diff --git a/code/data/cyq_perf.ipynb b/code/data/cyq_perf.ipynb new file mode 100644 index 0000000..70d220b --- /dev/null +++ b/code/data/cyq_perf.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:31:25.004019Z", + "start_time": "2025-03-12T15:31:24.322440Z" + } + }, + "outputs": [], + "source": [ + "from operator import index\n", + "\n", + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "972a5ac9f79fe373", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:31:40.917015Z", + "start_time": "2025-03-12T15:31:35.958771Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date his_low his_high cost_5pct cost_15pct \\\n", + "0 000001.SZ 20180104 0.2 12.7 7.2 7.9 \n", + "1 000002.SZ 20180104 0.3 31.8 14.1 15.6 \n", + "2 000004.SZ 20180104 0.8 53.2 21.6 22.0 \n", + "3 000008.SZ 20180104 0.1 13.9 7.2 7.8 \n", + "4 000009.SZ 20180104 0.3 15.0 5.9 5.9 \n", + "... ... ... ... ... ... ... \n", + "3091 603991.SH 20180104 12.0 67.8 26.4 27.0 \n", + "3092 603993.SH 20180104 1.5 8.1 5.6 5.8 \n", + "3093 603997.SH 20180104 5.4 31.5 9.9 10.2 \n", + "3094 603998.SH 20180104 3.9 18.9 9.8 10.1 \n", + "3095 603999.SH 20180104 5.4 30.9 6.9 7.2 \n", + "\n", + " cost_50pct cost_85pct cost_95pct weight_avg winner_rate \n", + "0 10.6 11.3 11.9 9.93 71.97 \n", + "1 20.1 23.1 24.3 19.62 99.34 \n", + "2 23.6 27.6 29.6 24.71 45.41 \n", + "3 8.6 9.2 10.5 8.64 47.04 \n", + "4 6.6 7.6 7.9 6.76 38.14 \n", + "... ... ... ... ... ... \n", + "3091 27.6 30.6 34.2 28.54 57.36 \n", + "3092 6.3 7.1 7.6 6.34 73.50 \n", + "3093 10.5 11.7 11.7 10.84 11.28 \n", + "3094 11.9 13.5 15.7 12.13 17.93 \n", + "3095 7.8 9.6 9.9 8.17 21.83 \n", + "\n", + "[3096 rows x 11 columns]\n" + ] + } + ], + "source": [ + "\n", + "df = pro.cyq_perf(trade_date='20180104')\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1b5a82fbf4e380de", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:30:20.421604Z", + "start_time": "2025-03-12T15:30:20.224851Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import time\n", + "\n", + "h5_filename = '../../../data/sw_daily.h5'\n", + "\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal['cal_date'].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f448da220816bf98", + "metadata": { + "ExecuteTime": { + "start_time": "2025-03-12T15:30:20.436796Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + 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time.sleep(0.1)\n", + " data = pro.cyq_perf(trade_date=trade_date)\n", + " if data is not None and not data.empty:\n", + " return data\n", + "\n", + "\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " trade_date = future_to_date[future] # 获取对应的交易日期\n", + " try:\n", + " result = future.result() # 获取任务执行的结果\n", + " all_daily_data.append(result)\n", + " print(f\"任务 {trade_date} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "907f732d3c397bf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:31:10.381348500Z", + "start_time": "2025-03-12T15:23:41.345460Z" + } + }, + "outputs": [], + "source": [ + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf('../../data/cyq_perf.h5', key='cyq_perf', mode='w', format='table', data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "73e829ac-ff3d-408e-beb3-0b87f5b00b19", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "0 000001.SZ 20250312\n", + "1 000002.SZ 20250312\n", + "2 000004.SZ 20250312\n", + "3 000006.SZ 20250312\n", + "4 000007.SZ 20250312\n", + "... ... ...\n", + "7465732 603991.SH 20180102\n", + "7465733 603993.SH 20180102\n", + "7465734 603997.SH 20180102\n", + "7465735 603998.SH 20180102\n", + "7465736 603999.SH 20180102\n", + "\n", + "[7465737 rows x 2 columns]\n" + ] + } + ], + "source": [ + "h5_filename = '../../data/cyq_perf.h5'\n", + "key = '/cyq_perf'\n", + "max_date = None\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date']]\n", + " print(df)\n", + " max_date = df['trade_date'].min()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/index_and_industry.ipynb b/code/data/index_and_industry.ipynb new file mode 100644 index 0000000..35613dd --- /dev/null +++ b/code/data/index_and_industry.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:23.864275Z", + "start_time": "2025-03-30T16:42:22.963221Z" + } + }, + "source": [ + "from operator import index\n", + "\n", + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "f448da220816bf98", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:25.559047Z", + "start_time": "2025-03-30T16:42:23.868783Z" + } + }, + "source": [ + "# 定义四个指数\n", + "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n", + "\n", + "# 获取并存储数据\n", + "all_data = []\n", + "\n", + "for ts_code in index_list:\n", + " df = pro.index_daily(ts_code=ts_code) # 可根据需要设置日期\n", + " df['ts_code'] = ts_code # 添加ts_code列来区分数据\n", + " all_data.append(df)\n", + "\n", + "# 合并所有数据\n", + "final_df = pd.concat(all_data, ignore_index=True)\n", + "\n", + "# 存储到H5文件\n", + "final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n", + "\n", + "print(\"数据已经成功存储到index_data.h5文件中\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "数据已经成功存储到index_data.h5文件中\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6192\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " final_df = pd.concat(all_data, ignore_index=True)\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "907f732d3c397bf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:25.802535Z", + "start_time": "2025-03-30T16:42:25.766399Z" + } + }, + "source": [ + "h5_filename = '../../data/index_data.h5'\n", + "key = '/index_data'\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key]\n", + " print(df)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date close open high low \\\n", + "0 000905.SH 20250328 5916.0314 5954.7297 5973.8015 5904.9159 \n", + "1 000905.SH 20250327 5957.6017 5932.5165 6000.6615 5891.7664 \n", + "2 000905.SH 20250326 5948.4986 5935.8537 5983.4739 5935.8537 \n", + "3 000905.SH 20250325 5946.9510 5969.4164 5993.9312 5929.6734 \n", + "4 000905.SH 20250324 5969.0789 5973.0466 5987.0606 5882.8780 \n", + "... ... ... ... ... ... ... \n", + "13423 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", + "13424 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", + "13425 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", + "13426 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", + "13427 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "\n", + " pre_close change pct_chg vol amount \n", + "0 5957.6017 -41.5703 -0.6978 1.342619e+08 1.688995e+08 \n", + "1 5948.4986 9.1031 0.1530 1.347089e+08 1.765905e+08 \n", + "2 5946.9510 1.5476 0.0260 1.367021e+08 1.716958e+08 \n", + "3 5969.0789 -22.1279 -0.3707 1.474839e+08 1.922270e+08 \n", + "4 5971.9302 -2.8513 -0.0477 1.691924e+08 2.200943e+08 \n", + "... ... ... ... ... ... \n", + "13423 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", + "13424 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", + "13425 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", + "13426 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", + "13427 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + "\n", + "[13428 rows x 11 columns]\n" + ] + } + ], + "execution_count": 3 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/industry_daily.ipynb b/code/data/industry_daily.ipynb new file mode 100644 index 0000000..94bfc94 --- /dev/null +++ b/code/data/industry_daily.ipynb @@ -0,0 +1,2167 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d7b022b7-8ffb-46ce-9967-f9421aa3a69f", + "metadata": {}, + "outputs": [], + "source": [ + "import tushare as ts\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6ed0dac4-4188-44a1-974c-782b40fcaf79", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "成功获取并保存 20250221 的每日基础数据\n", + "成功获取并保存 20250220 的每日基础数据\n", + "成功获取并保存 20250219 的每日基础数据\n", + "成功获取并保存 20250218 的每日基础数据\n", + "成功获取并保存 20250217 的每日基础数据\n", + "成功获取并保存 20250214 的每日基础数据\n", + "成功获取并保存 20250213 的每日基础数据\n", + "成功获取并保存 20250212 的每日基础数据\n", + "成功获取并保存 20250211 的每日基础数据\n", + "成功获取并保存 20250210 的每日基础数据\n", + "成功获取并保存 20250207 的每日基础数据\n", + "成功获取并保存 20250206 的每日基础数据\n", + "成功获取并保存 20250205 的每日基础数据\n", + "成功获取并保存 20250127 的每日基础数据\n", + "成功获取并保存 20250124 的每日基础数据\n", + "成功获取并保存 20250123 的每日基础数据\n", + "成功获取并保存 20250122 的每日基础数据\n", + "成功获取并保存 20250121 的每日基础数据\n", + "成功获取并保存 20250120 的每日基础数据\n", + "成功获取并保存 20250117 的每日基础数据\n", + "成功获取并保存 20250116 的每日基础数据\n", + "成功获取并保存 20250115 的每日基础数据\n", + "成功获取并保存 20250114 的每日基础数据\n", + "成功获取并保存 20250113 的每日基础数据\n", + "成功获取并保存 20250110 的每日基础数据\n", + "成功获取并保存 20250109 的每日基础数据\n", + "成功获取并保存 20250108 的每日基础数据\n", + "成功获取并保存 20250107 的每日基础数据\n", + "成功获取并保存 20250106 的每日基础数据\n", + "成功获取并保存 20250103 的每日基础数据\n", + "成功获取并保存 20250102 的每日基础数据\n", + "成功获取并保存 20241231 的每日基础数据\n", + "成功获取并保存 20241230 的每日基础数据\n", + "成功获取并保存 20241227 的每日基础数据\n", + "成功获取并保存 20241226 的每日基础数据\n", + "成功获取并保存 20241225 的每日基础数据\n", + "成功获取并保存 20241224 的每日基础数据\n", + "成功获取并保存 20241223 的每日基础数据\n", + "成功获取并保存 20241220 的每日基础数据\n", + "成功获取并保存 20241219 的每日基础数据\n", + "成功获取并保存 20241218 的每日基础数据\n", + "成功获取并保存 20241217 的每日基础数据\n", + "成功获取并保存 20241216 的每日基础数据\n", + "成功获取并保存 20241213 的每日基础数据\n", + "成功获取并保存 20241212 的每日基础数据\n", + "成功获取并保存 20241211 的每日基础数据\n", + "成功获取并保存 20241210 的每日基础数据\n", + "成功获取并保存 20241209 的每日基础数据\n", + "成功获取并保存 20241206 的每日基础数据\n", + "成功获取并保存 20241205 的每日基础数据\n", + "成功获取并保存 20241204 的每日基础数据\n", + "成功获取并保存 20241203 的每日基础数据\n", + "成功获取并保存 20241202 的每日基础数据\n", + "成功获取并保存 20241129 的每日基础数据\n", + "成功获取并保存 20241128 的每日基础数据\n", + "成功获取并保存 20241127 的每日基础数据\n", + "成功获取并保存 20241126 的每日基础数据\n", + "成功获取并保存 20241125 的每日基础数据\n", + "成功获取并保存 20241122 的每日基础数据\n", + "成功获取并保存 20241121 的每日基础数据\n", + "成功获取并保存 20241120 的每日基础数据\n", + "成功获取并保存 20241119 的每日基础数据\n", + "成功获取并保存 20241118 的每日基础数据\n", + "成功获取并保存 20241115 的每日基础数据\n", + "成功获取并保存 20241114 的每日基础数据\n", + "成功获取并保存 20241113 的每日基础数据\n", + "成功获取并保存 20241112 的每日基础数据\n", + "成功获取并保存 20241111 的每日基础数据\n", + "成功获取并保存 20241108 的每日基础数据\n", + "成功获取并保存 20241107 的每日基础数据\n", + "成功获取并保存 20241106 的每日基础数据\n", + "成功获取并保存 20241105 的每日基础数据\n", + "成功获取并保存 20241104 的每日基础数据\n", + "成功获取并保存 20241101 的每日基础数据\n", + "成功获取并保存 20241031 的每日基础数据\n", + "成功获取并保存 20241030 的每日基础数据\n", + "成功获取并保存 20241029 的每日基础数据\n", + "成功获取并保存 20241028 的每日基础数据\n", + "成功获取并保存 20241025 的每日基础数据\n", + "成功获取并保存 20241024 的每日基础数据\n", + "成功获取并保存 20241023 的每日基础数据\n", + "成功获取并保存 20241022 的每日基础数据\n", + "成功获取并保存 20241021 的每日基础数据\n", + "成功获取并保存 20241018 的每日基础数据\n", + "成功获取并保存 20241017 的每日基础数据\n", + "成功获取并保存 20241016 的每日基础数据\n", + "成功获取并保存 20241015 的每日基础数据\n", + "成功获取并保存 20241014 的每日基础数据\n", + "成功获取并保存 20241011 的每日基础数据\n", + "成功获取并保存 20241010 的每日基础数据\n", + "成功获取并保存 20241009 的每日基础数据\n", + "成功获取并保存 20241008 的每日基础数据\n", + "成功获取并保存 20240930 的每日基础数据\n", + "成功获取并保存 20240927 的每日基础数据\n", + "成功获取并保存 20240926 的每日基础数据\n", + "成功获取并保存 20240925 的每日基础数据\n", + "成功获取并保存 20240924 的每日基础数据\n", + "成功获取并保存 20240923 的每日基础数据\n", + "成功获取并保存 20240920 的每日基础数据\n", + "成功获取并保存 20240919 的每日基础数据\n", + "成功获取并保存 20240918 的每日基础数据\n", + "成功获取并保存 20240913 的每日基础数据\n", + "成功获取并保存 20240912 的每日基础数据\n", + "成功获取并保存 20240911 的每日基础数据\n", + "成功获取并保存 20240910 的每日基础数据\n", + "成功获取并保存 20240909 的每日基础数据\n", + "成功获取并保存 20240906 的每日基础数据\n", + "成功获取并保存 20240905 的每日基础数据\n", + "成功获取并保存 20240904 的每日基础数据\n", + "成功获取并保存 20240903 的每日基础数据\n", + "成功获取并保存 20240902 的每日基础数据\n", + "成功获取并保存 20240830 的每日基础数据\n", + "成功获取并保存 20240829 的每日基础数据\n", + "成功获取并保存 20240828 的每日基础数据\n", + "成功获取并保存 20240827 的每日基础数据\n", + "成功获取并保存 20240826 的每日基础数据\n", + "成功获取并保存 20240823 的每日基础数据\n", + "成功获取并保存 20240822 的每日基础数据\n", + "成功获取并保存 20240821 的每日基础数据\n", + "成功获取并保存 20240820 的每日基础数据\n", + "成功获取并保存 20240819 的每日基础数据\n", + "成功获取并保存 20240816 的每日基础数据\n", + "成功获取并保存 20240815 的每日基础数据\n", + "成功获取并保存 20240814 的每日基础数据\n", + "成功获取并保存 20240813 的每日基础数据\n", + "成功获取并保存 20240812 的每日基础数据\n", + "成功获取并保存 20240809 的每日基础数据\n", + "成功获取并保存 20240808 的每日基础数据\n", + "成功获取并保存 20240807 的每日基础数据\n", + "成功获取并保存 20240806 的每日基础数据\n", + "成功获取并保存 20240805 的每日基础数据\n", + "成功获取并保存 20240802 的每日基础数据\n", + "成功获取并保存 20240801 的每日基础数据\n", + "成功获取并保存 20240731 的每日基础数据\n", + "成功获取并保存 20240730 的每日基础数据\n", + "成功获取并保存 20240729 的每日基础数据\n", + "成功获取并保存 20240726 的每日基础数据\n", + "成功获取并保存 20240725 的每日基础数据\n", + "成功获取并保存 20240724 的每日基础数据\n", + "成功获取并保存 20240723 的每日基础数据\n", + "成功获取并保存 20240722 的每日基础数据\n", + "成功获取并保存 20240719 的每日基础数据\n", + "成功获取并保存 20240718 的每日基础数据\n", + "成功获取并保存 20240717 的每日基础数据\n", + "成功获取并保存 20240716 的每日基础数据\n", + "已调用 150 次 API，等待 28.88 秒以满足速率限制...\n", + "成功获取并保存 20240715 的每日基础数据\n", + "成功获取并保存 20240712 的每日基础数据\n", + "成功获取并保存 20240711 的每日基础数据\n", + "成功获取并保存 20240710 的每日基础数据\n", + "成功获取并保存 20240709 的每日基础数据\n", + "成功获取并保存 20240708 的每日基础数据\n", + "成功获取并保存 20240705 的每日基础数据\n", + "成功获取并保存 20240704 的每日基础数据\n", + "成功获取并保存 20240703 的每日基础数据\n", + "成功获取并保存 20240702 的每日基础数据\n", + "成功获取并保存 20240701 的每日基础数据\n", + "成功获取并保存 20240628 的每日基础数据\n", + "成功获取并保存 20240627 的每日基础数据\n", + "成功获取并保存 20240626 的每日基础数据\n", + "成功获取并保存 20240625 的每日基础数据\n", + "成功获取并保存 20240624 的每日基础数据\n", + "成功获取并保存 20240621 的每日基础数据\n", + "成功获取并保存 20240620 的每日基础数据\n", + "成功获取并保存 20240619 的每日基础数据\n", + "成功获取并保存 20240618 的每日基础数据\n", + "成功获取并保存 20240617 的每日基础数据\n", + "成功获取并保存 20240614 的每日基础数据\n", + "成功获取并保存 20240613 的每日基础数据\n", + "成功获取并保存 20240612 的每日基础数据\n", + "成功获取并保存 20240611 的每日基础数据\n", + "成功获取并保存 20240607 的每日基础数据\n", + "成功获取并保存 20240606 的每日基础数据\n", + "成功获取并保存 20240605 的每日基础数据\n", + "成功获取并保存 20240604 的每日基础数据\n", + "成功获取并保存 20240603 的每日基础数据\n", + "成功获取并保存 20240531 的每日基础数据\n", + "成功获取并保存 20240530 的每日基础数据\n", + "成功获取并保存 20240529 的每日基础数据\n", + "成功获取并保存 20240528 的每日基础数据\n", + "成功获取并保存 20240527 的每日基础数据\n", + "成功获取并保存 20240524 的每日基础数据\n", + "成功获取并保存 20240523 的每日基础数据\n", + "成功获取并保存 20240522 的每日基础数据\n", + "成功获取并保存 20240521 的每日基础数据\n", + "成功获取并保存 20240520 的每日基础数据\n", + "成功获取并保存 20240517 的每日基础数据\n", + "成功获取并保存 20240516 的每日基础数据\n", + "成功获取并保存 20240515 的每日基础数据\n", + "成功获取并保存 20240514 的每日基础数据\n", + "成功获取并保存 20240513 的每日基础数据\n", + "成功获取并保存 20240510 的每日基础数据\n", + "成功获取并保存 20240509 的每日基础数据\n", + "成功获取并保存 20240508 的每日基础数据\n", + "成功获取并保存 20240507 的每日基础数据\n", + "成功获取并保存 20240506 的每日基础数据\n", + "成功获取并保存 20240430 的每日基础数据\n", + "成功获取并保存 20240429 的每日基础数据\n", + "成功获取并保存 20240426 的每日基础数据\n", + "成功获取并保存 20240425 的每日基础数据\n", + "成功获取并保存 20240424 的每日基础数据\n", + "成功获取并保存 20240423 的每日基础数据\n", + "成功获取并保存 20240422 的每日基础数据\n", + "成功获取并保存 20240419 的每日基础数据\n", + "成功获取并保存 20240418 的每日基础数据\n", + "成功获取并保存 20240417 的每日基础数据\n", + "成功获取并保存 20240416 的每日基础数据\n", + "成功获取并保存 20240415 的每日基础数据\n", + "成功获取并保存 20240412 的每日基础数据\n", + "成功获取并保存 20240411 的每日基础数据\n", + "成功获取并保存 20240410 的每日基础数据\n", + "成功获取并保存 20240409 的每日基础数据\n", + "成功获取并保存 20240408 的每日基础数据\n", + "成功获取并保存 20240403 的每日基础数据\n", + "成功获取并保存 20240402 的每日基础数据\n", + "成功获取并保存 20240401 的每日基础数据\n", + "成功获取并保存 20240329 的每日基础数据\n", + "成功获取并保存 20240328 的每日基础数据\n", + "成功获取并保存 20240327 的每日基础数据\n", + "成功获取并保存 20240326 的每日基础数据\n", + "成功获取并保存 20240325 的每日基础数据\n", + "成功获取并保存 20240322 的每日基础数据\n", + "成功获取并保存 20240321 的每日基础数据\n", + "成功获取并保存 20240320 的每日基础数据\n", + "成功获取并保存 20240319 的每日基础数据\n", + "成功获取并保存 20240318 的每日基础数据\n", + "成功获取并保存 20240315 的每日基础数据\n", + "成功获取并保存 20240314 的每日基础数据\n", + "成功获取并保存 20240313 的每日基础数据\n", + "成功获取并保存 20240312 的每日基础数据\n", + "成功获取并保存 20240311 的每日基础数据\n", + "成功获取并保存 20240308 的每日基础数据\n", + "成功获取并保存 20240307 的每日基础数据\n", + "成功获取并保存 20240306 的每日基础数据\n", + "成功获取并保存 20240305 的每日基础数据\n", + "成功获取并保存 20240304 的每日基础数据\n", + "成功获取并保存 20240301 的每日基础数据\n", + "成功获取并保存 20240229 的每日基础数据\n", + "成功获取并保存 20240228 的每日基础数据\n", + "成功获取并保存 20240227 的每日基础数据\n", + "成功获取并保存 20240226 的每日基础数据\n", + "成功获取并保存 20240223 的每日基础数据\n", + "成功获取并保存 20240222 的每日基础数据\n", + "成功获取并保存 20240221 的每日基础数据\n", + "成功获取并保存 20240220 的每日基础数据\n", + "成功获取并保存 20240219 的每日基础数据\n", + "成功获取并保存 20240208 的每日基础数据\n", + "成功获取并保存 20240207 的每日基础数据\n", + "成功获取并保存 20240206 的每日基础数据\n", + "成功获取并保存 20240205 的每日基础数据\n", + "成功获取并保存 20240202 的每日基础数据\n", + "成功获取并保存 20240201 的每日基础数据\n", + "成功获取并保存 20240131 的每日基础数据\n", + "成功获取并保存 20240130 的每日基础数据\n", + "成功获取并保存 20240129 的每日基础数据\n", + "成功获取并保存 20240126 的每日基础数据\n", + "成功获取并保存 20240125 的每日基础数据\n", + "成功获取并保存 20240124 的每日基础数据\n", + "成功获取并保存 20240123 的每日基础数据\n", + "成功获取并保存 20240122 的每日基础数据\n", + "成功获取并保存 20240119 的每日基础数据\n", + "成功获取并保存 20240118 的每日基础数据\n", + "成功获取并保存 20240117 的每日基础数据\n", + "成功获取并保存 20240116 的每日基础数据\n", + "成功获取并保存 20240115 的每日基础数据\n", + "成功获取并保存 20240112 的每日基础数据\n", + "成功获取并保存 20240111 的每日基础数据\n", + "成功获取并保存 20240110 的每日基础数据\n", + "成功获取并保存 20240109 的每日基础数据\n", + "成功获取并保存 20240108 的每日基础数据\n", + "成功获取并保存 20240105 的每日基础数据\n", + "成功获取并保存 20240104 的每日基础数据\n", + "成功获取并保存 20240103 的每日基础数据\n", + "成功获取并保存 20240102 的每日基础数据\n", + "成功获取并保存 20231229 的每日基础数据\n", + "成功获取并保存 20231228 的每日基础数据\n", + "成功获取并保存 20231227 的每日基础数据\n", + "成功获取并保存 20231226 的每日基础数据\n", + "成功获取并保存 20231225 的每日基础数据\n", + "成功获取并保存 20231222 的每日基础数据\n", + "成功获取并保存 20231221 的每日基础数据\n", + "成功获取并保存 20231220 的每日基础数据\n", + "成功获取并保存 20231219 的每日基础数据\n", + "成功获取并保存 20231218 的每日基础数据\n", + "成功获取并保存 20231215 的每日基础数据\n", + "成功获取并保存 20231214 的每日基础数据\n", + "成功获取并保存 20231213 的每日基础数据\n", + "成功获取并保存 20231212 的每日基础数据\n", + "成功获取并保存 20231211 的每日基础数据\n", + "成功获取并保存 20231208 的每日基础数据\n", + "成功获取并保存 20231207 的每日基础数据\n", + "成功获取并保存 20231206 的每日基础数据\n", + "成功获取并保存 20231205 的每日基础数据\n", + "成功获取并保存 20231204 的每日基础数据\n", + "成功获取并保存 20231201 的每日基础数据\n", + "成功获取并保存 20231130 的每日基础数据\n", + "已调用 150 次 API，等待 29.24 秒以满足速率限制...\n", + "成功获取并保存 20231129 的每日基础数据\n", + "成功获取并保存 20231128 的每日基础数据\n", + "成功获取并保存 20231127 的每日基础数据\n", + "成功获取并保存 20231124 的每日基础数据\n", + "成功获取并保存 20231123 的每日基础数据\n", + "成功获取并保存 20231122 的每日基础数据\n", + "成功获取并保存 20231121 的每日基础数据\n", + "成功获取并保存 20231120 的每日基础数据\n", + "成功获取并保存 20231117 的每日基础数据\n", + "成功获取并保存 20231116 的每日基础数据\n", + "成功获取并保存 20231115 的每日基础数据\n", + "成功获取并保存 20231114 的每日基础数据\n", + "成功获取并保存 20231113 的每日基础数据\n", + "成功获取并保存 20231110 的每日基础数据\n", + "成功获取并保存 20231109 的每日基础数据\n", + "成功获取并保存 20231108 的每日基础数据\n", + "成功获取并保存 20231107 的每日基础数据\n", + "成功获取并保存 20231106 的每日基础数据\n", + "成功获取并保存 20231103 的每日基础数据\n", + "成功获取并保存 20231102 的每日基础数据\n", + "成功获取并保存 20231101 的每日基础数据\n", + "成功获取并保存 20231031 的每日基础数据\n", + "成功获取并保存 20231030 的每日基础数据\n", + "成功获取并保存 20231027 的每日基础数据\n", + "成功获取并保存 20231026 的每日基础数据\n", + "成功获取并保存 20231025 的每日基础数据\n", + "成功获取并保存 20231024 的每日基础数据\n", + "成功获取并保存 20231023 的每日基础数据\n", + "成功获取并保存 20231020 的每日基础数据\n", + "成功获取并保存 20231019 的每日基础数据\n", + "成功获取并保存 20231018 的每日基础数据\n", + "成功获取并保存 20231017 的每日基础数据\n", + "成功获取并保存 20231016 的每日基础数据\n", + "成功获取并保存 20231013 的每日基础数据\n", + "成功获取并保存 20231012 的每日基础数据\n", + "成功获取并保存 20231011 的每日基础数据\n", + "成功获取并保存 20231010 的每日基础数据\n", + "成功获取并保存 20231009 的每日基础数据\n", + "成功获取并保存 20230928 的每日基础数据\n", + "成功获取并保存 20230927 的每日基础数据\n", + "成功获取并保存 20230926 的每日基础数据\n", + "成功获取并保存 20230925 的每日基础数据\n", + "成功获取并保存 20230922 的每日基础数据\n", + "成功获取并保存 20230921 的每日基础数据\n", + "成功获取并保存 20230920 的每日基础数据\n", + "成功获取并保存 20230919 的每日基础数据\n", + "成功获取并保存 20230918 的每日基础数据\n", + "成功获取并保存 20230915 的每日基础数据\n", + "成功获取并保存 20230914 的每日基础数据\n", + "成功获取并保存 20230913 的每日基础数据\n", + "成功获取并保存 20230912 的每日基础数据\n", + "成功获取并保存 20230911 的每日基础数据\n", + "成功获取并保存 20230908 的每日基础数据\n", + "成功获取并保存 20230907 的每日基础数据\n", + "成功获取并保存 20230906 的每日基础数据\n", + "成功获取并保存 20230905 的每日基础数据\n", + "成功获取并保存 20230904 的每日基础数据\n", + "成功获取并保存 20230901 的每日基础数据\n", + "成功获取并保存 20230831 的每日基础数据\n", + "成功获取并保存 20230830 的每日基础数据\n", + "成功获取并保存 20230829 的每日基础数据\n", + "成功获取并保存 20230828 的每日基础数据\n", + "成功获取并保存 20230825 的每日基础数据\n", + "成功获取并保存 20230824 的每日基础数据\n", + "成功获取并保存 20230823 的每日基础数据\n", + "成功获取并保存 20230822 的每日基础数据\n", + "成功获取并保存 20230821 的每日基础数据\n", + "成功获取并保存 20230818 的每日基础数据\n", + "成功获取并保存 20230817 的每日基础数据\n", + "成功获取并保存 20230816 的每日基础数据\n", + "成功获取并保存 20230815 的每日基础数据\n", + "成功获取并保存 20230814 的每日基础数据\n", + "成功获取并保存 20230811 的每日基础数据\n", + "成功获取并保存 20230810 的每日基础数据\n", + "成功获取并保存 20230809 的每日基础数据\n", + "成功获取并保存 20230808 的每日基础数据\n", + "成功获取并保存 20230807 的每日基础数据\n", + "成功获取并保存 20230804 的每日基础数据\n", + "成功获取并保存 20230803 的每日基础数据\n", + "成功获取并保存 20230802 的每日基础数据\n", + "成功获取并保存 20230801 的每日基础数据\n", + "成功获取并保存 20230731 的每日基础数据\n", + "成功获取并保存 20230728 的每日基础数据\n", + "成功获取并保存 20230727 的每日基础数据\n", + "成功获取并保存 20230726 的每日基础数据\n", + "成功获取并保存 20230725 的每日基础数据\n", + "成功获取并保存 20230724 的每日基础数据\n", + "成功获取并保存 20230721 的每日基础数据\n", + "成功获取并保存 20230720 的每日基础数据\n", + "成功获取并保存 20230719 的每日基础数据\n", + "成功获取并保存 20230718 的每日基础数据\n", + "成功获取并保存 20230717 的每日基础数据\n", + "成功获取并保存 20230714 的每日基础数据\n", + "成功获取并保存 20230713 的每日基础数据\n", + "成功获取并保存 20230712 的每日基础数据\n", + "成功获取并保存 20230711 的每日基础数据\n", + "成功获取并保存 20230710 的每日基础数据\n", + "成功获取并保存 20230707 的每日基础数据\n", + "成功获取并保存 20230706 的每日基础数据\n", + "成功获取并保存 20230705 的每日基础数据\n", + "成功获取并保存 20230704 的每日基础数据\n", + "成功获取并保存 20230703 的每日基础数据\n", + "成功获取并保存 20230630 的每日基础数据\n", + "成功获取并保存 20230629 的每日基础数据\n", + "成功获取并保存 20230628 的每日基础数据\n", + "成功获取并保存 20230627 的每日基础数据\n", + "成功获取并保存 20230626 的每日基础数据\n", + "成功获取并保存 20230621 的每日基础数据\n", + "成功获取并保存 20230620 的每日基础数据\n", + "成功获取并保存 20230619 的每日基础数据\n", + "成功获取并保存 20230616 的每日基础数据\n", + "成功获取并保存 20230615 的每日基础数据\n", + "成功获取并保存 20230614 的每日基础数据\n", + "成功获取并保存 20230613 的每日基础数据\n", + "成功获取并保存 20230612 的每日基础数据\n", + "成功获取并保存 20230609 的每日基础数据\n", + "成功获取并保存 20230608 的每日基础数据\n", + "成功获取并保存 20230607 的每日基础数据\n", + "成功获取并保存 20230606 的每日基础数据\n", + "成功获取并保存 20230605 的每日基础数据\n", + "成功获取并保存 20230602 的每日基础数据\n", + "成功获取并保存 20230601 的每日基础数据\n", + "成功获取并保存 20230531 的每日基础数据\n", + "成功获取并保存 20230530 的每日基础数据\n", + "成功获取并保存 20230529 的每日基础数据\n", + "成功获取并保存 20230526 的每日基础数据\n", + "成功获取并保存 20230525 的每日基础数据\n", + "成功获取并保存 20230524 的每日基础数据\n", + "成功获取并保存 20230523 的每日基础数据\n", + "成功获取并保存 20230522 的每日基础数据\n", + "成功获取并保存 20230519 的每日基础数据\n", + "成功获取并保存 20230518 的每日基础数据\n", + "成功获取并保存 20230517 的每日基础数据\n", + "成功获取并保存 20230516 的每日基础数据\n", + "成功获取并保存 20230515 的每日基础数据\n", + "成功获取并保存 20230512 的每日基础数据\n", + "成功获取并保存 20230511 的每日基础数据\n", + "成功获取并保存 20230510 的每日基础数据\n", + "成功获取并保存 20230509 的每日基础数据\n", + "成功获取并保存 20230508 的每日基础数据\n", + "成功获取并保存 20230505 的每日基础数据\n", + "成功获取并保存 20230504 的每日基础数据\n", + "成功获取并保存 20230428 的每日基础数据\n", + "成功获取并保存 20230427 的每日基础数据\n", + "成功获取并保存 20230426 的每日基础数据\n", + "成功获取并保存 20230425 的每日基础数据\n", + "成功获取并保存 20230424 的每日基础数据\n", + "成功获取并保存 20230421 的每日基础数据\n", + "成功获取并保存 20230420 的每日基础数据\n", + "成功获取并保存 20230419 的每日基础数据\n", + "已调用 150 次 API，等待 27.56 秒以满足速率限制...\n", + "成功获取并保存 20230418 的每日基础数据\n", + "成功获取并保存 20230417 的每日基础数据\n", + "成功获取并保存 20230414 的每日基础数据\n", + "成功获取并保存 20230413 的每日基础数据\n", + "成功获取并保存 20230412 的每日基础数据\n", + "成功获取并保存 20230411 的每日基础数据\n", + "成功获取并保存 20230410 的每日基础数据\n", + "成功获取并保存 20230407 的每日基础数据\n", + "成功获取并保存 20230406 的每日基础数据\n", + "成功获取并保存 20230404 的每日基础数据\n", + "成功获取并保存 20230403 的每日基础数据\n", + "成功获取并保存 20230331 的每日基础数据\n", + "成功获取并保存 20230330 的每日基础数据\n", + "成功获取并保存 20230329 的每日基础数据\n", + "成功获取并保存 20230328 的每日基础数据\n", + "成功获取并保存 20230327 的每日基础数据\n", + "成功获取并保存 20230324 的每日基础数据\n", + "成功获取并保存 20230323 的每日基础数据\n", + "成功获取并保存 20230322 的每日基础数据\n", + "成功获取并保存 20230321 的每日基础数据\n", + "成功获取并保存 20230320 的每日基础数据\n", + "成功获取并保存 20230317 的每日基础数据\n", + "成功获取并保存 20230316 的每日基础数据\n", + "成功获取并保存 20230315 的每日基础数据\n", + "成功获取并保存 20230314 的每日基础数据\n", + "成功获取并保存 20230313 的每日基础数据\n", + "成功获取并保存 20230310 的每日基础数据\n", + "成功获取并保存 20230309 的每日基础数据\n", + "成功获取并保存 20230308 的每日基础数据\n", + "成功获取并保存 20230307 的每日基础数据\n", + "成功获取并保存 20230306 的每日基础数据\n", + "成功获取并保存 20230303 的每日基础数据\n", + "成功获取并保存 20230302 的每日基础数据\n", + "成功获取并保存 20230301 的每日基础数据\n", + "成功获取并保存 20230228 的每日基础数据\n", + "成功获取并保存 20230227 的每日基础数据\n", + "成功获取并保存 20230224 的每日基础数据\n", + "成功获取并保存 20230223 的每日基础数据\n", + "成功获取并保存 20230222 的每日基础数据\n", + "成功获取并保存 20230221 的每日基础数据\n", + "成功获取并保存 20230220 的每日基础数据\n", + "成功获取并保存 20230217 的每日基础数据\n", + "成功获取并保存 20230216 的每日基础数据\n", + "成功获取并保存 20230215 的每日基础数据\n", + "成功获取并保存 20230214 的每日基础数据\n", + "成功获取并保存 20230213 的每日基础数据\n", + "成功获取并保存 20230210 的每日基础数据\n", + "成功获取并保存 20230209 的每日基础数据\n", + "成功获取并保存 20230208 的每日基础数据\n", + "成功获取并保存 20230207 的每日基础数据\n", + "成功获取并保存 20230206 的每日基础数据\n", + "成功获取并保存 20230203 的每日基础数据\n", + "成功获取并保存 20230202 的每日基础数据\n", + "成功获取并保存 20230201 的每日基础数据\n", + "成功获取并保存 20230131 的每日基础数据\n", + "成功获取并保存 20230130 的每日基础数据\n", + "成功获取并保存 20230120 的每日基础数据\n", + "成功获取并保存 20230119 的每日基础数据\n", + "成功获取并保存 20230118 的每日基础数据\n", + "成功获取并保存 20230117 的每日基础数据\n", + "成功获取并保存 20230116 的每日基础数据\n", + "成功获取并保存 20230113 的每日基础数据\n", + "成功获取并保存 20230112 的每日基础数据\n", + "成功获取并保存 20230111 的每日基础数据\n", + "成功获取并保存 20230110 的每日基础数据\n", + "成功获取并保存 20230109 的每日基础数据\n", + "成功获取并保存 20230106 的每日基础数据\n", + "成功获取并保存 20230105 的每日基础数据\n", + "成功获取并保存 20230104 的每日基础数据\n", + "成功获取并保存 20230103 的每日基础数据\n", + "成功获取并保存 20221230 的每日基础数据\n", + "成功获取并保存 20221229 的每日基础数据\n", + "成功获取并保存 20221228 的每日基础数据\n", + "成功获取并保存 20221227 的每日基础数据\n", + "成功获取并保存 20221226 的每日基础数据\n", + "成功获取并保存 20221223 的每日基础数据\n", + "成功获取并保存 20221222 的每日基础数据\n", + "成功获取并保存 20221221 的每日基础数据\n", + "成功获取并保存 20221220 的每日基础数据\n", + "成功获取并保存 20221219 的每日基础数据\n", + "成功获取并保存 20221216 的每日基础数据\n", + "成功获取并保存 20221215 的每日基础数据\n", + "成功获取并保存 20221214 的每日基础数据\n", + "成功获取并保存 20221213 的每日基础数据\n", + "成功获取并保存 20221212 的每日基础数据\n", + "成功获取并保存 20221209 的每日基础数据\n", + "成功获取并保存 20221208 的每日基础数据\n", + "成功获取并保存 20221207 的每日基础数据\n", + "成功获取并保存 20221206 的每日基础数据\n", + "成功获取并保存 20221205 的每日基础数据\n", + "成功获取并保存 20221202 的每日基础数据\n", + "成功获取并保存 20221201 的每日基础数据\n", + "成功获取并保存 20221130 的每日基础数据\n", + "成功获取并保存 20221129 的每日基础数据\n", + "成功获取并保存 20221128 的每日基础数据\n", + "成功获取并保存 20221125 的每日基础数据\n", + "成功获取并保存 20221124 的每日基础数据\n", + "成功获取并保存 20221123 的每日基础数据\n", + "成功获取并保存 20221122 的每日基础数据\n", + "成功获取并保存 20221121 的每日基础数据\n", + "成功获取并保存 20221118 的每日基础数据\n", + "成功获取并保存 20221117 的每日基础数据\n", + "成功获取并保存 20221116 的每日基础数据\n", + "成功获取并保存 20221115 的每日基础数据\n", + "成功获取并保存 20221114 的每日基础数据\n", + "成功获取并保存 20221111 的每日基础数据\n", + "成功获取并保存 20221110 的每日基础数据\n", + "成功获取并保存 20221109 的每日基础数据\n", + "成功获取并保存 20221108 的每日基础数据\n", + "成功获取并保存 20221107 的每日基础数据\n", + "成功获取并保存 20221104 的每日基础数据\n", + "成功获取并保存 20221103 的每日基础数据\n", + "成功获取并保存 20221102 的每日基础数据\n", + "成功获取并保存 20221101 的每日基础数据\n", + "成功获取并保存 20221031 的每日基础数据\n", + "成功获取并保存 20221028 的每日基础数据\n", + "成功获取并保存 20221027 的每日基础数据\n", + "成功获取并保存 20221026 的每日基础数据\n", + "成功获取并保存 20221025 的每日基础数据\n", + "成功获取并保存 20221024 的每日基础数据\n", + "成功获取并保存 20221021 的每日基础数据\n", + "成功获取并保存 20221020 的每日基础数据\n", + "成功获取并保存 20221019 的每日基础数据\n", + "成功获取并保存 20221018 的每日基础数据\n", + "成功获取并保存 20221017 的每日基础数据\n", + "成功获取并保存 20221014 的每日基础数据\n", + "成功获取并保存 20221013 的每日基础数据\n", + "成功获取并保存 20221012 的每日基础数据\n", + "成功获取并保存 20221011 的每日基础数据\n", + "成功获取并保存 20221010 的每日基础数据\n", + "成功获取并保存 20220930 的每日基础数据\n", + "成功获取并保存 20220929 的每日基础数据\n", + "成功获取并保存 20220928 的每日基础数据\n", + "成功获取并保存 20220927 的每日基础数据\n", + "成功获取并保存 20220926 的每日基础数据\n", + "成功获取并保存 20220923 的每日基础数据\n", + "成功获取并保存 20220922 的每日基础数据\n", + "成功获取并保存 20220921 的每日基础数据\n", + "成功获取并保存 20220920 的每日基础数据\n", + "成功获取并保存 20220919 的每日基础数据\n", + "成功获取并保存 20220916 的每日基础数据\n", + "成功获取并保存 20220915 的每日基础数据\n", + "成功获取并保存 20220914 的每日基础数据\n", + "成功获取并保存 20220913 的每日基础数据\n", + "成功获取并保存 20220909 的每日基础数据\n", + "成功获取并保存 20220908 的每日基础数据\n", + "成功获取并保存 20220907 的每日基础数据\n", + "成功获取并保存 20220906 的每日基础数据\n", + "成功获取并保存 20220905 的每日基础数据\n", + "成功获取并保存 20220902 的每日基础数据\n", + "已调用 150 次 API，等待 28.51 秒以满足速率限制...\n", + "成功获取并保存 20220901 的每日基础数据\n", + "成功获取并保存 20220831 的每日基础数据\n", + "成功获取并保存 20220830 的每日基础数据\n", + "成功获取并保存 20220829 的每日基础数据\n", + "成功获取并保存 20220826 的每日基础数据\n", + "成功获取并保存 20220825 的每日基础数据\n", + "成功获取并保存 20220824 的每日基础数据\n", + "成功获取并保存 20220823 的每日基础数据\n", + "成功获取并保存 20220822 的每日基础数据\n", + "成功获取并保存 20220819 的每日基础数据\n", + "成功获取并保存 20220818 的每日基础数据\n", + "成功获取并保存 20220817 的每日基础数据\n", + "成功获取并保存 20220816 的每日基础数据\n", + "成功获取并保存 20220815 的每日基础数据\n", + "成功获取并保存 20220812 的每日基础数据\n", + "成功获取并保存 20220811 的每日基础数据\n", + "成功获取并保存 20220810 的每日基础数据\n", + "成功获取并保存 20220809 的每日基础数据\n", + "成功获取并保存 20220808 的每日基础数据\n", + "成功获取并保存 20220805 的每日基础数据\n", + "成功获取并保存 20220804 的每日基础数据\n", + "成功获取并保存 20220803 的每日基础数据\n", + "成功获取并保存 20220802 的每日基础数据\n", + "成功获取并保存 20220801 的每日基础数据\n", + "成功获取并保存 20220729 的每日基础数据\n", + "成功获取并保存 20220728 的每日基础数据\n", + "成功获取并保存 20220727 的每日基础数据\n", + "成功获取并保存 20220726 的每日基础数据\n", + "成功获取并保存 20220725 的每日基础数据\n", + "成功获取并保存 20220722 的每日基础数据\n", + "成功获取并保存 20220721 的每日基础数据\n", + "成功获取并保存 20220720 的每日基础数据\n", + "成功获取并保存 20220719 的每日基础数据\n", + "成功获取并保存 20220718 的每日基础数据\n", + "成功获取并保存 20220715 的每日基础数据\n", + "成功获取并保存 20220714 的每日基础数据\n", + "成功获取并保存 20220713 的每日基础数据\n", + "成功获取并保存 20220712 的每日基础数据\n", + "成功获取并保存 20220711 的每日基础数据\n", + "成功获取并保存 20220708 的每日基础数据\n", + "成功获取并保存 20220707 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的每日基础数据\n", + "成功获取并保存 20210629 的每日基础数据\n", + "成功获取并保存 20210628 的每日基础数据\n", + "成功获取并保存 20210625 的每日基础数据\n", + "成功获取并保存 20210624 的每日基础数据\n", + "成功获取并保存 20210623 的每日基础数据\n", + "成功获取并保存 20210622 的每日基础数据\n", + "成功获取并保存 20210621 的每日基础数据\n", + "成功获取并保存 20210618 的每日基础数据\n", + "成功获取并保存 20210617 的每日基础数据\n", + "成功获取并保存 20210616 的每日基础数据\n", + "成功获取并保存 20210615 的每日基础数据\n", + "成功获取并保存 20210611 的每日基础数据\n", + "已调用 150 次 API，等待 26.43 秒以满足速率限制...\n", + "成功获取并保存 20210610 的每日基础数据\n", + "成功获取并保存 20210609 的每日基础数据\n", + "成功获取并保存 20210608 的每日基础数据\n", + "成功获取并保存 20210607 的每日基础数据\n", + "成功获取并保存 20210604 的每日基础数据\n", + "成功获取并保存 20210603 的每日基础数据\n", + "成功获取并保存 20210602 的每日基础数据\n", + "成功获取并保存 20210601 的每日基础数据\n", + "成功获取并保存 20210531 的每日基础数据\n", + "成功获取并保存 20210528 的每日基础数据\n", + "成功获取并保存 20210527 的每日基础数据\n", + "成功获取并保存 20210526 的每日基础数据\n", + "成功获取并保存 20210525 的每日基础数据\n", + "成功获取并保存 20210524 的每日基础数据\n", + "成功获取并保存 20210521 的每日基础数据\n", + "成功获取并保存 20210520 的每日基础数据\n", + "成功获取并保存 20210519 的每日基础数据\n", + "成功获取并保存 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的每日基础数据\n", + "成功获取并保存 20210329 的每日基础数据\n", + "成功获取并保存 20210326 的每日基础数据\n", + "成功获取并保存 20210325 的每日基础数据\n", + "成功获取并保存 20210324 的每日基础数据\n", + "成功获取并保存 20210323 的每日基础数据\n", + "成功获取并保存 20210322 的每日基础数据\n", + "成功获取并保存 20210319 的每日基础数据\n", + "成功获取并保存 20210318 的每日基础数据\n", + "成功获取并保存 20210317 的每日基础数据\n", + "成功获取并保存 20210316 的每日基础数据\n", + "成功获取并保存 20210315 的每日基础数据\n", + "成功获取并保存 20210312 的每日基础数据\n", + "成功获取并保存 20210311 的每日基础数据\n", + "成功获取并保存 20210310 的每日基础数据\n", + "成功获取并保存 20210309 的每日基础数据\n", + "成功获取并保存 20210308 的每日基础数据\n", + "成功获取并保存 20210305 的每日基础数据\n", + "成功获取并保存 20210304 的每日基础数据\n", + "成功获取并保存 20210303 的每日基础数据\n", + "成功获取并保存 20210302 的每日基础数据\n", + "成功获取并保存 20210301 的每日基础数据\n", + "成功获取并保存 20210226 的每日基础数据\n", + "成功获取并保存 20210225 的每日基础数据\n", + "成功获取并保存 20210224 的每日基础数据\n", + "成功获取并保存 20210223 的每日基础数据\n", + "成功获取并保存 20210222 的每日基础数据\n", + "成功获取并保存 20210219 的每日基础数据\n", + "成功获取并保存 20210218 的每日基础数据\n", + "成功获取并保存 20210210 的每日基础数据\n", + "成功获取并保存 20210209 的每日基础数据\n", + "成功获取并保存 20210208 的每日基础数据\n", + "成功获取并保存 20210205 的每日基础数据\n", + "成功获取并保存 20210204 的每日基础数据\n", + "成功获取并保存 20210203 的每日基础数据\n", + "成功获取并保存 20210202 的每日基础数据\n", + "成功获取并保存 20210201 的每日基础数据\n", + "成功获取并保存 20210129 的每日基础数据\n", + "成功获取并保存 20210128 的每日基础数据\n", + "成功获取并保存 20210127 的每日基础数据\n", + "成功获取并保存 20210126 的每日基础数据\n", + "成功获取并保存 20210125 的每日基础数据\n", + "成功获取并保存 20210122 的每日基础数据\n", + "成功获取并保存 20210121 的每日基础数据\n", + "成功获取并保存 20210120 的每日基础数据\n", + "成功获取并保存 20210119 的每日基础数据\n", + "成功获取并保存 20210118 的每日基础数据\n", + "成功获取并保存 20210115 的每日基础数据\n", + "成功获取并保存 20210114 的每日基础数据\n", + "成功获取并保存 20210113 的每日基础数据\n", + "成功获取并保存 20210112 的每日基础数据\n", + "成功获取并保存 20210111 的每日基础数据\n", + "成功获取并保存 20210108 的每日基础数据\n", + "成功获取并保存 20210107 的每日基础数据\n", + "成功获取并保存 20210106 的每日基础数据\n", + "成功获取并保存 20210105 的每日基础数据\n", + "成功获取并保存 20210104 的每日基础数据\n", + "成功获取并保存 20201231 的每日基础数据\n", + "成功获取并保存 20201230 的每日基础数据\n", + "成功获取并保存 20201229 的每日基础数据\n", + "成功获取并保存 20201228 的每日基础数据\n", + "成功获取并保存 20201225 的每日基础数据\n", + "成功获取并保存 20201224 的每日基础数据\n", + "成功获取并保存 20201223 的每日基础数据\n", + "成功获取并保存 20201222 的每日基础数据\n", + "成功获取并保存 20201221 的每日基础数据\n", + "成功获取并保存 20201218 的每日基础数据\n", + "成功获取并保存 20201217 的每日基础数据\n", + "成功获取并保存 20201216 的每日基础数据\n", + "成功获取并保存 20201215 的每日基础数据\n", + "成功获取并保存 20201214 的每日基础数据\n", + "成功获取并保存 20201211 的每日基础数据\n", + "成功获取并保存 20201210 的每日基础数据\n", + "成功获取并保存 20201209 的每日基础数据\n", + "成功获取并保存 20201208 的每日基础数据\n", + "成功获取并保存 20201207 的每日基础数据\n", + "成功获取并保存 20201204 的每日基础数据\n", + "成功获取并保存 20201203 的每日基础数据\n", + "成功获取并保存 20201202 的每日基础数据\n", + "成功获取并保存 20201201 的每日基础数据\n", + "成功获取并保存 20201130 的每日基础数据\n", + "成功获取并保存 20201127 的每日基础数据\n", + "成功获取并保存 20201126 的每日基础数据\n", + "成功获取并保存 20201125 的每日基础数据\n", + "成功获取并保存 20201124 的每日基础数据\n", + "成功获取并保存 20201123 的每日基础数据\n", + "成功获取并保存 20201120 的每日基础数据\n", + "成功获取并保存 20201119 的每日基础数据\n", + "成功获取并保存 20201118 的每日基础数据\n", + "成功获取并保存 20201117 的每日基础数据\n", + "成功获取并保存 20201116 的每日基础数据\n", + "成功获取并保存 20201113 的每日基础数据\n", + "成功获取并保存 20201112 的每日基础数据\n", + "成功获取并保存 20201111 的每日基础数据\n", + "成功获取并保存 20201110 的每日基础数据\n", + "成功获取并保存 20201109 的每日基础数据\n", + "成功获取并保存 20201106 的每日基础数据\n", + "成功获取并保存 20201105 的每日基础数据\n", + "成功获取并保存 20201104 的每日基础数据\n", + "成功获取并保存 20201103 的每日基础数据\n", + "成功获取并保存 20201102 的每日基础数据\n", + "成功获取并保存 20201030 的每日基础数据\n", + "已调用 150 次 API，等待 24.84 秒以满足速率限制...\n", + "成功获取并保存 20201029 的每日基础数据\n", + "成功获取并保存 20201028 的每日基础数据\n", + "成功获取并保存 20201027 的每日基础数据\n", + "成功获取并保存 20201026 的每日基础数据\n", + "成功获取并保存 20201023 的每日基础数据\n", + "成功获取并保存 20201022 的每日基础数据\n", + "成功获取并保存 20201021 的每日基础数据\n", + "成功获取并保存 20201020 的每日基础数据\n", + "成功获取并保存 20201019 的每日基础数据\n", + "成功获取并保存 20201016 的每日基础数据\n", + "成功获取并保存 20201015 的每日基础数据\n", + "成功获取并保存 20201014 的每日基础数据\n", + "成功获取并保存 20201013 的每日基础数据\n", + "成功获取并保存 20201012 的每日基础数据\n", + "成功获取并保存 20201009 的每日基础数据\n", + "成功获取并保存 20200930 的每日基础数据\n", + "成功获取并保存 20200929 的每日基础数据\n", + "成功获取并保存 20200928 的每日基础数据\n", + "成功获取并保存 20200925 的每日基础数据\n", + "成功获取并保存 20200924 的每日基础数据\n", + "成功获取并保存 20200923 的每日基础数据\n", + "成功获取并保存 20200922 的每日基础数据\n", + "成功获取并保存 20200921 的每日基础数据\n", + "成功获取并保存 20200918 的每日基础数据\n", + "成功获取并保存 20200917 的每日基础数据\n", + "成功获取并保存 20200916 的每日基础数据\n", + "成功获取并保存 20200915 的每日基础数据\n", + "成功获取并保存 20200914 的每日基础数据\n", + "成功获取并保存 20200911 的每日基础数据\n", + "成功获取并保存 20200910 的每日基础数据\n", + "成功获取并保存 20200909 的每日基础数据\n", + "成功获取并保存 20200908 的每日基础数据\n", + "成功获取并保存 20200907 的每日基础数据\n", + "成功获取并保存 20200904 的每日基础数据\n", + "成功获取并保存 20200903 的每日基础数据\n", + "成功获取并保存 20200902 的每日基础数据\n", + "成功获取并保存 20200901 的每日基础数据\n", + "成功获取并保存 20200831 的每日基础数据\n", + "成功获取并保存 20200828 的每日基础数据\n", + "成功获取并保存 20200827 的每日基础数据\n", + "成功获取并保存 20200826 的每日基础数据\n", + "成功获取并保存 20200825 的每日基础数据\n", + "成功获取并保存 20200824 的每日基础数据\n", + "成功获取并保存 20200821 的每日基础数据\n", + "成功获取并保存 20200820 的每日基础数据\n", + "成功获取并保存 20200819 的每日基础数据\n", + "成功获取并保存 20200818 的每日基础数据\n", + "成功获取并保存 20200817 的每日基础数据\n", + "成功获取并保存 20200814 的每日基础数据\n", + "成功获取并保存 20200813 的每日基础数据\n", + "成功获取并保存 20200812 的每日基础数据\n", + "成功获取并保存 20200811 的每日基础数据\n", + "成功获取并保存 20200810 的每日基础数据\n", + "成功获取并保存 20200807 的每日基础数据\n", + "成功获取并保存 20200806 的每日基础数据\n", + "成功获取并保存 20200805 的每日基础数据\n", + "成功获取并保存 20200804 的每日基础数据\n", + "成功获取并保存 20200803 的每日基础数据\n", + "成功获取并保存 20200731 的每日基础数据\n", + "成功获取并保存 20200730 的每日基础数据\n", + "成功获取并保存 20200729 的每日基础数据\n", + "成功获取并保存 20200728 的每日基础数据\n", + "成功获取并保存 20200727 的每日基础数据\n", + "成功获取并保存 20200724 的每日基础数据\n", + "成功获取并保存 20200723 的每日基础数据\n", + "成功获取并保存 20200722 的每日基础数据\n", + "成功获取并保存 20200721 的每日基础数据\n", + "成功获取并保存 20200720 的每日基础数据\n", + "成功获取并保存 20200717 的每日基础数据\n", + "成功获取并保存 20200716 的每日基础数据\n", + "成功获取并保存 20200715 的每日基础数据\n", + "成功获取并保存 20200714 的每日基础数据\n", + "成功获取并保存 20200713 的每日基础数据\n", + "成功获取并保存 20200710 的每日基础数据\n", + "成功获取并保存 20200709 的每日基础数据\n", + "成功获取并保存 20200708 的每日基础数据\n", + "成功获取并保存 20200707 的每日基础数据\n", + "成功获取并保存 20200706 的每日基础数据\n", + "成功获取并保存 20200703 的每日基础数据\n", + "成功获取并保存 20200702 的每日基础数据\n", + "成功获取并保存 20200701 的每日基础数据\n", + "成功获取并保存 20200630 的每日基础数据\n", + "成功获取并保存 20200629 的每日基础数据\n", + "成功获取并保存 20200624 的每日基础数据\n", + "成功获取并保存 20200623 的每日基础数据\n", + "成功获取并保存 20200622 的每日基础数据\n", + "成功获取并保存 20200619 的每日基础数据\n", + "成功获取并保存 20200618 的每日基础数据\n", + "成功获取并保存 20200617 的每日基础数据\n", + "成功获取并保存 20200616 的每日基础数据\n", + "成功获取并保存 20200615 的每日基础数据\n", + "成功获取并保存 20200612 的每日基础数据\n", + "成功获取并保存 20200611 的每日基础数据\n", + "成功获取并保存 20200610 的每日基础数据\n", + "成功获取并保存 20200609 的每日基础数据\n", + "成功获取并保存 20200608 的每日基础数据\n", + "成功获取并保存 20200605 的每日基础数据\n", + "成功获取并保存 20200604 的每日基础数据\n", + "成功获取并保存 20200603 的每日基础数据\n", + "成功获取并保存 20200602 的每日基础数据\n", + "成功获取并保存 20200601 的每日基础数据\n", + "成功获取并保存 20200529 的每日基础数据\n", + "成功获取并保存 20200528 的每日基础数据\n", + "成功获取并保存 20200527 的每日基础数据\n", + "成功获取并保存 20200526 的每日基础数据\n", + "成功获取并保存 20200525 的每日基础数据\n", + "成功获取并保存 20200522 的每日基础数据\n", + "成功获取并保存 20200521 的每日基础数据\n", + "成功获取并保存 20200520 的每日基础数据\n", + "成功获取并保存 20200519 的每日基础数据\n", + "成功获取并保存 20200518 的每日基础数据\n", + "成功获取并保存 20200515 的每日基础数据\n", + "成功获取并保存 20200514 的每日基础数据\n", + "成功获取并保存 20200513 的每日基础数据\n", + "成功获取并保存 20200512 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的每日基础数据\n", + "成功获取并保存 20170613 的每日基础数据\n", + "成功获取并保存 20170612 的每日基础数据\n", + "成功获取并保存 20170609 的每日基础数据\n", + "成功获取并保存 20170608 的每日基础数据\n", + "成功获取并保存 20170607 的每日基础数据\n", + "成功获取并保存 20170606 的每日基础数据\n", + "成功获取并保存 20170605 的每日基础数据\n", + "成功获取并保存 20170602 的每日基础数据\n", + "成功获取并保存 20170601 的每日基础数据\n", + "成功获取并保存 20170531 的每日基础数据\n", + "成功获取并保存 20170526 的每日基础数据\n", + "成功获取并保存 20170525 的每日基础数据\n", + "成功获取并保存 20170524 的每日基础数据\n", + "成功获取并保存 20170523 的每日基础数据\n", + "成功获取并保存 20170522 的每日基础数据\n", + "成功获取并保存 20170519 的每日基础数据\n", + "成功获取并保存 20170518 的每日基础数据\n", + "成功获取并保存 20170517 的每日基础数据\n", + "成功获取并保存 20170516 的每日基础数据\n", + "成功获取并保存 20170515 的每日基础数据\n", + "成功获取并保存 20170512 的每日基础数据\n", + "成功获取并保存 20170511 的每日基础数据\n", + "成功获取并保存 20170510 的每日基础数据\n", + "成功获取并保存 20170509 的每日基础数据\n", + "成功获取并保存 20170508 的每日基础数据\n", + "成功获取并保存 20170505 的每日基础数据\n", + "成功获取并保存 20170504 的每日基础数据\n", + "成功获取并保存 20170503 的每日基础数据\n", + "成功获取并保存 20170502 的每日基础数据\n", + "成功获取并保存 20170428 的每日基础数据\n", + "成功获取并保存 20170427 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20170123 的每日基础数据\n", + "成功获取并保存 20170120 的每日基础数据\n", + "成功获取并保存 20170119 的每日基础数据\n", + "成功获取并保存 20170118 的每日基础数据\n", + "成功获取并保存 20170117 的每日基础数据\n", + "成功获取并保存 20170116 的每日基础数据\n", + "成功获取并保存 20170113 的每日基础数据\n", + "成功获取并保存 20170112 的每日基础数据\n", + "成功获取并保存 20170111 的每日基础数据\n", + "成功获取并保存 20170110 的每日基础数据\n", + "成功获取并保存 20170109 的每日基础数据\n", + "成功获取并保存 20170106 的每日基础数据\n", + "成功获取并保存 20170105 的每日基础数据\n", + "成功获取并保存 20170104 的每日基础数据\n", + "成功获取并保存 20170103 的每日基础数据\n" + ] + } + ], + "source": [ + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "\n", + "# 获取交易日历\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250301')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal['cal_date'].tolist() # 获取所有交易日期列表\n", + "\n", + "# 使用 HDFStore 存储数据\n", + "all_daily_data = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "# 遍历每个交易日期并获取数据\n", + "for trade_date in trade_dates:\n", + " try:\n", + " # 获取每日基础数据\n", + " sw_daily = pro.sw_daily(trade_date=trade_date)\n", + " if sw_daily is not None and not sw_daily.empty:\n", + " all_daily_data.append(sw_daily)\n", + " print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用 300 次，检查时间是否少于 1 分钟，如果少于则等待剩余时间\n", + " if api_call_count % 150 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用 150 次 API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "260641c7-5e5b-407c-8cbf-8371a3c98961", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有每日基础数据获取并保存完毕！\n" + ] + } + ], + "source": [ + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf('../../data/sw_daily.h5', key='sw_daily', mode='w', format='table', data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "86961191-2337-4ef4-a84a-a790408bca57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date name open low high close \\\n", + "0 801001.SI 20250221 申万50 3035.90 3032.87 3082.66 3080.21 \n", + "1 801002.SI 20250221 申万中小 6669.62 6661.71 6764.47 6756.56 \n", + "2 801003.SI 20250221 申万Ａ指 3820.27 3804.30 3868.00 3866.24 \n", + "3 801005.SI 20250221 申万创业 2948.20 2940.50 3015.09 3011.72 \n", + "4 801010.SI 20250221 农林牧渔 2484.68 2474.55 2509.74 2482.52 \n", + ".. ... ... ... ... ... ... ... \n", + "434 859811.SI 20250221 生活用纸 1246.80 1232.31 1262.77 1242.81 \n", + "435 859821.SI 20250221 化妆品制造及其他 2556.69 2509.55 2571.30 2541.22 \n", + "436 859822.SI 20250221 品牌化妆品 8094.33 8012.65 8187.53 8097.73 \n", + "437 859852.SI 20250221 培训教育 697.66 681.08 712.29 701.34 \n", + "438 859951.SI 20250221 电视广播Ⅲ 1065.62 1052.04 1100.17 1082.18 \n", + "\n", + " change pct_change vol amount pe pb float_mv \\\n", + "0 52.96 1.75 816982.0 23041217.0 14.09 1.99 8.250593e+08 \n", + "1 103.90 1.56 3829971.0 44318003.0 26.50 2.36 6.496748e+08 \n", + "2 53.69 1.41 15127544.0 219063211.0 18.24 1.81 3.690715e+09 \n", + "3 68.00 2.31 3330400.0 66930876.0 38.42 3.77 6.188568e+08 \n", + "4 2.94 0.12 176011.0 1500583.0 22.17 2.25 5.354195e+07 \n", + ".. ... ... ... ... ... ... ... \n", + "434 -2.94 -0.24 4569.0 66382.0 40.52 2.05 2.102047e+06 \n", + "435 -27.71 -1.08 14028.0 100506.0 40.09 2.10 1.550297e+06 \n", + "436 20.03 0.25 4185.0 98655.0 27.34 3.10 3.700208e+06 \n", + "437 2.40 0.34 64215.0 481622.0 128.82 7.13 3.987335e+06 \n", + "438 26.90 2.55 65040.0 407776.0 60.36 1.09 6.848231e+06 \n", + "\n", + " total_mv \n", + "0 2.124415e+09 \n", + "1 1.216920e+09 \n", + "2 8.808614e+09 \n", + "3 1.386123e+09 \n", + "4 1.206622e+08 \n", + ".. ... \n", + "434 5.901333e+06 \n", + "435 3.119048e+06 \n", + "436 1.032154e+07 \n", + "437 6.559061e+06 \n", + "438 1.663464e+07 \n", + "\n", + "[439 rows x 15 columns]\n" + ] + } + ], + "source": [ + "print(pro.sw_daily(trade_date='20250221'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/industry_data.ipynb b/code/data/industry_data.ipynb new file mode 100644 index 0000000..a3fa419 --- /dev/null +++ b/code/data/industry_data.ipynb @@ -0,0 +1,5894 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ac5ca4b4-2ec8-4919-ab95-0af825efb6c1", + "metadata": {}, + "outputs": [], + "source": [ + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f0e4ee09-c6c2-474f-a881-01268eaa1d4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " l1_code l1_name l2_code l2_name l3_code l3_name ts_code name \\\n", + "0 801080.SI 电子 801082.SI 其他电子Ⅱ 850841.SI 其他电子Ⅲ 600751.SH 海航科技 \n", + "1 801170.SI 交通运输 801992.SI 航运港口 851761.SI 航运 600751.SH 海航科技 \n", + "\n", + " in_date out_date is_new \n", + "0 20180713 None Y \n", + "1 20240730 None Y \n" + ] + } + ], + "source": [ + "print(pro.index_member_all(ts_code='600751.SH'))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fc09abd2-2404-4a37-9357-5576930a9d3a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "成功获取 000001.SZ 的数据\n", + "成功获取 000002.SZ 的数据\n", + "成功获取 000004.SZ 的数据\n", + "成功获取 000006.SZ 的数据\n", + "成功获取 000007.SZ 的数据\n", + "成功获取 000008.SZ 的数据\n", + "成功获取 000009.SZ 的数据\n", + "成功获取 000010.SZ 的数据\n", + "成功获取 000011.SZ 的数据\n", + "成功获取 000012.SZ 的数据\n", + "成功获取 000014.SZ 的数据\n", + "成功获取 000016.SZ 的数据\n", + "成功获取 000017.SZ 的数据\n", + "成功获取 000019.SZ 的数据\n", + "成功获取 000020.SZ 的数据\n", + "成功获取 000021.SZ 的数据\n", + "成功获取 000025.SZ 的数据\n", + "成功获取 000026.SZ 的数据\n", + "成功获取 000027.SZ 的数据\n", + "成功获取 000028.SZ 的数据\n", + "成功获取 000029.SZ 的数据\n", + "成功获取 000030.SZ 的数据\n", + "成功获取 000031.SZ 的数据\n", + "成功获取 000032.SZ 的数据\n", + "成功获取 000034.SZ 的数据\n", + "成功获取 000035.SZ 的数据\n", + "成功获取 000036.SZ 的数据\n", + "成功获取 000037.SZ 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{ts_code} 的数据\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 300 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "afaca5f2-1b44-435c-81c6-7432443844c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有日线数据已保存到 industry_data.h5\n" + ] + } + ], + "source": [ + "# 合并所有获取到的日线数据\n", + "if daily_data_list:\n", + " all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n", + " # 使用 HDF5 格式保存到本地文件（文件名：daily_data.h5，key设为 'daily_data'）\n", + " all_daily_data.to_hdf('../../data/industry_data.h5', key='industry_data', mode='w', format='table')\n", + " print(\"所有日线数据已保存到 industry_data.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "57e28839-128c-4916-9f59-9121d3b5f537", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " l1_code l1_name l2_code l2_name l3_code l3_name ts_code \\\n", + "0 801780.SI 银行 801783.SI 股份制银行Ⅱ 857831.SI 股份制银行Ⅲ 000001.SZ \n", + "1 801180.SI 房地产 801181.SI 房地产开发 851811.SI 住宅开发 000002.SZ \n", + "2 801750.SI 计算机 801104.SI 软件开发 851042.SI 横向通用软件 000004.SZ \n", + "3 801180.SI 房地产 801181.SI 房地产开发 851811.SI 住宅开发 000006.SZ \n", + "4 801200.SI 商贸零售 801203.SI 一般零售 852034.SI 商业物业经营 000007.SZ \n", + "... ... ... ... ... ... ... ... \n", + "5733 801750.SI 计算机 801104.SI 软件开发 851041.SI 垂直应用软件 688555.SH \n", + "5734 801740.SI 国防军工 801742.SI 航空装备Ⅱ 857421.SI 航空装备Ⅲ 832317.BJ \n", + "5735 801880.SI 汽车 801093.SI 汽车零部件 850923.SI 底盘与发动机系统 833874.BJ \n", + "5736 801080.SI 电子 801084.SI 光学光电子 850831.SI 面板 833994.BJ \n", + "5737 801170.SI 交通运输 801992.SI 航运港口 851711.SI 港口 T00018.SH \n", + "\n", + " name in_date out_date is_new \n", + "0 平安银行 19910403 None Y \n", + "1 万科A 19910129 None Y \n", + "2 国华网安 20210730 None Y \n", + "3 深振业A 20150930 None Y \n", + "4 全新好 20190724 None Y \n", + "... ... ... ... ... \n", + "5733 退市泽达(退市) 20200605 None Y \n", + "5734 观典防务(退市) 20200727 None Y \n", + "5735 泰祥股份(退市) 20200727 None Y \n", + "5736 翰博高新(退市) 20200727 None Y \n", + "5737 上港集箱(退市) 20000719 None Y \n", + "\n", + "[5738 rows x 11 columns]\n" + ] + } + ], + "source": [ + "print(all_daily_data)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/kpl_concept.ipynb b/code/data/kpl_concept.ipynb new file mode 100644 index 0000000..ce37f42 --- /dev/null +++ b/code/data/kpl_concept.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:28:49.275220Z", + "start_time": "2025-03-12T15:28:48.624632Z" + } + }, + "outputs": [], + "source": [ + "from operator import index\n", + "\n", + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "972a5ac9f79fe373", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:28:49.280632Z", + "start_time": "2025-03-12T15:28:49.275220Z" + } + }, + "outputs": [], + "source": [ + "\n", + "# df = pro.cyq_perf(start_date='20220101', end_date='20220429')\n", + "# print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f448da220816bf98", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:39:50.128089Z", + "start_time": "2025-03-12T15:28:49.437760Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "成功获取并保存 20250228 的每日基础数据\n", + "成功获取并保存 20250227 的每日基础数据\n", + "成功获取并保存 20250226 的每日基础数据\n", + "成功获取并保存 20250225 的每日基础数据\n", + "成功获取并保存 20250224 的每日基础数据\n", + "成功获取并保存 20250221 的每日基础数据\n", + "成功获取并保存 20250220 的每日基础数据\n", + "成功获取并保存 20250219 的每日基础数据\n", + "成功获取并保存 20250218 的每日基础数据\n", + "成功获取并保存 20250217 的每日基础数据\n", + "成功获取并保存 20250214 的每日基础数据\n", + "成功获取并保存 20250213 的每日基础数据\n", + "成功获取并保存 20250212 的每日基础数据\n", + "成功获取并保存 20250211 的每日基础数据\n", + "成功获取并保存 20250210 的每日基础数据\n", + "成功获取并保存 20250207 的每日基础数据\n", + "成功获取并保存 20250206 的每日基础数据\n", + "成功获取并保存 20250205 的每日基础数据\n", + "成功获取并保存 20250127 的每日基础数据\n", + "成功获取并保存 20250124 的每日基础数据\n", + "成功获取并保存 20250123 的每日基础数据\n", + "成功获取并保存 20250122 的每日基础数据\n", + "成功获取并保存 20250121 的每日基础数据\n", + "成功获取并保存 20250120 的每日基础数据\n", + "成功获取并保存 20250117 的每日基础数据\n", + "成功获取并保存 20250116 的每日基础数据\n", + "成功获取并保存 20250115 的每日基础数据\n", + "成功获取并保存 20250114 的每日基础数据\n", + "成功获取并保存 20250113 的每日基础数据\n", + "成功获取并保存 20250110 的每日基础数据\n", + "成功获取并保存 20250109 的每日基础数据\n", + "成功获取并保存 20250108 的每日基础数据\n", + "成功获取并保存 20250107 的每日基础数据\n", + "成功获取并保存 20250106 的每日基础数据\n", + "成功获取并保存 20250103 的每日基础数据\n", + "成功获取并保存 20250102 的每日基础数据\n", + "成功获取并保存 20241231 的每日基础数据\n", + "成功获取并保存 20241230 的每日基础数据\n", + "成功获取并保存 20241227 的每日基础数据\n", + "成功获取并保存 20241226 的每日基础数据\n", + "成功获取并保存 20241225 的每日基础数据\n", + "成功获取并保存 20241224 的每日基础数据\n", + "成功获取并保存 20241223 的每日基础数据\n", + "成功获取并保存 20241220 的每日基础数据\n", + "成功获取并保存 20241219 的每日基础数据\n", + "成功获取并保存 20241218 的每日基础数据\n", + "成功获取并保存 20241217 的每日基础数据\n", + "成功获取并保存 20241216 的每日基础数据\n", + "成功获取并保存 20241213 的每日基础数据\n", + "成功获取并保存 20241212 的每日基础数据\n", + "成功获取并保存 20241211 的每日基础数据\n", + "成功获取并保存 20241210 的每日基础数据\n", + "成功获取并保存 20241209 的每日基础数据\n", + "成功获取并保存 20241206 的每日基础数据\n", + "成功获取并保存 20241205 的每日基础数据\n", + "成功获取并保存 20241204 的每日基础数据\n", + "成功获取并保存 20241203 的每日基础数据\n", + "成功获取并保存 20241202 的每日基础数据\n", + "成功获取并保存 20241129 的每日基础数据\n", + "成功获取并保存 20241128 的每日基础数据\n", + "成功获取并保存 20241127 的每日基础数据\n", + "成功获取并保存 20241126 的每日基础数据\n", + "成功获取并保存 20241125 的每日基础数据\n", + "成功获取并保存 20241122 的每日基础数据\n", + "成功获取并保存 20241121 的每日基础数据\n", + "成功获取并保存 20241120 的每日基础数据\n", + "成功获取并保存 20241119 的每日基础数据\n", + "成功获取并保存 20241118 的每日基础数据\n", + "成功获取并保存 20241115 的每日基础数据\n", + "成功获取并保存 20241114 的每日基础数据\n", + "成功获取并保存 20241113 的每日基础数据\n", + "成功获取并保存 20241112 的每日基础数据\n", + "成功获取并保存 20241111 的每日基础数据\n", + "成功获取并保存 20241108 的每日基础数据\n", + "成功获取并保存 20241107 的每日基础数据\n", + "成功获取并保存 20241106 的每日基础数据\n", + "成功获取并保存 20241105 的每日基础数据\n", + "成功获取并保存 20241104 的每日基础数据\n", + "成功获取并保存 20241101 的每日基础数据\n", + "成功获取并保存 20241031 的每日基础数据\n", + "成功获取并保存 20241030 的每日基础数据\n", + "成功获取并保存 20241029 的每日基础数据\n", + "成功获取并保存 20241028 的每日基础数据\n", + "成功获取并保存 20241025 的每日基础数据\n", + "成功获取并保存 20241024 的每日基础数据\n", + "成功获取并保存 20241023 的每日基础数据\n", + "成功获取并保存 20241022 的每日基础数据\n", + "成功获取并保存 20241021 的每日基础数据\n", + "成功获取并保存 20241014 的每日基础数据\n", + "150 1741835004.3988936 1741834982.2357981\n", + "已调用 150 次 API，等待 37.84 秒以满足速率限制...\n", + "300 1741835064.0700593 1741835042.2372077\n", + "已调用 150 次 API，等待 38.17 秒以满足速率限制...\n", + "450 1741835124.4976892 1741835102.2381623\n", + "已调用 150 次 API，等待 37.74 秒以满足速率限制...\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 22\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m trade_date \u001b[38;5;129;01min\u001b[39;00m trade_dates:\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 21\u001b[0m \u001b[38;5;66;03m# 获取每日基础数据\u001b[39;00m\n\u001b[1;32m---> 22\u001b[0m kpl_concept \u001b[38;5;241m=\u001b[39m pro\u001b[38;5;241m.\u001b[39mkpl_concept(trade_date\u001b[38;5;241m=\u001b[39mtrade_date)\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kpl_concept \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kpl_concept\u001b[38;5;241m.\u001b[39mempty:\n\u001b[0;32m 24\u001b[0m all_daily_data\u001b[38;5;241m.\u001b[39mappend(kpl_concept)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\tushare\\pro\\client.py:41\u001b[0m, in \u001b[0;36mDataApi.query\u001b[1;34m(self, api_name, fields, **kwargs)\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mquery\u001b[39m(\u001b[38;5;28mself\u001b[39m, api_name, fields\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 34\u001b[0m req_params \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 35\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_name\u001b[39m\u001b[38;5;124m'\u001b[39m: api_name,\n\u001b[0;32m 36\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__token,\n\u001b[0;32m 37\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m: kwargs,\n\u001b[0;32m 38\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfields\u001b[39m\u001b[38;5;124m'\u001b[39m: fields\n\u001b[0;32m 39\u001b[0m }\n\u001b[1;32m---> 41\u001b[0m res \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__http_url\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mapi_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, json\u001b[38;5;241m=\u001b[39mreq_params, timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__timeout)\n\u001b[0;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m res:\n\u001b[0;32m 43\u001b[0m result \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(res\u001b[38;5;241m.\u001b[39mtext)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\api.py:115\u001b[0m, in \u001b[0;36mpost\u001b[1;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 104\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request.\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \n\u001b[0;32m 106\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, data\u001b[38;5;241m=\u001b[39mdata, json\u001b[38;5;241m=\u001b[39mjson, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m 587\u001b[0m }\n\u001b[0;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:724\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[0;32m 722\u001b[0m \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[0;32m 723\u001b[0m gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 724\u001b[0m history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 726\u001b[0m history \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:724\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m allow_redirects:\n\u001b[0;32m 722\u001b[0m \u001b[38;5;66;03m# Redirect resolving generator.\u001b[39;00m\n\u001b[0;32m 723\u001b[0m gen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresolve_redirects(r, request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 724\u001b[0m history \u001b[38;5;241m=\u001b[39m [resp \u001b[38;5;28;01mfor\u001b[39;00m resp \u001b[38;5;129;01min\u001b[39;00m gen]\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 726\u001b[0m history \u001b[38;5;241m=\u001b[39m []\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:265\u001b[0m, in \u001b[0;36mSessionRedirectMixin.resolve_redirects\u001b[1;34m(self, resp, req, stream, timeout, verify, cert, proxies, yield_requests, **adapter_kwargs)\u001b[0m\n\u001b[0;32m 263\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m req\n\u001b[0;32m 264\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 265\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(\n\u001b[0;32m 266\u001b[0m req,\n\u001b[0;32m 267\u001b[0m stream\u001b[38;5;241m=\u001b[39mstream,\n\u001b[0;32m 268\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 269\u001b[0m verify\u001b[38;5;241m=\u001b[39mverify,\n\u001b[0;32m 270\u001b[0m cert\u001b[38;5;241m=\u001b[39mcert,\n\u001b[0;32m 271\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m 272\u001b[0m allow_redirects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 273\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39madapter_kwargs,\n\u001b[0;32m 274\u001b[0m )\n\u001b[0;32m 276\u001b[0m extract_cookies_to_jar(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcookies, prepared_request, resp\u001b[38;5;241m.\u001b[39mraw)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;66;03m# extract redirect url, if any, for the next loop\u001b[39;00m\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[0;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\requests\\adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 664\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 667\u001b[0m resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m 668\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 669\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m 670\u001b[0m body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m 671\u001b[0m headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m 672\u001b[0m redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 673\u001b[0m assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 674\u001b[0m preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 675\u001b[0m decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 676\u001b[0m retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m 677\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 678\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 679\u001b[0m )\n\u001b[0;32m 681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\urllib3\\connectionpool.py:787\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m 784\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 786\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[1;32m--> 787\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m 788\u001b[0m conn,\n\u001b[0;32m 789\u001b[0m method,\n\u001b[0;32m 790\u001b[0m url,\n\u001b[0;32m 791\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m 792\u001b[0m body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m 793\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m 794\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 795\u001b[0m retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[0;32m 796\u001b[0m response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[0;32m 797\u001b[0m preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m 798\u001b[0m decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m 799\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[0;32m 800\u001b[0m )\n\u001b[0;32m 802\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[0;32m 803\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\urllib3\\connectionpool.py:534\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m 532\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 534\u001b[0m response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 535\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 536\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\urllib3\\connection.py:516\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 513\u001b[0m _shutdown \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshutdown\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 515\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[1;32m--> 516\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 518\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 519\u001b[0m assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\http\\client.py:1395\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1393\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1394\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1395\u001b[0m response\u001b[38;5;241m.\u001b[39mbegin()\n\u001b[0;32m 1396\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[0;32m 1397\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\http\\client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 325\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_status()\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[0;32m 327\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\http\\client.py:286\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 286\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[0;32m 288\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\socket.py:718\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 716\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m 717\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 718\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m 720\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "\n", + "# 获取交易日历\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250301')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal['cal_date'].tolist() # 获取所有交易日期列表\n", + "\n", + "# 使用 HDFStore 存储数据\n", + "all_daily_data = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "# 遍历每个交易日期并获取数据\n", + "for trade_date in trade_dates:\n", + " try:\n", + " # 获取每日基础数据\n", + " kpl_concept = pro.kpl_concept(trade_date=trade_date)\n", + " if kpl_concept is not None and not kpl_concept.empty:\n", + " all_daily_data.append(kpl_concept)\n", + " print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用 300 次，检查时间是否少于 1 分钟，如果少于则等待剩余时间\n", + " if api_call_count % 150 == 0:\n", + " print(api_call_count,time.time(), batch_start_time)\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用 150 次 API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "907f732d3c397bf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-12T15:39:50.141920800Z", + "start_time": "2025-03-12T15:23:41.345460Z" + } + }, + "outputs": [], + "source": [ + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf('../../data/kpl_concept.h5', key='kpl_concept', mode='w', format='table', data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/update/cyq-perf.ipynb b/code/data/update/cyq-perf.ipynb new file mode 100644 index 0000000..499cbf1 --- /dev/null +++ b/code/data/update/cyq-perf.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:31.596637Z", + "start_time": "2025-03-30T16:42:30.883319Z" + } + }, + "source": [ + "import tushare as ts\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:37.590148Z", + "start_time": "2025-03-30T16:42:31.596637Z" + } + }, + "source": [ + "import pandas as pd\n", + "import time\n", + "\n", + "h5_filename = '../../../data/cyq_perf.h5'\n", + "key = '/cyq_perf'\n", + "max_date = None\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date']]\n", + " print(df)\n", + " max_date = df['trade_date'].max()\n", + "\n", + "print(max_date)\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", + "start_date = min(trade_dates)\n", + "print(f'start_date: {start_date}')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "0 000001.SZ 20250312\n", + "1 000002.SZ 20250312\n", + "2 000004.SZ 20250312\n", + "3 000006.SZ 20250312\n", + "4 000007.SZ 20250312\n", + "... ... ...\n", + "32304 920108.BJ 20250314\n", + "32305 920111.BJ 20250314\n", + "32306 920116.BJ 20250314\n", + "32307 920118.BJ 20250314\n", + "32308 920128.BJ 20250314\n", + "\n", + "[7503415 rows x 2 columns]\n", + "20250321\n", + "start_date: 20250324\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "747acc47-0884-4f76-90fb-276f6494e31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:43:29.275885Z", + "start_time": "2025-03-30T16:42:37.858763Z" + } + }, + "source": [ + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", + "all_daily_data = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "\n", + "def get_data(trade_date):\n", + " time.sleep(0.1)\n", + " data = pro.cyq_perf(trade_date=trade_date)\n", + " if data is not None and not data.empty:\n", + " return data\n", + "\n", + "\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " trade_date = future_to_date[future] # 获取对应的交易日期\n", + " try:\n", + " result = future.result() # 获取任务执行的结果\n", + " all_daily_data.append(result)\n", + " print(f\"任务 {trade_date} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "任务 20250418 完成\n", + "任务 20250417 完成\n", + "任务 20250415 完成\n", + "任务 20250416 完成\n", + "任务 20250411 完成\n", + "任务 20250414 完成\n", + "任务 20250409 完成\n", + "任务 20250410 完成\n", + "任务 20250408 完成\n", + "任务 20250407 完成\n", + "任务 20250403 完成\n", + "任务 20250402 完成\n", + "任务 20250401 完成\n", + "任务 20250331 完成\n", + "任务 20250328 完成\n", + "任务 20250327 完成\n", + "任务 20250326 完成\n", + "任务 20250325 完成\n", + "任务 20250324 完成\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "c6765638-481f-40d8-a259-2e7b25362618", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:43:30.100678Z", + "start_time": "2025-03-30T16:43:29.311710Z" + } + }, + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有每日基础数据获取并保存完毕！\n" + ] + } + ], + "execution_count": 4 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/update/index_data.ipynb b/code/data/update/index_data.ipynb new file mode 100644 index 0000000..f32b914 --- /dev/null +++ b/code/data/update/index_data.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", + "metadata": {}, + "outputs": [], + "source": [ + "import tushare as ts\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "0 801001.SI 20250221\n", + "1 801002.SI 20250221\n", + "2 801003.SI 20250221\n", + "3 801005.SI 20250221\n", + "4 801010.SI 20250221\n", + "... ... ...\n", + "1044388 857344.SI 20170103\n", + "1044389 857411.SI 20170103\n", + "1044390 857421.SI 20170103\n", + "1044391 857431.SI 20170103\n", + "1044392 858811.SI 20170103\n", + "\n", + "[1044393 rows x 2 columns]\n", + "20250221\n", + "start_date: 20250224\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import time\n", + "\n", + "h5_filename = '../../../data/sw_daily.h5'\n", + "key = '/sw_daily'\n", + "max_date = None\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date']]\n", + " print(df)\n", + " max_date = df['trade_date'].max()\n", + "\n", + "print(max_date)\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", + "start_date = min(trade_dates)\n", + "print(f'start_date: {start_date}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "747acc47-0884-4f76-90fb-276f6494e31d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "任务 20250417 完成\n", + "任务 20250418 完成\n", + "任务 20250416 完成\n", + "任务 20250415 完成\n", + "任务 20250411 完成\n", + "任务 20250414 完成\n", + "任务 20250410 完成\n", + "任务 20250409 完成\n", + "任务 20250408 完成\n", + "任务 20250403 完成\n", + "任务 20250407 完成\n", + "任务 20250402 完成\n", + "任务 20250401 完成\n", + "任务 20250331 完成\n", + "任务 20250328 完成\n", + "任务 20250327 完成\n", + "任务 20250326 完成\n", + "任务 20250325 完成\n", + "任务 20250324 完成\n", + "任务 20250321 完成\n", + "任务 20250320 完成\n", + "任务 20250319 完成\n", + "任务 20250317 完成\n", + "任务 20250314 完成\n", + "任务 20250318 完成\n", + "任务 20250313 完成\n", + "任务 20250312 完成\n", + "任务 20250311 完成\n", + "任务 20250310 完成\n", + "任务 20250307 完成\n", + "任务 20250306 完成\n", + "任务 20250305 完成\n", + "任务 20250304 完成\n", + "任务 20250303 完成\n", + "任务 20250228 完成\n", + "任务 20250227 完成\n", + "任务 20250226 完成\n", + "任务 20250225 完成\n", + "任务 20250224 完成\n" + ] + } + ], + "source": [ + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", + "all_daily_data = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n", + "def get_data(trade_date):\n", + " time.sleep(0.1)\n", + " data = pro.sw_daily(trade_date=trade_date)\n", + " if data is not None and not data.empty:\n", + " return data\n", + "\n", + "\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " trade_date = future_to_date[future] # 获取对应的交易日期\n", + " try:\n", + " result = future.result() # 获取任务执行的结果\n", + " all_daily_data.append(result)\n", + " print(f\"任务 {trade_date} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c6765638-481f-40d8-a259-2e7b25362618", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有每日基础数据获取并保存完毕！\n" + ] + } + ], + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/data/update/sw_daily.ipynb b/code/data/update/sw_daily.ipynb new file mode 100644 index 0000000..1ce7b7f --- /dev/null +++ b/code/data/update/sw_daily.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:32.996500Z", + "start_time": "2025-03-30T16:42:32.209631Z" + } + }, + "source": [ + "import tushare as ts\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:34.591433Z", + "start_time": "2025-03-30T16:42:32.996500Z" + } + }, + "source": [ + "import pandas as pd\n", + "import time\n", + "\n", + "h5_filename = '../../../data/sw_daily.h5'\n", + "key = '/sw_daily'\n", + "max_date = None\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date']]\n", + " print(df)\n", + " max_date = df['trade_date'].max()\n", + "\n", + "print(max_date)\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", + "start_date = min(trade_dates)\n", + "print(f'start_date: {start_date}')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "0 801001.SI 20250221\n", + "1 801002.SI 20250221\n", + "2 801003.SI 20250221\n", + "3 801005.SI 20250221\n", + "4 801010.SI 20250221\n", + "... ... ...\n", + "2629 859811.SI 20250314\n", + "2630 859821.SI 20250314\n", + "2631 859822.SI 20250314\n", + "2632 859852.SI 20250314\n", + "2633 859951.SI 20250314\n", + "\n", + "[1053173 rows x 2 columns]\n", + "20250321\n", + "start_date: 20250324\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "747acc47-0884-4f76-90fb-276f6494e31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:37.718270Z", + "start_time": "2025-03-30T16:42:34.817305Z" + } + }, + "source": [ + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", + "all_daily_data = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "\n", + "def get_data(trade_date):\n", + " time.sleep(0.1)\n", + " data = pro.sw_daily(trade_date=trade_date)\n", + " if data is not None and not data.empty:\n", + " return data\n", + "\n", + "\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " trade_date = future_to_date[future] # 获取对应的交易日期\n", + " try:\n", + " result = future.result() # 获取任务执行的结果\n", + " all_daily_data.append(result)\n", + " print(f\"任务 {trade_date} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + "\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "任务 20250417 完成\n", + "任务 20250418 完成\n", + "任务 20250416 完成\n", + "任务 20250415 完成\n", + "任务 20250414 完成\n", + "任务 20250411 完成\n", + "任务 20250410 完成\n", + "任务 20250409 完成\n", + "任务 20250408 完成\n", + "任务 20250407 完成\n", + "任务 20250403 完成\n", + "任务 20250402 完成\n", + "任务 20250401 完成\n", + "任务 20250331 完成\n", + "任务 20250328 完成\n", + "任务 20250327 完成\n", + "任务 20250326 完成\n", + "任务 20250325 完成\n", + "任务 20250324 完成\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "c6765638-481f-40d8-a259-2e7b25362618", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-30T16:42:37.922827Z", + "start_time": "2025-03-30T16:42:37.739040Z" + } + }, + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有每日基础数据获取并保存完毕！\n" + ] + } + ], + "execution_count": 4 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/model/lightgbm_model_UpdateRegression_2025-2-25.pkl b/code/model/lightgbm_model_UpdateRegression_2025-2-25.pkl new file mode 100644 index 0000000..79b4ed7 Binary files /dev/null and b/code/model/lightgbm_model_UpdateRegression_2025-2-25.pkl differ diff --git a/code/test.py b/code/test.py new file mode 100644 index 0000000..797c6f4 --- /dev/null +++ b/code/test.py @@ -0,0 +1,5 @@ +import torch +print(torch.__version__) + +print(torch.version.cuda) +print(torch.backends.cudnn.version()) diff --git a/code/train/ClassifyLR.ipynb b/code/train/ClassifyLR.ipynb new file mode 100644 index 0000000..df5178e --- /dev/null +++ b/code/train/ClassifyLR.ipynb @@ -0,0 +1,1317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:19:35.036653Z", + "start_time": "2025-02-23T14:19:34.973432Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:20:18.946211Z", + "start_time": "2025-02-23T14:19:35.036653Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8296325 entries, 0 to 8296324\n", + "Data columns (total 21 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 turnover_rate float64 \n", + " 8 pe_ttm float64 \n", + " 9 circ_mv float64 \n", + " 10 volume_ratio float64 \n", + " 11 is_st bool \n", + " 12 up_limit float64 \n", + " 13 down_limit float64 \n", + " 14 buy_sm_vol float64 \n", + " 15 sell_sm_vol float64 \n", + " 16 buy_lg_vol float64 \n", + " 17 sell_lg_vol float64 \n", + " 18 buy_elg_vol float64 \n", + " 19 sell_elg_vol float64 \n", + " 20 net_mf_vol float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(18), object(1)\n", + "memory usage: 1.2+ GB\n", + "None\n" + ] + } + ], + "source": [ + "from utils.utils import read_and_merge_h5_data\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "38879273d3574ae3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:20:21.108154Z", + "start_time": "2025-02-23T14:20:19.025121Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n", + "left merge on ['ts_code']\n" + ] + } + ], + "source": [ + "print('industry')\n", + "df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code'],\n", + " df=df, on=['ts_code'], join='left')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a4eec8c93f6a7cc3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:20:21.712957Z", + "start_time": "2025-02-23T14:20:21.602953Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "\n", + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " return df\n", + "\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line', 'MACD_hist'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:20:21.860318Z", + "start_time": "2025-02-23T14:20:21.735442Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "\n", + "def get_technical_factor(df):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # 计算 up 和 down\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " # 计算 ATR\n", + " df['atr_14'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", + " index=x.index)\n", + " )\n", + " df['atr_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", + " index=x.index)\n", + " )\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " # 计算 RSI\n", + " df['rsi_3'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " )\n", + " df['rsi_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['rsi_9'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # 计算 avg_close_5\n", + " df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_act_factor(df, cat=True):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + " # 计算 EMA 指标\n", + " df['ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " if cat:\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", + "\n", + " # 根据 trade_date 截面计算排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_money_flow_factor(df):\n", + " # 计算资金流相关因子（字段名称见 tushare 数据说明）\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + " return df\n", + "\n", + "\n", + "def get_alpha_factor(df):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code')\n", + "\n", + " # alpha_022: 当前 close 与 5 日前 close 差值\n", + " df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))\n", + "\n", + " # alpha_003: (close - open) / (high - low)\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " return df\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:20:25.061140Z", + "start_time": "2025-02-23T14:20:21.880078Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " # industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # industry_data = get_act_factor(industry_data, cat=False)\n", + " # industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + "\n", + " for factor in factor_columns:\n", + " if factor in industry_data.columns:\n", + " # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " lambda x: x - x.median())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:20:25.128062Z", + "start_time": "2025-02-23T14:20:25.076707Z" + }, + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'l2_code']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5f3d9aece75318cd", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:22.858178Z", + "start_time": "2025-02-23T14:20:25.145555Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 5538535 entries, 1962 to 5538534\n", + "Data columns (total 71 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 turnover_rate float64 \n", + " 8 pe_ttm float64 \n", + " 9 circ_mv float64 \n", + " 10 volume_ratio float64 \n", + " 11 is_st bool \n", + " 12 up_limit float64 \n", + " 13 down_limit float64 \n", + " 14 buy_sm_vol float64 \n", + " 15 sell_sm_vol float64 \n", + " 16 buy_lg_vol float64 \n", + " 17 sell_lg_vol float64 \n", + " 18 buy_elg_vol float64 \n", + " 19 sell_elg_vol float64 \n", + " 20 net_mf_vol float64 \n", + " 21 cat_l2_code object \n", + " 22 up float64 \n", + " 23 down float64 \n", + " 24 atr_14 float64 \n", + " 25 atr_6 float64 \n", + " 26 obv float64 \n", + " 27 maobv_6 float64 \n", + " 28 obv-maobv_6 float64 \n", + " 29 rsi_3 float64 \n", + " 30 rsi_6 float64 \n", + " 31 rsi_9 float64 \n", + " 32 return_5 float64 \n", + " 33 return_10 float64 \n", + " 34 return_20 float64 \n", + " 35 avg_close_5 float64 \n", + " 36 std_return_5 float64 \n", + " 37 std_return_15 float64 \n", + " 38 std_return_25 float64 \n", + " 39 std_return_90 float64 \n", + " 40 std_return_90_2 float64 \n", + " 41 std_return_5 / std_return_90 float64 \n", + " 42 std_return_5 / std_return_25 float64 \n", + " 43 std_return_90 - std_return_90_2 float64 \n", + " 44 ema_5 float64 \n", + " 45 ema_13 float64 \n", + " 46 ema_20 float64 \n", + " 47 ema_60 float64 \n", + " 48 act_factor1 float64 \n", + " 49 act_factor2 float64 \n", + " 50 act_factor3 float64 \n", + " 51 act_factor4 float64 \n", + " 52 cat_af1 bool \n", + " 53 cat_af2 bool \n", + " 54 cat_af3 bool \n", + " 55 cat_af4 bool \n", + " 56 act_factor5 float64 \n", + " 57 act_factor6 float64 \n", + " 58 rank_act_factor1 float64 \n", + " 59 rank_act_factor2 float64 \n", + " 60 rank_act_factor3 float64 \n", + " 61 active_buy_volume_large float64 \n", + " 62 active_buy_volume_big float64 \n", + " 63 active_buy_volume_small float64 \n", + " 64 buy_lg_vol_minus_sell_lg_vol float64 \n", + " 65 buy_elg_vol_minus_sell_elg_vol float64 \n", + " 66 log(circ_mv) float64 \n", + " 67 alpha_022 float64 \n", + " 68 alpha_003 float64 \n", + " 69 alpha_007 float64 \n", + " 70 alpha_013 float64 \n", + "dtypes: bool(5), datetime64[ns](1), float64(63), object(2)\n", + "memory usage: 2.8+ GB\n", + "None\n" + ] + } + ], + "source": [ + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "df = get_technical_factor(df)\n", + "df = get_act_factor(df)\n", + "df = get_money_flow_factor(df)\n", + "df = get_alpha_factor(df)\n", + "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:23.090304Z", + "start_time": "2025-02-23T14:21:22.943303Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['turnover_rate', 'pe_ttm', 'volume_ratio', 'cat_l2_code', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n" + ] + } + ], + "source": [ + "feature_columns = [col for col in df.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(feature_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0ebdfb92-d88b-4b5c-a715-675dab876fc0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:23.168842Z", + "start_time": "2025-02-23T14:21:23.122002Z" + } + }, + "outputs": [], + "source": [ + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " grouped_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n", + " deviation_col_name = f'deviation_median_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_median\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(groupby_col)[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # if feature in df.columns and f'industry_{feature}' in df:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fbb968383f8cf2c7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:39.397925Z", + "start_time": "2025-02-23T14:21:23.168842Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removed 123852 outliers.\n" + ] + } + ], + "source": [ + "def get_qcuts(series, quantiles):\n", + " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", + " return q[-1] # 返回窗口最后一个元素的分位数标签\n", + "\n", + "\n", + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n", + " df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return df['sharpe_ratio']\n", + "\n", + "\n", + "future_close = df.groupby('ts_code')['close'].shift(-4)\n", + "future_return = (future_close - df['close']) / df['close']\n", + "labels = future_return >= 0.03\n", + "df['label'] = labels\n", + "df['lr_label'] = future_return\n", + "df['lr_label'] = remove_outliers_label_percentile(df['lr_label'])\n", + "\n", + "# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\n", + "df = df.sort_values(by=['trade_date', 'ts_code'])\n", + "train_data = df[(df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2016-01-01')]\n", + "test_data = df[df['trade_date'] >= '2023-01-01']\n", + "\n", + "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "# train_data = train_data.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "# test_data = test_data.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "train_data = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "test_data = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "\n", + "# train_data = get_future_data(train_data)\n", + "\n", + "# df = df[['ts_code', 'trade_date', 'open', 'close']]\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "35238cb4f45ce756", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:56.438803Z", + "start_time": "2025-02-23T14:21:39.431593Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_columns size: 149\n", + "feature_columns size: 149\n", + "1171702\n", + "最小日期: 2017-03-21\n", + "最大日期: 2022-12-30\n", + "402634\n", + "最小日期: 2023-01-03\n", + "最大日期: 2025-02-12\n" + ] + } + ], + "source": [ + "# feature_columns_new = feature_columns[:]\n", + "train_data, feature_columns_new = create_deviation_within_dates(train_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "test_data, feature_columns_new = create_deviation_within_dates(test_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns_new)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "\n", + "test_data = test_data.dropna(subset=feature_columns_new)\n", + "test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f679ccccecd09a06", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:56.621062Z", + "start_time": "2025-02-23T14:21:56.470090Z" + } + }, + "outputs": [], + "source": [ + "cat_columns = [col for col in df.columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8f134d435f71e9e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:58.157508Z", + "start_time": "2025-02-23T14:21:56.638939Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from catboost import Pool\n", + "import lightgbm as lgb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import optuna\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import mean_absolute_error\n", + "import os\n", + "import json\n", + "import pickle\n", + "import hashlib\n", + "\n", + "\n", + "def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n", + " print_feature_importance=True, num_boost_round=100,\n", + " use_optuna=False):\n", + " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", + " X_train = train_data_df[feature_columns]\n", + " y_train = train_data_df['label']\n", + "\n", + " X_val = test_data_df[feature_columns]\n", + " y_val = test_data_df['label']\n", + "\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " print(f'categorical_feature: {categorical_feature}')\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + " val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n", + " model = lgb.train(\n", + " params, train_data, num_boost_round=num_boost_round,\n", + " valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + "\n", + " if print_feature_importance:\n", + " lgb.plot_metric(evals)\n", + " # lgb.plot_tree(model, figsize=(20, 8))\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + " return model\n", + "\n", + "\n", + "from catboost import CatBoostClassifier\n", + "import pandas as pd\n", + "\n", + "\n", + "def train_catboost(train_data_df, test_data_df, feature_columns, params=None, plot=False):\n", + " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", + " X_train = train_data_df[feature_columns]\n", + " y_train = train_data_df['label']\n", + "\n", + " X_val = test_data_df[feature_columns]\n", + " y_val = test_data_df['label']\n", + "\n", + " cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " print(f'cat_features: {cat_features}')\n", + " train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n", + " val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n", + "\n", + " model = CatBoostClassifier(**params)\n", + " model.fit(train_pool,\n", + " eval_set=val_pool, plot=plot)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4a4542e1ed6afe7d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:21:58.418136Z", + "start_time": "2025-02-23T14:21:58.339405Z" + } + }, + "outputs": [], + "source": [ + "light_params = {\n", + " 'objective': 'binary',\n", + " 'metric': 'average_precision',\n", + " 'learning_rate': 0.05,\n", + " 'is_unbalance': True,\n", + " 'num_leaves': 2048,\n", + " 'min_data_in_leaf': 1024,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 0.7,\n", + " 'bagging_freq': 5,\n", + " # 'lambda_l1': 80,\n", + " # 'lambda_l2': 65,\n", + " 'verbosity': -1,\n", + " 'num_threads': 16\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "beeb098799ecfa6a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:23:12.347650Z", + "start_time": "2025-02-23T14:21:58.449422Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 1171702\n", + "categorical_feature: [3, 34, 35, 36, 37]\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[89]\ttrain's average_precision: 0.473589\tvalid's average_precision: 0.284686\n", + "Evaluated only: average_precision\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "evals = {}\n", + "model = train_light_model(train_data, test_data, light_params, feature_columns_new,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, use_optuna=False,\n", + " print_feature_importance=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "445dff84-70b2-4fc9-a9b6-1251993324d6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:23:12.546231Z", + "start_time": "2025-02-23T14:23:12.397923Z" + } + }, + "outputs": [], + "source": [ + "catboost_params = {\n", + " 'loss_function': 'Logloss', # 适用于二分类\n", + " 'eval_metric': 'AUC', # 评估指标\n", + " 'iterations': 5000,\n", + " 'learning_rate': 0.01,\n", + " 'depth': 10, # 控制模型复杂度\n", + " # 'l2_leaf_reg': 3, # L2 正则化\n", + " 'verbose': 500,\n", + " 'early_stopping_rounds': 100,\n", + " # 'one_hot_max_size': 50,\n", + " # 'class_weights': [0.6, 1.2]\n", + " 'task_type': 'GPU'\n", + "}\n", + "\n", + "# model = train_catboost(train_data, test_data, feature_columns_new, catboost_params, plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c8783e36d104ec15", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:23:12.687156Z", + "start_time": "2025-02-23T14:23:12.546231Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import SGDClassifier, LinearRegression, LogisticRegression\n", + "\n", + "\n", + "def train_gbdt_lr(model, X_train: pd.DataFrame, y_train: pd.Series, model_type: str = 'lightgbm'):\n", + " if model_type not in ['lightgbm', 'catboost']:\n", + " raise ValueError(\"model_type must be either 'lightgbm' or 'catboost'\")\n", + "\n", + " # Step 1: Use the pre-trained GBDT model to transform the data into leaf indices\n", + " X_train_lr = predict(model, X_train, model_type=model_type)\n", + " # Raw predictions\n", + " # Convert raw predictions to leaf indices (CatBoost doesn't directly support leaf indices)\n", + " # X_train_lr = np.array([np.argmax(row) for row in X_train_lr])\n", + "\n", + " # # Step 2: One-hot encode the leaf indices to create new features\n", + " # X_train_lr = pd.DataFrame(X_train_lr).add_prefix('tree_') # Add prefix for clarity\n", + "\n", + " # # One-hot encoding for leaf indices\n", + " # X_train_lr = pd.get_dummies(X_train_lr, columns=X_train_lr.columns)\n", + "\n", + " # Step 3: Train a Logistic Regression model on the transformed features\n", + " lr_model = SGDClassifier(loss='log_loss')\n", + " lr_model.fit(X_train_lr, y_train)\n", + "\n", + " return lr_model\n", + "\n", + "\n", + "def predict(model, X: pd.DataFrame, model_type: str = 'lightgbm'):\n", + " if model_type == 'lightgbm':\n", + " X_train_lr = model.predict(X, pred_leaf=True) # Get leaf indices for training data\n", + " elif model_type == 'catboost':\n", + " X_train_lr = model.calc_leaf_indexes(X, ntree_start=int(model.tree_count_ * 0.3),\n", + " ntree_end=int(model.tree_count_ * 0.7))\n", + " return X_train_lr" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1a169a7d962c7ba7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:23:12.799750Z", + "start_time": "2025-02-23T14:23:12.720458Z" + } + }, + "outputs": [], + "source": [ + "\n", + "# train_data = train_data.dropna(subset=['lr_label'])\n", + "# lr_model = train_gbdt_lr(model, train_data[feature_columns_new], train_data['label'], model_type='lightgbm')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "52a6ffb090e92f84", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:23:12.911717Z", + "start_time": "2025-02-23T14:23:12.834986Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.14485596707818926\n" + ] + } + ], + "source": [ + "print(train_data['lr_label'].min())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "63cd2f8d16f05b38", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:37:28.748351Z", + "start_time": "2025-02-23T14:37:28.538732Z" + } + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "\n", + "def incremental_training(test_data: pd.DataFrame,\n", + " model, lr_model,\n", + " days: int,\n", + " back_days: int,\n", + " feature_columns: list,\n", + " params: dict,\n", + " model_type: str = 'lightgbm'):\n", + "\n", + " test_data = test_data.sort_values(by='trade_date')\n", + " scores = []\n", + " unique_trade_dates = sorted(test_data['trade_date'].unique())\n", + "\n", + " for i in tqdm(range(0, len(unique_trade_dates), days)):\n", + " # Get the current window of trade dates\n", + " current_dates = unique_trade_dates[i:i + days]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " X = window_data[feature_columns]\n", + "\n", + " if lr_model is not None:\n", + " window_scores = lr_model.predict_proba(model.predict(X, pred_leaf=True, num_iteration=1000))[:, -1]\n", + " scores.extend(window_scores)\n", + "\n", + " # Prepare data for incremental training\n", + " current_dates = unique_trade_dates[max(0, i - back_days):i + days]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " X_train = window_data[feature_columns]\n", + " y_train = window_data['label'] # Assuming 'label' is what you're predicting\n", + " # Incrementally train the model\n", + " if len(y_train.unique()) > 1:\n", + " if model_type == 'lightgbm':\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + " model = lgb.train(params,\n", + " train_set=train_data,\n", + " num_boost_round=100,\n", + " init_model=model,\n", + " keep_training_booster=True)\n", + " elif model_type == 'catboost':\n", + " from catboost import Pool\n", + " train_data = Pool(data=X_train, label=y_train,\n", + " cat_features=[col for col in feature_columns if col.startswith('cat')])\n", + " # model.set_params(**params)\n", + " model.fit(train_data, init_model=model)\n", + " lr_model = LogisticRegression(max_iter=10000)\n", + " lr_model.fit(model.predict(X_train, pred_leaf=True, num_iteration=1000), y_train)\n", + " else:\n", + " print(current_dates)\n", + "\n", + " # Add the scores as a new 'score' column to the test_data\n", + " test_data['score'] = scores\n", + " return test_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "751a6df9-d90b-4053-8769-c6c3b6654406", + "metadata": { + "ExecuteTime": { + "start_time": "2025-02-23T14:37:28.771726Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 9%|▉ | 9/102 [00:56<13:10, 8.50s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 10%|▉ | 10/102 [01:10<15:47, 10.29s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 12%|█▏ | 12/102 [01:37<17:54, 11.94s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 14%|█▎ | 14/102 [02:06<19:15, 13.13s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 15%|█▍ | 15/102 [02:21<20:00, 13.80s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 18%|█▊ | 18/102 [03:06<20:25, 14.59s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 21%|██ | 21/102 [03:48<18:57, 14.05s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 24%|██▎ | 24/102 [05:13<30:33, 23.51s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 25%|██▍ | 25/102 [05:50<35:04, 27.33s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 26%|██▋ | 27/102 [06:19<25:45, 20.60s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 27%|██▋ | 28/102 [06:33<22:54, 18.58s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 28%|██▊ | 29/102 [06:46<20:50, 17.13s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 29%|██▉ | 30/102 [06:59<18:49, 15.68s/it]E:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + " 30%|███ | 31/102 [07:26<17:01, 14.39s/it]\n" + ] + } + ], + "source": [ + "predictions_test = incremental_training(test_data, model, None, 5, 0, feature_columns_new, light_params, model_type='lightgbm')\n", + "predictions_test = predictions_test.loc[predictions_test.groupby('trade_date')['score'].idxmax()]\n", + "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6329ae6f358dabe7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-23T14:44:59.546639600Z", + "start_time": "2025-02-22T21:50:47.121759Z" + } + }, + "outputs": [], + "source": [ + "from lightgbm import LGBMClassifier" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/DoubleQuntile.ipynb b/code/train/DoubleQuntile.ipynb new file mode 100644 index 0000000..fe08a2c --- /dev/null +++ b/code/train/DoubleQuntile.ipynb @@ -0,0 +1,967 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "79a7758178bafdd3", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-02-16T08:24:31.295363Z", + "start_time": "2025-02-16T08:24:31.248141Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n" + ], + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:26:37.921393Z", + "start_time": "2025-02-16T08:26:37.822365Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " # 计算当日涨跌幅\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + "\n", + " # 计算5日移动平均\n", + " df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + "\n", + " # 计算RSI（14日）\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " return df\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " \"\"\"\n", + " 从H5文件中读取指数行情数据，计算相关指标，返回结果。\n", + "\n", + " 参数:\n", + " h5_filename (str): 存储指数行情数据的H5文件路径\n", + "\n", + " 返回:\n", + " DataFrame: 包含计算结果的DataFrame，每行代表一天，包含所有指数的指标\n", + " \"\"\"\n", + " # 读取指数行情数据\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " # 获取某个指数的日线数据\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + "\n", + " # 计算相关指标\n", + " df_index = calculate_indicators(df_index)\n", + "\n", + " # 将结果添加到df_indicators列表\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', '5_day_ma', 'RSI', 'MACD', 'Signal_line', 'MACD_hist'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + " df_final['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n", + "# 打印结果\n", + "print(index_data.head())\n" + ], + "id": "b216cc5529d07cad", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " trade_date 000852.SH_5_day_ma 000905.SH_5_day_ma 399006.SZ_5_day_ma \\\n", + "1314 2019-09-06 4108.5648 4064.9986 1013.1790 \n", + "1315 2019-09-05 4132.3992 4081.7042 1031.9632 \n", + "1316 2019-09-04 4164.2720 4107.3572 1048.1040 \n", + "1317 2019-09-03 4204.0682 4141.2734 1071.9208 \n", + "1318 2019-09-02 4232.8298 4163.0654 1090.8244 \n", + "\n", + " 000852.SH_MACD 000905.SH_MACD 399006.SZ_MACD 000852.SH_MACD_hist \\\n", + "1314 20.380810 34.702099 15.173396 -2.837620 \n", + "1315 28.830595 41.792560 21.448935 4.902761 \n", + "1316 32.263643 44.346834 26.714792 9.561498 \n", + "1317 39.294963 49.978947 32.917542 18.983194 \n", + "1318 44.192628 52.871877 37.373758 28.626657 \n", + "\n", + " 000905.SH_MACD_hist 399006.SZ_MACD_hist 000852.SH_RSI 000905.SH_RSI \\\n", + "1314 1.270353 -9.734351 39.526629 45.136516 \n", + "1315 8.678401 -5.892400 49.265364 53.813061 \n", + "1316 13.402276 -2.099643 53.026120 56.084097 \n", + "1317 22.384958 3.578197 66.615288 68.521794 \n", + "1318 30.874127 8.928962 68.827733 70.274913 \n", + "\n", + " 399006.SZ_RSI 000852.SH_Signal_line 000905.SH_Signal_line \\\n", + "1314 38.536512 23.218429 33.431746 \n", + "1315 47.245649 23.927834 33.114158 \n", + "1316 51.408682 22.702144 30.944558 \n", + "1317 63.606060 20.311770 27.593989 \n", + "1318 69.525065 15.565971 21.997750 \n", + "\n", + " 399006.SZ_Signal_line 000852.SH_daily_return 000905.SH_daily_return \\\n", + "1314 24.907747 -2.133006 -1.914426 \n", + "1315 27.341335 1.676448 1.436414 \n", + "1316 28.814435 -0.741755 -0.725607 \n", + "1317 29.339346 0.839908 0.864581 \n", + "1318 28.444796 -0.331791 -0.402711 \n", + "\n", + " 399006.SZ_daily_return \n", + "1314 -2.676700 \n", + "1315 2.454294 \n", + "1316 0.127868 \n", + "1317 2.933411 \n", + "1318 4.066145 \n" + ] + } + ], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:25:13.872207Z", + "start_time": "2025-02-16T08:24:31.906361Z" + } + }, + "source": [ + "from utils.utils import read_and_merge_h5_data\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "stk limit\n", + "money flow\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:26:43.228123Z", + "start_time": "2025-02-16T08:26:41.895312Z" + } + }, + "cell_type": "code", + "source": "df = df.merge(index_data, on='trade_date', how='left')", + "id": "7357147395bda969", + "outputs": [], + "execution_count": 6 + }, + { + "cell_type": "code", + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:26:43.275563Z", + "start_time": "2025-02-16T08:26:43.228123Z" + } + }, + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio']]" + ], + "outputs": [], + "execution_count": 7 + }, + { + "cell_type": "code", + "id": "a735bc02ceb4d872", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-02-16T08:26:43.388955Z", + "start_time": "2025-02-16T08:26:43.310081Z" + } + }, + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "def get_technical_factor(df):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # 计算 up 和 down\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " # 计算 ATR\n", + " df['atr_14'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), index=x.index)\n", + " )\n", + " df['atr_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " # 计算 RSI\n", + " df['rsi_3'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " )\n", + " df['rsi_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['rsi_9'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # 计算 avg_close_5\n", + " df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_act_factor(df):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + " # 计算 EMA 指标\n", + " df['ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(df['act_factor1']**2 + df['act_factor2']**2)\n", + "\n", + " # 根据 trade_date 截面计算排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_money_flow_factor(df):\n", + " # 计算资金流相关因子（字段名称见 tushare 数据说明）\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + " return df\n", + "\n", + "\n", + "def get_alpha_factor(df):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code')\n", + "\n", + " # alpha_022: 当前 close 与 5 日前 close 差值\n", + " df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))\n", + "\n", + " # alpha_003: (close - open) / (high - low)\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " return df\n", + "\n" + ], + "outputs": [], + "execution_count": 8 + }, + { + "cell_type": "code", + "id": "dbe2fd8021b9417f", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-02-16T08:27:52.356590Z", + "start_time": "2025-02-16T08:26:43.420269Z" + } + }, + "source": [ + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "df = get_technical_factor(df)\n", + "df = get_act_factor(df)\n", + "df = get_money_flow_factor(df)\n", + "df = get_alpha_factor(df)\n", + "print(df.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 5453316 entries, 1962 to 5453315\n", + "Data columns (total 87 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 turnover_rate float64 \n", + " 8 pe_ttm float64 \n", + " 9 circ_mv float64 \n", + " 10 volume_ratio float64 \n", + " 11 is_st bool \n", + " 12 up_limit float64 \n", + " 13 down_limit float64 \n", + " 14 buy_sm_vol float64 \n", + " 15 sell_sm_vol float64 \n", + " 16 buy_lg_vol float64 \n", + " 17 sell_lg_vol float64 \n", + " 18 buy_elg_vol float64 \n", + " 19 sell_elg_vol float64 \n", + " 20 net_mf_vol float64 \n", + " 21 000852.SH_5_day_ma float64 \n", + " 22 000905.SH_5_day_ma float64 \n", + " 23 399006.SZ_5_day_ma float64 \n", + " 24 000852.SH_MACD float64 \n", + " 25 000905.SH_MACD float64 \n", + " 26 399006.SZ_MACD float64 \n", + " 27 000852.SH_MACD_hist float64 \n", + " 28 000905.SH_MACD_hist float64 \n", + " 29 399006.SZ_MACD_hist float64 \n", + " 30 000852.SH_RSI float64 \n", + " 31 000905.SH_RSI float64 \n", + " 32 399006.SZ_RSI float64 \n", + " 33 000852.SH_Signal_line float64 \n", + " 34 000905.SH_Signal_line float64 \n", + " 35 399006.SZ_Signal_line float64 \n", + " 36 000852.SH_daily_return float64 \n", + " 37 000905.SH_daily_return float64 \n", + " 38 399006.SZ_daily_return float64 \n", + " 39 up float64 \n", + " 40 down float64 \n", + " 41 atr_14 float64 \n", + " 42 atr_6 float64 \n", + " 43 obv float64 \n", + " 44 maobv_6 float64 \n", + " 45 obv-maobv_6 float64 \n", + " 46 rsi_3 float64 \n", + " 47 rsi_6 float64 \n", + " 48 rsi_9 float64 \n", + " 49 return_10 float64 \n", + " 50 return_20 float64 \n", + " 51 avg_close_5 float64 \n", + " 52 std_return_5 float64 \n", + " 53 std_return_15 float64 \n", + " 54 std_return_25 float64 \n", + " 55 std_return_90 float64 \n", + " 56 std_return_90_2 float64 \n", + " 57 std_return_5 / std_return_90 float64 \n", + " 58 std_return_5 / std_return_25 float64 \n", + " 59 std_return_90 - std_return_90_2 float64 \n", + " 60 ema_5 float64 \n", + " 61 ema_13 float64 \n", + " 62 ema_20 float64 \n", + " 63 ema_60 float64 \n", + " 64 act_factor1 float64 \n", + " 65 act_factor2 float64 \n", + " 66 act_factor3 float64 \n", + " 67 act_factor4 float64 \n", + " 68 cat_af1 bool \n", + " 69 cat_af2 bool \n", + " 70 cat_af3 bool \n", + " 71 cat_af4 bool \n", + " 72 act_factor5 float64 \n", + " 73 act_factor6 float64 \n", + " 74 rank_act_factor1 float64 \n", + " 75 rank_act_factor2 float64 \n", + " 76 rank_act_factor3 float64 \n", + " 77 active_buy_volume_large float64 \n", + " 78 active_buy_volume_big float64 \n", + " 79 active_buy_volume_small float64 \n", + " 80 buy_lg_vol_minus_sell_lg_vol float64 \n", + " 81 buy_elg_vol_minus_sell_elg_vol float64 \n", + " 82 log(circ_mv) float64 \n", + " 83 alpha_022 float64 \n", + " 84 alpha_003 float64 \n", + " 85 alpha_007 float64 \n", + " 86 alpha_013 float64 \n", + "dtypes: bool(5), datetime64[ns](1), float64(80), object(1)\n", + "memory usage: 3.4+ GB\n", + "None\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:27:52.874223Z", + "start_time": "2025-02-16T08:27:52.717759Z" + } + }, + "cell_type": "code", + "source": [ + "feature_columns = [col for col in df.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "print(feature_columns)" + ], + "id": "2132103543a77819", + "outputs": [], + "execution_count": 10 + }, + { + "cell_type": "code", + "id": "5f3d9aece75318cd", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.569134Z", + "start_time": "2025-02-16T08:42:37.956365Z" + } + }, + "source": [ + "def get_qcuts(series, quantiles):\n", + " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", + " return q[-1] # 返回窗口最后一个元素的分位数标签\n", + "\n", + "\n", + "window = 5\n", + "quantiles = 20\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(level=0, drop=True)\n", + " df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n", + " df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return df['sharpe_ratio']\n", + "\n", + "def get_label(df):\n", + " # labels = df['future_af13'] - df['act_factor1']\n", + " labels = df['future_close5']\n", + " # labels = df['future_af11']\n", + " # labels = df['ema_5'].shift(-1) - df['close']\n", + " # df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\n", + " # df['label'] = calculate_risk_adjusted_target(df, days=5)\n", + " # lower_percentile = df['label'].quantile(0.01) # 1%分位数\n", + " # upper_percentile = df['label'].quantile(0.99) # 99%分位数\n", + " # labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\n", + " # labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\n", + " return labels\n", + "\n", + "df['label'] = get_label(df)\n", + "train_data = df[df['trade_date'] <= '2023-01-01']\n", + "test_data = df[df['trade_date'] >= '2023-01-01']\n", + "\n", + "train_data = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "test_data = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "\n", + "# train_data = get_future_data(train_data)\n", + "\n", + "# df = df[['ts_code', 'trade_date', 'open', 'close']]\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "train_data = train_data.dropna(subset=feature_columns)\n", + "train_data = train_data.reset_index(drop=True)\n", + "train_data['label'] = get_label(train_data)\n", + "\n", + "test_data = test_data.dropna(subset=feature_columns)\n", + "test_data = test_data.reset_index(drop=True)\n", + "test_data['label'] = get_label(test_data)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n" + ], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[1], line 34\u001B[0m\n\u001B[0;32m 24\u001B[0m \u001B[38;5;66;03m# labels = df['future_af11']\u001B[39;00m\n\u001B[0;32m 25\u001B[0m \u001B[38;5;66;03m# labels = df['ema_5'].shift(-1) - df['close']\u001B[39;00m\n\u001B[0;32m 26\u001B[0m \u001B[38;5;66;03m# df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 30\u001B[0m \u001B[38;5;66;03m# labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\u001B[39;00m\n\u001B[0;32m 31\u001B[0m \u001B[38;5;66;03m# labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\u001B[39;00m\n\u001B[0;32m 32\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m labels\n\u001B[1;32m---> 34\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m get_label(\u001B[43mdf\u001B[49m)\n\u001B[0;32m 35\u001B[0m train_data \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2023-01-01\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m 36\u001B[0m test_data \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2023-01-01\u001B[39m\u001B[38;5;124m'\u001B[39m]\n", + "\u001B[1;31mNameError\u001B[0m: name 'df' is not defined" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "8f134d435f71e9e2", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.569134800Z", + "start_time": "2025-02-16T08:28:25.333768Z" + } + }, + "source": [ + "import lightgbm as lgb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import optuna\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import mean_absolute_error\n", + "import os\n", + "import json\n", + "import pickle\n", + "import hashlib\n", + "\n", + "def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n", + " print_feature_importance=True, num_boost_round=100,\n", + " use_optuna=False):\n", + "\n", + " X_train = train_data_df[feature_columns]\n", + " y_train = train_data_df['label']\n", + "\n", + " X_val = test_data_df[feature_columns]\n", + " y_val = test_data_df['label']\n", + "\n", + " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + " val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n", + " model = lgb.train(\n", + " params, train_data, num_boost_round=num_boost_round,\n", + " valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + "\n", + " if print_feature_importance:\n", + " lgb.plot_metric(evals)\n", + " # lgb.plot_tree(model, figsize=(20, 8))\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + " return model\n", + "\n", + "\n", + "from catboost import CatBoostRegressor\n", + "import pandas as pd\n", + "\n", + "\n", + "def train_catboost(df, feature_columns, params=None):\n", + " \"\"\"\n", + " 训练 CatBoost 排序模型\n", + " - df: 包含因子、date、instrument 和 label 的 DataFrame\n", + " - num_boost_round: 训练的轮数\n", + " - print_feature_importance: 是否打印特征重要性\n", + " - plot: 是否绘制特征重要性图\n", + " - split_date: 用于划分训练集和验证集的日期（比如 '2020-01-01'）\n", + "\n", + " 返回训练好的模型\n", + " \"\"\"\n", + " df_sorted = df.sort_values(by=['trade_date', 'label'], ascending=[True, False])\n", + "\n", + " df_sorted = df_sorted.sort_values(by='trade_date')\n", + " unique_dates = df_sorted['trade_date'].unique()\n", + " val_date_count = int(len(unique_dates) * 0.1)\n", + " val_dates = unique_dates[-val_date_count:]\n", + " val_indices = df_sorted[df_sorted['trade_date'].isin(val_dates)].index\n", + " train_indices = df_sorted[~df_sorted['trade_date'].isin(val_dates)].index\n", + "\n", + " # 获取训练集和验证集的样本\n", + " train_df = df_sorted.iloc[train_indices].sort_values(by=['trade_date', 'label'], ascending=[True, False])\n", + " val_df = df_sorted.iloc[val_indices].sort_values(by=['trade_date', 'label'], ascending=[True, False])\n", + "\n", + " X_train = train_df[feature_columns]\n", + " y_train = train_df['label']\n", + "\n", + " X_val = val_df[feature_columns]\n", + " y_val = val_df['label']\n", + "\n", + " model = CatBoostRegressor(**params)\n", + " model.fit(X_train,\n", + " y_train,\n", + " eval_set=(X_val, y_val))\n", + "\n", + " return model" + ], + "outputs": [], + "execution_count": 12 + }, + { + "cell_type": "code", + "id": "beeb098799ecfa6a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.569134800Z", + "start_time": "2025-02-16T08:29:41.943281Z" + } + }, + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "light_params9 = {\n", + " # 'objective': 'regression',\n", + " # 'metric': 'l2',\n", + " 'objective': 'quantile', # 分位回归\n", + " 'metric': 'quantile', # 使用 quantile 作为评估指标\n", + " 'alpha': 0.75, # 90% 分位数\n", + " 'learning_rate': 0.05,\n", + " 'is_unbalance': True,\n", + " 'num_leaves': 2048,\n", + " 'min_data_in_leaf': 256,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " # 'feature_fraction': 0.7,\n", + " # 'bagging_fraction': 0.7,\n", + " # 'bagging_freq': 5,\n", + " # 'lambda_l1': 10,\n", + " # 'lambda_l2': 10,\n", + " 'verbosity': -1,\n", + " # 'device': 'gpu'\n", + "}\n", + "evals = {}\n", + "light_model9 = train_light_model(train_data, test_data, light_params9, feature_columns,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=10000, use_optuna=False,\n", + " print_feature_importance=True)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 1170833\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[147]\ttrain's quantile: 0.00152696\tvalid's quantile: 0.00164099\n", + "Evaluated only: quantile\n" + ] + }, + { + "data": { + "text/plain": [ + "
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EBgZSr149tm7dyuOPP87gwYNLTHYXERGpbEVFReTl5Tm0hvz8fJydnTl79iyFhYUOreVKWSwWzGZzhRzLoUFq+PDhpKen8/LLL5OUlETLli2ZP38+9evXByApKYnExERb+8jISObPn8+ECRN4//33CQsL49133y22FlTnzp2ZNWsWzz33HM8//zwNGzZk9uzZtjWkwDr5u0ePHrb75+c1jRw5kpkzZ9qee+LEiaSkpBAaGsr999/P888/fy1fDhERkcvKy8sjISGBoqIih9ZhGAYhISEcPny4Sq6V6OvrS0hIyFXX7vDJ5uPGjWPcuHGlPnY+1FyoW7dubNiw4ZLHHDp0KEOHDi3z8e7du9smqZflscce47HHHrtkGxERkcpkGAZJSUmYzWbCw8Mvv1jkNVRUVERWVhZeXl4OreNKGYZBTk4OqampAISGhl7V8RwepERERKR8CgoKyMnJsV2+zJHOn150c3OrUkEKwN3dHbB+GS0oKOiqTvNVrZ6LiIjUYOfnIrm4uDi4kqrvfBDNz8+/quMoSImIiFQxVXFO0vWmol5DBSkREREROylIiYiISJURERFxRdfPvdY02VxERESuqe7du9OmTZsKCUBr167F09Pz6ouqIApSIiIi4lCGYVBYWIiz8+VjSWBgYCVUVH46tSciIiLXzKhRo1i2bBnvvPMOJpMJk8nEzJkzMZlMLFiwgNjYWFxdXVmxYgX79+9n0KBBBAcH4+XlRfv27Vm0aFGx4118as9kMvHxxx8zZMgQPDw8aNy4MT/88EOl9U9BSkREpIoyDIOcvAKH3C63sPV577zzDp06dWLMmDEkJSWRlJREeHg4AE8//TSTJ09m586dtG7dmqysLPr378+iRYvYuHEjffv2ZeDAgcWuclKal156iWHDhrFlyxb69+/Pvffey4kTJ6769S0PndoTERGpos7kFxL1twUOee5tL/YuVzsfHx9cXFzw8PAgJCQEgF27dgHw8ssv07v3H8fx9/cnOjradv/VV19l7ty5/PDDDzz66KNlPseoUaO4++67AXjttdf497//zZo1a7jllluuuF9XSiNSIiIi4hCxsbHF7mdnZ/P0008TFRWFr68vXl5e7Nq167IjUq1bt7b97unpibe3t+0SMNeaRqRERESqKHeLmR0v93XIc7uaTWSevbpjXPztu6eeeooFCxbw5ptv0qhRI9zd3Rk6dCh5eXmXPI7FYil232QyVdpFnRWkREREqiiTyYSHi2P+Kb+SoOLi4mK7vM2lrFixglGjRjFkyBAAsrKyOHjwoL0lVgqd2hMREZFrKiIigtWrV3Pw4EHS0tLKDGGNGjXi22+/ZdOmTWzevJl77rmn0kaW7KUgJSIiItfUk08+idlsJioqisDAwDLnPL399tvUrl2bzp07M3DgQPr27Uu7du0qudoro1N7IiIick01adKE+Pj4YttGjRpVol1ERASLFy8utu2RRx4pdv/iU32lLcNw6tQpu+q0h0akREREROykICUiIiJiJwUpERERETspSImIiIjYSUFKRERExE4KUiIiIiJ2UpASERERsZOClIiIiIidFKRERERE7KQgJSIiIte1iIgIpkyZYrtvMpn47rvvymx/8OBBTCYTmzZtuua16RIxIiIiUqUkJSVRu3ZtR5cBKEiJiIhIFRMSEuLoEmx0ak9ERESumQ8//JA6depQVFRUbPttt93GyJEj2b9/P4MGDSI4OBgvLy/at2/PokWLLnnMi0/trVmzhrZt2+Lm5kZsbCwbN268Fl0plYKUiIhIVWUYkJftmJthlKvEO++8k7S0NJYsWWLbdvLkSRYsWMC9995LVlYW/fv3Z9GiRWzcuJG+ffsycOBAEhMTy3X87OxsBgwYQNOmTVm/fj0vvvgiTz75pF0vpz10ak9ERKSqys+B18Ic89zPHClXMz8/P2655Rb+97//0atXLwC+/vpr/Pz86NWrF2azmejoaFv7V199lblz5/LDDz/w6KOPXvb4X3zxBYWFhcyYMQMPDw9atGjBkSNHePjhh+3r1xXSiJSIiIhcU/feey9z5swhNzcXsIafu+66C7PZTHZ2Nk8//TRRUVH4+vri5eXFrl27yj0itXPnTqKjo/Hw8LBt69Sp0zXpR2k0IiUiIlJVWTzgr8cc89xmNzibWa6mAwcOpKioiHnz5tG+fXtWrFjBW2+9BcBTTz3FggULePPNN2nUqBHu7u4MHTqUvLy8ch3bKOcpxmtFQUpERKSqMpnAxdMxz33R5PFLcXd35/bbb+eLL75g3759NGnShJiYGABWrFjBqFGjGDJkCABZWVkcPHiw3MeOiori888/58yZM7i7uwPw+++/l78fV0mn9kREROSau/fee5k3bx4zZszgvvvus21v1KgR3377LZs2bWLz5s3cc889Jb7hdyn33HMPTk5OjB49mh07djB//nzefPPNa9GFUilIiYiIyDXXs2dP/Pz82L17N/fcc49t+9tvv03t2rXp3LkzAwcOpG/fvrRr167cx/Xy8uLHH39kx44dtG3blmeffZY33njjWnShVDq1JyIiItec2Wzm2LGS87kiIiJYvHhxsW2PPPJIsfsXn+q7eF5Ux44dS1wOprLmTmlESkRERMROClIiIiIidlKQEhEREbGTgpSIiIiInRSkREREqhhHL0JZHVTUa6ggJSIiUkWYzWaAcq/6LWXLyckBwGKxXNVxtPxBJTibX+joEkREpBpwdnbGw8OD48ePY7FYcHJy3HhIUVEReXl5nD171qF1XCnDMMjJySE1NRVfX19bOLWXglQl+D3hBHeEBDm6DBERqeJMJhOhoaEkJCRw6NAhh9ZiGIbtsiwmk8mhtdjD19eXkJCQqz6OglQlWLo7jTsq70LUIiJSjbm4uNC4cWOHn97Lz89n+fLldO3a9apPj1U2i8Vy1SNR5ylIVYJle45TUFiEs7nqDH2KiMj1y8nJCTc3N4fWYDabKSgowM3NrcoFqYqkf9krwakzBaw/dNLRZYiIiEgFU5CqJAt3pDi6BBEREalgClKVZMH2ZK37ISIiUs0oSFUCV4sTR06eYWdSpqNLERERkQqkIFUJOkXWBqyjUiIiIlJ9KEhVgp5NrWtIzd+a5OBKREREpCIpSFWC7k0DcDE7sTc1iz0pOr0nIiJSXShIVQJvNwtdmwQC8NPmYw6uRkRERCqKglQlGdA6FICftibp23siIiLVhIJUJenVPAgXZycOHM9mV7JO74mIiFQHClKVxNvNQvfzp/e26PSeiIhIdaAgVYkGRIcBMG+LTu+JiIhUBwpSlahXsyDcLE4cTM9h85HTji5HRERErpKCVCXydHWmb4sQAOasP+LgakRERORqKUhVsjva1QXgh83HyC0odHA1IiIicjUUpCrZjY0CCKnlxukz+SzemerockREROQqKEhVMrOTicFt6wAwZ4NO74mIiFRlClIOMDTGGqSW7j5OWlaug6sREREReylIOUCjIG+i6/pQUGTw/SatKSUiIlJVOTxITZ06lcjISNzc3IiJiWHFihWXbL9s2TJiYmJwc3OjQYMGTJs2rUSbOXPmEBUVhaurK1FRUcydO7fY48uXL2fgwIGEhYVhMpn47rvvShwjKyuLRx99lLp16+Lu7k7z5s354IMPrqqvFxoaY510/tXaw1pTSkREpIpyaJCaPXs248eP59lnn2Xjxo106dKFfv36kZiYWGr7hIQE+vfvT5cuXdi4cSN//etfeeyxx5gzZ46tTXx8PMOHD2fEiBFs3ryZESNGMGzYMFavXm1rk52dTXR0NO+9916ZtU2YMIFffvmF//73v+zcuZMJEybw5z//me+//75C+n5bmzq4WZzYnZLJxsOnKuSYIiIiUrkcGqTeeustRo8ezUMPPUTz5s2ZMmUK4eHhZY78TJs2jXr16jFlyhSaN2/OQw89xIMPPsibb75pazNlyhR69+7NpEmTaNasGZMmTaJXr15MmTLF1qZfv368+uqr3H777WXWFh8fz8iRI+nevTsRERH86U9/Ijo6mnXr1lVI333cLdzayrrS+ZerSw+OIiIicn1zdtQT5+XlsX79ep555pli2/v06cOqVatK3Sc+Pp4+ffoU29a3b1+mT59Ofn4+FouF+Ph4JkyYUKLNhUGqPG666SZ++OEHHnzwQcLCwli6dCl79uzhnXfeKXOf3NxccnP/mDyekZEBQH5+Pvn5+SXa39kulDkbjvDTlmNMuqUx3m6WK6rxena+v6X1u7pT39X3mkZ9V9+rmyvpk8OCVFpaGoWFhQQHBxfbHhwcTHJycqn7JCcnl9q+oKCAtLQ0QkNDy2xT1jHL8u677zJmzBjq1q2Ls7MzTk5OfPzxx9x0001l7jN58mReeumlEtuXLFmCh4dHie2GASHuZpLPFDH5f4u4KaT6zZWKi4tzdAkOo77XTOp7zaS+Vy85OTnlbuuwIHWeyWQqdt8wjBLbLtf+4u1XeszSvPvuu/z+++/88MMP1K9fn+XLlzNu3DhCQ0O5+eabS91n0qRJTJw40XY/IyOD8PBwevTogb+/f6n7pNY+xGs/72b7WV9e69/pimq8nuXn5xMXF0fv3r2xWKrPSFt5qO/qu/pec6jv1bPv588olYfDglRAQABms7nESFFqamqJEaXzQkJCSm3v7OxsCypltSnrmKU5c+YMf/3rX5k7dy633norAK1bt2bTpk28+eabZQYpV1dXXF1dS2y3WCxlfsjujK3Hm3F72ZGUyfbkbNqE+5a7zqrgUn2v7tR39b2mUd/V9+riSvrjsMnmLi4uxMTElBgSjIuLo3PnzqXu06lTpxLtFy5cSGxsrK3TZbUp65ilOT+nycmp+MtjNpspKioq93HKo7anCwNahwLw2aqDFXpsERERubYc+q29iRMn8vHHHzNjxgzbEgOJiYmMHTsWsJ4qu//++23tx44dy6FDh5g4cSI7d+5kxowZTJ8+nSeffNLW5vHHH2fhwoW88cYb7Nq1izfeeINFixYxfvx4W5usrCw2bdrEpk2bAOuyCps2bbItu1CrVi26devGU089xdKlS0lISGDmzJl89tlnDBkypMJfh1GdIwD4aUuSVjoXERGpQhw6R2r48OGkp6fz8ssvk5SURMuWLZk/fz7169cHICkpqdiaUpGRkcyfP58JEybw/vvvExYWxrvvvssdd9xha9O5c2dmzZrFc889x/PPP0/Dhg2ZPXs2HTp0sLVZt24dPXr0sN0/P69p5MiRzJw5E4BZs2YxadIk7r33Xk6cOEH9+vX5+9//bgt5Fal1XV/ahPuy6fApZq1J5NGejSv8OURERKTiOXyy+bhx4xg3blypj50PNRfq1q0bGzZsuOQxhw4dytChQ8t8vHv37pddTTwkJIRPPvnkkm0q0sjO9dk0+xT//T2Rsd0a4mx2+KLzIiIichn61/o60b9VKAFeLiRnnGXhjhRHlyMiIiLloCB1nXB1NnP3DfUAmLEywcHViIiISHkoSF1HRnSsj8VsYt2hk2xIPOnockREROQyFKSuI0G13Bjcpg4AH6844OBqRERE5HIUpK4zY7o2AOCXbckcSs92cDUiIiJyKQpS15kmwd50bxpIkaG5UiIiItc7Banr0Jgu1lGpr9Yd4WR2noOrERERkbIoSF2HOjf0p0VYLc7kF/KJLhsjIiJy3VKQug6ZTCYe6dEIgJm/JZCVW+DgikRERKQ0ClLXqb4tQmgQ6EnG2QL++/shR5cjIiIipVCQuk6ZnUyM624dlfp4RQJn8wsdXJGIiIhcTEHqOjaoTRh1fN1Jy8pl9trDji5HRERELqIgdR2zmJ0Y2836Db73l+zjTJ5GpURERK4nClLXuWHtw6lb253UzFxm/KZ1pURERK4nClLXOVdnM0/0aQLAtGX7OZWjdaVERESuFwpSVcCg6Do0C/Em82wBU5fud3Q5IiIico6CVBXg5GTiL7c0A2DmqoMcO3XGwRWJiIgIKEhVGd2bBnJDpB95BUVMWbTH0eWIiIgIClJVhslk4pl+1lGpb9YfYW9KpoMrEhEREQWpKqRdvdr0bRFMkQH/XLDb0eWIiIjUeApSVcxTfZviZIKFO1JYf+iEo8sRERGp0RSkqphGQd7cGRMOwN/n7cQwDAdXJCIiUnMpSFVBE3o3wcPFzIbEU8zdeNTR5YiIiNRYClJVUIiPG4/2tF7QePLPu8g8m+/gikRERGomBakqavRNkUQGeHI8M5d/L97n6HJERERqJAWpKsrV2czfBkQBMGNlAvtSsxxckYiISM2jIFWF9WgWRK9mQRQUGbz043ZNPBcREalkClJV3PMDonAxO7FibxoLd6Q4uhwREZEaRUGqiosI8GRM10gAXvlpB2fzCx1ckYiISM2hIFUNPNKjEaE+bhw5eYZpy/Y7uhwREZEaQ0GqGvBwcebZW5sDMHXJfk08FxERqSQKUtXEra1C6d40kLzCIiZ9u4WiIk08FxERudYUpKoJk8nE34e0wtPFzNqDJ/li9SFHlyQiIlLtKUhVI3V83Xn6lmYAvP7zLo6eOuPgikRERKo3BalqZkTH+sTUr012XiFPfrVZp/hERESuIQWpasbJycS/7ozG3WIm/kA6M35LcHRJIiIi1ZaCVDUUEeDJcwOs3+L7x4Ld7EnJdHBFIiIi1ZOCVDV1zw316NE0kLyCIsbP2kReQZGjSxIREal2FKSqKZPJxBtDW1Pbw8KOpAymLNrj6JJERESqHQWpaizI243Jt7cCYNqy/aw7eMLBFYmIiFQvClLV3C0tQ7m9XR2KDJj41WaycgscXZKIiEi1oSBVA7x4Wwvq+LqTeCKHv3yzBcPQkggiIiIVQUGqBqjlZuHdu9tiMZuYtzWJ6Su1JIKIiEhFUJCqIWLq1+a5W6MAmPzzLlYfSHdwRSIiIlWfglQNcn+n+gxuE0ZhkcGjX24kNeOso0sSERGp0hSkahCTycRrt7eiabA3xzNzGffFBvILtb6UiIiIvRSkahgPF2emjYjB29WZdYdOMnn+LkeXJCIiUmUpSFWG6+xbcpEBnvxrWDQAM35LYO7GIw6uSEREpGpSkKoEpr2/OLqEEvq0COGRHg0B+Ms3W1l/SIt1ioiIXCkFqUpgXvFPKMx3dBklPNG7KX2igskrLOJPn63n8IkcR5ckIiJSpShIVQLTyQTY8JmjyyjBycnE28PbEBVai/TsPEZ/upbTOddf4BMREbleKUhVlqWvQ26Wo6sowdPVmemjYgnydmVPShZjPlvH2fxCR5clIiJSJShIVQLDpx5kp0L8+44upVShPu58+uANeLs5s+bgCR6ftZHCoutrgryIiMj1SEGqEhR2edL6y29T4PRRh9ZSluahtfjP/bG4ODuxYHsKz3+/TdfkExERuQwFqUpgNB0A4R0hPwcWvejocsrUsYE/7wxvg8kE/1udyLu/7nN0SSIiItc1BanKYDJBv9cBE2z9Cg6vcXRFZerXKpSXb2sBwNuL9vDlmkQHVyQiInL9UpCqLGFtoe291t9//gsUXb8Tukd0iuDPPRsB8OzcrcTtSHFwRSIiItcnBanK1PNv4OINxzbA6g8dXc0lTezdhOGx4RQZ8Oj/NrDuoBbsFBERuZiCVGXyDobeL1l///VlSN/v2HouwWQy8fchLenVLIjcgiJGf7qO3cmZji5LRETkuqIgVdliH4TIblBwBr5/BIqKHF1RmZzNTrx3Tzva1fPl9Jl87v34d/alKkyJiIicpyBV2UwmuO3f4OIFifGw+gNHV3RJ7i5mPhl1Ay3CapGWlcfd/1nN/uPX38KiIiIijqAg5Qi160OfV6y/x70ARzc4tp7L8PGw8N/RHWgW4s3xzFzu+c/vHEzLdnRZIiIiDqcg5SgxD0CzAVCUD988AGdPO7qiS6rt6cIXD3WgSbAXKRnWMKWLHIuISE2nIOUoJhMMeg986sHJg/Dj43CdryTu7+XKFw91pGGgJ8dOn+WujxSmRESkZlOQciT32jB0Bjg5w/a5sH6moyu6rEBvV74c05HIAE+OnjrDndPi2ZuiCegiIlIzOTxITZ06lcjISNzc3IiJiWHFihWXbL9s2TJiYmJwc3OjQYMGTJs2rUSbOXPmEBUVhaurK1FRUcydO7fY48uXL2fgwIGEhYVhMpn47rvvShzDZDKVevvnP/95Vf0tIbw99Pqb9fdfnoGU7RV7/GsgqJYbX47pSOMgL5IzzjLsw3g2Hz7l6LJEREQqnUOD1OzZsxk/fjzPPvssGzdupEuXLvTr14/ExNIvS5KQkED//v3p0qULGzdu5K9//SuPPfYYc+bMsbWJj49n+PDhjBgxgs2bNzNixAiGDRvG6tWrbW2ys7OJjo7mvffeK7O2pKSkYrcZM2ZgMpm44447Ku4FOK/Tn6FRbyg4C1+PgrzrfyJ3iI8bX/1fJ6Lr+nAyJ597/vM7q/anObosERGRSuXQIPXWW28xevRoHnroIZo3b86UKVMIDw/ngw9KXxJg2rRp1KtXjylTptC8eXMeeughHnzwQd58801bmylTptC7d28mTZpEs2bNmDRpEr169WLKlCm2Nv369ePVV1/l9ttvL7O2kJCQYrfvv/+eHj160KBBgwrrv42TEwyZBt6hkLYHfnjsup8vBecmoI/pSOeG/mTnFTLqk7Us3J7s6LJEREQqjbOjnjgvL4/169fzzDPPFNvep08fVq1aVeo+8fHx9OnTp9i2vn37Mn36dPLz87FYLMTHxzNhwoQSbS4MUlcqJSWFefPm8emnn16yXW5uLrm5ubb7GRkZAOTn55Ofn3/pJ3HxwTT4Q8xf3I5p2zcUBjanqPPjdtdcWVyd4KN72zDh663E7Uzl4S82MHlwCwa0DAS4fL+rofN9Vt9rFvVdfa9pqnPfr6RPDgtSaWlpFBYWEhwcXGx7cHAwycmlj2okJyeX2r6goIC0tDRCQ0PLbFPWMcvj008/xdvb+5IjWACTJ0/mpZdeKrF9yZIleHh4lOu5IsLuJfrIpzgteZW1h7JI8WlrV82VrZ8PnA50Ys1xJ57+dhu/byikWyjExcU5ujSHUd9rJvW9ZlLfq5ecnPJ/I91hQeo8k8lU7L5hGCW2Xa79xduv9JiXM2PGDO69917c3Nwu2W7SpElMnDjRdj8jI4Pw8HB69OiBv79/OZ+tP4U/O2He8AkdjnxEYbfvMMKqRpi6tcjgtV9282l8It8eNJNdUMRbD/TExcXF0aVVqvz8fOLi4ujduzcWi8XR5VQq9V19V99rjurc9/NnlMrDYUEqICAAs9lcYqQoNTW1xIjSeSEhIaW2d3Z2tgWVstqUdczLWbFiBbt372b27NmXbevq6oqrq2uJ7RaL5co+ZLf+E04ewJSwDOdZw+HBXyCw6ZWU7TAv3tYSfy833orbw4IjTjzz/S7+eWcb3CxmR5dW6a74fa9G1Hf1vaZR36tX36+kPw6bbO7i4kJMTEyJIcG4uDg6d+5c6j6dOnUq0X7hwoXExsbaOl1Wm7KOeTnTp08nJiaG6Ohou/a3i9kCd30BYW3hzAn4bDCcPFR5z38VTCYTj/VqzCu3ReFkMvhxSzJ3ffQ7qZlnHV2aiIhIhbvqIHX2rP3/QE6cOJGPP/6YGTNmsHPnTiZMmEBiYiJjx44FrKfK7r//flv7sWPHcujQISZOnMjOnTuZMWMG06dP58knn7S1efzxx1m4cCFvvPEGu3bt4o033mDRokWMHz/e1iYrK4tNmzaxadMmwLqswqZNm0osu5CRkcHXX3/NQw89ZHcf7ebqDffOgcBmkHkMPh8MmSmVX4ed7mpfl4ebF+Hj7symw6cY/N5vbD92fV8GR0RE5ErZFaSKiop45ZVXqFOnDl5eXhw4cACA559/nunTp5f7OMOHD2fKlCm8/PLLtGnThuXLlzN//nzq168PWNdyujDcREZGMn/+fJYuXUqbNm145ZVXePfdd4ut7dS5c2dmzZrFJ598QuvWrZk5cyazZ8+mQ4cOtjbr1q2jbdu2tG1rnXs0ceJE2rZty9/+9rdi9c2aNQvDMLj77ruv/EWqCJ7+MGIu+NaDEwfgv7fDmZOOqcUOTXwMvvm/DjQIsF5SZugH8SzQ8ggiIlKN2BWkXn31VWbOnMk//vGPYhOJW7Vqxccff3xFxxo3bhwHDx4kNzeX9evX07VrV9tjM2fOZOnSpcXad+vWjQ0bNpCbm0tCQoJt9OpCQ4cOZdeuXeTl5bFz584S37br3r07hmGUuM2cObNYuz/96U/k5OTg4+NzRX2qULXCYMR34BUMKdvgi2GQW3UuyRLh78nccTdyU6MAzuQXMva/6/l01UFHlyUiIlIh7ApSn332GR999BH33nsvZvMfk4hbt27Nrl27Kqw4Oce/oXVkys0XjqyBz2+Hs1XnNJmPh4VPHmjPPR3qYRjwwg/b+eeCXbZvXIqIiFRVdgWpo0eP0qhRoxLbi4qKquXCXNeF4BbFw9Rng6vUaT6L2Ym/D27JE72bAPD+kv08+uVGsnMLHFyZiIiI/ewKUi1atCj14sJff/21bd6RXAN12sHIH8HDH45tgBm3wMmDjq6q3EwmE3/u1Zh/3NEaZycT87YkMWTqbxw4nuXo0kREROxi1zpSL7zwAiNGjODo0aMUFRXx7bffsnv3bj777DN++umniq5RLhTaGkb+ZJ14fnwX/KcX3P0lhN/g6MrKbVj7cBoEejLuiw3sScli0Hu/8dbwNvSOsm+tLxEREUexa0Rq4MCBzJ49m/nz52Mymfjb3/7Gzp07+fHHH+ndu3dF1ygXC46CMYshpDXkpMHMAbDhM0dXdUViI/z46c830T6iNpm5BYz5bB1vLthNYZHmTYmISNVh9zpSffv2ZdmyZWRlZZGTk8PKlStLXFBYrqFaYfDAz9BsABTmwg9/hu8fgfwzjq6s3IJqufG/MR0Z1TkCgPeW7OPBmWs5naN5diIiUjU4bGVzqQCuXjDsc+j5PJicYON/4T89IXmroysrN4vZiRdva8GU4W1wszixbM9xbnt/JbuTq84SDyIiUnOVO0jVrl0bPz+/ct2kEjk5Qdcn4b5vwTMQUnfARz1g5dtQVOjo6sptcNs6zHm4M3Vru3MoPYchU39j3pYkR5clIiJySeWebD5lypRrWIZctYY94OF4+PFx2D0PFr0IexbAkGlQO8LR1ZVLizAffnz0Jh79cgO/7Uvnkf9tYNuxhjzZpylmJ5OjyxMRESmh3EFq5MiR17IOqQhegdaLHW/6An5+BhLj4YMb4ZbJ0HYEmK7/MFLb04VPH7iBfy7YzYfLD/DB0v1sPnyKKXe1IcjbzdHliYiIFFPuU3sZGRnFfr/UTRzIZIK298HDK6FeZ8jLsk5En3UPZB13dHXl4mx2YlL/5rx7d1s8XMys2p9O/3dWsGJv1ahfRERqjiuaI5WamgqAr68vtWvXLnE7v12uA7UjYNRPcPNLYHaB3fNhakfrMglVZO7UbdFh/PDoTTQL8SYtK4/7Z6zhnwt2UVBY5OjSREREgCs4tbd48WLbRPIlS5Zcs4KkAjmZ4abx0Ohm+PZPkLrdOjq15j/Q7w2o39nRFV5WoyAvvnvkRl7+aQf/W53I+0v2sybhBO/c1ZYwX3dHlyciIjVcuYNUt27dbL9HRkYSHh6O6aI5N4ZhcPjw4YqrTipGSEv401JY8xEs+wckb4FP+kHUYOj9MtSu7+gKL8nNYua1Ia3o1MCfSd9uZe3Bk9z67gr+NSyans20GrqIiDiOXetIRUZGcvx4yfkqJ06cIDIy8qqLkmvA2QU6PwqPbYCYB6zrTu34Dt5rD7++ArnX//XuBkaHMe+xm2hVx4eTOfk8OHMdf5+3g7P5VeNUpYiIVD92BSnDMEqMRgFkZWXh5qZvVl3XPANg4BT4vxUQ0cW6KvqKN+HfMbBuxnW/Mnp9f0++ebiTbTX0/6xIYMC/V7Ih8aRjCxMRkRrpii5aPHHiRABMJhPPP/88Hh4etscKCwtZvXo1bdq0qdAC5RoJaQkjf4RdP8HC5+DkQfhpAix+Fdo/ZL15BTm6ylK5Opt58bYW3NgogEnfbmVfahZDP1jF6JsieaJPU9wsZkeXKCIiNcQVBamNGzcC1hGprVu34uLiYnvMxcWF6OhonnzyyYqtUK4dkwmaD4TGfWDtdPj9AzidCMvesK6M3noYtB8DodHX5RpUvaOCaR9Rm5d/3MG3G4/ynxUJLNqZyht3tOaGSK2wLyIi194VBanz39Z74IEHeOedd6hVq9Y1KUoqmbMrdBoHN/wJdv0Iq96Do+us1+7b+F8IaAKthkGrO8CvgaOrLcbXw4W3hrfh1tah/HXuVhLSshn+UTwjO0Xw9C1N8XC5oo+4iIjIFbFrjtQnn3yiEFUdmZ2hxRAY8yuMjoMWt4PZFdL2wJJX4d228J9e8Ps0yEp1dLXF9GoezMIJ3RgWWxfDgJmrDtLvnRWsPpDu6NJERKQas+u/69nZ2bz++uv8+uuvpKamUlRUfIHEAwcOVEhx4kDhN1hvZzOs86i2fAUJy6wjVUfXwYJJ0KA7tLoTmg0AN8cHax93C/8YGk3/VqFM+nYrh9JzGP7R74zqrNEpERG5Nuz6l+Whhx5i2bJljBgxgtDQ0FK/wSfVhFstaHOP9ZaZAtvnwtav4Oh62L/YenOeAA17WoNVg+7WU4EO1L1pEAsmdOW1eTuZtfYwM1cdZPGuVN69uy1twn0dWpuIiFQvdgWpn3/+mXnz5nHjjTdWdD1yPfMOho5jrbf0/bBtjnWkKn2v9RI0u+efaxeKOaIr4ad9IKMt+Ner9FJruVl4/Y7W9GsVyqQ5W0g8kcPQD1bxl1uaMfqmSJycFP5FROTq2TVHqnbt2rbLxUgN5d8Quj0Nj66F/1sOvf4Gkd2sc6oyk3DaOpt2iR9h+XcreO8GmP8U7JoHZ09XapndmgTy8/iu9G8VQkGRwd/n7+S+6avZl5pZqXWIiEj1ZNeI1CuvvMLf/vY3Pv3002JrSUkNZDJZl0cIjYYuT1gX9Ez8ncL9S8jY9AO+OQcxpe2GtN3WS9SYnCCs3bnTgN0gvIP1W4PXkI+7hffvaccXqxN55acdrNqfzi1TVvDgTZE81qsxXq6aOyUiIvax61+Qf/3rX+zfv5/g4GAiIiKwWCzFHt+wYUOFFCdVkMUdGvagqN5NLD8TQ/8enbAc+d06Uf3AUkjf98eE9RVvgrM71I21BrGQVhDS2jrHylyx4cZkMnFfx/p0bRzIyz9tZ9HOVD5afoDvNh7l2Vubc1t0mOb6iYjIFbPrX6vBgwdXcBlSbbnXhqjbrDeA00fgwLlQlbAMslLg4Arr7TyzK4S2tp4qjOwKwS3Bw69CFgWt5+/BxyPbs3hXCi/9uIND6Tk8PmsTX6xO5OVBLWgW4vhvH4qISNVhV5B64YUXKroOqSl86kLbe603w4Dju6zfAEzaAslbrbe8TDiy1npb8aZ1P9da1nlZwS2tI1fBLSG4Bbj72lVGz2bBdG4YwMcrDvDekn2sSTjBre+u5P5O9XmyT1M8dbpPRETKQf9aiOOYTBDU3Hpre25bURGcOgiH4q0jVodWwenDkJsBxzZabxfyCgaPAOuIlU9dqB0JtSPAL9L6u2dAmSNZbhYzj/ZszOC2dfj7vJ38vC2ZT347yKKdKfzrzja6zIyIiFyWXUGqsLCQt99+m6+++orExETy8vKKPX7ixIkKKU5qICcn62Vo/BpYR63AOoH95CHr6FXKNkjeZv15+rD11GBWStnHc/GyBivf+uAbDj7h537WBZ964BlA3doefHBfDMv3HGfSt1s5fOIMwz+K5/6O9Rl/cxNqe7qUfXwREanR7ApSL730Eh9//DETJ07k+eef59lnn+XgwYN89913/O1vf6voGqWms7hDUDPrrcXgP7afOQmnEiHnBGSnwalDcPKg9XYiATKOQl6WNXSlbCv92M5u50JVXbr61OXX2DB+SHDi+wQTy39PYt7GBP7UM4qRnSNwdTZXQmdFRKQqsStIffHFF/znP//h1ltv5aWXXuLuu++mYcOGtG7dmt9//53HHnusousUKcm9tvVWlvyz1qB1PlydPmyd7H76iPX3zGQoOGv9JmH6PgDcgGHAsAsGodJ+rcXBJQHUCokkpG4DTF7B4BUE3iHWn17B4BkIZkspRYiISHVmV5BKTk6mVatWAHh5eXH6tHWRxQEDBvD8889XXHUiV8PiBoFNrLfSFORZR61OH4bTR60BK+OILWwZp49gyssiwJRBgJEBSQcg6dcynsxknafl4Y/Z3Y8bMvIw/7QAvAJt2/HwB/dzv9cKBRfPa9Z1ERGpHHYFqbp165KUlES9evVo1KgRCxcupF27dqxduxZX12u7uKJIhXF2sU5K94ss9WGTYcDZ05xJP0TcqvVs2rYV36ITBHGKFrXO0sQzG9ezaZCVCkYh5KRDTjpOQCjA5susp+bqY50M71YLXL2t30x0rWUNXrXqQK2wP356BlrrFRGR64pdQWrIkCH8+uuvdOjQgccff5y7776b6dOnk5iYyIQJEyq6RhHHMJnA3Rf3ur7cNiyajrec5e1Fe5iy9jBFJ8By2sT9nSL4c/dIfMmyTno/c4KCzFS2r11BywahmHNPnwtYJ2xBi5x069yt3NPWW3k5OVtHsTz8wTPIGsI8A603r3P33f3A1QtcvK2jYW6+FbL+loiIlM6uIPX666/bfh86dCh169Zl1apVNGrUiNtuu63CihO5ngTVcmPy7a0Z2TmCyfN3sWzPcaavTOCb9Uf4c89G3N8pChdnJ4z8fA4mWIi6qT9mSxnzps5mQMYxOHMCcjOt93PP3bLTrI+dv2Ueg6IC6+3saevtxIHyFe3sbg1UFk9w8QCLhzWMObtZ53Q5WazBy83XuiaXm4/1dxdP6yR/ZzfrPhY367Es525OmngvIgIVtI5Ux44d6dixY0UcSuS61yykFp8+eAPL9xzntfk72ZWcyavzdvL574f4yy3NuLmp/+UP4lbLeiuPokJr2MrPgdwsyEmD7OPnbudOLZ7//cwJyMs+F8xOQ8EZ64T7iuZkuSBg/RG2zGZXOp3Kwvz1rHPB7dxjzm5/BDMnM5jMF/x0+uO+k/Mft/NBz+x87qellMcsl25rctKInIhcmmFYb5z7eYV/Z9gVpD777LNLPn7//ffbc1iRKqVrk0BubBTAN+sP8+bCPRxKz2HcFxuIqedL14q80oyT2TpaZFvFvYzJ8xfLPwOZSZCdDvnZkJdjDWN52dZvKxYVQGHeuRGx03DmFJw9Zf2Zf8batuDsud/PQGHuH8cuyi/11KQTEASQWcZyE45wPnyZnC64mS66fy7MmZzOBbsL75svuG+66P4fj5sx0Sn9BOYvPwWz+Y/HTabif0ljWOsqa5vJVDx4WjysF/a2hULTpX8a547FRf84XNG2i5T2D8sFbZ2KCmlxJAGnuFXnRiuNi9pc7n5521xc14WvyUWvj8npgtrLetx0iccvem3Pvz5G0bnfrTU6FRbQNGkPTsu3Wj87F7+vxfa56HUurW2xn0WXeAw79rnE85da4yX2BcxFhdx4Ih3z5x+ce43KqulStfHHYyXf4Avew4u3ldLOMKzzVYsKrP8BNQqtP4vObbvwvnHR9gt1ehQ6/aWUekpnMoxLfUpLV7t28a+c5+fnk5OTg4uLCx4eHlqQ85yMjAx8fHxIS0vD378coxTVSH5+PvPnz6d///4lLmpdHWXnFvDR8gN8tPwAZ/KtfyhvbRnCM/2bE+7n4eDqKkhRkTVYFZy1hqz8s9YRr/NBq+AsBWcz2bJuNdEtmmAuyi8Zxgpy//gLrNjPogv+AiyAwgJrWCvMP/fzwvuFF/xeYP1ZmFfyL0MREXt0fISMzs/g4+PD6dOnqVXr0v8ztmtE6uTJkyW27d27l4cffpinnnrKnkOKVGmers5M6N2Eu2+ox5sLdjFnwxHmbUsmbmcqo26M4JEejfBxr+KB0snJerrOxQMo/fI5Rn4+hxMstGp3iflh14ph/BG8ii4IZefD1oX/IzaKLvq90PqzqPCP/9Xa7l/4eFEp7a33Cwry2bxxA9GtW+PsxB+Pn1dihKOMbRjFw+e5kFq+kQLjguNR/NiX3HZxLRe9rsU3cPFIQWGRwYH9+2nQsCFm8/n5cxePJlzufnnbXFjXxaMcF414FBtl4TKPl3Ys/ni8tFEqk4nCIoPEw4epV6/+ub5f1K7YyNfFr/OlfpY2Aokd+5gu//wlRufKd4yCwkI2btxE23ZtcT7f9yt+Di6q/cL313anHNvPPe/5KQK26QIXTCUoa3ux0WWTdST4TH7Jz1wZKuxae40bN+b111/nvvvuY9euXRV1WJEqJcTHjclDWtCg4BArs4NYtf8EHy0/wFfrDvNYz8bc27GeVki/Vkymc0tEOGaZCCM/nyOH3Gnduj/UgFHYCxXl57Pj7HwiejogQDtYUX4+W+bPp26/mtd3Iz+fYwddaNO8Gn7mryBIOVXk85rNZo4dO1aRhxSpkup4wsyRMXzyQHsaB3lxKiefl3/aQY9/LmXWmkTyC0ubDyAiIlWNXSNSP/zwQ7H7hmGQlJTEe++9x4033lghhYlUdSaTiR5Ng+jSKICv1x/hnUV7OXb6LM98u5Vpy/YzoXcTBrYOw8mplNMWIiJSJdgVpAYPHlzsvslkIjAwkJ49e/Kvf/2rIuoSqTaczU7cfUM9hrStwxerE5m6ZB8H03N4fNYmpi7ZzxN9mtA7KhiTvqYvIlLl2BWkiop0WkLkSrlZzIy+KZK72ofzyW8JfLj8ALtTMvnT5+uJDvflyT5NuKlRgAKViEgVYleQmjhxYrnbvvXWW/Y8hUi15enqzKM9GzOiYwQfrdjPJ78dZPPhU4yYvoYOkX481bcpsRGlfytORESuL3YFqY0bN7J+/XoKCwtp2rQpAHv27MFsNtOuXTtbO/3PWqRsPh4WnurbjFGdI5m6dB9f/J7I6oQTDJ0WT/emgTzZpykt6/g4ukwREbkEu4LUwIED8fb25tNPP7Utznny5EkeeOABunTpwhNPPFGhRYpUZ4HerrwwsAVjujTg34v38tW6IyzdfZylu4/Tr2UIE3s3oXGwt6PLFBGRUti1/MG//vUvJk+eXGyF89q1a/Pqq69qsrmIncJ83Zl8e2t+ndiNwW3CMJng523J9J2ynIlfbSIxPcfRJYqIyEXsClIZGRmkpKSU2J6amkpmZuZVFyVSk0UEeDLlrrb88nhX+kQFU2TAtxuO0vNfS3l27laST591dIkiInKOXUFqyJAhPPDAA3zzzTccOXKEI0eO8M033zB69Ghuv/32iq5RpEZqGuLNR/fH8v0jN9KlcQAFRQZfrE6k2z+X8OpPO0jPyr38QURE5Jqya47UtGnTePLJJ7nvvvvIz7cuo+7s7Mzo0aP55z//WaEFitR00eG+fD66A78fSOfNBbtZd+gkH69M4Ms1ifypa0Me6hKJp2uFXe1JRESugF0jUh4eHkydOpX09HQ2btzIhg0bOHHiBFOnTsXT07OiaxQRoGMDf74e24lPHmhPi7BaZOcV8vaiPXT751K+WnuYoqKLLy4rIiLX2lX9N9bT05PWrVtXVC0ichnnLzvTrXEgP21N4s0Fu0k8kcPTc7bw5dpEXhnUUksmiIhUogq9aLGIVA4nJxO3RYexaGI3nu3fHE8XMxsTTzHg3ysZOWMNK/YexzA0QiUicq0pSIlUYS7OTozp2oDFT3Zn0LklE5btOc6I6WsYOi2erUdOO7pEEZFqTUFKpBoIruXGO3e1ZemT3RnVOQJ3i5n1h05y2/srefLrzew/nuXoEkVEqiUFKZFqpL6/Jy/e1oLFT1oX9TQM+Gb9EXr9axmjZ65l/aETji5RRKRaUZASqYZCfdyZcldbvh3XmZubB2Mywa+7Urnjg3jun7GGjYknHV2iiEi1oMVnRKqxdvVq8/HIWA4cz+Kj5Qf4ev0Rlu85zvI9x2lVx4e7bghncJs6WodKRMROGpESqQEaBHrx+h2tWfxEN+5oVxeL2cTWo6d5du42ur+5lK/XaR0qERF7KEiJ1CD1/T3517Bofp/Ui+dubU49Pw+OZ+by1DdbuO39lcTtSFGgEhG5AgpSIjWQv5crD3VpQNzErvy1fzO8XZ3ZdjSDMZ+to987K/hq7WFy8gocXaaIyHVPQUqkBnN1NvOnrg1Z8lR3Hu7eEC9XZ3anZPL0nC10+PuvPP/dNrYf01pUIiJl0QxTESHAy5W/3NKMsd0a8r/ViXy5JpHEEzl8/vshPv/9ENF1fbj7hnoMjA7TxHQRkQvob0QRsfFxt/Bw94b8X9cGxB9I539rElm4PZnNR06z+chWXvlpB4Pa1uGeG+rpmn4iIlwHp/amTp1KZGQkbm5uxMTEsGLFiku2X7ZsGTExMbi5udGgQQOmTZtWos2cOXOIiorC1dWVqKgo5s6dW+zx5cuXM3DgQMLCwjCZTHz33XelPtfOnTu57bbb8PHxwdvbm44dO5KYmGh3X0WqCicnEzc2CuD9e9oRP6kXk/o1I8Lfg+y8Qv63OpEB/17JoPdWai6ViNR4Dg1Ss2fPZvz48Tz77LNs3LiRLl260K9fvzLDSkJCAv3796dLly5s3LiRv/71rzz22GPMmTPH1iY+Pp7hw4czYsQINm/ezIgRIxg2bBirV6+2tcnOziY6Opr33nuvzNr279/PTTfdRLNmzVi6dCmbN2/m+eefx83NreJeAJEqIMDLlf/r1pAlT3bnf2M6MDA6DIvZxOYjp61zqV77lRe+38bu5ExHlyoiUukcemrvrbfeYvTo0Tz00EMATJkyhQULFvDBBx8wefLkEu2nTZtGvXr1mDJlCgDNmzdn3bp1vPnmm9xxxx22Y/Tu3ZtJkyYBMGnSJJYtW8aUKVP48ssvAejXrx/9+vW7ZG3PPvss/fv35x//+IdtW4MGDa66zyJVlclkonPDADo3DCAtK4pv1h/hf6utc6k+jT/Ep/GHiK3vSzOLiV75hVgsFkeXLCJyzTksSOXl5bF+/XqeeeaZYtv79OnDqlWrSt0nPj6ePn36FNvWt29fpk+fTn5+PhaLhfj4eCZMmFCizfnwVR5FRUXMmzePp59+mr59+7Jx40YiIyOZNGkSgwcPLnO/3NxccnNzbfczMjIAyM/PJz8/v9zPXx2c729N6zfUjL77uDoxunM9HugYzm8H0pm19gi/7jrOukOnWIeZH/+5jDva1uG26FCiQr0xmUyOLvmaqwnve1nUd/W9urmSPjksSKWlpVFYWEhwcHCx7cHBwSQnJ5e6T3JycqntCwoKSEtLIzQ0tMw2ZR2zNKmpqWRlZfH666/z6quv8sYbb/DLL79w++23s2TJErp161bqfpMnT+all14qsX3JkiV4eHiU+/mrk7i4OEeX4DA1qe+3+sBNbeH3VBOrUpw4daaAGasOMWPVIQLdDNoHFtE52MC7BgxS1aT3/WLqe81UHfuek5NT7rYO/9bexf9TNQzjkv97La39xduv9JgXKyoqAmDQoEG20a02bdqwatUqpk2bVmaQmjRpEhMnTrTdz8jIIDw8nB49euDv71/u568O8vPziYuLo3fv3jXuFE9N7vvQ/HwWLIzDLaIt329NZcnu4xw/W8T8w2YWJTkxsHUIt7cNI7ZebZycqtcoVU1+39V39b269f38GaXycFiQCggIwGw2lxgpSk1NLTGidF5ISEip7Z2dnW1Bpaw2ZR2zrNqcnZ2Jiooqtr158+asXLmyzP1cXV1xdXUtsd1isVS7D1l5qe81r+9OJri5RSj92tQjO7eABduT+XTVQTYfOc2cDceYs+EYIbXcuLV1KLdFh9G6rk+1OvVXU993UN/V9+rjSvrjsG/tubi4EBMTU2JIMC4ujs6dO5e6T6dOnUq0X7hwIbGxsbZOl9WmrGOWVVv79u3ZvXt3se179uyhfv365T6OSE3n6erM7e3q8t0jNzLn4U4MjamLt6szyRlnmb4ygUHv/0b3N5fy71/3kpJx1tHliohcMYee2ps4cSIjRowgNjaWTp068dFHH5GYmMjYsWMB66myo0eP8tlnnwEwduxY3nvvPSZOnMiYMWOIj49n+vTptm/jATz++ON07dqVN954g0GDBvH999+zaNGiYiNJWVlZ7Nu3z3Y/ISGBTZs24efnR7169QB46qmnGD58OF27dqVHjx788ssv/PjjjyxdurQSXhmR6sVkMhFT34+Y+n68Orgly/cc54fNx1i0M4VD6Tn8K24PU37dS4+mQdzTIZxuTYIwV7NTfyJSPTk0SA0fPpz09HRefvllkpKSaNmyJfPnz7eN+iQlJRVbUyoyMpL58+czYcIE3n//fcLCwnj33XdtSx8AdO7cmVmzZvHcc8/x/PPP07BhQ2bPnk2HDh1sbdatW0ePHj1s98/Paxo5ciQzZ84EYMiQIUybNo3Jkyfz2GOP0bRpU+bMmcNNN910LV8SkWrPzWKmT4sQ+rQIITu3gF+2JTNrbSJrD55k0c4UFu1MIdTHjWGx4QxrH04dX3dHlywiUiaHTzYfN24c48aNK/Wx86HmQt26dWPDhg2XPObQoUMZOnRomY93797dNkn9Uh588EEefPDBy7YTEft4ujpzR0xd7oipy96UTGatPcy3G46QdPos7/y6l3cX76Vn0yBGd4mkUwP/ajWXSkSqB4cHKRERgMbB3jw/IIqnb2nKgu0pfLk6kfgD6fy6K5Vfd6XSIqwW93aoz8DoULzdqtfEVhGpuhSkROS64ups5rboMG6LDuPA8Sw++e0gX68/zPZjGfx1rvXCybe2DmV4+3Bi69fWKJWIOJSClIhctxoEevHK4JZM7N2Er9cfZvbaw+w/ns0364/wzfojNAj0ZECrUPq0CKFFWC2FKhGpdApSInLdq+3pwp+6NmRMlwZsSDzJrDWHmbc1iQPHs3l38T7eXbyP+v4ejOocwbDYcDxd9VebiFQO/W0jIlXGhcsovHBbCxZsSyZuRwrL9hznUHoOL/24g7fj9jAgOox+LUPo2MAfi9lhy+WJSA2gICUiVZLXBd/4O5NXyDcbjjBjZQIJadn8b3Ui/1udiJ+nC0Pa1uHO2Lo0C6nl6JJFpBpSkBKRKs/dxcyIjvW594Z6/LY/jflbk1m4PZn07Dymr0xg+soEWtf14c7YcG6LDsPHXd/6E5GKoSAlItWGk5OJLo0D6dI4kFcGtWD53uN8ve4Ii3amsOXIabYcOc0rP+3glhYhDIsNp1NDf62gLiJXRUFKRKolZ7MTPZsF07NZMOlZuXy36RhfrzvMruRMfth8jB82H8PP04UeTYPo1TyILo0DtD6ViFwxBSkRqfb8vVwZfVMkD94YwbajGXy17jA/bD7Giew85mw4wpwNR7CYTXSI9KdX8yB6NQumnr+Ho8sWkSpAQUpEagyTyUSruj60quvD3wZGse7gSRbvSuHXnakcSMtm5b40Vu5L46Ufd9A4yIuezYO4uXkw7erV1ilAESmVgpSI1EgWsxOdGvrTqaE/z94axYHjWSzelcqvO1NZc/AEe1Oz2JuaxYfLDuDv6cLNzYPp2zKYzg0DcLOYHV2+iFwnFKRERLCuot4g0IuHujTg9Jl8lu85zq87U1iy+zjp2XnMXneY2esO4+lipnuzIHo3D6Zrk0D8PF0cXbqIOJCClIjIRXzcLQyMDmNgdBgFhUWsSTjBgu3JLNieQnLGWeZtSWLeliRMJmgT7kuPpkF0aehHkeHoykWksilIiYhcgrPZic6NAujcKIAXb2vBliOnWbA9mSW7j7MzKYONiafYmHiKt+LA22JmRe42ejUP4abGAVqvSqQGUJASESknk8lEdLgv0eG+PH1LM5JPn2Xp7lSW7E5l5b40MnML+XbjMb7deAyzk4mYerXp2TyIvi1CiAzwdHT5InINKEiJiNgpxMeNu26ox1031CP7TC4ffL2AM7UbsHxvOntTs1hz8ARrDp7g9Z930STYi74tQujbIoQWYbUwmfQtQJHqQEFKRKQCuDg70djHoP8tTXl+oIXDJ3JYujuVhTtSiN+fzp6ULPak7OPfi/dRx9edPi2C6dsihPYRflpaQaQKU5ASEbkGwv08GNEpghGdIjidk8/i3Sks2JbCsj3HOXrqDJ/8dpBPfjuIn6cLN587/XdjIy2tIFLVKEiJiFxjPh4WhrSty5C2dTmTV8iKvcdZsD2FX3elcCI7j6/WHeGrdUesSys0DeKOmDp0bRyIs9nJ0aWLyGUoSImIVCJ3FzN9WoTQp0VIsaUVFu5IIen0WeZtTWLe1iSCa7kyuE0dejUPpl09X4UqkeuUgpSIiIOUtrTCD5uP8e2GI6Rk5PLh8gN8uPwAPu4WujUJpFfzILo1CcTXQ4uAilwvFKRERK4DxZdWaMqvO1OJ25HCkt2pnMrJ54fNx/hh8zGcTBBb34+ezYPo1SyIRkFe+gagiAMpSImIXGdcnc30bxVK/1ahFBYZbEw8ya+7Ulm8M5XdKZnFllUI93OnZ9MgejYPpkOknyari1QyBSkRkeuY2clEbIQfsRF+/OWWZhw5mcOSXan8uiuVVfvTOXziDJ/GH+LT+EN4uJi5qVEAvZoH0aNpEEG13Bxdvki1pyAlIlKF1K39x7IKOXkF/LYvncW7Uli8K5WUjFwW7khh4Y4UAFrV8aFnsyB6RwVrEVCRa0RBSkSkivJwcaZ3VDC9o4IxDIPtxzJYfG60avPhU2w9epqtR0/zzq97CfNxo3dUMH1ahHBDpB8WfQtQpEIoSImIVAMmk4mWdXxoWceHx3o15nhmLkt3p7JoZwrL96Rx7PRZ2ynAWm7O9Dg3UtWtSSDebrq4soi9FKRERKqhQG9X7owN587YcM7mF7JybxpxO1JYtDOF9Ow8vt90jO83HcNiNtGpYQC9mwdxc1QwoT7uji5dpEpRkBIRqebcLGZujgrm5qhgCosMNiSeZNGOFOJ2pHAgLZvle46zfM9xnv9+O63q+NA7KpibmwfTPNRb86pELkNBSkSkBjE7mWgf4Uf7CD8m9W/OvtQsFu20hqoNiSdt86reittD3dru9Gxm/QZgxwb+uLtoaQWRiylIiYjUYI2CvGgU5MXYbg05npnLkl2pLNyRwsp9xzly8gyfxR/is/hDuDo70bGBP92bBtKjaRARAZ6OLl3kuqAgJSIigHVe1bD24QxrH86ZvEJW7ktj6e5Ulu4+ztFTZ1i25zjL9hznpR93EOHvQfemQXRvGkhMeC1Hly7iMApSIiJSgruLudjSCvtSs1hyLlStPXiCg+k5zFx1kJmrDuJmcaKBpxMn/BPp1TyUev4eji5fpNIoSImIyCWZTCYaB3vTONibP3VtSFZuAb+dG61asus4yRln2XHKiZd+2sVLP+2iQaAn3ZsE0aNZIDdE+uHqrLlVUn0pSImIyBXxcnWmb4sQ+rYIsS4EeuQkH/64khSnANYnnuLA8WwOHE9gxm8JuFvM3NjI33YasG5tjVZJ9aIgJSIidjOZTDQN8ebmOgb9+7fnTCH8tjfNdhowNTOXRTtTWbQzFYDGQV62CeuxEX64OGuFdanaFKRERKTC1HKz0K9VKP1ahWIYBjuSMli6+zhLd6eyIfEUe1Oz2JuaxX9WJODpYubGRgG20aowXy0GKlWPgpSIiFwTJpOJFmE+tAjz4ZEejTidk8+KfcfPBavjpGUVv8hy02BvujcLpHuTIGIjaut6gFIlKEiJiEil8PGwMKB1GANah1FUZL3I8tLdqSzZncqmw6fYnZLJ7pRMPlx2AG9XZ25sFECPZoF0axJEiI+bo8sXKZWClIiIVDonJxOt6vrQqq4Pf+7VmJPZeSzfe5xlu61rVaVn5/HL9mR+2Z4MQPPQWra5Ve3q+eKs0Sq5TihIiYiIw9X2dGFQmzoMalOHoiKDrUdP2yasbz5yip1JGexMyuCDpfvxdnOmS+MAujXRaJU4noKUiIhcV5ycTESH+xId7sv4m5uQnpXLinPfBFy+5zgnc/KZvzWZ+Vuto1XNQrzp1iSQHs2CiK1fW6NVUqkUpERE5Lrm7+XK4LZ1GNy2DoVFBpuPnLKdAtx85BS7kjPZlZzJh8sPUMvNme5Ng+jVPIhuTQLx9XBxdPlSzSlIiYhIlWF2MtGuXm3a1avNhN5NbHOrzi+xcDInnx82H+OHzccwO5mIqV+bXs2C6N40iMZBXjg5mRzdBalmFKRERKTKunBuVWGRwcbEk/y6K5XFO1PZnZLJmoQTrEk4weSfd1Hbw0L7CD9uiPSjQ6Q/zUO9dRpQrpqClIiIVAtmJxOxEX7ERvjxl1uacfhEDot3pfLrrlTWJpzgZE5+sXWrvFydialfmw4N/OgQ6UerOr5aaV2umIKUiIhUS+F+HozsHMHIzhHkFRSx7dhp2wjV2oMnyDxbwLI91rlWAG4WJ9qG1z43YuVHu/q1cbPogstyaQpSIiJS7bk4O9nmVo3t1pDCIoNdyRmsPmANVmsOnuBEdh7xB9KJP5Bu3cfsRJtwXzo28KNjQ3/a1VOwkpIUpEREpMYxO/1x+ZoHb4rEMAz2H89i9bkRq98PpJOSkcuag9aQ9e7ifdZgVc+Xjg386dTAn7b1fFGsEgUpERGp8UwmE42CvGkU5M29HepjGAaH0nOIP5DO7wfSid+fTmpmru3U4Lu/7sXF2Yk2dX3wK3DCP+EEsZEBGrGqgRSkRERELmIymYgI8CQiwJO7b6iHYRgcSMtm9QHraFX8gXSOZ+ay5uBJwIlfZqzD1dmJ9hF+3NQ4gJsaBRAVWkvLLdQAClIiIiKXYTKZaBjoRcNAL+7pYA1WCWnZrNybyne/bedwrhvHs/JYuS+NlfvSAKjtYaFzowC6NArgpsYB1K3t4eBeyLWgICUiInKFTCYTDQK9CPd1xef4Vvr168ahk7nWILU3jd8PpHMyJ595W5KYtyUJgMgAT25s5M9NjQLp1NAfH3eLg3shFUFBSkRE5CqZTCYaB3vTONibB26MJL+wiE2HT7Fibxq/7Utj0+FTJKRlk5CWzX9/T8TJBK3r+tKpoT83RPoRU782tdwUrKoiBSkREZEKZjFb50u1j/BjYu8mZJzN5/f96fy2L40V+9I4cDybTYdPsenwKT5Yuh8nEzQLqcUNkdZ92kfWJsjbzdHdkHJQkBIREbnGarlZ6NMihD4tQgA4duoMv+1Ls61hdSg9hx1JGexIymDmqoOA9VRg+4jatsva1PPzwGTS5PXrjYKUiIhIJQvzdefO2HDujA0HICXjLGsPnmBtwglWJ5xgd0qm7VTgV+uOABBcy9UWqtpH+NE02FvfCrwOKEiJiIg4WHAtNwa0DmNA6zAATp/JZ/2hE6xJOMnagyfYcuQUKRm5/LQliZ/OTV6v5eZM7LnTh+3q+dKqrg8eLvpnvbLpFRcREbnO+Lhb6NksmJ7NggE4m1/IxsRT1lGrgydYf+gkGWcLWLwrlcW7UgHrau1Ng71pU8+XtuG+xEb4EeGv04HXmoKUiIjIdc7NYqZTQ386NfQHoKCwiB1JGaxJOMG6gyfZePgkKRm5tnlW/1udCECgt6ttnlX7CD8tEnoNKEiJiIhUMc5mJ1rX9aV1XV8e6mLdlnT6DJsST7Hx8Ck2HDrJliOnOZ6Zy/ytyczfmgyAr4eFTg386dwogBsb+hMZ4KkRq6ukICUiIlINhPq4E9rKnX6tQgHr6cDNh62nA9ccPMn6gyc4lZPPz9uS+Xlb8rl93OjcMIAbG1lHu0J93B3ZhSpJQUpERKQacrOY6dDAnw4NrKcD8wuL2HLkNKv2pfHb/jQ2HDpF0umzzNlwhDkbrN8MDPNxo1392sScuzUPrYXF7OTIblz3FKRERERqAIvZyRaQ/tyrMWfyCll36AS/7Utn1f40th/L4Njpsxy74JuBbhYnouv6WsNVvdq0q18bP08XB/fk+uLwmDl16lQiIyNxc3MjJiaGFStWXLL9smXLiImJwc3NjQYNGjBt2rQSbebMmUNUVBSurq5ERUUxd+7cYo8vX76cgQMHEhYWhslk4rvvvitxjFGjRmEymYrdOnbseFV9FRERuV64u5jp0jiQZ/o144dHb2LLC33435gOPNmnCT2aBuLjbuFsfhGrE07wwdL9PPTZOtq9Ekf3fy7hsS838smqQ+zPgJy8Akd3xaEcOiI1e/Zsxo8fz9SpU7nxxhv58MMP6devHzt27KBevXol2ickJNC/f3/GjBnDf//7X3777TfGjRtHYGAgd9xxBwDx8fEMHz6cV155hSFDhjB37lyGDRvGypUr6dChAwDZ2dlER0fzwAMP2PYrzS233MInn3xiu+/iohQuIiLVk6erM50bBtC5YQAARUUGB9Ky2XDoJOsPnWR94kn2pWZxMD2Hg+k5/LAZwJn3diymSbA37erXJrZ+bWLr+xHu515jJrE7NEi99dZbjB49moceegiAKVOmsGDBAj744AMmT55cov20adOoV68eU6ZMAaB58+asW7eON9980xaIpkyZQu/evZk0aRIAkyZNYtmyZUyZMoUvv/wSgH79+tGvX7/L1ufq6kpISEhFdFVERKRKcXIy0SjIi0ZBXgxrb12B/VROHpuPnGbrkVNsSjzJ2gOpnM4zsSs5k13JmcWWXYg9dxox9tyyCy7ODj8Jdk04LEjl5eWxfv16nnnmmWLb+/Tpw6pVq0rdJz4+nj59+hTb1rdvX6ZPn05+fj4Wi4X4+HgmTJhQos358HUlli5dSlBQEL6+vnTr1o2///3vBAUFldk+NzeX3Nxc2/2MjAwA8vPzyc/Pv+Lnr8rO97em9RvU9wt/1iTqu/peE3haTHSO9KVzpC/5+XWIi4sjumNXtidnsyHxFOsTT7H9WAbHM3OLfTvQYjbRPMSbVnV8aFWnFq3r+NAg0BPzdbqm1ZW8nw4LUmlpaRQWFhIcHFxse3BwMMnJyaXuk5ycXGr7goIC0tLSCA0NLbNNWccsS79+/bjzzjupX78+CQkJPP/88/Ts2ZP169fj6upa6j6TJ0/mpZdeKrF9yZIleHh4XNHzVxdxcXGOLsFh1PeaSX2vmWpy3zf/vhyA1kDrupAXCoez4UCmiYRzt5wC2HI0gy1HM2z7uToZ1PWEel6G7ebvCtfDGcGcnJxyt3X4t/YuPodqGMYlz6uW1v7i7Vd6zNIMHz7c9nvLli2JjY2lfv36zJs3j9tvv73UfSZNmsTEiRNt9zMyMggPD6dHjx74+/tf0fNXdfn5+cTFxdG7d28sFoujy6lU6rv6rr7XHOr75ftuGAaJJ8+w9chpth7NYMvR0+xIyiQnr5D9mbA/849/n2t7WGgZVovouj50iPSjbbgPrhZzZXSnmPNnlMrDYUEqICAAs9lcYqQoNTW1xIjSeSEhIaW2d3Z2tgWVstqUdczyCg0NpX79+uzdu7fMNq6urqWOVlkslhr3B+w89V19r2nUd/W9pilP3xsFu9Ao2IchMdb7hUUG+1Kz2HzkFFuOnGLLkdPsTMrgZE4+K/als2JfOu8tPYCLsxOx9WvTqYF1wdAWYT64u1z7YHUl76XDgpSLiwsxMTHExcUxZMgQ2/a4uDgGDRpU6j6dOnXixx9/LLZt4cKFxMbG2jrdqVMn4uLiis2TWrhwIZ07d76qetPT0zl8+DChoaFXdRwREZGazuxkommIN01DvBkWa53InltQyK6kTLYcOcW6QyeJ359OamYuq/ans2p/OsRZ92sU6EXLOj60rutDyzo+NA/1xsPFcSfYHHpqb+LEiYwYMYLY2Fg6derERx99RGJiImPHjgWsp8qOHj3KZ599BsDYsWN57733mDhxImPGjCE+Pp7p06fbvo0H8Pjjj9O1a1feeOMNBg0axPfff8+iRYtYuXKlrU1WVhb79u2z3U9ISGDTpk34+flRr149srKyePHFF7njjjsIDQ3l4MGD/PWvfyUgIKBY6BMREZGK4epsJjrcl+hwX0Z0isAwDPYfzyZ+fxrxB9JZk3CStKxcdqdksjsl07Yau8kEDQI8aRHmQ1RYLVqG+dCmni9erpUTcRwapIYPH056ejovv/wySUlJtGzZkvnz51O/fn0AkpKSSExMtLWPjIxk/vz5TJgwgffff5+wsDDefffdYmtBde7cmVmzZvHcc8/x/PPP07BhQ2bPnm1bQwpg3bp19OjRw3b//LymkSNHMnPmTMxmM1u3buWzzz7j1KlThIaG0qNHD2bPno23t/e1fllERERqPJPpj+UXzgerlIxcth49bb0dOcW2c98Q3H88m/3Hs/lh8zHAOnIVFVqLtvV8aRFWi6hQH5qEeOHqXPGnBR0+2XzcuHGMGzeu1MdmzpxZYlu3bt3YsGHDJY85dOhQhg4dWubj3bt3t01SL427uzsLFiy45HOIiIhI5TGZTIT4uBHi40bvqD/mPadmnmX7sQx2HMtg+7HTbD58mqOnztgC13nO59bFigqtRVTYuVtoLXw9rm6xbYcHKRERERF7BXm7EdTUjR5N/1jn8dipM6w9eIJtR0+zIymD7ccyOJWTb1s49NuNR21t6/i6Ex3uY5vQ3jDQ64qeX0FKREREqpUwX3cGtanDoDZ1AOsSDEmnz54btcpgR5I1YB0+cYajp6y3+Vut3/i/vW0dXuzXoNzPpSAlIiIi1ZrJZCLM150wX3duvuC04Okz+ew4lsG6gyeIP5DO+kMnaVHH54qOrSAlIiIiNZKPu4VODa2n9P7cqzFn8wspLDIozK1CK5uLiIiIXA/czq2inpF7mYYXqJ6XYhYRERGpBApSIiIiInZSkBIRERGxk4KUiIiIiJ0UpERERETspCAlIiIiYicFKRERERE7KUiJiIiI2ElBSkRERMROClIiIiIidlKQEhEREbGTgpSIiIiInRSkREREROykICUiIiJiJwUpERERETspSImIiIjYSUFKRERExE4KUiIiIiJ2UpASERERsZOClIiIiIidFKRERERE7KQgJSIiImInBSkREREROylIiYiIiNhJQUpERETETgpSIiIiInZSkBIRERGxk4KUiIiIiJ0UpERERETspCAlIiIiYicFKRERERE7KUiJiIiI2ElBSkRERMROClIiIiIidlKQEhEREbGTgpSIiIiInRSkREREROykICUiIiJiJwUpERERETspSImIiIjYSUFKRERExE4KUiIiIiJ2UpASERERsZOClIiIiIidFKRERERE7KQgJSIiImInBSkREREROylIiYiIiNhJQUpERETETs6OLqA6MwwDgMzMTCwWi4OrqVz5+fnk5OSQkZGhvtcg6rv6rr7XHNW57xkZGcAf/45fioLUNZSeng5AZGSkgysRERGRK5WZmYmPj88l2yhIXUN+fn4AJCYmXvaNqG4yMjIIDw/n8OHD1KpVy9HlVCr1XX1X32sO9b169t0wDDIzMwkLC7tsWwWpa8jJyToFzcfHp9p9yMqrVq1a6nsNpL6r7zWN+l79+l7eARBNNhcRERGxk4KUiIiIiJ0UpK4hV1dXXnjhBVxdXR1dSqVT39X3mkZ9V99rmprc9wuZjPJ8t09EREREStCIlIiIiIidFKRERERE7KQgJSIiImInBSkREREROylIXSNTp04lMjISNzc3YmJiWLFihaNLqnCTJ0+mffv2eHt7ExQUxODBg9m9e3exNqNGjcJkMhW7dezY0UEVV5wXX3yxRL9CQkJsjxuGwYsvvkhYWBju7u50796d7du3O7DiihMREVGi7yaTiUceeQSoXu/58uXLGThwIGFhYZhMJr777rtij5fnfc7NzeXPf/4zAQEBeHp6ctttt3HkyJFK7IV9LtX3/Px8/vKXv9CqVSs8PT0JCwvj/vvv59ixY8WO0b179xKfhbvuuquSe3LlLve+l+czXh3fd6DUP/smk4l//vOftjZV9X23l4LUNTB79mzGjx/Ps88+y8aNG+nSpQv9+vUjMTHR0aVVqGXLlvHII4/w+++/ExcXR0FBAX369CE7O7tYu1tuuYWkpCTbbf78+Q6quGK1aNGiWL+2bt1qe+wf//gHb731Fu+99x5r164lJCSE3r17k5mZ6cCKK8batWuL9TsuLg6AO++809amurzn2dnZREdH895775X6eHne5/HjxzN37lxmzZrFypUrycrKYsCAARQWFlZWN+xyqb7n5OSwYcMGnn/+eTZs2MC3337Lnj17uO2220q0HTNmTLHPwocfflgZ5V+Vy73vcPnPeHV834FifU5KSmLGjBmYTCbuuOOOYu2q4vtuN0Mq3A033GCMHTu22LZmzZoZzzzzjIMqqhypqakGYCxbtsy2beTIkcagQYMcV9Q18sILLxjR0dGlPlZUVGSEhIQYr7/+um3b2bNnDR8fH2PatGmVVGHlefzxx42GDRsaRUVFhmFU3/ccMObOnWu7X573+dSpU4bFYjFmzZpla3P06FHDycnJ+OWXXyqt9qt1cd9Ls2bNGgMwDh06ZNvWrVs34/HHH7+2xV1jpfX9cp/xmvS+Dxo0yOjZs2exbdXhfb8SGpGqYHl5eaxfv54+ffoU296nTx9WrVrloKoqx+nTp4E/LtZ83tKlSwkKCqJJkyaMGTOG1NRUR5RX4fbu3UtYWBiRkZHcddddHDhwAICEhASSk5OLfQZcXV3p1q1btfsM5OXl8d///pcHH3wQk8lk215d3/MLled9Xr9+Pfn5+cXahIWF0bJly2r3WTh9+jQmkwlfX99i27/44gsCAgJo0aIFTz75ZLUYlYVLf8ZryvuekpLCvHnzGD16dInHquv7XhpdtLiCpaWlUVhYSHBwcLHtwcHBJCcnO6iqa88wDCZOnMhNN91Ey5Ytbdv79evHnXfeSf369UlISOD555+nZ8+erF+/vkqvhtuhQwc+++wzmjRpQkpKCq+++iqdO3dm+/bttve5tM/AoUOHHFHuNfPdd99x6tQpRo0aZdtWXd/zi5XnfU5OTsbFxYXatWuXaFOd/j44e/YszzzzDPfcc0+xi9fee++9REZGEhISwrZt25g0aRKbN2+2nQ6uqi73Ga8p7/unn36Kt7c3t99+e7Ht1fV9L4uC1DVy4f/OwRo0Lt5WnTz66KNs2bKFlStXFts+fPhw2+8tW7YkNjaW+vXrM2/evBJ/+KqSfv362X5v1aoVnTp1omHDhnz66ae2Sac14TMwffp0+vXrR1hYmG1bdX3Py2LP+1ydPgv5+fncddddFBUVMXXq1GKPjRkzxvZ7y5Ytady4MbGxsWzYsIF27dpVdqkVxt7PeHV63wFmzJjBvffei5ubW7Ht1fV9L4tO7VWwgIAAzGZzif91pKamlvifa3Xx5z//mR9++IElS5ZQt27dS7YNDQ2lfv367N27t5Kqqxyenp60atWKvXv32r69V90/A4cOHWLRokU89NBDl2xXXd/z8rzPISEh5OXlcfLkyTLbVGX5+fkMGzaMhIQE4uLiio1GlaZdu3ZYLJZq91m4+DNe3d93gBUrVrB79+7L/vmH6vu+n6cgVcFcXFyIiYkpMYQZFxdH586dHVTVtWEYBo8++ijffvstixcvJjIy8rL7pKenc/jwYUJDQyuhwsqTm5vLzp07CQ0NtQ1pX/gZyMvLY9myZdXqM/DJJ58QFBTErbfeesl21fU9L8/7HBMTg8ViKdYmKSmJbdu2VfnPwvkQtXfvXhYtWoS/v/9l99m+fTv5+fnV7rNw8We8Or/v502fPp2YmBiio6Mv27a6vu82DpzoXm3NmjXLsFgsxvTp040dO3YY48ePNzw9PY2DBw86urQK9fDDDxs+Pj7G0qVLjaSkJNstJyfHMAzDyMzMNJ544glj1apVRkJCgrFkyRKjU6dORp06dYyMjAwHV391nnjiCWPp0qXGgQMHjN9//90YMGCA4e3tbXuPX3/9dcPHx8f49ttvja1btxp33323ERoaWuX7fV5hYaFRr1494y9/+Uux7dXtPc/MzDQ2btxobNy40QCMt956y9i4caPtm2nleZ/Hjh1r1K1b11i0aJGxYcMGo2fPnkZ0dLRRUFDgqG6Vy6X6np+fb9x2221G3bp1jU2bNhX785+bm2sYhmHs27fPeOmll4y1a9caCQkJxrx584xmzZoZbdu2rdJ9L+9nvDq+7+edPn3a8PDwMD744IMS+1fl991eClLXyPvvv2/Ur1/fcHFxMdq1a1dsSYDqAij19sknnxiGYRg5OTlGnz59jMDAQMNisRj16tUzRo4caSQmJjq28AowfPhwIzQ01LBYLEZYWJhx++23G9u3b7c9XlRUZLzwwgtGSEiI4erqanTt2tXYunWrAyuuWAsWLDAAY/fu3cW2V7f3fMmSJaV+xkeOHGkYRvne5zNnzhiPPvqo4efnZ7i7uxsDBgyoEq/HpfqekJBQ5p//JUuWGIZhGImJiUbXrl0NPz8/w8XFxWjYsKHx2GOPGenp6Y7tWDlcqu/l/YxXx/f9vA8//NBwd3c3Tp06VWL/qvy+28tkGIZxTYe8RERERKopzZESERERsZOClIiIiIidFKRERERE7KQgJSIiImInBSkREREROylIiYiIiNhJQUpERETETgpSIiIVKCIigilTpji6DBGpJApSIlJljRo1isGDBwPQvXt3xo8fX2nPPXPmTHx9fUtsX7t2LX/6058qrQ4RcSxnRxcgInI9ycvLw8XFxe79AwMDK7AaEbneaURKRKq8UaNGsWzZMt555x1MJhMmk4mDBw8CsGPHDvr374+XlxfBwcGMGDGCtLQ0277du3fn0UcfZeLEiQQEBNC7d28A3nrrLVq1aoWnpyfh4eGMGzeOrKwsAJYuXcoDDzzA6dOnbc/34osvAiVP7SUmJjJo0CC8vLyoVasWw4YNIyUlxfb4iy++SJs2bfj888+JiIjAx8eHu+66i8zMTFubb775hlatWuHu7o6/vz8333wz2dnZ1+jVFJEroSAlIlXeO++8Q6dOnRgzZgxJSUkkJSURHh5OUlIS3bp1o02bNqxbt45ffvmFlJQUhg0bVmz/Tz/9FGdnZ3777Tc+/PBDAJycnHj33XfZtm0bn376KYsXL+bpp58GoHPnzkyZMoVatWrZnu/JJ58sUZdhGAwePJgTJ06wbNky4uLi2L9/P8OHDy/Wbv/+/Xz33Xf89NNP/PTTTyxbtozXX38dgKSkJO6++24efPBBdu7cydKlS7n99tvRZVJFrg86tSciVZ6Pjw8uLi54eHgQEhJi2/7BBx/Qrl07XnvtNdu2GTNmEB4ezp49e2jSpAkAjRo14h//+EexY1443yoyMpJXXnmFhx9+mKlTp+Li4oKPjw8mk6nY811s0aJFbNmyhYSEBMLDwwH4/PPPadGiBWvXrqV9+/YAFBUVMXPmTLy9vQEYMWIEv/76K3//+99JSkqioKCA22+/nfr16wPQqlWrq3i1RKQiaURKRKqt9evXs2TJEry8vGy3Zs2aAdZRoPNiY2NL7LtkyRJ69+5NnTp18Pb25v777yc9Pf2KTqnt3LmT8PBwW4gCiIqKwtfXl507d9q2RURE2EIUQGhoKKmpqQBER0fTq1cvWrVqxZ133sl//vMfTp48Wf4XQUSuKQUpEam2ioqKGDhwIJs2bSp227t3L127drW18/T0LLbfoUOH6N+/Py1btmTOnDmsX7+e999/H4D8/PxyP79hGJhMpstut1gsxR43mUwUFRUBYDabiYuL4+effyYqKop///vfNG3alISEhHLXISLXjoKUiFQLLi4uFBYWFtvWrl07tm/fTkREBI0aNSp2uzg8XWjdunUUFBTwr3/9i44dO9KkSROOHTt22ee7WFRUFImJiRw+fNi2bceOHZw+fZrmzZuXu28mk4kbb7yRl156iY0bN+Li4sLcuXPLvb+IXDsKUiJSLURERLB69WoOHjxIWloaRUVFPPLII5w4cYK7776bNWvWcODAARYuXMiDDz54yRDUsGFDCgoK+Pe//82BAwf4/PPPmTZtWonny8rK4tdffyUtLY2cnJwSx7n55ptp3bo19957Lxs2bGDNmjXcf//9dOvWrdTTiaVZvXo1r732GuvWrSMxMZFvv/2W48ePX1EQE5FrR0FKRKqFJ598ErPZTFRUFIGBgSQmJhIWFsZvv/1GYWEhffv2pWXLljz++OP4+Pjg5FT2X39t2rThrbfe4o033qBly5Z88cUXTJ48uVibzp07M3bsWIYPH05gYGCJyepgHUn67rvvqF27Nl27duXmm2+mQYMGzJ49u9z9qlWrFsuXL6d///40adKE5557jn/961/069ev/C+OiFwzJkPfoRURERGxi0akREREROykICUiIiJiJwUpERERETspSImIiIjYSUFKRERExE4KUiIiIiJ2UpASERERsZOClIiIiIidFKRERERE7KQgJSIiImInBSkREREROylIiYiIiNjp/wH0YSbdowgh7gAAAABJRU5ErkJggg==" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 14 + }, + { + "cell_type": "code", + "id": "10bdf199fcff6b48", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.569134800Z", + "start_time": "2025-02-16T08:30:40.002290Z" + } + }, + "source": [ + "light_params1 = {\n", + " # 'objective': 'regression',\n", + " # 'metric': 'l2',\n", + " 'objective': 'quantile', # 分位回归\n", + " 'metric': 'quantile', # 使用 quantile 作为评估指标\n", + " 'alpha': 0.25, # 90% 分位数\n", + " 'learning_rate': 0.05,\n", + " 'is_unbalance': True,\n", + " 'num_leaves': 2048,\n", + " 'min_data_in_leaf': 256,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " # 'feature_fraction': 0.7,\n", + " # 'bagging_fraction': 0.7,\n", + " # 'bagging_freq': 5,\n", + " # 'lambda_l1': 10,\n", + " # 'lambda_l2': 10,\n", + " 'verbosity': -1,\n", + " # 'device': 'gpu'\n", + "}\n", + "evals = {}\n", + "light_model1 = train_light_model(train_data, test_data, light_params1, feature_columns,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=10000, use_optuna=False,\n", + " print_feature_importance=True)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[82]\ttrain's quantile: 0.00160062\tvalid's quantile: 0.00150239\n", + "Evaluated only: quantile\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 15 + }, + { + "cell_type": "code", + "id": "5bb96ca8492e74d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.584794600Z", + "start_time": "2025-02-16T08:31:20.861518Z" + } + }, + "source": [ + "score_df = train_data\n", + "score_df['score'] = light_model9.predict(score_df[feature_columns]) + light_model1.predict(score_df[feature_columns])\n", + "# train_data['score'] = catboost_model.predict(train_data[feature_columns])\n", + "# score_df = score_df[score_df['score'] > 0]\n", + "predictions_train = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", + "predictions_train[['trade_date', 'score', 'ts_code']].to_csv('predictions_train.tsv', index=False)\n" + ], + "outputs": [], + "execution_count": 16 + }, + { + "cell_type": "code", + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.584794600Z", + "start_time": "2025-02-16T08:31:26.697459Z" + } + }, + "source": [ + "score_df = test_data\n", + "score_df['score'] = light_model9.predict(score_df[feature_columns]) / light_model1.predict(score_df[feature_columns])\n", + "# test_data['score'] = catboost_model.predict(test_data[feature_columns])\n", + "# score_df = score_df[score_df['score'] > 0]\n", + "predictions_test = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n", + "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)" + ], + "outputs": [], + "execution_count": 17 + }, + { + "cell_type": "code", + "id": "b427ce41-9739-4e9e-bea8-5f2551fec5d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-16T08:42:38.584794600Z", + "start_time": "2025-02-16T08:31:28.801289Z" + } + }, + "source": [], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/DoubleRank.ipynb b/code/train/DoubleRank.ipynb new file mode 100644 index 0000000..cff6b9c --- /dev/null +++ b/code/train/DoubleRank.ipynb @@ -0,0 +1,1385 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "79a7758178bafdd3", + "metadata": {}, + "source": [ + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "import pandas as pd\n", + "import warnings\n", + "\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "pd.set_option('display.max_columns', None)\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "a79cafb06a7e0e43", + "metadata": { + "scrolled": true + }, + "source": [ + "from utils.utils import read_and_merge_h5_data\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)\n", + "print('cyq perf')\n", + "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct',\n", + " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", + " df=df)\n", + "print(df.info())" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "cac01788dac10678", + "metadata": {}, + "source": [ + "print('industry')\n", + "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "c4e9e1d31da6dba6", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df = df.sort_values('trade_date')\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " return df\n", + "\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line',\n", + " 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "a735bc02ceb4d872", + "metadata": {}, + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "\n", + "def get_rolling_factor(df):\n", + " old_columns = df.columns.tolist()[:]\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # df[\"gap_next_open\"] = (df[\"open\"].shift(-1) - df[\"close\"]) / df[\"close\"]\n", + "\n", + " df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n", + " df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n", + "\n", + " # 因子 1：短期成交量变化率\n", + " df['volume_change_rate'] = (\n", + " grouped['vol'].rolling(window=2).mean() /\n", + " grouped['vol'].rolling(window=10).mean() - 1\n", + " ).reset_index(level=0, drop=True) # 确保索引对齐\n", + "\n", + " # 因子 2：成交量突破信号\n", + " max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n", + " df['cat_volume_breakout'] = (df['vol'] > max_volume)\n", + "\n", + " # 因子 3：换手率均线偏离度\n", + " mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + " std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n", + " df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n", + "\n", + " # 因子 4：换手率激增信号\n", + " df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n", + "\n", + " # 因子 5：量比均值\n", + " df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + "\n", + " # 因子 6：量比突破信号\n", + " max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n", + " df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n", + "\n", + " df['vol_spike'] = grouped.apply(\n", + " lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n", + " )\n", + " df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()\n", + "\n", + " # 计算 ATR\n", + " df['atr_14'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", + " index=x.index)\n", + " )\n", + " df['atr_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", + " index=x.index)\n", + " )\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + "\n", + " df['rsi_3'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " )\n", + " df['rsi_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['rsi_9'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算 EMA 指标\n", + " df['_ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['_ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['_ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['_ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['_ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['_ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['_ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['_ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " # 根据 trade_date 截面计算排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + "\n", + " def rolling_covariance(x, y, window):\n", + " return x.rolling(window).cov(y)\n", + "\n", + " def delta(series, period):\n", + " return series.diff(period)\n", + "\n", + " def rank(series):\n", + " return series.rank(pct=True)\n", + "\n", + " def stddev(series, window):\n", + " return series.rolling(window).std()\n", + "\n", + " window_high_volume = 5\n", + " window_close_stddev = 20\n", + " period_delta = 5\n", + " df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)\n", + " df['delta_cov'] = delta(df['cov'], period_delta)\n", + " df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))\n", + " df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n", + "\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）\n", + " df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）\n", + " df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + " df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + "\n", + " # 3. 最近连续涨跌停天数\n", + " def calculate_consecutive_limits(series):\n", + " \"\"\"\n", + " 计算连续涨停/跌停天数。\n", + " \"\"\"\n", + " consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " return consecutive_up, consecutive_down\n", + "\n", + " # 连续涨停天数\n", + " df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n", + " lambda x: calculate_consecutive_limits(x)[0]\n", + " ).reset_index(level=0, drop=True)\n", + "\n", + " df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n", + "\n", + " df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n", + "\n", + " def rolling_corr(group):\n", + " roc_close = group['close'].pct_change()\n", + " roc_weight = group['weight_avg'].pct_change()\n", + " return roc_close.rolling(10).corr(roc_weight)\n", + "\n", + " df['price_cost_divergence'] = grouped.apply(rolling_corr)\n", + "\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " # 16. 筹码稳定性指数 (20日波动率)\n", + " df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n", + " df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n", + "\n", + " # 17. 成本区间突破标记\n", + " df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n", + "\n", + " # 20. 筹码-流动性风险\n", + " df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n", + " 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n", + "\n", + " # 7. 市值波动率因子\n", + " df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)\n", + " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " # 8. 市值成长性因子\n", + " df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)\n", + " df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n", + " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", + "\n", + " return df, new_columns\n", + "\n", + "\n", + "def get_simple_factor(df):\n", + " old_columns = df.columns.tolist()[:]\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " alpha = 0.5\n", + " df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n", + " df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']\n", + " df['log_close'] = np.log(df['close'])\n", + "\n", + " df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']\n", + "\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", + "\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + "\n", + " df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n", + "\n", + " df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['lock_factor'] = df['turnover_rate'] * (\n", + " 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n", + "\n", + " df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)\n", + "\n", + " df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n", + "\n", + " # 12. 小盘股筹码集中度\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n", + " (df['volume_ratio'] > 1.5) &\n", + " (df['winner_rate'] > 0.7))\n", + "\n", + " df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']\n", + "\n", + " df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])\n", + "\n", + " df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']\n", + "\n", + " df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']\n", + "\n", + " drop_columns = [col for col in df.columns if col.startswith('_')]\n", + " df.drop(columns=drop_columns, inplace=True, errors='ignore')\n", + "\n", + " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", + " return df, new_columns\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "53f86ddc0677a6d7", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "source": [ + "from utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "dbe2fd8021b9417f", + "metadata": {}, + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if\n", + " col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n", + "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", + "print(origin_columns)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "92d84ce15a562ec6", + "metadata": {}, + "source": [ + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '20180101']\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "# df = get_technical_factor(df)\n", + "# df = get_act_factor(df)\n", + "# df = get_money_flow_factor(df)\n", + "# df = get_alpha_factor(df)\n", + "# df = get_limit_factor(df)\n", + "# df = get_cyp_perf_factor(df)\n", + "# df = get_mv_factors(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "b87b938028afa206", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.1, wasserstein_threshold=0.15,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold and wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "f4f16d63ad18d1bc", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", + " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", + " num_features = [col for col in num_features if 'limit' not in col]\n", + " num_features = [col for col in num_features if 'cyq' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", + " # deviation_col_name = f'deviation_mean_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "40e6b68a91b30c79", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n", + " log=True):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " if log:\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " volatility = stock_df['close'].pct_change().rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + "\n", + " return score\n", + "\n", + " scores = df.groupby('ts_code').apply(lambda x: compute_stock_score(x))\n", + " scores = scores.reset_index(level=0, drop=True)\n", + "\n", + " return scores\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "47c12bb34062ae7a", + "metadata": {}, + "source": [ + "days = 2\n", + "validation_days=120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + " df.groupby('ts_code')['open'].shift(-1)\n", + "df['future_volatility'] = (\n", + " df.groupby('ts_code')['future_return']\n", + " .transform(lambda x: x.rolling(days).std())\n", + ")\n", + "\n", + "df['future_score'] = (\n", + " 0.7 * df['future_return'] +\n", + " 0.15 * df['future_volatility']\n", + ")\n", + "\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "\n", + "df['label'] = df.groupby('trade_date', group_keys=False)['future_volatility'].transform(\n", + " lambda x: pd.qcut(x, q=30, labels=False, duplicates='drop')\n", + ")\n", + "\n", + "df['label2'] = df.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + " lambda x: pd.qcut(x, q=30, labels=False, duplicates='drop')\n", + ")\n", + "\n", + "# df['1_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(days)\n", + "# df['2_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(1 + days)\n", + "# df['3_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(3 + days - 1)\n", + "\n", + "def symmetric_log_transform(values):\n", + " return np.sign(values) * np.log1p(np.abs(values))\n", + "\n", + "\n", + "train_data = df[filter_index & (df['trade_date'] <= '2024-06-01') & (df['trade_date'] >= '2021-01-01')]\n", + "test_data = df[filter_index & (df['trade_date'] >= '2024-06-01')]\n", + "\n", + "\n", + "def select_pre_zt_stocks_dynamic(stock_df):\n", + " # 排序数据\n", + " stock_df = stock_df.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # avg_vol_3 = stock_df.groupby('ts_code')['vol'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + " # avg_vol_5 = stock_df.groupby('ts_code')['vol'].rolling(window=5).mean().shift(3).reset_index(level=0, drop=True)\n", + "\n", + " # stock_df = stock_df[\n", + " # (stock_df['cat_up_limit'] == 1) |\n", + " # (stock_df['vol'] > vol_spike_multiplier * stock_df['vol_spike'])\n", + " # ]\n", + " # cd1 = stock_df[\"close\"] > stock_df[\"close\"].shift(1)\n", + "\n", + " # cd2 = stock_df[\"close\"] > stock_df[\"close\"].rolling(window=10).mean()\n", + " #\n", + " # cd3 = (avg_vol_3 > avg_vol_5 * 2)\n", + " #\n", + " # cd4 = stock_df['gap_next_open'] < 0\n", + "\n", + " # stock_df = stock_df[(cd2 & cd4) | cd3]\n", + " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x.nlargest(1000, 'return_20')\n", + " )\n", + "\n", + " return stock_df\n", + "\n", + "\n", + "train_data = select_pre_zt_stocks_dynamic(train_data)\n", + "test_data = select_pre_zt_stocks_dynamic(test_data)\n", + "\n", + "# train_data, _ = get_simple_factor(train_data)\n", + "# test_data, _ = get_simple_factor(test_data)\n", + "\n", + "\n", + "# train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + "# )\n", + "# test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + "# )\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, feature_columns)\n", + "# test_data, _ = create_deviation_within_dates(test_data, feature_columns)\n", + "\n", + "feature_columns = [col for col in train_data.columns if col in train_data.columns]\n", + "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(f'feature_columns size: {len(feature_columns)}')\n", + "\n", + "feature_columns, _ = remove_shifted_features(train_data[train_data['label'] == train_data['label'].max()],\n", + " test_data[test_data['label'] == test_data['label'].max()],\n", + " feature_columns)\n", + "\n", + "feature_columns = remove_highly_correlated_features(train_data[train_data['label'] == train_data['label'].max()],\n", + " feature_columns)\n", + "keep_columns = [col for col in train_data.columns if\n", + " col in feature_columns or col in ['ts_code', 'trade_date', 'label', 'future_return',\n", + " 'future_score', 'future_volatility']]\n", + "# train_data = train_data[keep_columns]\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=feature_columns)\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "\n", + "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "\n", + "\n", + "# feature_columns_new.remove('cat_l2_code')" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "8f134d435f71e9e2", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "import lightgbm as lgb\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.decomposition import PCA\n", + "\n", + "\n", + "def train_light_model(train_data_df, params, feature_columns, callbacks, evals,\n", + " print_feature_importance=True, num_boost_round=100,\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " label_column='label'): # 新增参数：validation_days\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " numeric_columns = train_data_df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " # X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns])\n", + " # X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns])\n", + " train_data_df = cross_sectional_standardization(train_data_df, numeric_columns)\n", + "\n", + " # 去除标签为空的样本\n", + " train_data_df = train_data_df.dropna(subset=[label_column])\n", + " print('原始训练集大小: ', len(train_data_df))\n", + "\n", + " # 按时间顺序划分训练集和验证集\n", + " if split_date is None:\n", + " all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n", + " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + "\n", + " # 打印划分结果\n", + " print(f\"划分后的训练集大小: {len(train_data_split)}, 验证集大小: {len(val_data_split)}\")\n", + "\n", + " # 提取特征和标签\n", + " X_train = train_data_split[feature_columns]\n", + " y_train = train_data_split[label_column]\n", + "\n", + " X_val = val_data_split[feature_columns]\n", + " y_val = val_data_split[label_column]\n", + "\n", + " # 标准化数值特征\n", + " scaler = StandardScaler()\n", + "\n", + " # 计算每个 trade_date 内的样本数（LTR 需要 group 信息）\n", + " train_groups = train_data_split.groupby('trade_date').size().tolist()\n", + " val_groups = val_data_split.groupby('trade_date').size().tolist()\n", + "\n", + " # 处理类别特征\n", + " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", + "\n", + " pca = None\n", + " if use_pca:\n", + " pca = PCA(n_components=0.95) # 或指定 n_components=固定值（如 10）\n", + " numeric_features = [col for col in feature_columns if col not in categorical_feature]\n", + " numeric_pca = pca.fit_transform(X_train[numeric_features])\n", + " X_train = pd.concat([pd.DataFrame(numeric_pca, index=X_train.index), X_train[categorical_feature]], axis=1)\n", + "\n", + " numeric_pca = pca.transform(X_val[numeric_features])\n", + " X_val = pd.concat([pd.DataFrame(numeric_pca, index=X_val.index), X_val[categorical_feature]], axis=1)\n", + "\n", + " # 计算权重（基于时间）\n", + " # trade_date = train_data_split['trade_date'] # 交易日期\n", + " # weights = (trade_date - trade_date.min()).dt.days / (trade_date.max() - trade_date.min()).days + 1\n", + " # weights = train_data_split.groupby('trade_date')['std_return_5'].transform(\n", + " # lambda x: x / x.mean()\n", + " # )\n", + " ud = sorted(train_data_split[\"trade_date\"].unique().tolist())\n", + " date_weights = {date: weight * weight for date, weight in zip(ud, np.linspace(1, 10, len(ud)))}\n", + " params['weight'] = train_data_split[\"trade_date\"].map(date_weights).tolist()\n", + "\n", + " print('feature_columns size: ', len(X_train.columns.tolist()))\n", + "\n", + " train_dataset = lgb.Dataset(\n", + " X_train, label=y_train, group=train_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + "\n", + " # weights = val_data_split.groupby('trade_date')['std_return_5'].transform(\n", + " # lambda x: x / x.mean()\n", + " # )\n", + " val_dataset = lgb.Dataset(\n", + " X_val, label=y_val, group=val_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + "\n", + " # 训练模型\n", + " # 显式创建 LGBMRanker 模型\n", + " model = lgb.train(\n", + " params, train_dataset, num_boost_round=num_boost_round,\n", + " valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + "\n", + " # 打印特征重要性（如果需要）\n", + " if print_feature_importance:\n", + " lgb.plot_metric(evals)\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + "\n", + " return model, scaler, pca\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", + "metadata": {}, + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "if 'gen_volatility' in feature_columns:\n", + " feature_columns.remove('gen_volatility')\n", + "\n", + "label_gain = list(range(len(train_data['label'].unique())))\n", + "label_gain = [gain * gain for gain in label_gain]\n", + "light_params = {\n", + " 'label_gain': label_gain,\n", + " 'objective': 'lambdarank',\n", + " 'metric': 'lambdarank',\n", + " 'learning_rate': 0.03,\n", + " 'num_leaves': 1024,\n", + " 'min_data_in_leaf': 128,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 1,\n", + " 'bagging_freq': 5,\n", + " 'lambda_l1': 0.1,\n", + " 'lambda_l2': 0.1,\n", + " # 'boosting': 'dart',\n", + " 'verbosity': -1,\n", + " 'extra_trees': True,\n", + " 'max_position': 5,\n", + " 'ndcg_at': 1,\n", + " 'seed': 7\n", + "}\n", + "evals = {}\n", + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", + "light_params['feature_contri'] = feature_contri\n", + "print(f'feature_contri: {feature_contri}')\n", + "model, scaler, pca = train_light_model(train_data.copy().dropna(subset=['label']),\n", + " light_params, feature_columns,\n", + " [lgb.log_evaluation(period=100),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, validation_days=validation_days,\n", + " print_feature_importance=True, use_pca=use_pca)\n", + "\n", + "print('train data size: ', len(train_data))" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "5d1522a7538db91b", + "metadata": {}, + "source": [ + "days = 2\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "if 'gen_volatility' in feature_columns:\n", + " feature_columns.remove('gen_volatility')\n", + "\n", + "train_data = train_data.sort_values(['trade_date', 'ts_code'])\n", + "\n", + "score_df = train_data\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "score_df = cross_sectional_standardization(score_df, numeric_columns)\n", + "score_df['score'] = model.predict(score_df[feature_columns])\n", + "\n", + "train_data['gen_volatility'] = score_df['score']\n", + "# train_data['gen_volatility'] = train_data.groupby('trade_date')['gen_volatility'].rank(pct=True)\n", + "train_data['cat_gen_volatility'] = train_data.groupby('trade_date', group_keys=False)['gen_volatility'].transform(\n", + " lambda x: pd.qcut(x, q=3, labels=False, duplicates='drop')\n", + ")\n", + "\n", + "score_df = test_data\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "score_df = cross_sectional_standardization(score_df, numeric_columns)\n", + "score_df['score'] = model.predict(score_df[feature_columns])\n", + "\n", + "test_data['gen_volatility'] = score_df['score']\n", + "# test_data['gen_volatility'] = test_data.groupby('trade_date')['gen_volatility'].rank(pct=True)\n", + "test_data['cat_gen_volatility'] = test_data.groupby('trade_date', group_keys=False)['gen_volatility'].transform(\n", + " lambda x: pd.qcut(x, q=3, labels=False, duplicates='drop')\n", + ")\n", + "\n", + "# test_data['future_score'] = (\n", + "# 0.7 * test_data['future_return'] +\n", + "# 0.3 * test_data['future_volatility']\n", + "# )\n", + "\n", + "if 'gen_volatility' not in feature_columns:\n", + " feature_columns.append('gen_volatility')\n", + "\n", + "print(feature_columns)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "612d1bdc10933f64", + "metadata": {}, + "source": [ + "# print(test_data[['gen_volatility', 'future_volatility_r']].head(15))\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "d86af99d15cb3bdd", + "metadata": {}, + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "label_gain = list(range(len(train_data['label'].unique())))\n", + "light_params['label_gain'] = [gain * gain for gain in label_gain]\n", + "# light_params['max_depth'] = 32\n", + "# light_params = {\n", + "# 'label_gain': label_gain,\n", + "# 'objective': 'lambdarank',\n", + "# 'metric': 'lambdarank',\n", + "# 'learning_rate': 0.1,\n", + "# 'num_leaves': 1024,\n", + "# 'min_data_in_leaf': 128,\n", + "# 'max_depth': 16,\n", + "# 'max_bin': 1024,\n", + "# 'feature_fraction': 0.7,\n", + "# 'bagging_fraction': 1,\n", + "# 'bagging_freq': 5,\n", + "# 'lambda_l1': 1,\n", + "# 'lambda_l2': 1,\n", + "# # 'boosting': 'dart',\n", + "# 'verbosity': -1,\n", + "# 'extra_trees': True,\n", + "# 'max_position': 5,\n", + "# 'ndcg_at': 1,\n", + "# 'seed': 7\n", + "# }\n", + "evals = {}\n", + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "# feature_contri = [2 if feat.startswith('gen_volatility') else 1 for feat in feature_columns]\n", + "feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", + "light_params['feature_contri'] = feature_contri\n", + "print(f'feature_contri: {feature_contri}')\n", + "model2, scaler, pca = train_light_model(train_data.dropna(subset=['label2']),\n", + " light_params, feature_columns,\n", + " [lgb.log_evaluation(period=100),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, validation_days=validation_days,\n", + " print_feature_importance=True, use_pca=use_pca,\n", + " label_column='label2')\n", + "\n", + "print('train data size: ', len(train_data))" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "ef9d068e-67f7-412c-bbd8-cdee7492dbc9", + "metadata": {}, + "source": [ + "# train_data = train_data.sort_values(by='trade_date')\n", + "# all_dates = train_data['trade_date'].unique() # 获取所有唯一的 trade_date\n", + "# split_date = all_dates[-120] # 划分点为倒数第 validation_days 天\n", + "# print(split_date)\n", + "# print(all_dates)\n", + "# val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", + "\n", + "score_df = test_data\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n", + "score_df = cross_sectional_standardization(score_df, numeric_columns)\n", + "\n", + "if use_pca and pca is not None:\n", + " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", + " numeric_features = [col for col in feature_columns if col not in categorical_feature]\n", + " numeric_pca = pca.transform(score_df[numeric_features])\n", + " score_df = pd.concat([pd.DataFrame(numeric_pca), score_df[categorical_feature],\n", + " score_df[['trade_date', 'ts_code', 'future_return', 'future_score', 'label']]], axis=1)\n", + " score_df['score'] = model2.predict(score_df[[col for col in score_df.columns if\n", + " col not in ['trade_date', 'ts_code', 'future_return', 'future_score',\n", + " 'label']]])\n", + "else:\n", + " score_df['score'] = model2.predict(score_df[feature_columns])\n", + "# train_data['score'] = catboost_model.predict(train_data[feature_columns])\n", + "# 按 trade_date 分组，并对每组按 score 降序排序，取前 10\n", + "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x.nlargest(1, 'score')\n", + ")# score_df = score_df[score_df['score'] > 0]\n", + "score_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n", + "print(score_df['label2'].mean())\n", + "print(score_df['future_return'].mean())" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "7ea31b06eec3cac9", + "metadata": {}, + "source": [ + "print(score_df[score_df['trade_date'] >= '2024-08-01'][['trade_date', 'ts_code', 'future_return', 'future_score', 'label', 'label2']].head(15))\n" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "id": "bcc6a60c-a134-4eb2-9125-5a608ef88e27", + "metadata": {}, + "source": [ + "test_data['future_volatility_r'] = test_data.groupby('trade_date', group_keys=False)['future_volatility'].transform(\n", + " lambda x: pd.qcut(x, q=3, labels=False, duplicates='drop')\n", + ")\n", + "td = test_data.dropna(subset=['cat_gen_volatility'])\n", + "td = td.dropna(subset=['future_volatility'])\n", + "\n", + "gen_volatility = td['gen_volatility']\n", + "future_volatility = td['future_volatility_r']\n", + "\n", + "from scipy.stats import spearmanr, kendalltau\n", + "from sklearn.metrics import mean_squared_error, ndcg_score\n", + "import numpy as np\n", + "\n", + "# (1) Spearman 相关系数\n", + "spearman_corr, _ = spearmanr(gen_volatility, future_volatility)\n", + "print(f\"Spearman Correlation: {spearman_corr}\")\n", + "\n", + "# (2) Kendall Tau 系数\n", + "kendall_corr, _ = kendalltau(gen_volatility, future_volatility)\n", + "print(f\"Kendall Tau Correlation: {kendall_corr}\")\n", + "\n", + "# (3) 均方误差 (MSE)\n", + "mse = mean_squared_error(future_volatility, gen_volatility)\n", + "print(f\"Mean Squared Error (MSE): {mse}\")\n", + "\n", + "# (4) Top-K 准确率\n", + "def top_k_accuracy(y_true, y_pred, k=5):\n", + " correct = 0\n", + " for true, pred in zip(y_true, y_pred):\n", + " if abs(true - pred) <= k:\n", + " correct += 1\n", + " return correct / len(y_true)\n", + "\n", + "top_5_acc = top_k_accuracy(future_volatility, gen_volatility, k=5)\n", + "top_10_acc = top_k_accuracy(future_volatility, gen_volatility, k=20)\n", + "# print(f\"Top-5 Accuracy: {top_5_acc}, Top-10 Accuracy: {top_10_acc}\")\n", + "\n", + "# (5) NDCG\n", + "true_scores = 50 - future_volatility\n", + "pred_scores = 50 - gen_volatility\n", + "ndcg = ndcg_score([true_scores.values], [pred_scores.values])\n", + "print(f\"NDCG Score: {ndcg}\")\n", + "\n", + "\n", + "# print(gen_volatility, future_volatility)" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/PlUpdateClassify.ipynb b/code/train/PlUpdateClassify.ipynb new file mode 100644 index 0000000..3111572 --- /dev/null +++ b/code/train/PlUpdateClassify.ipynb @@ -0,0 +1,1016 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:12.588477Z", + "start_time": "2025-02-21T15:09:12.513776Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "execution_count": 13 + }, + { + "cell_type": "code", + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:52.492559Z", + "start_time": "2025-02-21T15:09:12.603475Z" + } + }, + "source": [ + "from utils.utils import read_and_merge_h5_data_polars\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data_polars('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data_polars('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data_polars('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data_polars('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n" + ] + } + ], + "execution_count": 14 + }, + { + "cell_type": "code", + "id": "a4eec8c93f6a7cc3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:53.009911Z", + "start_time": "2025-02-21T15:09:52.621225Z" + } + }, + "source": [ + "print('industry')\n", + "df = read_and_merge_h5_data_polars('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code'],\n", + " df=df, on=['ts_code'], join='left')\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n", + "left merge on ['ts_code']\n" + ] + } + ], + "execution_count": 15 + }, + { + "cell_type": "code", + "id": "4243c838-1775-4c33-8ec5-38d3fae3e55c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:53.087600Z", + "start_time": "2025-02-21T15:09:53.016442Z" + } + }, + "source": [ + "import polars as pl\n", + "\n", + "def print_df_info(df: pl.DataFrame):\n", + " # 获取行列信息\n", + " shape = df.shape\n", + " print(f\"Shape: {shape[0]} rows, {shape[1]} columns\")\n", + " \n", + " # 获取内存占用大小\n", + " mem_size = df.estimated_size() # 单位是字节\n", + " print(f\"Memory usage: {mem_size / (1024 ** 2):.2f} MB\") # 转换为 MB\n", + " \n", + " # 获取列名和每列的类型\n", + " print(\"\\nColumn types:\")\n", + " for col_name, dtype in zip(df.columns, df.dtypes):\n", + " print(f\"{col_name}: {dtype}\")\n", + "\n", + "print_df_info(df)\n", + "\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: 8418162 rows, 22 columns\n", + "Memory usage: 1380.84 MB\n", + "\n", + "Column types:\n", + "ts_code: String\n", + "trade_date: String\n", + "open: Float64\n", + "close: Float64\n", + "high: Float64\n", + "low: Float64\n", + "vol: Float64\n", + "turnover_rate: Float64\n", + "pe_ttm: Float64\n", + "circ_mv: Float64\n", + "volume_ratio: Float64\n", + "is_st: Boolean\n", + "up_limit: Float64\n", + "down_limit: Float64\n", + "buy_sm_vol: Int64\n", + "sell_sm_vol: Int64\n", + "buy_lg_vol: Int64\n", + "sell_lg_vol: Int64\n", + "buy_elg_vol: Int64\n", + "sell_elg_vol: Int64\n", + "net_mf_vol: Int64\n", + "l2_code: String\n" + ] + } + ], + "execution_count": 16 + }, + { + "cell_type": "code", + "id": "99776e73-f310-47c0-953e-2cf73ff13310", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:53.229940Z", + "start_time": "2025-02-21T15:09:53.151622Z" + } + }, + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "\n", + "def get_technical_factor(df: pl.LazyFrame) -> pl.LazyFrame:\n", + " # 按股票和日期排序\n", + " df = df.sort(['ts_code', 'trade_date'])\n", + " \n", + " # 计算 up 和 down\n", + " df = df.with_columns([\n", + " ((pl.col('high') - pl.max_horizontal(['close', 'open'])) / pl.col('close')).alias('up'),\n", + " ((pl.min_horizontal(['close', 'open']) - pl.col('low')) / pl.col('close')).alias('down')\n", + " ])\n", + " \n", + " # 定义一个函数来计算分组指标（如 ATR、OBV、RSI 等）\n", + " def calculate_talib_indicator(col_names, func, *args, **kwargs):\n", + " return (\n", + " df.group_by('ts_code', maintain_order=True)\n", + " .agg(\n", + " pl.map_batches(\n", + " col_names,\n", + " lambda cols: pl.Series(func(*[col.to_numpy() for col in cols], **kwargs))\n", + " ).alias(kwargs.get('output_col'))\n", + " )\n", + " .explode(kwargs.get('output_col'))\n", + " )\n", + "\n", + " # 计算 ATR\n", + " atr_14_df = calculate_talib_indicator(\n", + " ['high', 'low', 'close'], talib.ATR, timeperiod=14, output_col='atr_14'\n", + " )\n", + " atr_6_df = calculate_talib_indicator(\n", + " ['high', 'low', 'close'], talib.ATR, timeperiod=6, output_col='atr_6'\n", + " )\n", + "\n", + " # 合并 ATR 列\n", + " df = df.join(atr_14_df, on='ts_code', how='left').join(atr_6_df, on='ts_code', how='left')\n", + "\n", + " # 计算 OBV 及其均线\n", + " obv_df = calculate_talib_indicator(\n", + " ['close', 'vol'], talib.OBV, output_col='obv'\n", + " )\n", + " maobv_6_df = obv_df.with_columns(\n", + " pl.map_batches(\n", + " ['obv'],\n", + " lambda cols: pl.Series(talib.SMA(cols[0].to_numpy(), timeperiod=6))\n", + " ).alias('maobv_6')\n", + " )\n", + "\n", + " # 合并 OBV 和 MAOBV 列\n", + " df = df.join(obv_df, on='ts_code', how='left').join(maobv_6_df, on='ts_code', how='left')\n", + " df = df.with_columns((pl.col('obv') - pl.col('maobv_6')).alias('obv-maobv_6'))\n", + "\n", + " # 计算 RSI\n", + " rsi_3_df = calculate_talib_indicator(\n", + " ['close'], talib.RSI, timeperiod=3, output_col='rsi_3'\n", + " )\n", + " rsi_6_df = calculate_talib_indicator(\n", + " ['close'], talib.RSI, timeperiod=6, output_col='rsi_6'\n", + " )\n", + " rsi_9_df = calculate_talib_indicator(\n", + " ['close'], talib.RSI, timeperiod=9, output_col='rsi_9'\n", + " )\n", + "\n", + " # 合并 RSI 列\n", + " df = (\n", + " df.join(rsi_3_df, on='ts_code', how='left')\n", + " .join(rsi_6_df, on='ts_code', how='left')\n", + " .join(rsi_9_df, on='ts_code', how='left')\n", + " )\n", + "\n", + " # 计算 return_5, return_10, return_20\n", + " df = df.with_columns([\n", + " (pl.col('close') / pl.col('close').shift(5) - 1).over('ts_code').alias('return_5'),\n", + " (pl.col('close') / pl.col('close').shift(10) - 1).over('ts_code').alias('return_10'),\n", + " (pl.col('close') / pl.col('close').shift(20) - 1).over('ts_code').alias('return_20')\n", + " ])\n", + "\n", + " # 计算 avg_close_5\n", + " df = df.with_columns(\n", + " (pl.col('close').rolling_mean(window_size=5) / pl.col('close')).over('ts_code').alias('avg_close_5')\n", + " )\n", + "\n", + " # 计算标准差指标\n", + " df = df.with_columns([\n", + " pl.col('close').pct_change().rolling_std(window_size=5).over('ts_code').alias('std_return_5'),\n", + " pl.col('close').pct_change().rolling_std(window_size=15).over('ts_code').alias('std_return_15'),\n", + " pl.col('close').pct_change().rolling_std(window_size=25).over('ts_code').alias('std_return_25'),\n", + " pl.col('close').pct_change().rolling_std(window_size=90).over('ts_code').alias('std_return_90'),\n", + " pl.col('close').shift(10).pct_change().rolling_std(window_size=90).over('ts_code').alias('std_return_90_2')\n", + " ])\n", + "\n", + " # 计算比值指标\n", + " df = df.with_columns([\n", + " (pl.col('std_return_5') / pl.col('std_return_90')).alias('std_return_5 / std_return_90'),\n", + " (pl.col('std_return_5') / pl.col('std_return_25')).alias('std_return_5 / std_return_25')\n", + " ])\n", + "\n", + " # 计算标准差差值\n", + " df = df.with_columns(\n", + " (pl.col('std_return_90') - pl.col('std_return_90_2')).alias('std_return_90 - std_return_90_2')\n", + " )\n", + "\n", + " return df\n", + "\n", + "def get_act_factor(df: pl.LazyFrame, cat=True) -> pl.LazyFrame:\n", + " # 按股票和日期排序\n", + " df = df.sort(['ts_code', 'trade_date'])\n", + " \n", + " # 定义一个函数来计算分组 EMA\n", + " def calculate_ema(col_name, timeperiod):\n", + " return (\n", + " df.group_by('ts_code', maintain_order=True)\n", + " .agg(\n", + " pl.map_batches(\n", + " [col_name],\n", + " lambda cols: pl.Series(talib.EMA(cols[0].to_numpy(), timeperiod=timeperiod))\n", + " ).alias(f\"ema_{timeperiod}\")\n", + " )\n", + " .explode(f\"ema_{timeperiod}\")\n", + " )\n", + " \n", + " # 计算 EMA 指标\n", + " ema_columns = []\n", + " for period in [5, 13, 20, 60]:\n", + " ema_df = calculate_ema('close', period)\n", + " ema_columns.append(ema_df.select([f\"ts_code\", f\"ema_{period}\"]))\n", + " \n", + " # 将所有 EMA 列合并到原始 DataFrame 中\n", + " for ema_col in ema_columns:\n", + " df = df.join(ema_col, on='ts_code', how='left')\n", + " \n", + " # 使用 NumPy 的 arctan 和 sqrt 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df = df.with_columns([\n", + " (pl.map_batches(\n", + " ['ema_5'],\n", + " lambda cols: pl.Series(np.arctan((cols[0] / cols[0].shift(1) - 1) * 100) * 57.3 / 50)\n", + " )).alias('act_factor1'),\n", + " (pl.map_batches(\n", + " ['ema_13'],\n", + " lambda cols: pl.Series(np.arctan((cols[0] / cols[0].shift(1) - 1) * 100) * 57.3 / 40)\n", + " )).alias('act_factor2'),\n", + " (pl.map_batches(\n", + " ['ema_20'],\n", + " lambda cols: pl.Series(np.arctan((cols[0] / cols[0].shift(1) - 1) * 100) * 57.3 / 21)\n", + " )).alias('act_factor3'),\n", + " (pl.map_batches(\n", + " ['ema_60'],\n", + " lambda cols: pl.Series(np.arctan((cols[0] / cols[0].shift(1) - 1) * 100) * 57.3 / 10)\n", + " )).alias('act_factor4')\n", + " ])\n", + " \n", + " if cat:\n", + " df = df.with_columns([\n", + " (pl.col('act_factor1') > 0).alias('cat_af1'),\n", + " (pl.col('act_factor2') > pl.col('act_factor1')).alias('cat_af2'),\n", + " (pl.col('act_factor3') > pl.col('act_factor2')).alias('cat_af3'),\n", + " (pl.col('act_factor4') > pl.col('act_factor3')).alias('cat_af4')\n", + " ])\n", + " \n", + " # 计算 act_factor5 和 act_factor6\n", + " df = df.with_columns([\n", + " (pl.col('act_factor1') + pl.col('act_factor2') + pl.col('act_factor3') + pl.col('act_factor4')).alias('act_factor5'),\n", + " ((pl.col('act_factor1') - pl.col('act_factor2')) / \n", + " pl.map_batches(\n", + " ['act_factor1', 'act_factor2'],\n", + " lambda cols: pl.Series(np.sqrt(cols[0]**2 + cols[1]**2))\n", + " )).alias('act_factor6')\n", + " ])\n", + " \n", + " # 根据 trade_date 截面计算排名\n", + " df = df.with_columns([\n", + " pl.col('act_factor1').rank(method='average', descending=True).over('trade_date').alias('rank_act_factor1'),\n", + " pl.col('act_factor2').rank(method='average', descending=True).over('trade_date').alias('rank_act_factor2'),\n", + " pl.col('act_factor3').rank(method='average', descending=True).over('trade_date').alias('rank_act_factor3')\n", + " ])\n", + " \n", + " return df" + ], + "outputs": [], + "execution_count": 17 + }, + { + "cell_type": "code", + "id": "53f86ddc0677a6d7", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-02-21T15:09:53.292850Z", + "start_time": "2025-02-21T15:09:53.234877Z" + } + }, + "source": [ + "origin_columns = df.columns\n", + "origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'l2_code']]\n", + "# origin_columns = [col for col in origin_columns if col not in index_data.columns]\n" + ], + "outputs": [], + "execution_count": 18 + }, + { + "cell_type": "code", + "id": "5f3d9aece75318cd", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:55.552350Z", + "start_time": "2025-02-21T15:09:53.310664Z" + } + }, + "source": [ + "def filter_data(df):\n", + " # 转换为 lazy frame\n", + " df = df.lazy()\n", + "\n", + " df = df.filter(~pl.col('is_st')) # 去掉 is_st 为 True 的行\n", + " df = df.filter(~pl.col('ts_code').str.contains('BJ$')) # ts_code 以 'BJ' 结尾的行\n", + " df = df.filter(~pl.col('ts_code').str.contains('^30')) # ts_code 以 '30' 开头的行\n", + " df = df.filter(~pl.col('ts_code').str.contains('^68')) # ts_code 以 '68' 开头的行\n", + " df = df.filter(~pl.col('ts_code').str.contains('^8')) # ts_code 以 '8' 开头的行\n", + "\n", + " return df\n", + "\n", + "\n", + "\n", + "tdf = filter_data(df)\n", + "tdf = get_technical_factor(tdf)\n", + "tdf = get_act_factor(tdf)\n", + "# df = get_money_flow_factor(df)\n", + "# df = get_alpha_factor(df)\n", + "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", + "# df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print_df_info(tdf.collect())" + ], + "outputs": [ + { + "ename": "PanicException", + "evalue": "python function failed: ATR() got an unexpected keyword argument 'output_col'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mPanicException\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[19], line 24\u001B[0m\n\u001B[0;32m 17\u001B[0m tdf \u001B[38;5;241m=\u001B[39m get_act_factor(tdf)\n\u001B[0;32m 18\u001B[0m \u001B[38;5;66;03m# df = get_money_flow_factor(df)\u001B[39;00m\n\u001B[0;32m 19\u001B[0m \u001B[38;5;66;03m# df = get_alpha_factor(df)\u001B[39;00m\n\u001B[0;32m 20\u001B[0m \u001B[38;5;66;03m# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\u001B[39;00m\n\u001B[0;32m 21\u001B[0m \u001B[38;5;66;03m# df = df.rename(columns={'l2_code': 'cat_l2_code'})\u001B[39;00m\n\u001B[0;32m 22\u001B[0m \u001B[38;5;66;03m# df = df.merge(index_data, on='trade_date', how='left')\u001B[39;00m\n\u001B[1;32m---> 24\u001B[0m print_df_info(\u001B[43mtdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcollect\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m)\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\try_trader\\lib\\site-packages\\polars\\lazyframe\\frame.py:2053\u001B[0m, in \u001B[0;36mLazyFrame.collect\u001B[1;34m(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, streaming, engine, background, _eager, **_kwargs)\u001B[0m\n\u001B[0;32m 2051\u001B[0m \u001B[38;5;66;03m# Only for testing purposes\u001B[39;00m\n\u001B[0;32m 2052\u001B[0m callback \u001B[38;5;241m=\u001B[39m _kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpost_opt_callback\u001B[39m\u001B[38;5;124m\"\u001B[39m, callback)\n\u001B[1;32m-> 2053\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m wrap_df(\u001B[43mldf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcollect\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcallback\u001B[49m\u001B[43m)\u001B[49m)\n", + "\u001B[1;31mPanicException\u001B[0m: python function failed: ATR() got an unexpected keyword argument 'output_col'" + ] + } + ], + "execution_count": 19 + }, + { + "cell_type": "code", + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:09:55.555352100Z", + "start_time": "2025-02-21T15:06:05.193979Z" + } + }, + "source": [ + "feature_columns = [col for col in tdf.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(feature_columns)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['turnover_rate', 'pe_ttm', 'volume_ratio', 'l2_code', 'up', 'down', 'atr_14', 'atr_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6468\\1049439272.py:1: PerformanceWarning: Determining the column names of a LazyFrame requires resolving its schema, which is a potentially expensive operation. Use `LazyFrame.collect_schema().names()` to get the column names without this warning.\n", + " feature_columns = [col for col in tdf.columns if col not in ['trade_date',\n" + ] + } + ], + "execution_count": 8 + }, + { + "cell_type": "code", + "id": "0ebdfb92-d88b-4b5c-a715-675dab876fc0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.360476Z", + "start_time": "2025-02-21T15:06:05.298420Z" + } + }, + "source": [ + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " grouped_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n", + " deviation_col_name = f'deviation_median_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_median\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(groupby_col)[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ], + "outputs": [], + "execution_count": 9 + }, + { + "cell_type": "code", + "id": "fbb968383f8cf2c7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.485541Z", + "start_time": "2025-02-21T15:06:05.392972Z" + } + }, + "source": [ + "def get_qcuts(series, quantiles):\n", + " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", + " return q[-1] # 返回窗口最后一个元素的分位数标签\n", + "\n", + "\n", + "window = 5\n", + "quantiles = 20\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(level=0, drop=True)\n", + " df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n", + " df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return df['sharpe_ratio']\n", + "\n", + "def get_label(df):\n", + " # labels = df['future_af13'] - df['act_factor1']\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-4)\n", + " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_high'] = df.groupby('ts_code')['high'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", + " labels = df['future_return'] >= 0.03\n", + " # labels = df['future_af11']\n", + " # labels = df['ema_5'].shift(-1) - df['close']\n", + " # df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\n", + " # df['label'] = calculate_risk_adjusted_target(df, days=5)\n", + " # lower_percentile = df['label'].quantile(0.01) # 1%分位数\n", + " # upper_percentile = df['label'].quantile(0.99) # 99%分位数\n", + " # labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\n", + " # labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\n", + " return labels\n", + "\n", + "df['label'] = get_label(df)\n", + "# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\n", + "df = df.sort_values(by=['trade_date', 'ts_code'])\n", + "train_data = df[(df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2016-01-01')]\n", + "test_data = df[df['trade_date'] >= '2023-01-01']\n", + "\n", + "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.rename(columns={'l2_code': 'cat_l2_code'})\n", + "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "train_data = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "test_data = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "\n", + "# train_data = get_future_data(train_data)\n", + "\n", + "# df = df[['ts_code', 'trade_date', 'open', 'close']]\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n" + ], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'DataFrame' object has no attribute 'groupby'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[10], line 38\u001B[0m\n\u001B[0;32m 28\u001B[0m \u001B[38;5;66;03m# labels = df['future_af11']\u001B[39;00m\n\u001B[0;32m 29\u001B[0m \u001B[38;5;66;03m# labels = df['ema_5'].shift(-1) - df['close']\u001B[39;00m\n\u001B[0;32m 30\u001B[0m \u001B[38;5;66;03m# df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 34\u001B[0m \u001B[38;5;66;03m# labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\u001B[39;00m\n\u001B[0;32m 35\u001B[0m \u001B[38;5;66;03m# labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\u001B[39;00m\n\u001B[0;32m 36\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m labels\n\u001B[1;32m---> 38\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mget_label\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 39\u001B[0m \u001B[38;5;66;03m# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\u001B[39;00m\n\u001B[0;32m 40\u001B[0m df \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39msort_values(by\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mts_code\u001B[39m\u001B[38;5;124m'\u001B[39m])\n", + "Cell \u001B[1;32mIn[10], line 23\u001B[0m, in \u001B[0;36mget_label\u001B[1;34m(df)\u001B[0m\n\u001B[0;32m 21\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_label\u001B[39m(df):\n\u001B[0;32m 22\u001B[0m \u001B[38;5;66;03m# labels = df['future_af13'] - df['act_factor1']\u001B[39;00m\n\u001B[1;32m---> 23\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfuture_close\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroupby\u001B[49m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mts_code\u001B[39m\u001B[38;5;124m'\u001B[39m)[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mclose\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mshift(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m4\u001B[39m)\n\u001B[0;32m 24\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfuture_open\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mts_code\u001B[39m\u001B[38;5;124m'\u001B[39m)[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mopen\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mshift(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m 25\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfuture_high\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mts_code\u001B[39m\u001B[38;5;124m'\u001B[39m)[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhigh\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mshift(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m)\n", + "\u001B[1;31mAttributeError\u001B[0m: 'DataFrame' object has no attribute 'groupby'" + ] + } + ], + "execution_count": 10 + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "de8c2f6c770d2439", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.491525100Z", + "start_time": "2025-02-20T16:12:48.951414Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio', 'is_st',\n", + " 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol',\n", + " 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol',\n", + " 'cat_l2_code', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6',\n", + " 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10',\n", + " 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15',\n", + " 'std_return_25', 'std_return_90', 'std_return_90_2',\n", + " 'std_return_5 / std_return_90', 'std_return_5 / std_return_25',\n", + " 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20',\n", + " 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4',\n", + " 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5',\n", + " 'act_factor6', 'rank_act_factor1', 'rank_act_factor2',\n", + " 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big',\n", + " 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol',\n", + " 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022',\n", + " 'alpha_003', 'alpha_007', 'alpha_013', 'future_close', 'future_open',\n", + " 'future_high', 'future_return', 'label', 'industry_obv',\n", + " 'industry_return_5', 'industry_obv_deviation',\n", + " 'industry_return_5_deviation', 'industry_return_5_percentile'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(train_data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "20ffa7229c9d2f86", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.492528800Z", + "start_time": "2025-02-20T16:12:49.145792Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_columns size: 149\n", + "feature_columns size: 149\n", + "1164839\n", + "最小日期: 2017-03-21\n", + "最大日期: 2022-12-30\n", + "397977\n", + "最小日期: 2023-01-03\n", + "最大日期: 2025-02-12\n" + ] + } + ], + "source": [ + "feature_columns_new = feature_columns[:]\n", + "train_data, feature_columns_new = create_deviation_within_dates(train_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "test_data, feature_columns_new = create_deviation_within_dates(test_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns_new)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "\n", + "test_data = test_data.dropna(subset=feature_columns_new)\n", + "test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "35238cb4f45ce756", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.507525300Z", + "start_time": "2025-02-20T16:13:07.227911Z" + } + }, + "outputs": [], + "source": [ + "cat_columns = [col for col in df.columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8f134d435f71e9e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.508526600Z", + "start_time": "2025-02-20T16:13:07.436171Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from catboost import Pool\n", + "import lightgbm as lgb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import optuna\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import mean_absolute_error\n", + "import os\n", + "import json\n", + "import pickle\n", + "import hashlib\n", + "\n", + "def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n", + " print_feature_importance=True, num_boost_round=100,\n", + " use_optuna=False):\n", + " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", + " X_train = train_data_df[feature_columns]\n", + " y_train = train_data_df['label']\n", + "\n", + " X_val = test_data_df[feature_columns]\n", + " y_val = test_data_df['label']\n", + "\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " print(f'categorical_feature: {categorical_feature}')\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + " val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n", + " model = lgb.train(\n", + " params, train_data, num_boost_round=num_boost_round,\n", + " valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + "\n", + " if print_feature_importance:\n", + " lgb.plot_metric(evals)\n", + " # lgb.plot_tree(model, figsize=(20, 8))\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + " return model\n", + "\n", + "\n", + "from catboost import CatBoostClassifier\n", + "import pandas as pd\n", + "\n", + "\n", + "def train_catboost(train_data_df, test_data_df, feature_columns, params=None, plot=False):\n", + "\n", + " train_data_df, test_data_df = train_data_df.dropna(subset=['label']), test_data_df.dropna(subset=['label'])\n", + " X_train = train_data_df[feature_columns]\n", + " y_train = train_data_df['label']\n", + "\n", + " X_val = test_data_df[feature_columns]\n", + " y_val = test_data_df['label']\n", + "\n", + " cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " print(f'cat_features: {cat_features}')\n", + " train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n", + " val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n", + "\n", + "\n", + " model = CatBoostClassifier(**params)\n", + " model.fit(train_pool,\n", + " eval_set=val_pool, plot=plot)\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4a4542e1ed6afe7d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.509525700Z", + "start_time": "2025-02-20T16:13:09.199245Z" + } + }, + "outputs": [], + "source": [ + "light_params = {\n", + " 'objective': 'binary',\n", + " 'metric': 'average_precision',\n", + " 'learning_rate': 0.05,\n", + " 'is_unbalance': True,\n", + " 'num_leaves': 2048,\n", + " 'min_data_in_leaf': 1024,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 0.7,\n", + " 'bagging_freq': 5,\n", + " # 'lambda_l1': 80,\n", + " # 'lambda_l2': 65,\n", + " 'verbosity': -1,\n", + " 'num_threads' : 16\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "beeb098799ecfa6a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.520525800Z", + "start_time": "2025-02-20T16:13:09.280097Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 1164839\n", + "categorical_feature: [3, 34, 35, 36, 37]\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[82]\ttrain's average_precision: 0.534659\tvalid's average_precision: 0.281957\n", + "Evaluated only: average_precision\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('train data size: ', len(train_data))\n", + " \n", + "evals = {}\n", + "model = train_light_model(train_data, test_data, light_params, feature_columns_new,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, use_optuna=False,\n", + " print_feature_importance=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "445dff84-70b2-4fc9-a9b6-1251993324d6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.522549500Z", + "start_time": "2025-02-20T16:14:20.677936Z" + } + }, + "outputs": [], + "source": [ + "# catboost_params = {\n", + "# 'loss_function': 'CrossEntropy', # 适用于二分类\n", + "# 'eval_metric': 'AUC', # 评估指标\n", + "# 'iterations': 1000,\n", + "# 'learning_rate': 0.01,\n", + "# 'depth': , # 控制模型复杂度\n", + "# # 'l2_leaf_reg': 3, # L2 正则化\n", + "# 'verbose': 500,\n", + "# 'early_stopping_rounds': 100,\n", + "# # 'one_hot_max_size': 50,\n", + "# # 'class_weights': [0.6, 1.2]\n", + "# # 'task_type': 'GPU'\n", + "# }\n", + "\n", + "# model = train_catboost(train_data, test_data, feature_columns_new, catboost_params, plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "751a6df9-d90b-4053-8769-c6c3b6654406", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.523526100Z", + "start_time": "2025-02-20T16:14:20.855152Z" + } + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "\n", + "def incremental_training(test_data: pd.DataFrame, model: lgb.Booster, days: int, back_days: int, feature_columns: list, params: dict):\n", + " test_data = test_data.sort_values(by='trade_date')\n", + "\n", + " scores = []\n", + "\n", + " unique_trade_dates = sorted(test_data['trade_date'].unique())\n", + " for i in tqdm(range(0, len(unique_trade_dates), days)):\n", + " # Get the current window of trade dates\n", + " current_dates = unique_trade_dates[i:i + days]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " X = window_data[feature_columns]\n", + "\n", + " window_scores = model.predict(X)\n", + " scores.extend(window_scores)\n", + "\n", + " current_dates = unique_trade_dates[max(0, i - back_days):i + days]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + "\n", + " # Incrementally train the model with the current window data\n", + " X_train = window_data[feature_columns]\n", + " y_train = window_data['label'] # Assuming 'score' is what you're predicting\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " # print(f'categorical_feature: {categorical_feature}')\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + "\n", + " model = lgb.train(params,\n", + " train_set=train_data,\n", + " num_boost_round=100,\n", + " init_model=model,\n", + " keep_training_booster=True)\n", + "\n", + " # Add the scores as a new 'score' column to the test_data\n", + " test_data['score'] = scores\n", + "\n", + " return test_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "660a24f74501f98f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.533542700Z", + "start_time": "2025-02-20T16:14:20.978664Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████| 102/102 [02:16<00:00, 1.34s/it]\n" + ] + } + ], + "source": [ + "predictions_test = incremental_training(test_data, model, 5, 0, feature_columns_new, light_params)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "36ccaa730ab46718", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-21T15:06:05.535526300Z", + "start_time": "2025-02-20T16:17:08.116647Z" + } + }, + "outputs": [], + "source": [ + "predictions_test = predictions_test.loc[predictions_test.groupby('trade_date')['score'].idxmax()]\n", + "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/RollingRank.py b/code/train/RollingRank.py new file mode 100644 index 0000000..a34f5ad --- /dev/null +++ b/code/train/RollingRank.py @@ -0,0 +1,1250 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +# %load_ext autoreload +# %autoreload 2 + +import pandas as pd +import warnings + +warnings.filterwarnings("ignore") + +pd.set_option('display.max_columns', None) + + +# In[2]: + + +from utils.utils import read_and_merge_h5_data + +print('daily data') +df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data', + columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'], + df=None) + +print('daily basic') +df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic', + columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio', + 'is_st'], df=df, join='inner') + +print('stk limit') +df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit', + columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'], + df=df) +print('money flow') +df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow', + columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', + 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'], + df=df) +print('cyq perf') +df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf', + columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', + 'cost_50pct', + 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'], + df=df) +print(df.info()) + + +# In[3]: + + +print('industry') +industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data', + columns=['ts_code', 'l2_code', 'in_date'], + df=None, on=['ts_code'], join='left') + + +def merge_with_industry_data(df, industry_df): + # 确保日期字段是 datetime 类型 + df['trade_date'] = pd.to_datetime(df['trade_date']) + industry_df['in_date'] = pd.to_datetime(industry_df['in_date']) + + # 对 industry_df 按 ts_code 和 in_date 排序 + industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code']) + + # 对原始 df 按 ts_code 和 trade_date 排序 + df_sorted = df.sort_values(['trade_date', 'ts_code']) + + # 使用 merge_asof 进行向后合并 + merged = pd.merge_asof( + df_sorted, + industry_df_sorted, + by='ts_code', # 按 ts_code 分组 + left_on='trade_date', + right_on='in_date', + direction='backward' + ) + + # 获取每个 ts_code 的最早 in_date 记录 + min_in_date_per_ts = (industry_df_sorted + .groupby('ts_code') + .first() + .reset_index()[['ts_code', 'l2_code']]) + + # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况） + merged['l2_code'] = merged['l2_code'].fillna( + merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code']) + ) + + # 保留需要的列并重置索引 + result = merged.reset_index(drop=True) + return result + + +# 使用示例 +df = merge_with_industry_data(df, industry_df) +# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']]) + + +# In[4]: + + +def calculate_indicators(df): + """ + 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。 + """ + df = df.sort_values('trade_date') + df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100 + # df['5_day_ma'] = df['close'].rolling(window=5).mean() + delta = df['close'].diff() + gain = delta.where(delta > 0, 0) + loss = -delta.where(delta < 0, 0) + avg_gain = gain.rolling(window=14).mean() + avg_loss = loss.rolling(window=14).mean() + rs = avg_gain / avg_loss + df['RSI'] = 100 - (100 / (1 + rs)) + + # 计算MACD + ema12 = df['close'].ewm(span=12, adjust=False).mean() + ema26 = df['close'].ewm(span=26, adjust=False).mean() + df['MACD'] = ema12 - ema26 + df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean() + df['MACD_hist'] = df['MACD'] - df['Signal_line'] + + # 4. 情绪因子1：市场上涨比例（Up Ratio） + df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0) + df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例 + + # 5. 情绪因子2：成交量变化率（Volume Change Rate） + df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量 + df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率 + + # 6. 情绪因子3：波动率（Volatility） + df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差 + + # 7. 情绪因子4：成交额变化率（Amount Change Rate） + df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额 + df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率 + + return df + + +def generate_index_indicators(h5_filename): + df = pd.read_hdf(h5_filename, key='index_data') + df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + df = df.sort_values('trade_date') + + # 计算每个ts_code的相关指标 + df_indicators = [] + for ts_code in df['ts_code'].unique(): + df_index = df[df['ts_code'] == ts_code].copy() + df_index = calculate_indicators(df_index) + df_indicators.append(df_index) + + # 合并所有指数的结果 + df_all_indicators = pd.concat(df_indicators, ignore_index=True) + + # 保留trade_date列，并将同一天的数据按ts_code合并成一行 + df_final = df_all_indicators.pivot_table( + index='trade_date', + columns='ts_code', + values=['daily_return', 'RSI', 'MACD', 'Signal_line', + 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility', + 'amount_change_rate', 'amount_mean'], + aggfunc='last' + ) + + df_final.columns = [f"{col[1]}_{col[0]}" for col in df_final.columns] + df_final = df_final.reset_index() + + return df_final + + +# 使用函数 +h5_filename = '../../data/index_data.h5' +index_data = generate_index_indicators(h5_filename) +index_data = index_data.dropna() + + +# In[5]: + + +import numpy as np +import talib + + +def get_rolling_factor(df): + old_columns = df.columns.tolist()[:] + # 按股票和日期排序 + df = df.sort_values(by=['ts_code', 'trade_date']) + grouped = df.groupby('ts_code', group_keys=False) + + # df["gap_next_open"] = (df["open"].shift(-1) - df["close"]) / df["close"] + + df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True) + df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True) + + # 因子 1：短期成交量变化率 + df['volume_change_rate'] = ( + grouped['vol'].rolling(window=2).mean() / + grouped['vol'].rolling(window=10).mean() - 1 + ).reset_index(level=0, drop=True) # 确保索引对齐 + + # 因子 2：成交量突破信号 + max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐 + df['cat_volume_breakout'] = (df['vol'] > max_volume) + + # 因子 3：换手率均线偏离度 + mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True) + std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True) + df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover + + # 因子 4：换手率激增信号 + df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover) + + # 因子 5：量比均值 + df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True) + + # 因子 6：量比突破信号 + max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True) + df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio) + + df['vol_spike'] = grouped.apply( + lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index) + ) + df['vol_std_5'] = df['vol'].pct_change().rolling(5).std() + + # 计算 ATR + df['atr_14'] = grouped.apply( + lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), + index=x.index) + ) + df['atr_6'] = grouped.apply( + lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), + index=x.index) + ) + + # 计算 OBV 及其均线 + df['obv'] = grouped.apply( + lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index) + ) + df['maobv_6'] = grouped.apply( + lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index) + ) + + df['rsi_3'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index) + ) + # df['rsi_6'] = grouped.apply( + # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index) + # ) + # df['rsi_9'] = grouped.apply( + # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index) + # ) + + # 计算 return_10 和 return_20 + df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1) + # df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1) + df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1) + + # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x) + + # 计算标准差指标 + df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std()) + # df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std()) + # df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std()) + df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std()) + df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std()) + + # 计算 EMA 指标 + df['_ema_5'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index) + ) + df['_ema_13'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index) + ) + df['_ema_20'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index) + ) + df['_ema_60'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index) + ) + + # 计算 act_factor1, act_factor2, act_factor3, act_factor4 + df['act_factor1'] = grouped['_ema_5'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50 + ) + df['act_factor2'] = grouped['_ema_13'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40 + ) + df['act_factor3'] = grouped['_ema_20'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21 + ) + df['act_factor4'] = grouped['_ema_60'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10 + ) + + # 根据 trade_date 截面计算排名 + df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True) + df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True) + df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True) + + df['log(circ_mv)'] = np.log(df['circ_mv']) + + def rolling_covariance(x, y, window): + return x.rolling(window).cov(y) + + def delta(series, period): + return series.diff(period) + + def rank(series): + return series.rank(pct=True) + + def stddev(series, window): + return series.rolling(window).std() + + window_high_volume = 5 + window_close_stddev = 20 + period_delta = 5 + df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume) + df['delta_cov'] = delta(df['cov'], period_delta) + df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev)) + df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev'] + + df['alpha_003'] = np.where(df['high'] != df['low'], + (df['close'] - df['open']) / (df['high'] - df['low']), + 0) + + df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True) + df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True) + + df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum()) + df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True) + + df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停） + df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停） + df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, + drop=True) + df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, + drop=True) + + # 3. 最近连续涨跌停天数 + def calculate_consecutive_limits(series): + """ + 计算连续涨停/跌停天数。 + """ + consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) + consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) + return consecutive_up, consecutive_down + + # 连续涨停天数 + df['consecutive_up_limit'] = grouped['cat_up_limit'].apply( + lambda x: calculate_consecutive_limits(x)[0] + ).reset_index(level=0, drop=True) + + df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0) + + df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5)) + + def rolling_corr(group): + roc_close = group['close'].pct_change() + roc_weight = group['weight_avg'].pct_change() + return roc_close.rolling(10).corr(roc_weight) + + df['price_cost_divergence'] = grouped.apply(rolling_corr) + + df['smallcap_concentration'] = (1 / df['log(circ_mv)']) * (df['cost_85pct'] - df['cost_15pct']) + + # 16. 筹码稳定性指数 (20日波动率) + df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std()) + df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean()) + + # 17. 成本区间突破标记 + df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum()) + + # 20. 筹码-流动性风险 + df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * ( + 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean())) + + # 7. 市值波动率因子 + df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True) + df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log(circ_mv)']).reset_index(level=0, drop=True) + + # 8. 市值成长性因子 + df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True) + df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['log(circ_mv)']).reset_index(level=0, drop=True) + + df["ar"] = df["high"].div(df["open"]).rolling(3).sum() / df["open"].div(df["low"]).rolling(3).sum() * 100 + # 计算 BR 指标 + df["pre_close"] = df["close"].shift(1) + df["br_up"] = (df["high"] - df["pre_close"]).clip(lower=0) + df["br_down"] = (df["pre_close"] - df["low"]).clip(lower=0) + df["br"] = df["br_up"].rolling(3).sum() / df["br_down"].rolling(3).sum() * 100 + df['arbr'] = df['ar'] - df['br'] + df.drop(columns=["pre_close", "br_up", "br_down", 'ar', 'br'], inplace=True) + + df.drop(columns=['weight_std20'], inplace=True, errors='ignore') + new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns] + + return df, new_columns + + +def get_simple_factor(df): + old_columns = df.columns.tolist()[:] + df = df.sort_values(by=['ts_code', 'trade_date']) + + alpha = 0.5 + df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation'] + df['resonance_factor'] = df['volume_ratio'] * df['pct_chg'] + df['log_close'] = np.log(df['close']) + + df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike'] + + df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close'] + df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close'] + + df['obv-maobv_6'] = df['obv'] - df['maobv_6'] + + # 计算比值指标 + df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90'] + # df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25'] + + # 计算标准差差值 + df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2'] + + # df['cat_af1'] = df['act_factor1'] > 0 + df['cat_af2'] = df['act_factor2'] > df['act_factor1'] + df['cat_af3'] = df['act_factor3'] > df['act_factor2'] + df['cat_af4'] = df['act_factor4'] > df['act_factor3'] + + # 计算 act_factor5 和 act_factor6 + df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4'] + df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt( + df['act_factor1'] ** 2 + df['act_factor2'] ** 2) + + df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol'] + df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol'] + df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol'] + + df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol'] + df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol'] + + df['log(circ_mv)'] = np.log(df['circ_mv']) + + df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low']) + + df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct']) + + df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct']) + + df['lock_factor'] = df['turnover_rate'] * ( + 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low'])) + + df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2) + + df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14'] + + # 12. 小盘股筹码集中度 + df['smallcap_concentration'] = (1 / df['log(circ_mv)']) * (df['cost_85pct'] - df['cost_15pct']) + + df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) & + (df['volume_ratio'] > 1.5) & + (df['winner_rate'] > 0.7)) + + df['mv_turnover_ratio'] = df['turnover_rate'] / df['log(circ_mv)'] + + df['mv_adjusted_volume'] = df['vol'] / df['log(circ_mv)'] + + df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['log(circ_mv)']) + + df['nonlinear_mv_volume'] = df['vol'] / df['log(circ_mv)'] + + df['mv_volume_ratio'] = df['volume_ratio'] / df['log(circ_mv)'] + + df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['log(circ_mv)'] + + drop_columns = [col for col in df.columns if col.startswith('_')] + df.drop(columns=drop_columns, inplace=True, errors='ignore') + + new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns] + return df, new_columns + + +# In[6]: + + +from utils.factor import get_act_factor + + +def read_industry_data(h5_filename): + # 读取 H5 文件中所有的行业数据 + industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[ + 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol' + ]) # 假设 H5 文件的键是 'industry_data' + industry_data = industry_data.sort_values(by=['ts_code', 'trade_date']) + industry_data = industry_data.reindex() + industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d') + + grouped = industry_data.groupby('ts_code', group_keys=False) + industry_data['obv'] = grouped.apply( + lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index) + ) + industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1) + industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1) + + industry_data = get_act_factor(industry_data, cat=False) + industry_data = industry_data.sort_values(by=['trade_date', 'ts_code']) + + # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差 + # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列 + # + # for factor in factor_columns: + # if factor in industry_data.columns: + # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差 + # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform( + # lambda x: x - x.mean()) + + industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform( + lambda x: x.rank(pct=True)) + industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform( + lambda x: x.rank(pct=True)) + industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol']) + + industry_data = industry_data.rename( + columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']}) + + industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'}) + return industry_data + + +industry_df = read_industry_data('../../data/sw_daily.h5') + + +# In[7]: + + +origin_columns = df.columns.tolist() +origin_columns = [col for col in origin_columns if + col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']] +origin_columns = [col for col in origin_columns if col not in index_data.columns] +origin_columns = [col for col in origin_columns if 'cyq' not in col] +print(origin_columns) + + +# In[8]: + + +def filter_data(df): + # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1')) + df = df[~df['is_st']] + df = df[~df['ts_code'].str.endswith('BJ')] + df = df[~df['ts_code'].str.startswith('30')] + df = df[~df['ts_code'].str.startswith('68')] + df = df[~df['ts_code'].str.startswith('8')] + df = df[df['trade_date'] >= '20180101'] + df = df.drop(columns=['in_date']) + df = df.reset_index(drop=True) + return df + + +df = filter_data(df) +# df = get_technical_factor(df) +# df = get_act_factor(df) +# df = get_money_flow_factor(df) +# df = get_alpha_factor(df) +# df = get_limit_factor(df) +# df = get_cyp_perf_factor(df) +# df = get_mv_factors(df) +df, _ = get_rolling_factor(df) +df, _ = get_simple_factor(df) +# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left') +df = df.rename(columns={'l2_code': 'cat_l2_code'}) +# df = df.merge(index_data, on='trade_date', how='left') + +print(df.info()) + + +# In[9]: + + +from scipy.stats import ks_2samp, wasserstein_distance +from sklearn.metrics import roc_auc_score +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler + + +def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1, + importance_threshold=0.05): + dropped_features = [] + + # **统计数据漂移** + numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns + numeric_columns = [col for col in numeric_columns if col in feature_columns] + for feature in numeric_columns: + ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature]) + wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature]) + + if p_value < ks_threshold and wasserstein_dist > wasserstein_threshold: + dropped_features.append(feature) + + print(f"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}") + + # **应用阈值进行最终筛选** + filtered_features = [f for f in feature_columns if f not in dropped_features] + + return filtered_features, dropped_features + + + +# In[10]: + + +def create_deviation_within_dates(df, feature_columns): + groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组 + new_columns = {} + ret_feature_columns = feature_columns[:] + + # 自动选择所有数值型特征 + num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col] + + # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013'] + num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col] + num_features = [col for col in num_features if 'limit' not in col] + num_features = [col for col in num_features if 'cyq' not in col] + + # 遍历所有数值型特征 + for feature in num_features: + if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差 + continue + + # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean') + # deviation_col_name = f'deviation_mean_{feature}' + # new_columns[deviation_col_name] = df[feature] - grouped_mean + # ret_feature_columns.append(deviation_col_name) + + grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean') + deviation_col_name = f'deviation_mean_{feature}' + new_columns[deviation_col_name] = df[feature] - grouped_mean + ret_feature_columns.append(deviation_col_name) + + # 将新计算的偏差特征与原始 DataFrame 合并 + df = pd.concat([df, pd.DataFrame(new_columns)], axis=1) + + # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']: + # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}'] + + return df, ret_feature_columns + + +# In[11]: + + +import pandas as pd + + +def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99, + log=True): + if not (0 <= lower_percentile < upper_percentile <= 1): + raise ValueError("Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.") + + # Calculate lower and upper bounds based on percentiles + lower_bound = label.quantile(lower_percentile) + upper_bound = label.quantile(upper_percentile) + + # Filter out values outside the bounds + filtered_label = label[(label >= lower_bound) & (label <= upper_bound)] + + # Print the number of removed outliers + if log: + print(f"Removed {len(label) - len(filtered_label)} outliers.") + return filtered_label + + +def calculate_risk_adjusted_target(df, days=5): + df = df.sort_values(by=['ts_code', 'trade_date']) + + df['future_close'] = df.groupby('ts_code')['close'].shift(-days) + df['future_open'] = df.groupby('ts_code')['open'].shift(-1) + df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open'] + + df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index( + level=0, drop=True) + sharpe_ratio = df['future_return'] * df['future_volatility'] + sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True) + + return sharpe_ratio + + +def calculate_score(df, days=5, lambda_param=1.0): + def calculate_max_drawdown(prices): + peak = prices.iloc[0] # 初始化峰值 + max_drawdown = 0 # 初始化最大回撤 + + for price in prices: + if price > peak: + peak = price # 更新峰值 + else: + drawdown = (peak - price) / peak # 计算当前回撤 + max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤 + + return max_drawdown + + def compute_stock_score(stock_df): + stock_df = stock_df.sort_values(by=['trade_date']) + future_return = stock_df['future_return'] + volatility = stock_df['close'].pct_change().rolling(days).std().shift(-days) + max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days) + score = future_return - lambda_param * max_drawdown + + return score + + scores = df.groupby('ts_code').apply(lambda x: compute_stock_score(x)) + scores = scores.reset_index(level=0, drop=True) + + return scores + + +def remove_highly_correlated_features(df, feature_columns, threshold=0.9): + numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist() + if not numeric_features: + raise ValueError("No numeric features found in the provided data.") + + # 先找出需要强制保留的特征 + always_keep = [col for col in feature_columns if 'act' in col or 'af' in col] + features_to_check = [col for col in numeric_features if col not in always_keep] + + corr_matrix = df[features_to_check].corr().abs() + upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)) + to_drop = [column for column in upper.columns if any(upper[column] > threshold)] + + remaining_features = [col for col in feature_columns if col not in to_drop or col in always_keep] + return remaining_features + + +import pandas as pd +from sklearn.preprocessing import StandardScaler + + +def cross_sectional_standardization(df, features): + df_sorted = df.sort_values(by='trade_date') # 按时间排序 + df_standardized = df_sorted.copy() + + for date in df_sorted['trade_date'].unique(): + # 获取当前时间点的数据 + current_data = df_standardized[df_standardized['trade_date'] == date] + + # 只对指定特征进行标准化 + scaler = StandardScaler() + standardized_values = scaler.fit_transform(current_data[features]) + + # 将标准化结果重新赋值回去 + df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values + + return df_standardized + + +import numpy as np +import pandas as pd +import statsmodels.api as sm + +from concurrent.futures import ProcessPoolExecutor + + +def neutralize_manual(df, features, industry_col, mkt_cap_col): + """ 手动实现简单回归以提升速度 """ + + for col in features: + residuals = [] + for _, group in df.groupby(industry_col): + if len(group) > 1: + x = np.log(group[mkt_cap_col]) # 市值对数 + y = group[col] # 因子值 + beta = np.cov(y, x)[0, 1] / np.var(x) # 计算斜率 + alpha = np.mean(y) - beta * np.mean(x) # 计算截距 + resid = y - (alpha + beta * x) # 计算残差 + residuals.extend(resid) + else: + residuals.extend(group[col]) # 样本不足时保留原值 + + df[col] = residuals + + return df + + +import gc + +gc.collect() + + +def mad_filter(df, features, n=3): + df = df.copy() + for col in features: + median = df[col].median() + mad = np.median(np.abs(df[col] - median)) + upper = median + n * mad + lower = median - n * mad + df[col] = np.clip(df[col], lower, upper) # 截断极值 + return df + + +def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99): + df = df.copy() + for col in features: + # 按日期分组计算上下百分位数 + lower_bound = df.groupby('trade_date')[col].transform( + lambda x: x.quantile(lower_percentile) + ) + upper_bound = df.groupby('trade_date')[col].transform( + lambda x: x.quantile(upper_percentile) + ) + # 截断超出范围的值 + df[col] = np.clip(df[col], lower_bound, upper_bound) + return df + + +from scipy.stats import iqr + + +def iqr_filter(df, features): + df = df.copy() + for col in features: + df[col] = df.groupby('trade_date')[col].transform( + lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x + ) + return df + + +def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60): + df = df.copy() + for col in features: + # 计算 rolling 统计量，需要按日期进行 groupby + rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile)) + rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile)) + + # 对数据进行裁剪 + df[col] = np.clip(df[col], rolling_lower, rolling_upper) + + return df + + +# In[12]: + + +# print(test_data.head()[['act_factor1', 'act_factor2', 'ts_code', 'trade_date']]) + + +# In[13]: + + +from sklearn.preprocessing import StandardScaler +import lightgbm as lgb +import matplotlib.pyplot as plt +from sklearn.decomposition import PCA + + +def train_light_model(train_data_df, params, feature_columns, callbacks, evals, + print_feature_importance=True, num_boost_round=100, + validation_days=180, use_pca=False, split_date=None): # 新增参数：validation_days + # 确保数据按时间排序 + train_data_df = train_data_df.sort_values(by='trade_date') + + numeric_columns = train_data_df.select_dtypes(include=['float64', 'int64']).columns + numeric_columns = [col for col in numeric_columns if col in feature_columns] + # X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns]) + # X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns]) + # train_data_df = cross_sectional_standardization(train_data_df, numeric_columns) + + # 去除标签为空的样本 + train_data_df = train_data_df.dropna(subset=['label']) + print('原始训练集大小: ', len(train_data_df)) + + # 按时间顺序划分训练集和验证集 + if split_date is None: + all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date + split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天 + train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集 + val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集 + + # 打印划分结果 + print(f"划分后的训练集大小: {len(train_data_split)}, 验证集大小: {len(val_data_split)}") + + # 提取特征和标签 + X_train = train_data_split[feature_columns] + y_train = train_data_split['label'] + + X_val = val_data_split[feature_columns] + y_val = val_data_split['label'] + + # 标准化数值特征 + scaler = StandardScaler() + + + # 计算每个 trade_date 内的样本数（LTR 需要 group 信息） + train_groups = train_data_split.groupby('trade_date').size().tolist() + val_groups = val_data_split.groupby('trade_date').size().tolist() + + # 处理类别特征 + categorical_feature = [col for col in feature_columns if 'cat' in col] + + pca = None + if use_pca: + pca = PCA(n_components=0.95) # 或指定 n_components=固定值（如 10） + numeric_features = [col for col in feature_columns if col not in categorical_feature] + numeric_pca = pca.fit_transform(X_train[numeric_features]) + X_train = pd.concat([pd.DataFrame(numeric_pca, index=X_train.index), X_train[categorical_feature]], axis=1) + + numeric_pca = pca.transform(X_val[numeric_features]) + X_val = pd.concat([pd.DataFrame(numeric_pca, index=X_val.index), X_val[categorical_feature]], axis=1) + + # 计算权重（基于时间） + # trade_date = train_data_split['trade_date'] # 交易日期 + # weights = (trade_date - trade_date.min()).dt.days / (trade_date.max() - trade_date.min()).days + 1 + # weights = train_data_split.groupby('trade_date')['std_return_5'].transform( + # lambda x: x / x.mean() + # ) + ud = sorted(train_data_split["trade_date"].unique().tolist()) + date_weights = {date: weight * weight for date, weight in zip(ud, np.linspace(1, 10, len(ud)))} + params['weight'] = train_data_split["trade_date"].map(date_weights).tolist() + + train_dataset = lgb.Dataset( + X_train, label=y_train, group=train_groups, + categorical_feature=categorical_feature + ) + + # weights = val_data_split.groupby('trade_date')['std_return_5'].transform( + # lambda x: x / x.mean() + # ) + val_dataset = lgb.Dataset( + X_val, label=y_val, group=val_groups, + categorical_feature=categorical_feature + ) + + # 训练模型 + model = lgb.train( + params, train_dataset, num_boost_round=num_boost_round, + valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'], + callbacks=callbacks + ) + + # 打印特征重要性（如果需要） + if print_feature_importance: + lgb.plot_metric(evals) + lgb.plot_importance(model, importance_type='split', max_num_features=20) + plt.show() + + return model, scaler, pca + + +# In[14]: + + +days = 2 +df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \ + df.groupby('ts_code')['open'].shift(-1) +df['future_volatility'] = ( + df.groupby('ts_code')['future_return'] + .transform(lambda x: x.rolling(days).std()) +) + +df['future_score'] = ( + 0.7 * df['future_return'] + + 0.3 * df['future_volatility'] +) + +filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99)) +filter_index = df['future_volatility'].between(df['future_volatility'].quantile(0.01), + df['future_volatility'].quantile(0.99)) | filter_index +df['label'] = df.groupby('trade_date', group_keys=False)['future_score'].transform( + lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop') +) +# industry_df = industry_df.sort_values(by=['trade_date']) + +# df = df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left') +df = df.sort_values(['trade_date']) + +df = df.replace([np.inf, -np.inf], np.nan) + +feature_columns = [col for col in df.columns] +feature_columns = [col for col in feature_columns if col not in ['trade_date', + 'ts_code', + 'label']] +feature_columns = [col for col in feature_columns if 'future' not in col] +feature_columns = [col for col in feature_columns if 'label' not in col] +feature_columns = [col for col in feature_columns if 'score' not in col] +feature_columns = [col for col in feature_columns if 'gen' not in col] +feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col] +feature_columns = [col for col in feature_columns if col not in origin_columns] +feature_columns = [col for col in feature_columns if not col.startswith('_')] + +numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns +numeric_columns = [col for col in numeric_columns if col in feature_columns] + +qdf = quantile_filter(df, numeric_columns, window=20) + + +# In[15]: + + +# print('train data size: ', len(train_data)) + +label_gain = list(range(len(df['label'].unique()))) +label_gain = [gain * gain for gain in label_gain] +light_params = { + 'label_gain': label_gain, + 'objective': 'lambdarank', + 'metric': 'lambdarank', + 'learning_rate': 0.05, + 'num_leaves': 1024, + 'min_data_in_leaf': 128, + 'max_depth': 16, + 'max_bin': 1024, + 'feature_fraction': 0.7, + 'bagging_fraction': 1, + 'bagging_freq': 5, + 'lambda_l1': 0.1, + 'lambda_l2': 0.1, + # 'boosting': 'dart', + 'verbosity': -1, + 'extra_trees': True, + 'max_position': 5, + 'ndcg_at': 1, + 'quant_train_renew_leaf': True, + 'lambdarank_truncation_level': 5, + 'lambdarank_position_bias_regularization': 1, + 'seed': 7 +} +evals = {} + +gc.collect() + + +# In[16]: + + +gc.collect() + + +def rolling_train_predict(df, train_days, test_days, industry_df, index_df, days=5, use_pca=False, validation_days=60, filter_index=None): + # 1. 按照交易日期排序 + unique_dates = df[df['trade_date'] >= '2020-01-01']['trade_date'].unique().tolist() + unique_dates = sorted(unique_dates) + n = len(unique_dates) + + # 2. 计算需要跳过的天数，使后续窗口对齐 + extra_days = (n - train_days) % test_days + start_index = extra_days # 从此索引开始滚动 + + predictions_list = [] + + + def select_pre_zt_stocks_dynamic( + stock_df, + ): + stock_df = stock_df.groupby('trade_date', group_keys=False).apply( + lambda x: x.nlargest(1000, 'return_20') + ) + return stock_df + + df = select_pre_zt_stocks_dynamic(df) + + feature_columns = [col for col in df.columns] + feature_columns = [col for col in feature_columns if col not in ['trade_date', + 'ts_code', + 'label']] + feature_columns = [col for col in feature_columns if 'future' not in col] + feature_columns = [col for col in feature_columns if 'label' not in col] + feature_columns = [col for col in feature_columns if 'score' not in col] + feature_columns = [col for col in feature_columns if 'gen' not in col] + feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col] + feature_columns = [col for col in feature_columns if col not in origin_columns] + feature_columns = [col for col in feature_columns if not col.startswith('_')] + + numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns + numeric_columns = [col for col in numeric_columns if col in feature_columns] + df = cross_sectional_standardization(df, numeric_columns) + + for start in range(start_index, n - train_days - test_days + 1, test_days): + gc.collect() + + train_dates = unique_dates[start: start + train_days] + test_dates = unique_dates[start + train_days: start + train_days + test_days] + + # 根据日期筛选数据 + train_data = df[filter_index & df['trade_date'].isin(train_dates)] + test_data = df[df['trade_date'].isin(test_dates)] + + train_data = train_data.sort_values('trade_date') + test_data = test_data.sort_values('trade_date') + + + industry_df = industry_df.sort_values(by=['trade_date']) + # index_df = index_df.sort_values(by=['trade_date']) + + train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left') + # # train_data = train_data.merge(index_df, on='trade_date', how='left') + test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left') + # # test_data = test_data.merge(index_df, on='trade_date', how='left') + + # train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], + # np.nan) + + feature_columns = [col for col in train_data.columns if col in train_data.columns] + feature_columns = [col for col in feature_columns if col not in ['trade_date', + 'ts_code', + 'label']] + feature_columns = [col for col in feature_columns if 'future' not in col] + feature_columns = [col for col in feature_columns if 'label' not in col] + feature_columns = [col for col in feature_columns if 'score' not in col] + feature_columns = [col for col in feature_columns if 'gen' not in col] + feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col] + feature_columns = [col for col in feature_columns if col not in origin_columns] + feature_columns = [col for col in feature_columns if not col.startswith('_')] + + numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns + numeric_columns = [col for col in numeric_columns if col in feature_columns] + # print('去极值') + train_data = quantile_filter(train_data, numeric_columns) # 去极值 + # # print('中性化') + # # train_data = neutralize_manual(train_data, numeric_columns, industry_col='cat_l2_code', mkt_cap_col='log(circ_mv)') # 中性化 + # print('去极值') + test_data = quantile_filter(test_data, numeric_columns) # 去极值 + + all_dates = train_data['trade_date'].unique() # 获取所有唯一的 trade_date + split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天 + train_data_split = train_data[train_data['trade_date'] < split_date] # 训练集 + val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集 + + feature_columns, _ = remove_shifted_features(train_data_split[train_data_split['label'] == train_data_split['label'].max()], + val_data_split[val_data_split['label'] == val_data_split['label'].max()], + feature_columns) + + feature_columns = remove_highly_correlated_features(train_data[train_data['label'] == train_data['label'].max()], + feature_columns) + + train_data = train_data.dropna(subset=feature_columns) + train_data = train_data.dropna(subset=['label']) + train_data = train_data.reset_index(drop=True) + + # print(test_data.tail()) + test_data = test_data.dropna(subset=feature_columns) + # test_data = test_data.dropna(subset=['label']) + test_data = test_data.reset_index(drop=True) + + # print(len(train_data)) + print(f"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}") + print(f"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}") + # print(len(test_data)) + print(f"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}") + print(f"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}") + + cat_columns = [col for col in df.columns if col.startswith('cat')] + for col in cat_columns: + train_data[col] = train_data[col].astype('category') + test_data[col] = test_data[col].astype('category') + + label_gain = list(range(len(train_data['label'].unique()))) + label_gain = [(gain + 1) * (gain + 1) for gain in label_gain] + light_params['label_gain'] = label_gain + + # ud = train_data["trade_date"].unique() + # date_weights = {date: weight for date, weight in zip(ud, np.linspace(1, 2, len(unique_dates)))} + # light_params['weight'] = train_data["trade_date"].map(date_weights).tolist() + + # print(f'feature_columns: {feature_columns}') + feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns] + light_params['feature_contri'] = feature_contri + model, _, _ = train_light_model(train_data.dropna(subset=['label']), + light_params, feature_columns, + [lgb.log_evaluation(period=100), + lgb.callback.record_evaluation(evals), + lgb.early_stopping(50, first_metric_only=True) + ], evals, + num_boost_round=3000, validation_days=validation_days, + print_feature_importance=False, use_pca=False) + + score_df = test_data.copy() + numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns + numeric_columns = [col for col in numeric_columns if col in feature_columns] + score_df = cross_sectional_standardization(score_df, numeric_columns) + score_df['score'] = model.predict(score_df[feature_columns]) + score_df = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()] + score_df = score_df[['trade_date', 'score', 'ts_code']] + predictions_list.append(score_df) + + # m = 5 + # all_data = [] + # for i, trade_date in enumerate(sorted(score_df['trade_date'].unique().tolist())): + # # 提取当前日期的数据 + # current_data = score_df[score_df['trade_date'] == trade_date] + # all_data.append(current_data) + # + # numeric_columns = [col for col in feature_columns if col in current_data.select_dtypes(include=['float64', 'int64']).columns] + # current_data = cross_sectional_standardization(current_data, numeric_columns) + # current_data['score'] = model.predict(current_data[feature_columns]) + # daily_top_score = current_data.loc[[current_data['score'].idxmax()]] + # predictions_list.append(daily_top_score[['trade_date', 'score', 'ts_code']]) + # + # if i % m == 0: + # train_data_split = pd.concat(all_data) + # train_data_split = train_data_split.dropna(subset=['label']) + # + # X_train = train_data_split[feature_columns] + # y_train = train_data_split['label'] + # + # train_groups = train_data_split.groupby('trade_date').size().tolist() + # categorical_feature = [col for col in feature_columns if 'cat' in col] + # + # train_dataset = lgb.Dataset( + # X_train, label=y_train, group=train_groups, + # categorical_feature=categorical_feature + # ) + # + # model = lgb.train( + # light_params, train_dataset, num_boost_round=36, + # init_model=model + # ) + # all_data = [] + + final_predictions = pd.concat(predictions_list, ignore_index=True) + return final_predictions + + +# In[ ]: + + +gc.collect() + +print(df[df['ts_code'] == '000001.SZ'].tail(1)[['act_factor1', 'act_factor2']]) +print('finish') +# qdf = qdf[qdf['trade_date'] >= '2022-01-01'] + +final_predictions = rolling_train_predict(df[df['trade_date'] >= '2022-01-01'], 90, 5, industry_df, None, + days=days, validation_days=10, filter_index=filter_index) +final_predictions.to_csv('predictions_test.tsv', index=False) + + +# In[ ]: + + +print(df[df['ts_code'] == '000001.SZ'].tail(1)[['act_factor1', 'act_factor2']]) +print('finish') + + +# In[ ]: + + + + diff --git a/code/train/TRank.ipynb b/code/train/TRank.ipynb new file mode 100644 index 0000000..2eae67f --- /dev/null +++ b/code/train/TRank.ipynb @@ -0,0 +1,1723 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "79a7758178bafdd3", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-19T14:44:14.217154Z", + "start_time": "2025-03-19T14:44:13.935883Z" + } + }, + "source": [ + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "import pandas as pd\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "pd.set_option('display.max_columns', None)\n" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "a79cafb06a7e0e43", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-03-19T14:45:19.808772Z", + "start_time": "2025-03-19T14:44:14.221159Z" + } + }, + "source": [ + "from utils.utils import read_and_merge_h5_data\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)\n", + "print('cyq perf')\n", + "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct',\n", + " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", + " df=df)\n", + "print(df.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8418207 entries, 0 to 8418206\n", + "Data columns (total 31 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 volume_ratio float64 \n", + " 12 is_st bool \n", + " 13 up_limit float64 \n", + " 14 down_limit float64 \n", + " 15 buy_sm_vol float64 \n", + " 16 sell_sm_vol float64 \n", + " 17 buy_lg_vol float64 \n", + " 18 sell_lg_vol float64 \n", + " 19 buy_elg_vol float64 \n", + " 20 sell_elg_vol float64 \n", + " 21 net_mf_vol float64 \n", + " 22 his_low float64 \n", + " 23 his_high float64 \n", + " 24 cost_5pct float64 \n", + " 25 cost_15pct float64 \n", + " 26 cost_50pct float64 \n", + " 27 cost_85pct float64 \n", + " 28 cost_95pct float64 \n", + " 29 weight_avg float64 \n", + " 30 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n", + "memory usage: 1.9+ GB\n", + "None\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "cac01788dac10678", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-19T14:45:35.459083Z", + "start_time": "2025-03-19T14:45:20.342424Z" + } + }, + "source": [ + "print('industry')\n", + "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "c4e9e1d31da6dba6", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-19T14:45:35.759821Z", + "start_time": "2025-03-19T14:45:35.492414Z" + } + }, + "source": [ + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df = df.sort_values('trade_date')\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " return df\n", + "\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line',\n", + " 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "code", + "id": "a735bc02ceb4d872", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-19T14:45:35.904531Z", + "start_time": "2025-03-19T14:45:35.790296Z" + } + }, + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "\n", + "def get_rolling_factor(df):\n", + " old_columns = df.columns.tolist()[:]\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n", + " df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n", + "\n", + " # 因子 1：短期成交量变化率\n", + " df['volume_change_rate'] = (\n", + " grouped['vol'].rolling(window=2).mean() /\n", + " grouped['vol'].rolling(window=10).mean() - 1\n", + " ).reset_index(level=0, drop=True) # 确保索引对齐\n", + "\n", + " # 因子 2：成交量突破信号\n", + " max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n", + " df['cat_volume_breakout'] = (df['vol'] > max_volume)\n", + "\n", + " # 因子 3：换手率均线偏离度\n", + " mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + " std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n", + " df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n", + "\n", + " # 因子 4：换手率激增信号\n", + " df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n", + "\n", + " # 因子 5：量比均值\n", + " df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + "\n", + " # 因子 6：量比突破信号\n", + " max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n", + " df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n", + "\n", + " df['vol_spike'] = grouped.apply(\n", + " lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n", + " )\n", + " df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + "\n", + " # df['rsi_3'] = grouped.apply(\n", + " # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " # )\n", + " # df['rsi_6'] = grouped.apply(\n", + " # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " # )\n", + " # df['rsi_9'] = grouped.apply(\n", + " # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " # )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算 EMA 指标\n", + " df['_ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['_ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['_ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['_ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['_ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['_ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['_ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['_ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + "\n", + " def rolling_covariance(x, y, window):\n", + " return x.rolling(window).cov(y)\n", + "\n", + " def delta(series, period):\n", + " return series.diff(period)\n", + "\n", + " def rank(series):\n", + " return series.rank(pct=True)\n", + "\n", + " def stddev(series, window):\n", + " return series.rolling(window).std()\n", + "\n", + " window_high_volume = 5\n", + " window_close_stddev = 20\n", + " period_delta = 5\n", + " df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)\n", + " df['delta_cov'] = delta(df['cov'], period_delta)\n", + " df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))\n", + " df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n", + "\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）\n", + " df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）\n", + " df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + " df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + "\n", + " # 3. 最近连续涨跌停天数\n", + " def calculate_consecutive_limits(series):\n", + " \"\"\"\n", + " 计算连续涨停/跌停天数。\n", + " \"\"\"\n", + " consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " return consecutive_up, consecutive_down\n", + "\n", + " # 连续涨停天数\n", + " df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n", + " lambda x: calculate_consecutive_limits(x)[0]\n", + " ).reset_index(level=0, drop=True)\n", + "\n", + " df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n", + "\n", + " df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n", + "\n", + " def rolling_corr(group):\n", + " roc_close = group['close'].pct_change()\n", + " roc_weight = group['weight_avg'].pct_change()\n", + " return roc_close.rolling(10).corr(roc_weight)\n", + "\n", + " df['price_cost_divergence'] = grouped.apply(rolling_corr)\n", + "\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " # 16. 筹码稳定性指数 (20日波动率)\n", + " df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n", + " df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n", + "\n", + " # 17. 成本区间突破标记\n", + " df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n", + "\n", + " # 20. 筹码-流动性风险\n", + " df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n", + " 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n", + "\n", + " # 7. 市值波动率因子\n", + " df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)\n", + " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " # 8. 市值成长性因子\n", + " df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)\n", + " df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n", + " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", + "\n", + " return df, new_columns\n", + "\n", + "\n", + "def get_simple_factor(df):\n", + " old_columns = df.columns.tolist()[:]\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " alpha = 0.5\n", + " df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n", + " df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']\n", + " df['log_close'] = np.log(df['close'])\n", + "\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", + "\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + "\n", + " df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n", + "\n", + " df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['lock_factor'] = df['turnover_rate'] * (\n", + " 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n", + "\n", + " df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)\n", + "\n", + " df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n", + "\n", + " # 12. 小盘股筹码集中度\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n", + " (df['volume_ratio'] > 1.5) &\n", + " (df['winner_rate'] > 0.7))\n", + "\n", + " df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']\n", + "\n", + " df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])\n", + "\n", + " df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']\n", + "\n", + " df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']\n", + "\n", + " drop_columns = [col for col in df.columns if col.startswith('_')]\n", + " df.drop(columns=drop_columns, inplace=True, errors='ignore')\n", + "\n", + " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", + " return df, new_columns\n" + ], + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "53f86ddc0677a6d7", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-03-19T14:45:43.390734Z", + "start_time": "2025-03-19T14:45:35.933466Z" + } + }, + "source": [ + "from utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ], + "outputs": [], + "execution_count": 6 + }, + { + "cell_type": "code", + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T14:45:43.435718Z", + "start_time": "2025-03-19T14:45:43.429472Z" + } + }, + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if\n", + " col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n", + "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", + "print(origin_columns)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "execution_count": 7 + }, + { + "cell_type": "code", + "id": "85c3e3d0235ffffa", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T14:45:43.684712Z", + "start_time": "2025-03-19T14:45:43.492971Z" + } + }, + "source": [ + "print(df[df['is_st']][['ts_code', 'trade_date', 'is_st']])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date is_st\n", + "29 000037.SZ 2017-01-03 True\n", + "72 000408.SZ 2017-01-03 True\n", + "95 000504.SZ 2017-01-03 True\n", + "96 000505.SZ 2017-01-03 True\n", + "101 000511.SZ 2017-01-03 True\n", + "... ... ... ...\n", + "8417201 603869.SH 2025-03-13 True\n", + "8417206 603879.SH 2025-03-13 True\n", + "8417253 603959.SH 2025-03-13 True\n", + "8417635 688282.SH 2025-03-13 True\n", + "8417639 688287.SH 2025-03-13 True\n", + "\n", + "[191519 rows x 3 columns]\n" + ] + } + ], + "execution_count": 8 + }, + { + "cell_type": "code", + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T14:47:42.395141Z", + "start_time": "2025-03-19T14:45:43.718152Z" + } + }, + "source": [ + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '20180101']\n", + " df = df.drop(columns=[col for col in df.columns if col.startswith('_')])\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "# df = get_technical_factor(df)\n", + "# df = get_act_factor(df)\n", + "# df = get_money_flow_factor(df)\n", + "# df = get_alpha_factor(df)\n", + "# df = get_limit_factor(df)\n", + "# df = get_cyp_perf_factor(df)\n", + "# df = get_mv_factors(df)\n", + "df, _ = get_rolling_factor(df)\n", + "\n", + "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 5084263 entries, 0 to 5084262\n", + "Data columns (total 85 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 volume_ratio float64 \n", + " 12 is_st bool \n", + " 13 up_limit float64 \n", + " 14 down_limit float64 \n", + " 15 buy_sm_vol float64 \n", + " 16 sell_sm_vol float64 \n", + " 17 buy_lg_vol float64 \n", + " 18 sell_lg_vol float64 \n", + " 19 buy_elg_vol float64 \n", + " 20 sell_elg_vol float64 \n", + " 21 net_mf_vol float64 \n", + " 22 his_low float64 \n", + " 23 his_high float64 \n", + " 24 cost_5pct float64 \n", + " 25 cost_15pct float64 \n", + " 26 cost_50pct float64 \n", + " 27 cost_85pct float64 \n", + " 28 cost_95pct float64 \n", + " 29 weight_avg float64 \n", + " 30 winner_rate float64 \n", + " 31 cat_l2_code object \n", + " 32 in_date datetime64[ns]\n", + " 33 return_skew float64 \n", + " 34 return_kurtosis float64 \n", + " 35 volume_change_rate float64 \n", + " 36 cat_volume_breakout bool \n", + " 37 turnover_deviation float64 \n", + " 38 cat_turnover_spike bool \n", + " 39 avg_volume_ratio float64 \n", + " 40 cat_volume_ratio_breakout bool \n", + " 41 vol_spike float64 \n", + " 42 vol_std_5 float64 \n", + " 43 obv float64 \n", + " 44 maobv_6 float64 \n", + " 45 return_5 float64 \n", + " 46 return_10 float64 \n", + " 47 return_20 float64 \n", + " 48 std_return_5 float64 \n", + " 49 std_return_15 float64 \n", + " 50 std_return_25 float64 \n", + " 51 std_return_90 float64 \n", + " 52 std_return_90_2 float64 \n", + " 53 _ema_5 float64 \n", + " 54 _ema_13 float64 \n", + " 55 _ema_20 float64 \n", + " 56 _ema_60 float64 \n", + " 57 act_factor1 float64 \n", + " 58 act_factor2 float64 \n", + " 59 act_factor3 float64 \n", + " 60 act_factor4 float64 \n", + " 61 log(circ_mv) float64 \n", + " 62 cov float64 \n", + " 63 delta_cov float64 \n", + " 64 _rank_stddev float64 \n", + " 65 alpha_22_improved float64 \n", + " 66 alpha_003 float64 \n", + " 67 alpha_007 float64 \n", + " 68 alpha_013 float64 \n", + " 69 cat_up_limit bool \n", + " 70 cat_down_limit bool \n", + " 71 up_limit_count_10d float64 \n", + " 72 down_limit_count_10d float64 \n", + " 73 consecutive_up_limit int64 \n", + " 74 vol_break int32 \n", + " 75 weight_roc5 float64 \n", + " 76 price_cost_divergence float64 \n", + " 77 smallcap_concentration float64 \n", + " 78 cost_stability float64 \n", + " 79 high_cost_break_days float64 \n", + " 80 liquidity_risk float64 \n", + " 81 turnover_std float64 \n", + " 82 mv_volatility float64 \n", + " 83 volume_growth float64 \n", + " 84 mv_growth float64 \n", + "dtypes: bool(6), datetime64[ns](2), float64(73), int32(1), int64(1), object(2)\n", + "memory usage: 3.0+ GB\n", + "None\n" + ] + } + ], + "execution_count": 9 + }, + { + "cell_type": "code", + "id": "f4f16d63ad18d1bc", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-19T14:47:42.645061Z", + "start_time": "2025-03-19T14:47:42.637235Z" + } + }, + "source": [ + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", + " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", + " num_features = [col for col in num_features if 'limit' not in col]\n", + " num_features = [col for col in num_features if 'cyq' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", + " # deviation_col_name = f'deviation_mean_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ], + "outputs": [], + "execution_count": 10 + }, + { + "cell_type": "code", + "id": "40e6b68a91b30c79", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T14:47:42.840210Z", + "start_time": "2025-03-19T14:47:42.740146Z" + } + }, + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n", + " log=True):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " if log:\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " volatility = stock_df['close'].pct_change().rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + "\n", + " return score\n", + "\n", + " scores = df.groupby('ts_code').apply(lambda x: compute_stock_score(x))\n", + " scores = scores.reset_index(level=0, drop=True)\n", + "\n", + " return scores\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 11 + }, + { + "cell_type": "code", + "id": "47c12bb34062ae7a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T15:54:02.848116Z", + "start_time": "2025-03-19T15:51:53.055184Z" + } + }, + "source": [ + "days = 5\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "\n", + "\n", + "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df.groupby('ts_code')['open'].shift(-1)\n", + "\n", + "\n", + "def symmetric_log_transform(values):\n", + " return np.sign(values) * np.log1p(np.abs(values))\n", + "\n", + "\n", + "# df['future_score'] = df['future_return']\n", + "df['future_score'] = calculate_score(df, days=days, lambda_param=0.5)\n", + "# df['future_score'] = df.groupby('ts_code')['future_return'].shift(-1).rolling(window=2).mean()\n", + "df['future_score'] = symmetric_log_transform(df['future_score'])\n", + "\n", + "# df['label'] = remove_outliers_label_percentile(df['label'])\n", + "train_data = df[(df['trade_date'] <= '2024-01-01') & (df['trade_date'] >= '2000-01-01')]\n", + "test_data = df[(df['trade_date'] >= '2024-01-01')]\n", + "\n", + "\n", + "def select_pre_zt_stocks_dynamic(\n", + " stock_df,\n", + " vol_spike_multiplier=1.5,\n", + " min_return=0.03, # 最小累计涨幅（例如 3%）\n", + " min_main_net_inflow=1e6, # 最小主力资金净流入（例如 100 万元）\n", + " window=30, # 计算历史均值的窗口大小\n", + " signal_days=1 # 异动信号需要连续出现的天数\n", + "):\n", + " # 排序数据\n", + " stock_df = stock_df.sort_values(by=['trade_date', 'ts_code'])\n", + " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x.nlargest(512, 'return_20')\n", + " )\n", + "\n", + " return stock_df\n", + "\n", + "\n", + "train_data = select_pre_zt_stocks_dynamic(train_data)\n", + "test_data = select_pre_zt_stocks_dynamic(test_data)\n", + "# # train_data = train_data[train_data['circ_mv'] < 2000000]\n", + "# # test_data = test_data[test_data['circ_mv'] < 2000000]\n", + "#\n", + "# train_data = train_data[train_data['vol'] > 1.5 * train_data['vol_spike']]\n", + "# test_data = test_data[test_data['vol'] > 1.5 * test_data['vol_spike']]\n", + "# train_data = train_data.groupby('trade_date', group_keys=False).apply(\n", + "# lambda x: x.nsmallest(500, 'return_20')\n", + "# )\n", + "#\n", + "# test_data = test_data.groupby('trade_date', group_keys=False).apply(\n", + "# lambda x: x.nsmallest(500, 'return_20')\n", + "# )\n", + "\n", + "# train_data, _ = get_simple_factor(train_data)\n", + "# test_data, _ = get_simple_factor(test_data)\n", + "\n", + "train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + " lambda x: pd.qcut(x, q=5, labels=False, duplicates='drop')\n", + ")\n", + "test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + " lambda x: pd.qcut(x, q=5, labels=False, duplicates='drop')\n", + ")\n", + "# train_data['label'] = train_data['label'] == 4\n", + "# test_data['label'] = test_data['label'] == 4\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "feature_columns = [col for col in train_data.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(feature_columns)\n", + "\n", + "feature_columns_new = feature_columns[:]\n", + "train_data, feature_columns_new = create_deviation_within_dates(train_data, feature_columns)\n", + "test_data, feature_columns_new = create_deviation_within_dates(test_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns_new)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=feature_columns_new)\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "\n", + "cat_columns = [col for col in df.columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "\n", + "# feature_columns_new.remove('cat_l2_code')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'volume_ratio', 'winner_rate', 'cat_l2_code', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'log(circ_mv)', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'cat_down_limit', 'up_limit_count_10d', 'down_limit_count_10d', 'consecutive_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", + "feature_columns size: 177\n", + "541133\n", + "最小日期: 2018-06-04\n", + "最大日期: 2023-12-29\n", + "106062\n", + "最小日期: 2024-01-02\n", + "最大日期: 2025-03-13\n" + ] + } + ], + "execution_count": 40 + }, + { + "cell_type": "code", + "id": "1c46817a-b5dd-4bec-8bb4-e6e80bfd9d66", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T15:54:03.016065Z", + "start_time": "2025-03-19T15:54:03.011956Z" + } + }, + "source": [ + "# test_data = df[(df['trade_date'] >= '2024-04-01')]\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')" + ], + "outputs": [], + "execution_count": 41 + }, + { + "cell_type": "code", + "id": "da2bb202843d9275", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T15:54:24.393873Z", + "start_time": "2025-03-19T15:54:03.049056Z" + } + }, + "source": [ + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "feature_columns_new = remove_highly_correlated_features(train_data, feature_columns_new)\n", + "keep_columns = [col for col in train_data.columns if\n", + " col in feature_columns_new or col in ['ts_code', 'trade_date', 'label', 'future_return',\n", + " 'future_score']]\n", + "train_data = train_data[keep_columns]\n", + "# test_data = test_data[keep_columns]\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "# print(feature_columns_new)\n", + "# 2. 按 trade_date 分组" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_columns size: 154\n" + ] + } + ], + "execution_count": 42 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T15:54:24.499139Z", + "start_time": "2025-03-19T15:54:24.490686Z" + } + }, + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader, Dataset\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "num_heads = 4 # Transformer头数\n", + "transformer_input_dim = 64\n", + "num_layers = 2 # Transformer层数\n", + "hidden_dim = 64 # 隐藏层维度\n", + "num_epochs = 10 # 训练轮数\n", + "learning_rate = 0.001 # 学习率\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "validation_days = 180\n", + "\n", + "print(device)" + ], + "id": "8113bba62de693cc", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n" + ] + } + ], + "execution_count": 43 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T15:57:09.407047Z", + "start_time": "2025-03-19T15:54:24.596522Z" + } + }, + "cell_type": "code", + "source": [ + "from tqdm import tqdm\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", + "from sklearn.metrics import accuracy_score\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "\n", + "\n", + "class StockPredictionModel(nn.Module):\n", + " def __init__(self, input_dim, hidden_dim, num_classes, num_heads, num_layers, transformer_input_dim):\n", + " super(StockPredictionModel, self).__init__()\n", + "\n", + " # 输入全连接层：将原始输入特征映射到 Transformer 的输入维度\n", + " self.fc_input = nn.Sequential(\n", + " nn.Linear(input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.3),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.3),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " )\n", + "\n", + " # Transformer Encoder\n", + " self.transformer = nn.Transformer(\n", + " d_model=transformer_input_dim,\n", + " nhead=num_heads,\n", + " num_encoder_layers=num_layers,\n", + " batch_first=True\n", + " )\n", + "\n", + " self.dropout = nn.Dropout(0.3)\n", + "\n", + " # 全连接层\n", + " self.fc_output = nn.Sequential(\n", + " nn.Linear(transformer_input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.3),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Dropout(0.3),\n", + " nn.Linear(hidden_dim, num_classes),\n", + " )\n", + "\n", + " def forward(self, x):\n", + " # x: (batch_size, num_stocks, input_dim)\n", + "\n", + " # 输入全连接层处理\n", + " x = (self.fc_input(x))\n", + "\n", + " # Transformer处理\n", + " transformer_out = self.transformer(x, x) # Self-attention\n", + "\n", + " # 全连接层处理\n", + " out = self.fc_output(transformer_out)\n", + "\n", + " return out\n", + "\n", + "\n", + "# 数据预处理函数\n", + "def preprocess_data(data, cat_columns, feature_columns, label_encoders=None, scaler=None):\n", + " \"\"\"\n", + " 预处理数据：\n", + " - 对分类特征进行 LabelEncoder 编码。\n", + " - 对数值特征进行标准化。\n", + " - 将数据按天分组并转换为 NumPy 数组。\n", + " \"\"\"\n", + " # 分离分类特征和数值特征\n", + " numeric_cols = [col for col in feature_columns if col not in cat_columns]\n", + "\n", + " # 初始化编码器和标准化器（如果是训练阶段）\n", + " if label_encoders is None:\n", + " label_encoders = {col: LabelEncoder() for col in cat_columns}\n", + " X_cat = np.array([label_encoders[col].fit_transform(data[col]) for col in cat_columns]).T\n", + " else:\n", + " X_cat = np.array([label_encoders[col].transform(data[col]) for col in cat_columns]).T\n", + " if scaler is None:\n", + " scaler = StandardScaler()\n", + " X_num = scaler.fit_transform(data[numeric_cols])\n", + " else:\n", + " X_num = scaler.transform(data[numeric_cols])\n", + "\n", + "\n", + " # 处理分类特征\n", + "\n", + " # 处理数值特征\n", + "\n", + " # 合并特征\n", + " X_processed = np.hstack([X_num, X_cat])\n", + "\n", + " # 将处理后的数据与日期对齐\n", + " processed_data = pd.DataFrame(X_processed, columns=numeric_cols + cat_columns)\n", + " processed_data['trade_date'] = data['trade_date'].values # 保留日期列\n", + " processed_data['label'] = data['label'].values # 保留标签列\n", + "\n", + " # 按天分组\n", + " grouped_X = []\n", + " grouped_y = []\n", + " for date, group in processed_data.groupby('trade_date'):\n", + " X_day = group[numeric_cols + cat_columns].values # 使用预处理后的特征\n", + " y_day = group['label'].values # 使用原始标签\n", + " grouped_X.append(X_day)\n", + " grouped_y.append(y_day)\n", + "\n", + " # 获取每个分类特征的最大类别数（用于嵌入层）\n", + " cat_dims = [len(label_encoders[col].classes_) for col in cat_columns]\n", + "\n", + " return grouped_X, grouped_y, label_encoders, scaler, cat_dims\n", + "\n", + "\n", + "# 训练函数（逐天训练）\n", + "def train_by_day(model, X_train, y_train, criterion, optimizer, device):\n", + " model.train()\n", + " total_loss = 0\n", + " all_preds, all_labels = [], []\n", + "\n", + " for day_idx in range(len(X_train)):\n", + " # 取出当天的数据\n", + " X_batch = torch.tensor(X_train[day_idx], dtype=torch.float32).to(device)\n", + " y_batch = torch.tensor(y_train[day_idx], dtype=torch.long).to(device)\n", + "\n", + " # 添加 batch 维度（从 2D 变为 3D）\n", + " if X_batch.dim() == 2:\n", + " X_batch = X_batch.unsqueeze(0) # 形状变为 (1, num_stocks, num_features)\n", + "\n", + " # 前向传播\n", + " outputs = model(X_batch) # (batch_size, num_stocks, num_classes)\n", + " loss = criterion(outputs.view(-1, outputs.size(-1)), y_batch.view(-1))\n", + "\n", + " # 反向传播\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # 统计损失和预测结果\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(outputs, dim=-1).cpu().numpy()\n", + " labels = y_batch.cpu().numpy()\n", + " all_preds.extend(preds.flatten())\n", + " all_labels.extend(labels.flatten())\n", + "\n", + " avg_loss = total_loss / len(X_train)\n", + " accuracy = accuracy_score(all_labels, all_preds)\n", + " return avg_loss, accuracy\n", + "\n", + "\n", + "# 验证/测试函数（逐天验证）\n", + "def evaluate_by_day(model, X_val, y_val, criterion, device):\n", + " model.eval()\n", + " total_loss = 0\n", + " all_preds, all_labels = [], []\n", + "\n", + " with torch.no_grad():\n", + " for day_idx in range(len(X_val)):\n", + " # 取出当天的数据\n", + " X_batch = torch.tensor(X_val[day_idx], dtype=torch.float32).to(device)\n", + " y_batch = torch.tensor(y_val[day_idx], dtype=torch.long).to(device)\n", + " # 添加 batch 维度（从 2D 变为 3D）\n", + " if X_batch.dim() == 2:\n", + " X_batch = X_batch.unsqueeze(0) # 形状变为 (1, num_stocks, num_features)\n", + "\n", + " # 前向传播\n", + " outputs = model(X_batch)\n", + " loss = criterion(outputs.view(-1, outputs.size(-1)), y_batch.view(-1))\n", + "\n", + " # 统计损失和预测结果\n", + " total_loss += loss.item()\n", + " preds = torch.argmax(outputs, dim=-1).cpu().numpy()\n", + " labels = y_batch.cpu().numpy()\n", + " all_preds.extend(preds.flatten())\n", + " all_labels.extend(labels.flatten())\n", + "\n", + " avg_loss = total_loss / len(X_val)\n", + " accuracy = accuracy_score(all_labels, all_preds)\n", + " return avg_loss, accuracy\n", + "\n", + "\n", + "# 主程序\n", + "gc.collect()\n", + "# 数据切分\n", + "all_dates = train_data['trade_date'].unique() # 获取所有唯一的 trade_date\n", + "split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + "train_data_split = train_data[train_data['trade_date'] < split_date] # 训练集\n", + "val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", + "\n", + "# 找到分类特征列\n", + "cat_columns = [col for col in train_data.columns if col.startswith(\"cat\")]\n", + "\n", + "# 预处理数据\n", + "X_train_processed, y_train_processed, label_encoders, scaler, cat_dims = preprocess_data(train_data_split, cat_columns,\n", + " feature_columns_new)\n", + "X_val_processed, y_val_processed, _, _, _ = preprocess_data(val_data_split, cat_columns, feature_columns_new,\n", + " label_encoders, scaler)\n", + "X_test_processed, y_test_processed, _, _, _ = preprocess_data(test_data, cat_columns, feature_columns_new,\n", + " label_encoders, scaler)\n", + "\n", + "# 超参数\n", + "input_dim = X_train_processed[0].shape[1] # 直接从数据获取输入维度\n", + "print(input_dim)\n", + "num_classes = len(train_data[\"label\"].unique())\n", + "\n", + "\n", + "# 模型、损失函数和优化器\n", + "model = StockPredictionModel(\n", + " input_dim=input_dim,\n", + " hidden_dim=hidden_dim,\n", + " num_classes=num_classes,\n", + " num_heads=num_heads,\n", + " num_layers=num_layers,\n", + " transformer_input_dim=transformer_input_dim,\n", + ").to(device)\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n", + "\n", + "# 训练和验证\n", + "best_val_accuracy = 0\n", + "for epoch in tqdm(range(num_epochs)):\n", + " train_loss, train_acc = train_by_day(model, X_train_processed, y_train_processed, criterion, optimizer, device)\n", + " val_loss, val_acc = evaluate_by_day(model, X_val_processed, y_val_processed, criterion, device)\n", + "\n", + " print(f\"Epoch {epoch + 1}/{num_epochs}: \"\n", + " f\"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, \"\n", + " f\"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\")\n", + "\n", + " # 保存最佳模型\n", + " # if val_acc > best_val_accuracy:\n", + " # best_val_accuracy = val_acc\n", + " # torch.save(model.state_dict(), \"best_model.pth\")\n", + "\n", + "# 测试\n", + "# model.load_state_dict(torch.load(\"best_model.pth\"))\n", + "# test_loss, test_acc = evaluate_by_day(model, X_test_processed, y_test_processed, criterion, device)\n", + "# print(f\"Test Loss: {test_loss:.4f}, Test Acc: {test_acc:.4f}\")" + ], + "id": "beeb098799ecfa6a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "154\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 10%|█ | 1/10 [00:26<03:58, 26.47s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10: Train Loss: 1.6094, Train Acc: 0.2037, Val Loss: 1.6091, Val Acc: 0.2088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|██ | 2/10 [00:53<03:33, 26.72s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/10: Train Loss: 1.6091, Train Acc: 0.2046, Val Loss: 1.6091, Val Acc: 0.2088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 30%|███ | 3/10 [01:20<03:07, 26.80s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 3/10: Train Loss: 1.6091, Train Acc: 0.2038, Val Loss: 1.6091, Val Acc: 0.2088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|████ | 4/10 [01:44<02:35, 25.92s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4/10: Train Loss: 1.6091, Train Acc: 0.2043, Val Loss: 1.6091, Val Acc: 0.2088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 50%|█████ | 5/10 [02:08<02:05, 25.18s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 5/10: Train Loss: 1.6091, Train Acc: 0.2046, Val Loss: 1.6091, Val Acc: 0.2088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 6/10 [02:35<01:42, 25.72s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/10: Train Loss: 1.6091, Train Acc: 0.2042, Val Loss: 1.6091, Val Acc: 0.2088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 6/10 [02:41<01:47, 26.93s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[44], line 223\u001B[0m\n\u001B[0;32m 221\u001B[0m best_val_accuracy \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m 222\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m epoch \u001B[38;5;129;01min\u001B[39;00m tqdm(\u001B[38;5;28mrange\u001B[39m(num_epochs)):\n\u001B[1;32m--> 223\u001B[0m train_loss, train_acc \u001B[38;5;241m=\u001B[39m train_by_day(model, X_train_processed, y_train_processed, criterion, optimizer, device)\n\u001B[0;32m 224\u001B[0m val_loss, val_acc \u001B[38;5;241m=\u001B[39m evaluate_by_day(model, X_val_processed, y_val_processed, criterion, device)\n\u001B[0;32m 226\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mEpoch \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mepoch\u001B[38;5;250m \u001B[39m\u001B[38;5;241m+\u001B[39m\u001B[38;5;250m \u001B[39m\u001B[38;5;241m1\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnum_epochs\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 227\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTrain Loss: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtrain_loss\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, Train Acc: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtrain_acc\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 228\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mVal Loss: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mval_loss\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, Val Acc: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mval_acc\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n", + "Cell \u001B[1;32mIn[44], line 132\u001B[0m, in \u001B[0;36mtrain_by_day\u001B[1;34m(model, X_train, y_train, criterion, optimizer, device)\u001B[0m\n\u001B[0;32m 129\u001B[0m X_batch \u001B[38;5;241m=\u001B[39m X_batch\u001B[38;5;241m.\u001B[39munsqueeze(\u001B[38;5;241m0\u001B[39m) \u001B[38;5;66;03m# 形状变为 (1, num_stocks, num_features)\u001B[39;00m\n\u001B[0;32m 131\u001B[0m \u001B[38;5;66;03m# 前向传播\u001B[39;00m\n\u001B[1;32m--> 132\u001B[0m outputs \u001B[38;5;241m=\u001B[39m model(X_batch) \u001B[38;5;66;03m# (batch_size, num_stocks, num_classes)\u001B[39;00m\n\u001B[0;32m 133\u001B[0m loss \u001B[38;5;241m=\u001B[39m criterion(outputs\u001B[38;5;241m.\u001B[39mview(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m, outputs\u001B[38;5;241m.\u001B[39msize(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m)), y_batch\u001B[38;5;241m.\u001B[39mview(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m))\n\u001B[0;32m 135\u001B[0m \u001B[38;5;66;03m# 反向传播\u001B[39;00m\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1737\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m 1738\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1739\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1745\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1746\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1747\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m 1748\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1749\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1750\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1752\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 1753\u001B[0m called_always_called_hooks \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m()\n", + "Cell \u001B[1;32mIn[44], line 54\u001B[0m, in \u001B[0;36mStockPredictionModel.forward\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m 50\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[0;32m 51\u001B[0m \u001B[38;5;66;03m# x: (batch_size, num_stocks, input_dim)\u001B[39;00m\n\u001B[0;32m 52\u001B[0m \n\u001B[0;32m 53\u001B[0m \u001B[38;5;66;03m# 输入全连接层处理\u001B[39;00m\n\u001B[1;32m---> 54\u001B[0m x \u001B[38;5;241m=\u001B[39m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfc_input(x))\n\u001B[0;32m 56\u001B[0m \u001B[38;5;66;03m# Transformer处理\u001B[39;00m\n\u001B[0;32m 57\u001B[0m transformer_out \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtransformer(x, x) \u001B[38;5;66;03m# Self-attention\u001B[39;00m\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1737\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m 1738\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1739\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1745\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1746\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1747\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m 1748\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1749\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1750\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1752\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 1753\u001B[0m called_always_called_hooks \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m()\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\container.py:250\u001B[0m, in \u001B[0;36mSequential.forward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m 248\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;28minput\u001B[39m):\n\u001B[0;32m 249\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m module \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m:\n\u001B[1;32m--> 250\u001B[0m \u001B[38;5;28minput\u001B[39m \u001B[38;5;241m=\u001B[39m module(\u001B[38;5;28minput\u001B[39m)\n\u001B[0;32m 251\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28minput\u001B[39m\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1739\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1737\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[0;32m 1738\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1739\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1750\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1745\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[0;32m 1746\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[0;32m 1747\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[0;32m 1748\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[0;32m 1749\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[1;32m-> 1750\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m forward_call(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1752\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 1753\u001B[0m called_always_called_hooks \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m()\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\torch\\nn\\modules\\linear.py:125\u001B[0m, in \u001B[0;36mLinear.forward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m 124\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforward\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;28minput\u001B[39m: Tensor) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Tensor:\n\u001B[1;32m--> 125\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m F\u001B[38;5;241m.\u001B[39mlinear(\u001B[38;5;28minput\u001B[39m, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mweight, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbias)\n", + "\u001B[1;31mKeyboardInterrupt\u001B[0m: " + ] + } + ], + "execution_count": 44 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-19T15:57:09.420051500Z", + "start_time": "2025-03-19T15:47:37.553874Z" + } + }, + "cell_type": "code", + "source": [ + "def preprocess_test_data(data, cat_columns, feature_columns, label_encoders, scaler):\n", + " \"\"\"对测试数据进行预处理\"\"\"\n", + " numeric_cols = [col for col in feature_columns if col not in cat_columns]\n", + "\n", + " # 处理分类特征\n", + " X_cat = np.array([label_encoders[col].transform(data[col]) for col in cat_columns]).T\n", + "\n", + " # 处理数值特征\n", + " X_num = scaler.transform(data[numeric_cols])\n", + "\n", + " # 合并特征\n", + " X_processed = np.hstack([X_num, X_cat])\n", + "\n", + " # 保留原始 ts_code 和 trade_date\n", + " processed_data = data[['ts_code', 'trade_date']].copy()\n", + " processed_data['features'] = list(X_processed)\n", + "\n", + " return processed_data\n", + "\n", + "\n", + "def predict_and_save(model, test_data, cat_columns, feature_columns, label_encoders, scaler, output_path):\n", + " # 预处理测试数据\n", + " processed_data = preprocess_test_data(test_data, cat_columns, feature_columns, label_encoders, scaler)\n", + "\n", + " # 按天分组\n", + " grouped_data = processed_data.groupby('trade_date')\n", + "\n", + " # 存储预测结果\n", + " results = []\n", + "\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for date, group in grouped_data:\n", + " # 准备输入数据\n", + " X_batch = np.stack(group['features'].values) # 形状 (num_stocks, num_features)\n", + "\n", + " X_batch = torch.tensor(X_batch, dtype=torch.float32).to(device)\n", + "\n", + " if X_batch.dim() == 2:\n", + " X_batch = X_batch.unsqueeze(0) # 形状变为 (1, num_stocks, num_features)\n", + "\n", + " # 预测\n", + " outputs = model(X_batch)\n", + " probs = torch.softmax(outputs, dim=-1).cpu().numpy() # 转换为概率\n", + "\n", + " # 取最后一列作为 score（假设最后一列是目标类别）\n", + " scores = probs[0, :, -1] # 形状 (num_stocks,)\n", + "\n", + " # 收集结果\n", + " for i in range(len(group)):\n", + " results.append({\n", + " 'trade_date': date,\n", + " 'score': scores[i],\n", + " 'ts_code': group['ts_code'].values[i],\n", + " })\n", + " # 保存为 DataFrame\n", + " result_df = pd.DataFrame(results)\n", + " result_df = result_df.loc[result_df.groupby('trade_date')['score'].idxmax()]\n", + " result_df.to_csv(output_path, index=False)\n", + " print(f\"预测结果已保存至 {output_path}\")\n", + "\n", + "\n", + "predict_and_save(\n", + " model=model,\n", + " test_data=test_data,\n", + " cat_columns=cat_columns,\n", + " feature_columns=feature_columns_new,\n", + " label_encoders=label_encoders,\n", + " scaler=scaler,\n", + " output_path=\"predictions_test.tsv\"\n", + ")" + ], + "id": "5d1522a7538db91b", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "预测结果已保存至 predictions_test.tsv\n" + ] + } + ], + "execution_count": 38 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/Transformer.ipynb b/code/train/Transformer.ipynb new file mode 100644 index 0000000..9118ecf --- /dev/null +++ b/code/train/Transformer.ipynb @@ -0,0 +1,1362 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:54.782179Z", + "start_time": "2025-03-01T04:00:54.471051Z" + } + }, + "source": [ + "%load_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "import pandas as pd\n", + " \n", + "\n", + " \n", + "pd.set_option('display.max_columns', None)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload module is not an IPython extension.\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "id": "a79cafb06a7e0e43", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-01T04:01:45.158165Z", + "start_time": "2025-03-01T04:00:54.784691Z" + } + }, + "source": [ + "\n", + "from utils.utils import read_and_merge_h5_data\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)\n", + "print(df.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8350183 entries, 0 to 8350182\n", + "Data columns (total 21 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 turnover_rate float64 \n", + " 8 pe_ttm float64 \n", + " 9 circ_mv float64 \n", + " 10 volume_ratio float64 \n", + " 11 is_st bool \n", + " 12 up_limit float64 \n", + " 13 down_limit float64 \n", + " 14 buy_sm_vol float64 \n", + " 15 sell_sm_vol float64 \n", + " 16 buy_lg_vol float64 \n", + " 17 sell_lg_vol float64 \n", + " 18 buy_elg_vol float64 \n", + " 19 sell_elg_vol float64 \n", + " 20 net_mf_vol float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(18), object(1)\n", + "memory usage: 1.3+ GB\n", + "None\n" + ] + } + ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "f7a55c19-b7dc-4d2f-a478-cffab11690df", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:01:49.907736Z", + "start_time": "2025-03-01T04:01:45.533679Z" + } + }, + "source": [ + "print('industry')\n", + "df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code'],\n", + " df=df, on=['ts_code'], join='left')\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n", + "left merge on ['ts_code']\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "4077d4449d406c86", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:01:50.070194Z", + "start_time": "2025-03-01T04:01:49.936309Z" + } + }, + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " return df\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line', 'MACD_hist'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "code", + "id": "c4e9e1d31da6dba6", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-03-01T04:12:27.210806Z", + "start_time": "2025-03-01T04:12:26.858559Z" + } + }, + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "def get_technical_factor(df):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # 计算 up 和 down\n", + " df['log_close'] = np.log(df['close'])\n", + "\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " # 计算 ATR\n", + " df['atr_14'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), index=x.index)\n", + " )\n", + " df['atr_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " # 计算 RSI\n", + " df['rsi_3'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " )\n", + " df['rsi_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['rsi_9'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # 计算 avg_close_5\n", + " df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_act_factor(df, cat=True):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + " # 计算 EMA 指标\n", + " df['ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " if cat:\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(df['act_factor1']**2 + df['act_factor2']**2)\n", + "\n", + " # 根据 trade_date 截面计算排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_money_flow_factor(df):\n", + " # 计算资金流相关因子（字段名称见 tushare 数据说明）\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + " return df\n", + "\n", + "\n", + "def get_alpha_factor(df):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code')\n", + "\n", + " # alpha_022: 当前 close 与 5 日前 close 差值\n", + " df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))\n", + "\n", + " # alpha_003: (close - open) / (high - low)\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " return df\n", + "\n" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.633429700Z", + "start_time": "2025-02-27T16:36:47.161749Z" + } + }, + "outputs": [], + "source": [ + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " # industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + "\n", + " for factor in factor_columns:\n", + " if factor in industry_data.columns:\n", + " # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(lambda x: x - x.median())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.633429700Z", + "start_time": "2025-02-27T16:36:56.276375Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'l2_code']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.633429700Z", + "start_time": "2025-02-27T16:36:56.417914Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 5567976 entries, 1962 to 5567975\n", + "Data columns (total 72 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 turnover_rate float64 \n", + " 8 pe_ttm float64 \n", + " 9 circ_mv float64 \n", + " 10 volume_ratio float64 \n", + " 11 is_st bool \n", + " 12 up_limit float64 \n", + " 13 down_limit float64 \n", + " 14 buy_sm_vol float64 \n", + " 15 sell_sm_vol float64 \n", + " 16 buy_lg_vol float64 \n", + " 17 sell_lg_vol float64 \n", + " 18 buy_elg_vol float64 \n", + " 19 sell_elg_vol float64 \n", + " 20 net_mf_vol float64 \n", + " 21 cat_l2_code object \n", + " 22 log_close float64 \n", + " 23 up float64 \n", + " 24 down float64 \n", + " 25 atr_14 float64 \n", + " 26 atr_6 float64 \n", + " 27 obv float64 \n", + " 28 maobv_6 float64 \n", + " 29 obv-maobv_6 float64 \n", + " 30 rsi_3 float64 \n", + " 31 rsi_6 float64 \n", + " 32 rsi_9 float64 \n", + " 33 return_5 float64 \n", + " 34 return_10 float64 \n", + " 35 return_20 float64 \n", + " 36 avg_close_5 float64 \n", + " 37 std_return_5 float64 \n", + " 38 std_return_15 float64 \n", + " 39 std_return_25 float64 \n", + " 40 std_return_90 float64 \n", + " 41 std_return_90_2 float64 \n", + " 42 std_return_5 / std_return_90 float64 \n", + " 43 std_return_5 / std_return_25 float64 \n", + " 44 std_return_90 - std_return_90_2 float64 \n", + " 45 ema_5 float64 \n", + " 46 ema_13 float64 \n", + " 47 ema_20 float64 \n", + " 48 ema_60 float64 \n", + " 49 act_factor1 float64 \n", + " 50 act_factor2 float64 \n", + " 51 act_factor3 float64 \n", + " 52 act_factor4 float64 \n", + " 53 cat_af1 bool \n", + " 54 cat_af2 bool \n", + " 55 cat_af3 bool \n", + " 56 cat_af4 bool \n", + " 57 act_factor5 float64 \n", + " 58 act_factor6 float64 \n", + " 59 rank_act_factor1 float64 \n", + " 60 rank_act_factor2 float64 \n", + " 61 rank_act_factor3 float64 \n", + " 62 active_buy_volume_large float64 \n", + " 63 active_buy_volume_big float64 \n", + " 64 active_buy_volume_small float64 \n", + " 65 buy_lg_vol_minus_sell_lg_vol float64 \n", + " 66 buy_elg_vol_minus_sell_elg_vol float64 \n", + " 67 log(circ_mv) float64 \n", + " 68 alpha_022 float64 \n", + " 69 alpha_003 float64 \n", + " 70 alpha_007 float64 \n", + " 71 alpha_013 float64 \n", + "dtypes: bool(5), datetime64[ns](1), float64(64), object(2)\n", + "memory usage: 2.8+ GB\n", + "None\n" + ] + } + ], + "source": [ + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "df = get_technical_factor(df)\n", + "df = get_act_factor(df)\n", + "df = get_money_flow_factor(df)\n", + "df = get_alpha_factor(df)\n", + "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d345bcc43b15579e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.648343Z", + "start_time": "2025-02-27T16:38:24.536432Z" + } + }, + "outputs": [], + "source": [ + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'industry' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_median = df.groupby(['trade_date', groupby_col])[feature].transform('median')\n", + " # deviation_col_name = f'deviation_median_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_median\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5f3d9aece75318cd", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.648343Z", + "start_time": "2025-02-27T16:38:24.876179Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removed 124442 outliers.\n" + ] + } + ], + "source": [ + "def get_qcuts(series, quantiles):\n", + " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", + " return q[-1] # 返回窗口最后一个元素的分位数标签\n", + "\n", + "\n", + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_return'] = (df['future_close'] - df['close']) / df['close']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n", + " df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return df['sharpe_ratio']\n", + "\n", + "\n", + "future_close = df.groupby('ts_code')['close'].shift(-4)\n", + "future_return = (future_close - df['close']) / df['close']\n", + "df['label'] = future_return\n", + "df['label'] = remove_outliers_label_percentile(df['label'])\n", + "\n", + "# df = df.apply(lambda x: x.astype('float32') if x.dtype in ['float64', 'float32'] else x)\n", + "df = df.sort_values(by=['trade_date', 'ts_code'])\n", + "train_data = df[(df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2016-01-01')]\n", + "test_data = df[(df['trade_date'] >= '2023-01-01') & (df['trade_date'] <= '2025-02-26')]\n", + "\n", + "train_data = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "test_data = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n", + "\n", + "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "93d47ef451968346", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.648343Z", + "start_time": "2025-02-27T16:38:53.216704Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['turnover_rate', 'pe_ttm', 'volume_ratio', 'cat_l2_code', 'log_close', 'up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_5', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'log(circ_mv)', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry_ema_5', 'industry_ema_13', 'industry_ema_20', 'industry_ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_obv_deviation', 'industry_return_5_deviation', 'industry_return_20_deviation', 'industry_act_factor1_deviation', 'industry_act_factor2_deviation', 'industry_act_factor3_deviation', 'industry_act_factor4_deviation', 'industry_return_5_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return']\n" + ] + } + ], + "source": [ + "feature_columns = [col for col in train_data.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(feature_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "572576eea818c865", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.648343Z", + "start_time": "2025-02-27T16:38:53.484283Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_columns size: 221\n", + "feature_columns size: 221\n", + "1136428\n", + "最小日期: 2017-04-06\n", + "最大日期: 2022-12-30\n", + "408601\n", + "最小日期: 2023-01-03\n", + "最大日期: 2025-02-26\n" + ] + } + ], + "source": [ + "feature_columns_new = feature_columns[:]\n", + "train_data, feature_columns_new = create_deviation_within_dates(train_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "test_data, feature_columns_new = create_deviation_within_dates(test_data, feature_columns)\n", + "print(f'feature_columns size: {len(feature_columns_new)}')\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns_new)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=feature_columns_new)\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2d7e37432f551aea", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.648343Z", + "start_time": "2025-02-27T16:39:44.144179Z" + } + }, + "outputs": [], + "source": [ + "cat_columns = [col for col in df.columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "class MultiTaskNNTransformerModel(nn.Module):\n", + " def __init__(self, input_dim, hidden_dim_nn, hidden_dim_transformer, num_heads, num_layers,\n", + " output_dim, task_type=\"regression\", num_classes=None):\n", + " super(MultiTaskNNTransformerModel, self).__init__()\n", + "\n", + " # 参数检查\n", + " if task_type not in [\"regression\", \"classification\", \"ranking\"]:\n", + " raise ValueError(\"task_type must be 'regression', 'classification', or 'ranking'\")\n", + " if task_type == \"classification\" and num_classes is None:\n", + " raise ValueError(\"num_classes must be specified for classification tasks\")\n", + "\n", + " # 3层全连接神经网络\n", + " self.fc1 = nn.Linear(input_dim, hidden_dim_nn)\n", + " self.fc2 = nn.Linear(hidden_dim_nn, hidden_dim_nn)\n", + " self.fc3 = nn.Linear(hidden_dim_nn, hidden_dim_transformer)\n", + " self.relu = nn.ReLU()\n", + "\n", + " # Transformer层\n", + " self.transformer = nn.Transformer(\n", + " d_model=hidden_dim_transformer, # Transformer的输入维度\n", + " nhead=num_heads, # 多头注意力机制的头数\n", + " num_encoder_layers=num_layers, # 编码器层数\n", + " num_decoder_layers=num_layers, # 解码器层数\n", + " dim_feedforward=hidden_dim_transformer * 2 # 前馈网络的隐藏层维度\n", + " )\n", + "\n", + " # 输出层\n", + " if task_type == \"classification\":\n", + " self.output_layer = nn.Linear(hidden_dim_transformer, num_classes) # 分类任务\n", + " else:\n", + " self.output_layer = nn.Linear(hidden_dim_transformer, output_dim) # 回归或排序任务\n", + "\n", + " # 激活函数（用于分类任务的softmax或排序任务的sigmoid）\n", + " self.task_type = task_type\n", + " if task_type == \"classification\":\n", + " self.activation = nn.Softmax(dim=1) # 分类任务使用Softmax\n", + " elif task_type == \"ranking\":\n", + " self.activation = nn.Sigmoid() # 排序任务使用Sigmoid\n", + " else:\n", + " self.activation = None # 回归任务不需要激活函数\n", + "\n", + " def forward(self, x):\n", + " \"\"\"\n", + " 前向传播函数。\n", + "\n", + " :param x: 输入数据 (batch_size, input_dim)\n", + " :return: 模型的输出 (batch_size, output_dim)\n", + " \"\"\"\n", + " # Step 1: 通过3层全连接神经网络提取特征\n", + " x = self.relu(self.fc1(x)) # 第一层\n", + " x = self.relu(self.fc2(x)) # 第二层\n", + " x = self.relu(self.fc3(x)) # 第三层\n", + "\n", + " # Step 2: 添加序列维度 (batch_size, seq_len=1, hidden_dim_transformer)\n", + " x = x.unsqueeze(1)\n", + "\n", + " # Step 3: 通过Transformer处理\n", + " transformer_output = self.transformer(x, x).squeeze(1) # 移除序列维度\n", + "\n", + " # Step 4: 通过输出层得到最终预测\n", + " output = self.output_layer(transformer_output)\n", + "\n", + " # Step 5: 根据任务类型应用激活函数\n", + " if self.activation is not None:\n", + " output = self.activation(output)\n", + "\n", + " return output" + ], + "id": "d8a5eba1e446b79a" + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8f134d435f71e9e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.668543Z", + "start_time": "2025-02-27T16:39:44.618725Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "# 自定义Dataset类\n", + "class TradeDateDataset(Dataset):\n", + " def __init__(self, data_df, feature_columns, label_column, scaler=None, one_hot_encoder=None):\n", + " self.data = []\n", + "\n", + " # 按trade_date分组\n", + " grouped = data_df.groupby('trade_date')\n", + " for trade_date, group in grouped:\n", + " X = group[feature_columns].values\n", + " y = group[label_column].values\n", + "\n", + " # 标准化数值型特征\n", + " if scaler is not None:\n", + " numeric_columns = group[feature_columns].select_dtypes(include=['float64', 'int64']).columns\n", + " X[:, [feature_columns.index(col) for col in numeric_columns]] = scaler.transform(\n", + " group[numeric_columns]\n", + " )\n", + "\n", + " # 对类别型特征进行One-Hot编码\n", + " if one_hot_encoder is not None:\n", + " categorical_columns = group[feature_columns].select_dtypes(include=['object', 'category']).columns\n", + " X_categorical = one_hot_encoder.transform(group[categorical_columns]).toarray()\n", + " X = np.hstack([X, X_categorical]) # 将One-Hot编码与数值型特征拼接\n", + "\n", + " self.data.append((X, y))\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " return self.data[idx]\n", + "\n", + "def train_transformer_model(train_data_df, test_data_df, feature_columns, label_column,\n", + " output_dim=1, task='regression',\n", + " hidden_dim=64, num_heads=4, num_layers=2,\n", + " dropout=0.1, learning_rate=0.001, num_epochs=10, batch_size=32):\n", + " # 数据预处理\n", + " train_data_df = train_data_df.dropna(subset=[label_column])\n", + " test_data_df = test_data_df.dropna(subset=[label_column])\n", + "\n", + " # 标准化数值型特征\n", + " scaler = StandardScaler()\n", + " numeric_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns\n", + " scaler.fit(train_data_df[numeric_columns])\n", + "\n", + " # 对类别型特征进行One-Hot编码\n", + " one_hot_encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')\n", + " categorical_columns = train_data_df[feature_columns].select_dtypes(include=['object', 'category']).columns\n", + " one_hot_encoder.fit(train_data_df[categorical_columns])\n", + "\n", + " # 创建Dataset\n", + " train_dataset = TradeDateDataset(train_data_df, feature_columns, label_column, scaler, one_hot_encoder)\n", + " test_dataset = TradeDateDataset(test_data_df, feature_columns, label_column, scaler, one_hot_encoder)\n", + "\n", + " # 创建DataLoader\n", + " train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + " test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n", + "\n", + " # 初始化模型、损失函数和优化器\n", + " input_dim = len(numeric_columns) + len(one_hot_encoder.get_feature_names_out())\n", + " model = TransformerModel(input_dim, hidden_dim, output_dim, num_heads, num_layers, dropout)\n", + "\n", + " if task == 'classification':\n", + " criterion = nn.CrossEntropyLoss()\n", + " elif task == 'regression':\n", + " criterion = nn.MSELoss()\n", + " else:\n", + " raise ValueError(\"Unsupported task type. Choose 'classification' or 'regression'.\")\n", + "\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", + "\n", + " # 训练循环\n", + " train_losses, val_losses = [], []\n", + " for epoch in range(num_epochs):\n", + " model.train()\n", + " running_loss = 0.0\n", + "\n", + " for X_batch, y_batch in train_loader:\n", + " X_batch = torch.tensor(X_batch, dtype=torch.float32)\n", + " if task == 'classification':\n", + " y_batch = torch.tensor(y_batch, dtype=torch.long)\n", + " elif task == 'regression':\n", + " y_batch = torch.tensor(y_batch, dtype=torch.float32).unsqueeze(1)\n", + "\n", + " # 前向传播\n", + " output = model(X_batch)\n", + "\n", + " # 计算损失\n", + " loss = criterion(output, y_batch)\n", + "\n", + " # 反向传播\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item()\n", + "\n", + " train_losses.append(running_loss / len(train_loader))\n", + " print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {train_losses[-1]:.4f}\")\n", + "\n", + " # 测试阶段\n", + " model.eval()\n", + " val_loss = 0.0\n", + " with torch.no_grad():\n", + " for X_batch, y_batch in test_loader:\n", + " X_batch = torch.tensor(X_batch, dtype=torch.float32)\n", + " if task == 'classification':\n", + " y_batch = torch.tensor(y_batch, dtype=torch.long)\n", + " elif task == 'regression':\n", + " y_batch = torch.tensor(y_batch, dtype=torch.float32).unsqueeze(1)\n", + "\n", + " output = model(X_batch)\n", + " loss = criterion(output, y_batch)\n", + " val_loss += loss.item()\n", + "\n", + " val_losses.append(val_loss / len(test_loader))\n", + " print(f\"Validation Loss: {val_losses[-1]:.4f}\")\n", + "\n", + " # 可视化损失曲线\n", + " plt.plot(train_losses, label=\"Training Loss\")\n", + " plt.plot(val_losses, label=\"Validation Loss\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " return model, scaler, one_hot_encoder" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4a4542e1ed6afe7d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.668543Z", + "start_time": "2025-02-27T16:39:44.838044Z" + } + }, + "outputs": [], + "source": [ + "light_params = {\n", + " # 'objective': 'regression',\n", + " # 'metric': 'l2',\n", + " 'objective': 'quantile', # 分位回归\n", + " 'metric': 'quantile', # 使用 quantile 作为评估指标\n", + " 'alpha': 0.75, # 90% 分位数\n", + " 'learning_rate': 0.05,\n", + " 'is_unbalance': True,\n", + " 'num_leaves': 1024,\n", + " 'min_data_in_leaf': 128,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 0.7,\n", + " 'bagging_freq': 5,\n", + " 'lambda_l1': 1,\n", + " 'lambda_l2': 1,\n", + " # 'boosting_type': 'dart',\n", + " 'verbosity': -1,\n", + " 'seed': 17\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "beeb098799ecfa6a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.668543Z", + "start_time": "2025-02-27T16:39:45.091206Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 1136428\n", + "categorical_feature: [3, 35, 36, 37, 38]\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[31]\ttrain's quantile: 0.014632\tvalid's quantile: 0.0152371\n", + "Evaluated only: quantile\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "evals = {}\n", + "model = train_light_model(train_data, test_data, light_params, feature_columns_new,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=500, use_optuna=False,\n", + " print_feature_importance=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "63235069-dc59-48fb-961a-e80373e41a61", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.668543Z", + "start_time": "2025-02-27T16:41:14.573661Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 1136428\n" + ] + } + ], + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "catboost_params = {\n", + " 'loss_function': 'MAE', # 90% 分位回归\n", + " 'iterations': 5000, # 训练轮数\n", + " 'learning_rate': 0.05, # 学习率，较低以防止过拟合\n", + " 'depth': 10, # 树的深度，防止过拟合\n", + " # 'l1_leaf_reg': 10.0, # l1 正则化，提高泛化能力\n", + " # 'bagging_temperature': 1, # 降低过拟合\n", + " # 'subsample': 0.8, # 每轮随机 80% 的样本，减少过拟合\n", + " 'colsample_bylevel': 0.8, # 每层 80% 特征子集，防止过拟合\n", + " 'random_seed': 42, # 固定随机种子，保证可复现\n", + " 'verbose': 500, # 每 100 轮打印一次信息\n", + " 'early_stopping_rounds': 100, # 早停，防止过拟合\n", + " # 'task_type': 'GPU'\n", + "}\n", + "\n", + "# model = train_catboost(train_data, test_data, feature_columns_new, catboost_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "465944b1d463e4b1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.668543Z", + "start_time": "2025-02-27T16:41:14.863552Z" + } + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "def incremental_training(test_data: pd.DataFrame,\n", + " model,\n", + " days: int,\n", + " back_days: int,\n", + " feature_columns: list,\n", + " params: dict,\n", + " model_type: str = 'lightgbm'):\n", + " if model_type not in ['lightgbm', 'catboost']:\n", + " raise ValueError(\"model_type must be either 'lightgbm' or 'catboost'\")\n", + "\n", + " test_data = test_data.sort_values(by='trade_date')\n", + " scores = []\n", + " unique_trade_dates = sorted(test_data['trade_date'].unique())\n", + "\n", + " new_model = None\n", + " for i in tqdm(range(0, len(unique_trade_dates), days)):\n", + " # Get the current window of trade dates\n", + " current_dates = unique_trade_dates[i:i + days]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " window_data = window_data.sort_values(by=['ts_code', 'trade_date'])\n", + " X = window_data[feature_columns]\n", + "\n", + " if new_model is not None:\n", + " window_scores = new_model.predict(X, prediction_type='RawFormulaVal')\n", + " else:\n", + " window_scores = model.predict(X, prediction_type='RawFormulaVal')\n", + " scores.extend(window_scores)\n", + "\n", + " # # Prepare data for incremental training\n", + " # current_dates = unique_trade_dates[max(0, i - back_days):i + days]\n", + " # window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " # X_train = window_data[feature_columns]\n", + " window_data = window_data.dropna(subset=['label'])\n", + " X_train = window_data[feature_columns]\n", + " y_train = window_data['label'] # Assuming 'label' is what you're predicting\n", + " # Incrementally train the model\n", + " if len(y_train.unique()) > 1:\n", + " if model_type == 'lightgbm':\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n", + " new_model = lgb.train(params,\n", + " train_set=train_data,\n", + " num_boost_round=100,\n", + " init_model=model,\n", + " keep_training_booster=True)\n", + " elif model_type == 'catboost':\n", + " from catboost import Pool\n", + " train_data = Pool(data=X_train, label=y_train, cat_features=[col for col in feature_columns if col.startswith('cat')])\n", + " # model.set_params(**params)\n", + " model.fit(train_data, init_model=model)\n", + " else:\n", + " print(current_dates)\n", + "\n", + " # Add the scores as a new 'score' column to the test_data\n", + " test_data['score'] = scores\n", + " return test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e3ac761d8f0b5d31", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.672656900Z", + "start_time": "2025-02-27T16:41:15.107891Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████| 104/104 [00:51<00:00, 2.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Timestamp('2025-02-25 00:00:00'), Timestamp('2025-02-26 00:00:00')]\n" + ] + } + ], + "source": [ + "\n", + "predictions_test = incremental_training(test_data, model, 5, 0, feature_columns_new, light_params, model_type='lightgbm')\n", + "predictions_test = predictions_test.loc[predictions_test.groupby('trade_date')['score'].idxmax()]\n", + "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b427ce41-9739-4e9e-bea8-5f2551fec5d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.672656900Z", + "start_time": "2025-02-27T16:42:45.229233Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "import joblib\n", + "import lightgbm as lgb\n", + "import pandas as pd\n", + "\n", + "# 假设你已经训练好了一个 LightGBM 模型\n", + "# model = lgb.train(params, train_data, ...)\n", + "\n", + "def save_model_with_info(model, params, feature_columns, train_data, info, save_path):\n", + " \"\"\"\n", + " 保存 LightGBM 模型及其相关信息。\n", + " \n", + " 参数:\n", + " model: 训练好的 LightGBM 模型 (lgb.Booster)。\n", + " params: 模型的参数 (dict)。\n", + " feature_columns: 特征列名列表 (list)。\n", + " train_data: 训练数据 (pd.DataFrame)，包含 'trade_date' 列。\n", + " info: 额外信息 (str 或 dict)。\n", + " save_path: 保存路径 (str)。\n", + " \"\"\"\n", + " # 提取训练数据的 trade_date 的最大值和最小值\n", + " if 'trade_date' not in train_data.columns:\n", + " raise ValueError(\"训练数据中必须包含 'trade_date' 列。\")\n", + " \n", + " trade_date_min = train_data['trade_date'].min()\n", + " trade_date_max = train_data['trade_date'].max()\n", + " \n", + " # 构建保存的信息字典\n", + " model_info = {\n", + " 'model': model, # 保存模型本身\n", + " 'params': params, # 模型参数\n", + " 'feature_columns': feature_columns, # 特征列名\n", + " 'trade_date_range': {\n", + " 'min': trade_date_min,\n", + " 'max': trade_date_max\n", + " }, # trade_date 的范围\n", + " 'info': info # 额外信息\n", + " }\n", + " \n", + " # 使用 joblib 保存模型及相关信息\n", + " joblib.dump(model_info, save_path)\n", + " print(f\"模型及相关信息已成功保存到 {save_path}\")\n", + "\n", + " \n", + "\n", + "# info = \"Update Regression + 滚动new model + 5days\"\n", + " \n", + "# # 保存模型及相关信息\n", + "# save_path = \"../model/lightgbm_model_UpdateRegression_2025-2-25.pkl\"\n", + "# save_model_with_info(model, light_params, feature_columns, train_data, info, save_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f9a2b7b-11fe-4eb5-aa11-c4066fe418a1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-01T04:00:27.672656900Z", + "start_time": "2025-02-27T16:42:45.450227Z" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/UpdateRank.ipynb b/code/train/UpdateRank.ipynb new file mode 100644 index 0000000..9e634e9 --- /dev/null +++ b/code/train/UpdateRank.ipynb @@ -0,0 +1,1570 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:10:25.019821Z", + "start_time": "2025-03-26T15:10:25.015412Z" + } + }, + "source": [ + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "\n", + "import pandas as pd\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "pd.set_option('display.max_columns', None)\n" + ], + "outputs": [], + "execution_count": 66 + }, + { + "cell_type": "code", + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:11:40.164945Z", + "start_time": "2025-03-26T15:10:25.138896Z" + } + }, + "source": [ + "from utils.utils import read_and_merge_h5_data\n", + "\n", + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df, join='inner')\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)\n", + "print('cyq perf')\n", + "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", + " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct',\n", + " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", + " df=df)\n", + "print(df.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8450470 entries, 0 to 8450469\n", + "Data columns (total 31 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 volume_ratio float64 \n", + " 12 is_st bool \n", + " 13 up_limit float64 \n", + " 14 down_limit float64 \n", + " 15 buy_sm_vol float64 \n", + " 16 sell_sm_vol float64 \n", + " 17 buy_lg_vol float64 \n", + " 18 sell_lg_vol float64 \n", + " 19 buy_elg_vol float64 \n", + " 20 sell_elg_vol float64 \n", + " 21 net_mf_vol float64 \n", + " 22 his_low float64 \n", + " 23 his_high float64 \n", + " 24 cost_5pct float64 \n", + " 25 cost_15pct float64 \n", + " 26 cost_50pct float64 \n", + " 27 cost_85pct float64 \n", + " 28 cost_95pct float64 \n", + " 29 weight_avg float64 \n", + " 30 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n", + "memory usage: 1.9+ GB\n", + "None\n" + ] + } + ], + "execution_count": 67 + }, + { + "cell_type": "code", + "id": "cac01788dac10678", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:11:55.524829Z", + "start_time": "2025-03-26T15:11:41.366630Z" + } + }, + "source": [ + "print('industry')\n", + "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "industry\n" + ] + } + ], + "execution_count": 68 + }, + { + "cell_type": "code", + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:11:55.794514Z", + "start_time": "2025-03-26T15:11:55.600258Z" + } + }, + "source": [ + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df = df.sort_values('trade_date')\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " return df\n", + "\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line',\n", + " 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ], + "outputs": [], + "execution_count": 69 + }, + { + "cell_type": "code", + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:11:56.026392Z", + "start_time": "2025-03-26T15:11:55.984754Z" + } + }, + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "\n", + "def get_rolling_factor(df):\n", + " old_columns = df.columns.tolist()[:]\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # df[\"gap_next_open\"] = (df[\"open\"].shift(-1) - df[\"close\"]) / df[\"close\"]\n", + "\n", + " df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n", + " df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n", + "\n", + " # 因子 1：短期成交量变化率\n", + " df['volume_change_rate'] = (\n", + " grouped['vol'].rolling(window=2).mean() /\n", + " grouped['vol'].rolling(window=10).mean() - 1\n", + " ).reset_index(level=0, drop=True) # 确保索引对齐\n", + "\n", + " # 因子 2：成交量突破信号\n", + " max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n", + " df['cat_volume_breakout'] = (df['vol'] > max_volume)\n", + "\n", + " # 因子 3：换手率均线偏离度\n", + " mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + " std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n", + " df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n", + "\n", + " # 因子 4：换手率激增信号\n", + " df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n", + "\n", + " # 因子 5：量比均值\n", + " df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + "\n", + " # 因子 6：量比突破信号\n", + " max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n", + " df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n", + "\n", + " df['vol_spike'] = grouped.apply(\n", + " lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n", + " )\n", + " df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()\n", + "\n", + " # 计算 ATR\n", + " df['atr_14'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", + " index=x.index)\n", + " )\n", + " df['atr_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", + " index=x.index)\n", + " )\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + "\n", + " df['rsi_3'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " )\n", + " df['rsi_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['rsi_9'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算 EMA 指标\n", + " df['_ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['_ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['_ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['_ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['_ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['_ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['_ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['_ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " # 根据 trade_date 截面计算排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + "\n", + " def rolling_covariance(x, y, window):\n", + " return x.rolling(window).cov(y)\n", + "\n", + " def delta(series, period):\n", + " return series.diff(period)\n", + "\n", + " def rank(series):\n", + " return series.rank(pct=True)\n", + "\n", + " def stddev(series, window):\n", + " return series.rolling(window).std()\n", + "\n", + " window_high_volume = 5\n", + " window_close_stddev = 20\n", + " period_delta = 5\n", + " df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)\n", + " df['delta_cov'] = delta(df['cov'], period_delta)\n", + " df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))\n", + " df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n", + "\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）\n", + " df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）\n", + " df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + " df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + "\n", + " # 3. 最近连续涨跌停天数\n", + " def calculate_consecutive_limits(series):\n", + " \"\"\"\n", + " 计算连续涨停/跌停天数。\n", + " \"\"\"\n", + " consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " return consecutive_up, consecutive_down\n", + "\n", + " # 连续涨停天数\n", + " df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n", + " lambda x: calculate_consecutive_limits(x)[0]\n", + " ).reset_index(level=0, drop=True)\n", + "\n", + " df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n", + "\n", + " df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n", + "\n", + " def rolling_corr(group):\n", + " roc_close = group['close'].pct_change()\n", + " roc_weight = group['weight_avg'].pct_change()\n", + " return roc_close.rolling(10).corr(roc_weight)\n", + "\n", + " df['price_cost_divergence'] = grouped.apply(rolling_corr)\n", + "\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " # 16. 筹码稳定性指数 (20日波动率)\n", + " df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n", + " df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n", + "\n", + " # 17. 成本区间突破标记\n", + " df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n", + "\n", + " # 20. 筹码-流动性风险\n", + " df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n", + " 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n", + "\n", + " # 7. 市值波动率因子\n", + " df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)\n", + " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " # 8. 市值成长性因子\n", + " df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)\n", + " df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n", + " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", + "\n", + " return df, new_columns\n", + "\n", + "\n", + "def get_simple_factor(df):\n", + " old_columns = df.columns.tolist()[:]\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " alpha = 0.5\n", + " df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n", + " df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']\n", + " df['log_close'] = np.log(df['close'])\n", + "\n", + " df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']\n", + "\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", + "\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + "\n", + " df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n", + "\n", + " df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['lock_factor'] = df['turnover_rate'] * (\n", + " 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n", + "\n", + " df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)\n", + "\n", + " df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n", + "\n", + " # 12. 小盘股筹码集中度\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n", + " (df['volume_ratio'] > 1.5) &\n", + " (df['winner_rate'] > 0.7))\n", + "\n", + " df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']\n", + "\n", + " df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])\n", + "\n", + " df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']\n", + "\n", + " df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']\n", + "\n", + " drop_columns = [col for col in df.columns if col.startswith('_')]\n", + " df.drop(columns=drop_columns, inplace=True, errors='ignore')\n", + "\n", + " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", + " return df, new_columns\n" + ], + "outputs": [], + "execution_count": 70 + }, + { + "cell_type": "code", + "id": "53f86ddc0677a6d7", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-03-26T15:12:02.787850Z", + "start_time": "2025-03-26T15:11:56.035514Z" + } + }, + "source": [ + "from utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " #\n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ], + "outputs": [], + "execution_count": 71 + }, + { + "cell_type": "code", + "id": "dbe2fd8021b9417f", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-03-26T15:12:02.870181Z", + "start_time": "2025-03-26T15:12:02.865559Z" + } + }, + "source": [ + "origin_columns = df.columns.tolist()\n", + "origin_columns = [col for col in origin_columns if\n", + " col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n", + "origin_columns = [col for col in origin_columns if col not in index_data.columns]\n", + "origin_columns = [col for col in origin_columns if 'cyq' not in col]\n", + "print(origin_columns)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "execution_count": 72 + }, + { + "cell_type": "code", + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:14:41.756942Z", + "start_time": "2025-03-26T15:12:03.028028Z" + } + }, + "source": [ + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '20180101']\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "# df = get_technical_factor(df)\n", + "# df = get_act_factor(df)\n", + "# df = get_money_flow_factor(df)\n", + "# df = get_alpha_factor(df)\n", + "# df = get_limit_factor(df)\n", + "# df = get_cyp_perf_factor(df)\n", + "# df = get_mv_factors(df)\n", + "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", + "# df = df.merge(industry_df, on=['l2_code', 'trade_date'], how='left')\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 5102787 entries, 0 to 5102786\n", + "Columns: 122 entries, ts_code to mv_momentum\n", + "dtypes: bool(13), datetime64[ns](2), float64(103), int32(1), int64(1), object(2)\n", + "memory usage: 4.2+ GB\n", + "None\n" + ] + } + ], + "execution_count": 73 + }, + { + "cell_type": "code", + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:14:42.493112Z", + "start_time": "2025-03-26T15:14:42.475598Z" + } + }, + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "from sklearn.metrics import roc_auc_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.1, wasserstein_threshold=0.15,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ], + "outputs": [], + "execution_count": 74 + }, + { + "cell_type": "code", + "id": "9d807cb2cde5d92c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:14:42.825272Z", + "start_time": "2025-03-26T15:14:42.814210Z" + } + }, + "source": [ + "def create_deviation_within_dates(df, feature_columns):\n", + " groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n", + " new_columns = {}\n", + " ret_feature_columns = feature_columns[:]\n", + "\n", + " # 自动选择所有数值型特征\n", + " num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n", + "\n", + " # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n", + " num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n", + " num_features = [col for col in num_features if 'limit' not in col]\n", + " num_features = [col for col in num_features if 'cyq' not in col]\n", + "\n", + " # 遍历所有数值型特征\n", + " for feature in num_features:\n", + " if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n", + " continue\n", + "\n", + " # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n", + " # deviation_col_name = f'deviation_mean_{feature}'\n", + " # new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " # ret_feature_columns.append(deviation_col_name)\n", + "\n", + " grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n", + " deviation_col_name = f'deviation_mean_{feature}'\n", + " new_columns[deviation_col_name] = df[feature] - grouped_mean\n", + " ret_feature_columns.append(deviation_col_name)\n", + "\n", + " # 将新计算的偏差特征与原始 DataFrame 合并\n", + " df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n", + "\n", + " # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n", + " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", + "\n", + " return df, ret_feature_columns\n" + ], + "outputs": [], + "execution_count": 75 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:14:44.049228Z", + "start_time": "2025-03-26T15:14:43.001080Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n", + " log=True):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " if log:\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " volatility = stock_df['close'].pct_change().rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + "\n", + " return score\n", + "\n", + " scores = df.groupby('ts_code').apply(lambda x: compute_stock_score(x))\n", + " scores = scores.reset_index(level=0, drop=True)\n", + "\n", + " return scores\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()" + ], + "id": "7ba833ee11a2f4cc", + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 76 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:14:44.297918Z", + "start_time": "2025-03-26T15:14:44.058234Z" + } + }, + "cell_type": "code", + "source": "print(df[['ts_code', 'trade_date', 'act_factor1']].head())", + "id": "8491afd6ea37782", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date act_factor1\n", + "0 000001.SZ 2018-01-02 NaN\n", + "2536 000001.SZ 2018-01-03 NaN\n", + "5079 000001.SZ 2018-01-04 NaN\n", + "7623 000001.SZ 2018-01-05 NaN\n", + "10167 000001.SZ 2018-01-08 NaN\n" + ] + } + ], + "execution_count": 77 + }, + { + "cell_type": "code", + "id": "097356cb-1cd8-4947-b870-9414abfdb3d8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:17:54.377205Z", + "start_time": "2025-03-26T15:14:44.467603Z" + } + }, + "source": [ + "days = 2\n", + "validation_days = 120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "# df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + " df.groupby('ts_code')['open'].shift(-1)\n", + "df['future_volatility'] = (\n", + " df.groupby('ts_code')['future_return']\n", + " .transform(lambda x: x.rolling(days).std())\n", + ")\n", + "\n", + "df['future_score'] = (\n", + " 0.7 * df['future_return'] +\n", + " 0.3 * df['future_volatility']\n", + ")\n", + "\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "filter_index = df['future_volatility'].between(df['future_volatility'].quantile(0.01),\n", + " df['future_volatility'].quantile(0.99)) | filter_index\n", + "\n", + "# df['label'] = df.groupby('trade_date', group_keys=False)['future_volatility'].transform(\n", + "# lambda x: pd.qcut(x, q=30, labels=False, duplicates='drop')\n", + "# )\n", + "\n", + "df['label'] = df.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + " lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + ")\n", + "\n", + "\n", + "# df['1_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(days)\n", + "# df['2_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(1 + days)\n", + "# df['3_score'] = df.groupby('ts_code', group_keys=False)['future_score'].shift(3 + days - 1)\n", + "\n", + "def symmetric_log_transform(values):\n", + " return np.sign(values) * np.log1p(np.abs(values))\n", + "\n", + "\n", + "train_data = df[filter_index & (df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2000-01-01')]\n", + "test_data = df[filter_index & (df['trade_date'] >= '2023-01-01')]\n", + "\n", + "\n", + "def select_pre_zt_stocks_dynamic(stock_df):\n", + " # 排序数据\n", + " stock_df = stock_df.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # avg_vol_3 = stock_df.groupby('ts_code')['vol'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + " # avg_vol_5 = stock_df.groupby('ts_code')['vol'].rolling(window=5).mean().shift(3).reset_index(level=0, drop=True)\n", + "\n", + " # stock_df = stock_df[\n", + " # (stock_df['cat_up_limit'] == 1) |\n", + " # (stock_df['vol'] > vol_spike_multiplier * stock_df['vol_spike'])\n", + " # ]\n", + " # cd1 = stock_df[\"close\"] > stock_df[\"close\"].shift(1)\n", + "\n", + " # cd2 = stock_df[\"close\"] > stock_df[\"close\"].rolling(window=10).mean()\n", + " #\n", + " # cd3 = (avg_vol_3 > avg_vol_5 * 2)\n", + " #\n", + " # cd4 = stock_df['gap_next_open'] < 0\n", + "\n", + " # stock_df = stock_df[(cd2 & cd4) | cd3]\n", + " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x.nlargest(1000, 'return_20')\n", + " )\n", + "\n", + " return stock_df\n", + "\n", + "\n", + "# train_data = select_pre_zt_stocks_dynamic(train_data)\n", + "# test_data = select_pre_zt_stocks_dynamic(test_data)\n", + "\n", + "train_data, _ = get_simple_factor(train_data)\n", + "test_data, _ = get_simple_factor(test_data)\n", + "\n", + "# train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + "# )\n", + "# test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_score'].transform(\n", + "# lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n", + "# )\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, feature_columns)\n", + "# test_data, _ = create_deviation_within_dates(test_data, feature_columns)\n", + "\n", + "feature_columns = [col for col in train_data.columns if col in train_data.columns]\n", + "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "feature_columns = [col for col in feature_columns if 'cat_l2_code' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "print(f'feature_columns size: {len(feature_columns)}')\n", + "\n", + "feature_columns, _ = remove_shifted_features(train_data[train_data['label'] == train_data['label'].max()],\n", + " test_data[test_data['label'] == test_data['label'].max()],\n", + " feature_columns)\n", + "\n", + "feature_columns = remove_highly_correlated_features(train_data[train_data['label'] == train_data['label'].max()],\n", + " feature_columns)\n", + "keep_columns = [col for col in train_data.columns if\n", + " col in feature_columns or col in ['ts_code', 'trade_date', 'label', 'future_return',\n", + " 'future_score', 'future_volatility']]\n", + "# train_data = train_data[keep_columns]\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=feature_columns)\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(test_data))\n", + "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "\n", + "cat_columns = [col for col in feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "\n", + "\n", + "# feature_columns_new.remove('cat_l2_code')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_columns size: 112\n", + "检测到 18 个可能漂移的特征: ['vol', 'pct_chg', 'turnover_rate', 'vol_std_5', 'obv', 'log(circ_mv)', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'up_limit_count_10d', 'log_close', 'up', 'down', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume']\n", + "feature_columns: ['pe_ttm', 'volume_ratio', 'winner_rate', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'atr_14', 'maobv_6', 'rsi_3', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'alpha_007', 'alpha_013', 'cat_up_limit', 'cat_down_limit', 'down_limit_count_10d', 'consecutive_up_limit', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'cat_vol_spike', 'obv-maobv_6', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'cat_af1', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_volume_ratio', 'mv_momentum', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile']\n", + "2543987\n", + "最小日期: 2018-06-04\n", + "最大日期: 2022-12-30\n", + "1234512\n", + "最小日期: 2023-01-03\n", + "最大日期: 2025-03-19\n" + ] + } + ], + "execution_count": 78 + }, + { + "cell_type": "code", + "id": "8f134d435f71e9e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:17:54.732350Z", + "start_time": "2025-03-26T15:17:54.705934Z" + } + }, + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "import lightgbm as lgb\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.decomposition import PCA\n", + "\n", + "\n", + "def train_light_model(train_data_df, params, feature_columns, callbacks, evals,\n", + " print_feature_importance=True, num_boost_round=100,\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " label_column='label'): # 新增参数：validation_days\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " numeric_columns = train_data_df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " # X_train.loc[:, numeric_columns] = scaler.fit_transform(X_train[numeric_columns])\n", + " # X_val.loc[:, numeric_columns] = scaler.transform(X_val[numeric_columns])\n", + " train_data_df = cross_sectional_standardization(train_data_df, numeric_columns)\n", + "\n", + " # 去除标签为空的样本\n", + " train_data_df = train_data_df.dropna(subset=[label_column])\n", + " print('原始训练集大小: ', len(train_data_df))\n", + "\n", + " # 按时间顺序划分训练集和验证集\n", + " if split_date is None:\n", + " all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n", + " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + "\n", + " # 打印划分结果\n", + " print(f\"划分后的训练集大小: {len(train_data_split)}, 验证集大小: {len(val_data_split)}\")\n", + "\n", + " # 提取特征和标签\n", + " X_train = train_data_split[feature_columns]\n", + " y_train = train_data_split[label_column]\n", + "\n", + " X_val = val_data_split[feature_columns]\n", + " y_val = val_data_split[label_column]\n", + "\n", + " # 标准化数值特征\n", + " scaler = StandardScaler()\n", + "\n", + " # 计算每个 trade_date 内的样本数（LTR 需要 group 信息）\n", + " train_groups = train_data_split.groupby('trade_date').size().tolist()\n", + " val_groups = val_data_split.groupby('trade_date').size().tolist()\n", + "\n", + " # 处理类别特征\n", + " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", + "\n", + " pca = None\n", + " if use_pca:\n", + " pca = PCA(n_components=0.95) # 或指定 n_components=固定值（如 10）\n", + " numeric_features = [col for col in feature_columns if col not in categorical_feature]\n", + " numeric_pca = pca.fit_transform(X_train[numeric_features])\n", + " X_train = pd.concat([pd.DataFrame(numeric_pca, index=X_train.index), X_train[categorical_feature]], axis=1)\n", + "\n", + " numeric_pca = pca.transform(X_val[numeric_features])\n", + " X_val = pd.concat([pd.DataFrame(numeric_pca, index=X_val.index), X_val[categorical_feature]], axis=1)\n", + "\n", + " # 计算权重（基于时间）\n", + " # trade_date = train_data_split['trade_date'] # 交易日期\n", + " # weights = (trade_date - trade_date.min()).dt.days / (trade_date.max() - trade_date.min()).days + 1\n", + " # weights = train_data_split.groupby('trade_date')['std_return_5'].transform(\n", + " # lambda x: x / x.mean()\n", + " # )\n", + " ud = sorted(train_data_split[\"trade_date\"].unique().tolist())\n", + " date_weights = {date: weight * weight for date, weight in zip(ud, np.linspace(1, 10, len(ud)))}\n", + " params['weight'] = train_data_split[\"trade_date\"].map(date_weights).tolist()\n", + "\n", + " print('feature_columns size: ', len(X_train.columns.tolist()))\n", + "\n", + " train_dataset = lgb.Dataset(\n", + " X_train, label=y_train, group=train_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + "\n", + " # weights = val_data_split.groupby('trade_date')['std_return_5'].transform(\n", + " # lambda x: x / x.mean()\n", + " # )\n", + " val_dataset = lgb.Dataset(\n", + " X_val, label=y_val, group=val_groups,\n", + " categorical_feature=categorical_feature\n", + " )\n", + "\n", + " # 训练模型\n", + " # 显式创建 LGBMRanker 模型\n", + " model = lgb.train(\n", + " params, train_dataset, num_boost_round=num_boost_round,\n", + " valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + "\n", + " # 打印特征重要性（如果需要）\n", + " if print_feature_importance:\n", + " lgb.plot_metric(evals)\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + "\n", + " return model, scaler, pca\n" + ], + "outputs": [], + "execution_count": 79 + }, + { + "cell_type": "code", + "id": "beeb098799ecfa6a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:21:10.639294Z", + "start_time": "2025-03-26T15:17:54.865846Z" + } + }, + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "if 'gen_volatility' in feature_columns:\n", + " feature_columns.remove('gen_volatility')\n", + "\n", + "label_gain = list(range(len(train_data['label'].unique())))\n", + "label_gain = [gain * gain for gain in label_gain]\n", + "light_params = {\n", + " 'label_gain': label_gain,\n", + " 'objective': 'lambdarank',\n", + " 'metric': 'lambdarank',\n", + " 'learning_rate': 0.03,\n", + " 'num_leaves': 1024,\n", + " 'min_data_in_leaf': 512,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 1,\n", + " 'bagging_freq': 5,\n", + " 'lambda_l1': 0.15,\n", + " 'lambda_l2': 0.15,\n", + " # 'boosting': 'dart',\n", + " 'verbosity': -1,\n", + " 'extra_trees': True,\n", + " 'max_position': 5,\n", + " 'ndcg_at': 1,\n", + " 'seed': 7\n", + "}\n", + "evals = {}\n", + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", + "light_params['feature_contri'] = feature_contri\n", + "print(f'feature_contri: {feature_contri}')\n", + "model, scaler, pca = train_light_model(train_data.copy().dropna(subset=['label']),\n", + " light_params, feature_columns,\n", + " [lgb.log_evaluation(period=100),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, validation_days=validation_days,\n", + " print_feature_importance=True, use_pca=use_pca)\n", + "\n", + "print('train data size: ', len(train_data))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 2543987\n", + "feature_contri: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "原始训练集大小: 2543987\n", + "划分后的训练集大小: 2262535, 验证集大小: 281452\n", + "feature_columns size: 87\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[18]\ttrain's ndcg@1: 0.649301\tvalid's ndcg@1: 0.586988\n", + "Evaluated only: ndcg@1\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 2543987\n" + ] + } + ], + "execution_count": 80 + }, + { + "cell_type": "code", + "id": "63235069-dc59-48fb-961a-e80373e41a61", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:21:10.902087Z", + "start_time": "2025-03-26T15:21:10.893389Z" + } + }, + "source": [ + "print('train data size: ', len(train_data))\n", + "\n", + "catboost_params = {\n", + " 'loss_function': 'QuerySoftMax', # 排序损失函数\n", + " 'custom_metric': ['NDCG', 'AverageGain:top=10'],\n", + " 'iterations': 5000, # 训练轮数\n", + " 'learning_rate': 0.05, # 学习率\n", + " 'depth': 10, # 树的深度，防止过拟合\n", + " # 'l2_leaf_reg': 10.0, # L2 正则化，提高泛化能力\n", + " # 'bagging_temperature': 1, # 降低过拟合\n", + " # 'subsample': 0.8, # 每轮随机 80% 样本\n", + " # 'colsample_bylevel': 0.8, # 每层 80% 特征\n", + " 'random_seed': 42, # 固定随机种子\n", + " 'verbose': 100, # 每 100 轮打印一次信息\n", + " 'early_stopping_rounds': 100, # 早停，防止过拟合\n", + " 'has_time': True, # 让模型知道数据有时间顺序\n", + " # 'task_type':\"GPU\",\n", + "}\n", + "\n", + "# model = train_catboost(train_data, test_data, feature_columns_new, catboost_params, plot=True)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 2543987\n" + ] + } + ], + "execution_count": 81 + }, + { + "cell_type": "code", + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:21:10.967045Z", + "start_time": "2025-03-26T15:21:10.950621Z" + } + }, + "source": [ + "from tqdm import tqdm\n", + "\n", + "\n", + "def incremental_training(test_data: pd.DataFrame,\n", + " model,\n", + " scaler,\n", + " days: int,\n", + " back_days: int,\n", + " feature_columns: list,\n", + " params: dict,\n", + " model_type: str = 'lightgbm',\n", + " times=10\n", + " ):\n", + " if model_type not in ['lightgbm', 'catboost']:\n", + " raise ValueError(\"model_type must be either 'lightgbm' or 'catboost'\")\n", + "\n", + " test_data = test_data.sort_values(by='trade_date')\n", + " scores = []\n", + " unique_trade_dates = sorted(test_data['trade_date'].unique())\n", + "\n", + " new_model = None\n", + " current_times = 0\n", + " for i in tqdm(range(0, len(unique_trade_dates))):\n", + " # Get the current window of trade dates\n", + " current_dates = [unique_trade_dates[i]]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " X = window_data[feature_columns]\n", + "\n", + " if new_model is not None:\n", + " window_scores = new_model.predict(X, prediction_type='RawFormulaVal')\n", + " else:\n", + " window_scores = model.predict(X, prediction_type='RawFormulaVal')\n", + " scores.extend(window_scores)\n", + " current_times += 1\n", + "\n", + " if current_times % times == 0:\n", + " current_dates = unique_trade_dates[max(0, i - days - back_days):i + 1 - back_days]\n", + " window_data = test_data[test_data['trade_date'].isin(current_dates)]\n", + " X_train = window_data[feature_columns]\n", + " y_train = window_data['label'] # Assuming 'label' is what you're predicting\n", + "\n", + " # Incrementally train the model\n", + " if len(y_train.unique()) > 1:\n", + " if model_type == 'lightgbm':\n", + " categorical_feature = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " train_groups = window_data.groupby('trade_date').size().tolist()\n", + " train_data = lgb.Dataset(X_train, label=y_train, group=train_groups,\n", + " categorical_feature=categorical_feature)\n", + " new_model = lgb.train(params,\n", + " train_set=train_data,\n", + " num_boost_round=24,\n", + " init_model=model,\n", + " keep_training_booster=True)\n", + " # print(f\"Number of trees: {model.num_trees()}\")\n", + " elif model_type == 'catboost':\n", + " from catboost import Pool\n", + " train_data = Pool(data=X_train, label=y_train,\n", + " cat_features=[col for col in feature_columns if col.startswith('cat')])\n", + " # model.set_params(**params)\n", + " model.fit(train_data, init_model=model)\n", + " # else:\n", + " # print(current_dates)\n", + "\n", + " # Add the scores as a new 'score' column to the test_data\n", + " test_data['score'] = scores\n", + " return test_data" + ], + "outputs": [], + "execution_count": 82 + }, + { + "cell_type": "code", + "id": "bbcc55a58ee063d6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-03-26T15:42:15.991756Z", + "start_time": "2025-03-26T15:36:18.479450Z" + } + }, + "source": [ + "numeric_columns = test_data.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "td = cross_sectional_standardization(test_data, numeric_columns)\n", + "predictions_test = incremental_training(td, model, scaler, 180, days, feature_columns, light_params,\n", + " model_type='lightgbm', times=20)\n", + "predictions_test = predictions_test.loc[predictions_test.groupby('trade_date')['score'].idxmax()]\n", + "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 533/533 [05:13<00:00, 1.70it/s]\n" + ] + } + ], + "execution_count": 86 + }, + { + "cell_type": "code", + "id": "020c3e3b-388b-42aa-a089-895057230122", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [], + "ExecuteTime": { + "end_time": "2025-03-26T15:34:41.558604Z", + "start_time": "2025-03-26T15:34:40.969894Z" + } + }, + "source": "print(df[['ts_code', 'trade_date', 'act_factor1']].head())\n", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date act_factor1\n", + "0 000001.SZ 2018-01-02 NaN\n", + "2536 000001.SZ 2018-01-03 NaN\n", + "5079 000001.SZ 2018-01-04 NaN\n", + "7623 000001.SZ 2018-01-05 NaN\n", + "10167 000001.SZ 2018-01-08 NaN\n" + ] + } + ], + "execution_count": 84 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/V1-copy.ipynb b/code/train/V1-copy.ipynb new file mode 100644 index 0000000..e0d0446 --- /dev/null +++ b/code/train/V1-copy.ipynb @@ -0,0 +1,896 @@ +{ + "cells": [ + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:52:54.170824Z", + "start_time": "2025-02-09T14:52:53.544850Z" + } + }, + "cell_type": "code", + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from utils.utils import read_and_merge_h5_data" + ], + "id": "79a7758178bafdd3", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:53:36.873700Z", + "start_time": "2025-02-09T14:52:54.170824Z" + } + }, + "cell_type": "code", + "source": [ + "print('daily data')\n", + "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", + " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n", + " df=None)\n", + "\n", + "print('daily basic')\n", + "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic_with_st',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st'], df=df)\n", + "\n", + "print('stk limit')\n", + "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", + " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", + " df=df)\n", + "print('money flow')\n", + "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", + " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", + " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", + " df=df)" + ], + "id": "a79cafb06a7e0e43", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "stk limit\n", + "money flow\n" + ] + } + ], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:53:37.426404Z", + "start_time": "2025-02-09T14:53:36.955552Z" + } + }, + "cell_type": "code", + "source": "origin_columns = df.columns.tolist()", + "id": "c4e9e1d31da6dba6", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:53:38.164112Z", + "start_time": "2025-02-09T14:53:38.070007Z" + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import talib\n", + "\n", + "\n", + "def get_technical_factor(df):\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " df['atr_14'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=14)\n", + " df['atr_6'] = talib.ATR(df['high'], df['low'], df['close'], timeperiod=6)\n", + "\n", + " df['obv'] = talib.OBV(df['close'], df['vol'])\n", + " df['maobv_6'] = talib.SMA(df['obv'], timeperiod=6)\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " df['rsi_3'] = talib.RSI(df['close'], timeperiod=3)\n", + " df['rsi_6'] = talib.RSI(df['close'], timeperiod=6)\n", + " df['rsi_9'] = talib.RSI(df['close'], timeperiod=9)\n", + "\n", + " df['return_10'] = df['close'] / df['close'].shift(10) - 1\n", + " df['return_20'] = df['close'] / df['close'].shift(20) - 1\n", + "\n", + " # # 计算 _rank_return_10 和 _rank_return_20\n", + " # df['_rank_return_10'] = df['return_10'].rank(pct=True)\n", + " # df['_rank_return_20'] = df['return_20'].rank(pct=True)\n", + "\n", + " # 计算 avg_close_5\n", + " df['avg_close_5'] = df['close'].rolling(window=5).mean() / df['close']\n", + "\n", + " # 计算 std_return_5, std_return_15, std_return_25, std_return_252, std_return_2522\n", + " df['std_return_5'] = df['close'].pct_change().shift(-1).rolling(window=5).std()\n", + " df['std_return_15'] = df['close'].pct_change().shift(-1).rolling(window=15).std()\n", + " df['std_return_25'] = df['close'].pct_change().shift(-1).rolling(window=25).std()\n", + " df['std_return_90'] = df['close'].pct_change().shift(-1).rolling(window=90).std()\n", + " df['std_return_90_2'] = df['close'].shift(10).pct_change().shift(-1).rolling(window=90).std()\n", + "\n", + " # 计算 std_return_5 / std_return_252 和 std_return_5 / std_return_25\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算 std_return_252 - std_return_2522\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + " return df\n", + "\n", + "\n", + "def get_act_factor(df):\n", + " # 计算 m_ta_ema(close, 5), m_ta_ema(close, 13), m_ta_ema(close, 20), m_ta_ema(close, 60)\n", + " df['ema_5'] = talib.EMA(df['close'], timeperiod=5)\n", + " df['ema_13'] = talib.EMA(df['close'], timeperiod=13)\n", + " df['ema_20'] = talib.EMA(df['close'], timeperiod=20)\n", + " df['ema_60'] = talib.EMA(df['close'], timeperiod=60)\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = np.arctan((df['ema_5'] / df['ema_5'].shift(1) - 1) * 100) * 57.3 / 50\n", + " df['act_factor2'] = np.arctan((df['ema_13'] / df['ema_13'].shift(1) - 1) * 100) * 57.3 / 40\n", + " df['act_factor3'] = np.arctan((df['ema_20'] / df['ema_20'].shift(1) - 1) * 100) * 57.3 / 21\n", + " df['act_factor4'] = np.arctan((df['ema_60'] / df['ema_60'].shift(1) - 1) * 100) * 57.3 / 10\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", + "\n", + " # 根据 'trade_date' 进行分组，在每个组内分别计算 'act_factor1', 'act_factor2', 'act_factor3' 的排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date')['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date')['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date')['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_money_flow_factor(df):\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol - sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol - sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " # # 你还提到了一些其他字段:\n", + " # df['net_active_buy_volume_main'] = df['net_mf_vol'] / df['buy_sm_vol']\n", + " # df['netflow_amount_main'] = df['net_mf_vol'] / df['buy_sm_vol'] # 这里假设 'net_mf_vol' 是主流资金流\n", + "\n", + " # df['active_sell_volume_large'] = df['sell_lg_vol'] / df['sell_sm_vol']\n", + " # df['active_sell_volume_big'] = df['sell_elg_vol'] / df['sell_sm_vol']\n", + " # df['active_sell_volume_small'] = df['sell_sm_vol'] / df['sell_sm_vol']\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_alpha_factor(df):\n", + " df['alpha_022'] = df['close'] - df['close'].shift(5)\n", + "\n", + " # alpha_003: (close - open) / (high - low)\n", + " df['alpha_003'] = (df['close'] - df['open']) / (df['high'] - df['low'])\n", + "\n", + " # alpha_007: rank(correlation(close, volume, 5))\n", + " df['alpha_007'] = df['close'].rolling(5).corr(df['vol']).rank(axis=1)\n", + "\n", + " # alpha_013: rank(sum(close, 5) - sum(close, 20))\n", + " df['alpha_013'] = (df['close'].rolling(5).sum() - df['close'].rolling(20).sum()).rank(axis=1)\n", + " return df\n", + "\n", + "\n", + "def get_future_data(df):\n", + " df['future_return1'] = (df['close'].shift(-1) - df['close']) / df['close']\n", + " df['future_return2'] = (df['open'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n", + " df['future_return3'] = (df['close'].shift(-2) - df['close'].shift(-1)) / df['close'].shift(-1)\n", + " df['future_return4'] = (df['close'].shift(-2) - df['open'].shift(-1)) / df['open'].shift(-1)\n", + " df['future_return5'] = (df['close'].shift(-5) - df['open'].shift(-1)) / df['open'].shift(-1)\n", + " df['future_return6'] = (df['close'].shift(-10) - df['open'].shift(-1)) / df['open'].shift(-1)\n", + " df['future_return7'] = (df['close'].shift(-20) - df['open'].shift(-1)) / df['open'].shift(-1)\n", + " df['future_close1'] = (df['close'].shift(-1) - df['close']) / df['close']\n", + " df['future_close2'] = (df['close'].shift(-2) - df['close']) / df['close']\n", + " df['future_close3'] = (df['close'].shift(-3) - df['close']) / df['close']\n", + " df['future_close4'] = (df['close'].shift(-4) - df['close']) / df['close']\n", + " df['future_close5'] = (df['close'].shift(-5) - df['close']) / df['close']\n", + " df['future_af11'] = df['act_factor1'].shift(-1)\n", + " df['future_af12'] = df['act_factor1'].shift(-2)\n", + " df['future_af13'] = df['act_factor1'].shift(-3)\n", + " df['future_af14'] = df['act_factor1'].shift(-4)\n", + " df['future_af15'] = df['act_factor1'].shift(-5)\n", + " df['future_af21'] = df['act_factor2'].shift(-1)\n", + " df['future_af22'] = df['act_factor2'].shift(-2)\n", + " df['future_af23'] = df['act_factor2'].shift(-3)\n", + " df['future_af24'] = df['act_factor2'].shift(-4)\n", + " df['future_af25'] = df['act_factor2'].shift(-5)\n", + " df['future_af31'] = df['act_factor3'].shift(-1)\n", + " df['future_af32'] = df['act_factor3'].shift(-2)\n", + " df['future_af33'] = df['act_factor3'].shift(-3)\n", + " df['future_af34'] = df['act_factor3'].shift(-4)\n", + " df['future_af35'] = df['act_factor3'].shift(-5)\n", + "\n", + " return df\n" + ], + "id": "a735bc02ceb4d872", + "outputs": [], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:53:49.153376Z", + "start_time": "2025-02-09T14:53:38.164112Z" + } + }, + "cell_type": "code", + "source": [ + "df = get_technical_factor(df)\n", + "df = get_act_factor(df)\n", + "df = get_money_flow_factor(df)\n", + "df = get_future_data(df)\n", + "# df = df.drop(columns=origin_columns)\n", + "\n", + "print(df.info())" + ], + "id": "53f86ddc0677a6d7", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8364308 entries, 0 to 8364307\n", + "Data columns (total 83 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 is_st object \n", + " 8 up_limit float64 \n", + " 9 down_limit float64 \n", + " 10 buy_sm_vol float64 \n", + " 11 sell_sm_vol float64 \n", + " 12 buy_lg_vol float64 \n", + " 13 sell_lg_vol float64 \n", + " 14 buy_elg_vol float64 \n", + " 15 sell_elg_vol float64 \n", + " 16 net_mf_vol float64 \n", + " 17 up float64 \n", + " 18 down float64 \n", + " 19 atr_14 float64 \n", + " 20 atr_6 float64 \n", + " 21 obv float64 \n", + " 22 maobv_6 float64 \n", + " 23 obv-maobv_6 float64 \n", + " 24 rsi_3 float64 \n", + " 25 rsi_6 float64 \n", + " 26 rsi_9 float64 \n", + " 27 return_10 float64 \n", + " 28 return_20 float64 \n", + " 29 avg_close_5 float64 \n", + " 30 std_return_5 float64 \n", + " 31 std_return_15 float64 \n", + " 32 std_return_25 float64 \n", + " 33 std_return_90 float64 \n", + " 34 std_return_90_2 float64 \n", + " 35 std_return_5 / std_return_90 float64 \n", + " 36 std_return_5 / std_return_25 float64 \n", + " 37 std_return_90 - std_return_90_2 float64 \n", + " 38 ema_5 float64 \n", + " 39 ema_13 float64 \n", + " 40 ema_20 float64 \n", + " 41 ema_60 float64 \n", + " 42 act_factor1 float64 \n", + " 43 act_factor2 float64 \n", + " 44 act_factor3 float64 \n", + " 45 act_factor4 float64 \n", + " 46 act_factor5 float64 \n", + " 47 act_factor6 float64 \n", + " 48 rank_act_factor1 float64 \n", + " 49 rank_act_factor2 float64 \n", + " 50 rank_act_factor3 float64 \n", + " 51 active_buy_volume_large float64 \n", + " 52 active_buy_volume_big float64 \n", + " 53 active_buy_volume_small float64 \n", + " 54 buy_lg_vol - sell_lg_vol float64 \n", + " 55 buy_elg_vol - sell_elg_vol float64 \n", + " 56 future_return1 float64 \n", + " 57 future_return2 float64 \n", + " 58 future_return3 float64 \n", + " 59 future_return4 float64 \n", + " 60 future_return5 float64 \n", + " 61 future_return6 float64 \n", + " 62 future_return7 float64 \n", + " 63 future_close1 float64 \n", + " 64 future_close2 float64 \n", + " 65 future_close3 float64 \n", + " 66 future_close4 float64 \n", + " 67 future_close5 float64 \n", + " 68 future_af11 float64 \n", + " 69 future_af12 float64 \n", + " 70 future_af13 float64 \n", + " 71 future_af14 float64 \n", + " 72 future_af15 float64 \n", + " 73 future_af21 float64 \n", + " 74 future_af22 float64 \n", + " 75 future_af23 float64 \n", + " 76 future_af24 float64 \n", + " 77 future_af25 float64 \n", + " 78 future_af31 float64 \n", + " 79 future_af32 float64 \n", + " 80 future_af33 float64 \n", + " 81 future_af34 float64 \n", + " 82 future_af35 float64 \n", + "dtypes: datetime64[ns](1), float64(80), object(2)\n", + "memory usage: 5.2+ GB\n", + "None\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:55:28.712343Z", + "start_time": "2025-02-09T14:53:49.279168Z" + } + }, + "cell_type": "code", + "source": [ + "def filter_data(df):\n", + " df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor3'))\n", + " df = df[df['is_st'] == False]\n", + " df = df[df['is_st'] == False]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "\n", + "df = filter_data(df)\n", + "print(df.info())" + ], + "id": "dbe2fd8021b9417f", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1136157 entries, 0 to 1136156\n", + "Data columns (total 83 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ts_code 1136157 non-null object \n", + " 1 trade_date 1136157 non-null datetime64[ns]\n", + " 2 open 1136157 non-null float64 \n", + " 3 close 1136157 non-null float64 \n", + " 4 high 1136157 non-null float64 \n", + " 5 low 1136157 non-null float64 \n", + " 6 vol 1136157 non-null float64 \n", + " 7 is_st 1136157 non-null object \n", + " 8 up_limit 1135878 non-null float64 \n", + " 9 down_limit 1135878 non-null float64 \n", + " 10 buy_sm_vol 1135663 non-null float64 \n", + " 11 sell_sm_vol 1135663 non-null float64 \n", + " 12 buy_lg_vol 1135663 non-null float64 \n", + " 13 sell_lg_vol 1135663 non-null float64 \n", + " 14 buy_elg_vol 1135663 non-null float64 \n", + " 15 sell_elg_vol 1135663 non-null float64 \n", + " 16 net_mf_vol 1135663 non-null float64 \n", + " 17 up 1136157 non-null float64 \n", + " 18 down 1136157 non-null float64 \n", + " 19 atr_14 1136157 non-null float64 \n", + " 20 atr_6 1136157 non-null float64 \n", + " 21 obv 1136157 non-null float64 \n", + " 22 maobv_6 1136157 non-null float64 \n", + " 23 obv-maobv_6 1136157 non-null float64 \n", + " 24 rsi_3 1136157 non-null float64 \n", + " 25 rsi_6 1136157 non-null float64 \n", + " 26 rsi_9 1136157 non-null float64 \n", + " 27 return_10 1136157 non-null float64 \n", + " 28 return_20 1136157 non-null float64 \n", + " 29 avg_close_5 1136157 non-null float64 \n", + " 30 std_return_5 1136157 non-null float64 \n", + " 31 std_return_15 1136157 non-null float64 \n", + " 32 std_return_25 1136157 non-null float64 \n", + " 33 std_return_90 1136131 non-null float64 \n", + " 34 std_return_90_2 1136129 non-null float64 \n", + " 35 std_return_5 / std_return_90 1136131 non-null float64 \n", + " 36 std_return_5 / std_return_25 1136157 non-null float64 \n", + " 37 std_return_90 - std_return_90_2 1136129 non-null float64 \n", + " 38 ema_5 1136157 non-null float64 \n", + " 39 ema_13 1136157 non-null float64 \n", + " 40 ema_20 1136157 non-null float64 \n", + " 41 ema_60 1136153 non-null float64 \n", + " 42 act_factor1 1136157 non-null float64 \n", + " 43 act_factor2 1136157 non-null float64 \n", + " 44 act_factor3 1136157 non-null float64 \n", + " 45 act_factor4 1136152 non-null float64 \n", + " 46 act_factor5 1136152 non-null float64 \n", + " 47 act_factor6 1136157 non-null float64 \n", + " 48 rank_act_factor1 1136157 non-null float64 \n", + " 49 rank_act_factor2 1136157 non-null float64 \n", + " 50 rank_act_factor3 1136157 non-null float64 \n", + " 51 active_buy_volume_large 1135659 non-null float64 \n", + " 52 active_buy_volume_big 1135636 non-null float64 \n", + " 53 active_buy_volume_small 1135663 non-null float64 \n", + " 54 buy_lg_vol - sell_lg_vol 1135660 non-null float64 \n", + " 55 buy_elg_vol - sell_elg_vol 1135640 non-null float64 \n", + " 56 future_return1 1136157 non-null float64 \n", + " 57 future_return2 1136157 non-null float64 \n", + " 58 future_return3 1136157 non-null float64 \n", + " 59 future_return4 1136157 non-null float64 \n", + " 60 future_return5 1136157 non-null float64 \n", + " 61 future_return6 1136157 non-null float64 \n", + " 62 future_return7 1136157 non-null float64 \n", + " 63 future_close1 1136157 non-null float64 \n", + " 64 future_close2 1136157 non-null float64 \n", + " 65 future_close3 1136157 non-null float64 \n", + " 66 future_close4 1136157 non-null float64 \n", + " 67 future_close5 1136157 non-null float64 \n", + " 68 future_af11 1136157 non-null float64 \n", + " 69 future_af12 1136157 non-null float64 \n", + " 70 future_af13 1136157 non-null float64 \n", + " 71 future_af14 1136157 non-null float64 \n", + " 72 future_af15 1136157 non-null float64 \n", + " 73 future_af21 1136157 non-null float64 \n", + " 74 future_af22 1136157 non-null float64 \n", + " 75 future_af23 1136157 non-null float64 \n", + " 76 future_af24 1136157 non-null float64 \n", + " 77 future_af25 1136157 non-null float64 \n", + " 78 future_af31 1136157 non-null float64 \n", + " 79 future_af32 1136157 non-null float64 \n", + " 80 future_af33 1136157 non-null float64 \n", + " 81 future_af34 1136157 non-null float64 \n", + " 82 future_af35 1136157 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(80), object(2)\n", + "memory usage: 719.5+ MB\n", + "None\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T15:00:45.828404Z", + "start_time": "2025-02-09T15:00:45.294830Z" + } + }, + "cell_type": "code", + "source": [ + "def remove_outliers_iqr(series, lower_quantile=0.05, upper_quantile=0.95, threshold=1.5):\n", + " Q1 = series.quantile(lower_quantile)\n", + " Q3 = series.quantile(upper_quantile)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - threshold * IQR\n", + " upper_bound = Q3 + threshold * IQR\n", + " # 过滤掉低于下边界或高于上边界的极值\n", + " return (series >= lower_bound) & (series <= upper_bound)\n", + "\n", + "\n", + "def neutralize_labels(labels, features, feature_columns, z_threshold=3, method='regression'):\n", + " labels_no_outliers = remove_outliers_iqr(labels)\n", + " return labels_no_outliers\n", + "\n", + "\n", + "train_data = df[df['trade_date'] <= '2023-01-01']\n", + "test_data = df[df['trade_date'] >= '2023-01-01']\n", + "\n", + "feature_columns = [col for col in df.columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "\n", + "# for column in [column for column in train_data.columns if 'future' in column]:\n", + "# label_index = neutralize_labels(train_data[column], train_data, feature_columns, z_threshold=3, method='regression')\n", + "# train_data = train_data[label_index]\n", + "# label_index = neutralize_labels(test_data[column], test_data, feature_columns, z_threshold=3, method='regression')\n", + "# test_data = test_data[label_index]\n", + "\n", + "print(len(train_data))\n", + "print(len(test_data))" + ], + "id": "5f3d9aece75318cd", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['up', 'down', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'obv-maobv_6', 'rsi_3', 'rsi_6', 'rsi_9', 'return_10', 'return_20', 'avg_close_5', 'std_return_5', 'std_return_15', 'std_return_25', 'std_return_90', 'std_return_90_2', 'std_return_5 / std_return_90', 'std_return_5 / std_return_25', 'std_return_90 - std_return_90_2', 'ema_5', 'ema_13', 'ema_20', 'ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol - sell_lg_vol', 'buy_elg_vol - sell_elg_vol']\n" + ] + } + ], + "execution_count": 19 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:56:05.319915Z", + "start_time": "2025-02-09T14:56:03.355725Z" + } + }, + "cell_type": "code", + "source": [ + "def get_qcuts(series, quantiles):\n", + " q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n", + " return q[-1] # 返回窗口最后一个元素的分位数标签\n", + "\n", + "\n", + "window = 5\n", + "quantiles = 20\n", + "\n", + "\n", + "def get_label(df):\n", + " labels = df['future_af13'] - df['act_factor1']\n", + " # labels = df['future_close3']\n", + " return labels\n", + "\n", + "\n", + "train_data['label'], test_data['label'] = get_label(train_data), get_label(test_data)\n", + "\n", + "train_data, test_data = train_data.dropna(subset=['label']), test_data.dropna(subset=['label'])\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan).dropna(), test_data.replace([np.inf, -np.inf],\n", + " np.nan).dropna()\n", + "train_data, test_data = train_data.reset_index(drop=True), test_data.reset_index(drop=True)\n", + "\n", + "print(len(train_data))\n", + "print(len(test_data))" + ], + "id": "f4f16d63ad18d1bc", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "875004\n", + "最小日期: 2017-01-03\n", + "最大日期: 2022-12-30\n", + "260581\n", + "最小日期: 2023-01-03\n", + "最大日期: 2025-01-27\n" + ] + } + ], + "execution_count": 13 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:56:05.480695Z", + "start_time": "2025-02-09T14:56:05.367238Z" + } + }, + "cell_type": "code", + "source": [ + "import lightgbm as lgb\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import optuna\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import mean_absolute_error\n", + "import os\n", + "import json\n", + "import pickle\n", + "import hashlib\n", + "\n", + "\n", + "def objective(trial, X, y, num_boost_round, params):\n", + " # 参数网格\n", + " X, y = X.reset_index(drop=True), y.reset_index(drop=True)\n", + " param_grid = {\n", + " \"n_estimators\": trial.suggest_categorical(\"n_estimators\", [10000]),\n", + " \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.3),\n", + " \"num_leaves\": trial.suggest_int(\"num_leaves\", 20, 3000, step=25),\n", + " \"max_depth\": trial.suggest_int(\"max_depth\", 3, 16),\n", + " \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 200, 10000, step=100),\n", + " \"lambda_l1\": trial.suggest_int(\"lambda_l1\", 0, 100, step=5),\n", + " \"lambda_l2\": trial.suggest_int(\"lambda_l2\", 0, 100, step=5),\n", + " \"min_gain_to_split\": trial.suggest_float(\"min_gain_to_split\", 0, 15),\n", + " \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.2, 0.95, step=0.1),\n", + " \"bagging_freq\": trial.suggest_categorical(\"bagging_freq\", [1]),\n", + " \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.2, 0.95, step=0.1),\n", + " \"random_state\": 1,\n", + " \"objective\": 'regression',\n", + " 'verbosity': -1\n", + " }\n", + " # 5折交叉验证\n", + " cv = KFold(n_splits=5, shuffle=False)\n", + "\n", + " cv_scores = np.empty(5)\n", + " for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)):\n", + " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", + " y_train, y_test = y[train_idx], y[test_idx]\n", + "\n", + " # LGBM建模\n", + " model = lgb.LGBMRegressor(**param_grid, num_boost_round=num_boost_round)\n", + " model.fit(\n", + " X_train,\n", + " y_train,\n", + " eval_set=[(X_test, y_test)],\n", + " eval_metric=\"l2\",\n", + " callbacks=[\n", + " # LightGBMPruningCallback(trial, \"l2\"),\n", + " lgb.early_stopping(50, first_metric_only=True),\n", + " lgb.log_evaluation(period=-1)\n", + " ],\n", + " )\n", + " # 模型预测\n", + " preds = model.predict(X_test)\n", + " # 优化指标logloss最小\n", + " cv_scores[idx] = mean_absolute_error(y_test, preds)\n", + "\n", + " return np.mean(cv_scores)\n", + "\n", + "def generate_key(params, feature_columns, num_boost_round):\n", + " key_data = {\n", + " \"params\": params,\n", + " \"feature_columns\": feature_columns,\n", + " \"num_boost_round\": num_boost_round\n", + " }\n", + " # 转换成排序后的 JSON 字符串，再生成 md5 hash\n", + " key_str = json.dumps(key_data, sort_keys=True)\n", + " return hashlib.md5(key_str.encode('utf-8')).hexdigest()\n", + "\n", + "def train_light_model(df, params, feature_columns, callbacks, evals,\n", + " print_feature_importance=True, num_boost_round=100,\n", + " use_optuna=False):\n", + " cache_file = 'light_model.pkl'\n", + " cache_key = generate_key(params, feature_columns, num_boost_round)\n", + "\n", + " # 检查缓存文件是否存在\n", + " if os.path.exists(cache_file):\n", + " try:\n", + " with open(cache_file, 'rb') as f:\n", + " cache_data = pickle.load(f)\n", + " if cache_data.get('key') == cache_key:\n", + " print(\"加载缓存模型...\")\n", + " return cache_data.get('model')\n", + " else:\n", + " print(\"缓存模型的参数与当前参数不匹配，重新训练模型。\")\n", + " except Exception as e:\n", + " print(f\"加载缓存失败: {e}，重新训练模型。\")\n", + " else:\n", + " print(\"未发现缓存模型，开始训练新模型。\")\n", + " # 确保数据按照 date 和 label 排序\n", + " df_sorted = df.sort_values(by=['trade_date', 'label'], ascending=[True, False]) # 按日期升序、标签降序排序\n", + " df_sorted = df_sorted.sort_values(by='trade_date')\n", + " unique_dates = df_sorted['trade_date'].unique()\n", + " val_date_count = int(len(unique_dates) * 0.1)\n", + " val_dates = unique_dates[-val_date_count:]\n", + " val_indices = df_sorted[df_sorted['trade_date'].isin(val_dates)].index\n", + " train_indices = df_sorted[~df_sorted['trade_date'].isin(val_dates)].index\n", + "\n", + " # 获取训练集和验证集的样本\n", + " train_df = df_sorted.iloc[train_indices]\n", + " val_df = df_sorted.iloc[val_indices]\n", + "\n", + " X_train = train_df[feature_columns]\n", + " y_train = train_df['label']\n", + "\n", + " X_val = val_df[feature_columns]\n", + " y_val = val_df['label']\n", + "\n", + " train_data = lgb.Dataset(X_train, label=y_train)\n", + " val_data = lgb.Dataset(X_val, label=y_val)\n", + " if use_optuna:\n", + " # study = optuna.create_study(direction='minimize' if classify else 'maximize')\n", + " study = optuna.create_study(direction='minimize')\n", + " study.optimize(lambda trial: objective(trial, X_train, y_train, num_boost_round, params), n_trials=20)\n", + "\n", + " print(f\"Best parameters: {study.best_trial.params}\")\n", + " print(f\"Best score: {study.best_trial.value}\")\n", + "\n", + " params.update(study.best_trial.params)\n", + " model = lgb.train(\n", + " params, train_data, num_boost_round=num_boost_round,\n", + " valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n", + " callbacks=callbacks\n", + " )\n", + "\n", + " # 打印特征重要性（如果需要）\n", + " if print_feature_importance:\n", + " lgb.plot_metric(evals)\n", + " lgb.plot_tree(model, figsize=(20, 8))\n", + " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", + " plt.show()\n", + " # with open(cache_file, 'wb') as f:\n", + " # pickle.dump({'key': cache_key,\n", + " # 'model': model,\n", + " # 'feature_columns': feature_columns}, f)\n", + " # print(\"模型训练完成并已保存缓存。\")\n", + " return model\n", + "\n", + "\n", + "from catboost import CatBoostRegressor\n", + "import pandas as pd\n", + "\n", + "\n", + "def train_catboost(df, num_boost_round, params=None):\n", + " \"\"\"\n", + " 训练 CatBoost 排序模型\n", + " - df: 包含因子、date、instrument 和 label 的 DataFrame\n", + " - num_boost_round: 训练的轮数\n", + " - print_feature_importance: 是否打印特征重要性\n", + " - plot: 是否绘制特征重要性图\n", + " - split_date: 用于划分训练集和验证集的日期（比如 '2020-01-01'）\n", + "\n", + " 返回训练好的模型\n", + " \"\"\"\n", + " df_sorted = df.sort_values(by=['date', 'label'], ascending=[True, False])\n", + "\n", + " # 提取特征和标签\n", + " feature_columns = [col for col in df.columns if col not in ['date',\n", + " 'instrument',\n", + " 'label']]\n", + " feature_columns = [col for col in feature_columns if 'future' not in col]\n", + " feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "\n", + " df_sorted = df_sorted.sort_values(by='date')\n", + " unique_dates = df_sorted['date'].unique()\n", + " val_date_count = int(len(unique_dates) * 0.1)\n", + " val_dates = unique_dates[-val_date_count:]\n", + " val_indices = df_sorted[df_sorted['date'].isin(val_dates)].index\n", + " train_indices = df_sorted[~df_sorted['date'].isin(val_dates)].index\n", + "\n", + " # 获取训练集和验证集的样本\n", + " train_df = df_sorted.iloc[train_indices].sort_values(by=['date', 'label'], ascending=[True, False])\n", + " val_df = df_sorted.iloc[val_indices].sort_values(by=['date', 'label'], ascending=[True, False])\n", + "\n", + " X_train = train_df[feature_columns]\n", + " y_train = train_df['label']\n", + "\n", + " X_val = val_df[feature_columns]\n", + " y_val = val_df['label']\n", + "\n", + " model = CatBoostRegressor(**params, iterations=num_boost_round)\n", + " model.fit(X_train,\n", + " y_train,\n", + " eval_set=(X_val, y_val))\n", + "\n", + " return model" + ], + "id": "8f134d435f71e9e2", + "outputs": [], + "execution_count": 14 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:56:05.576927Z", + "start_time": "2025-02-09T14:56:05.480695Z" + } + }, + "cell_type": "code", + "source": [ + "light_params = {\n", + " 'objective': 'regression',\n", + " 'metric': 'l2',\n", + " 'learning_rate': 0.05,\n", + " 'is_unbalance': True,\n", + " 'num_leaves': 2048,\n", + " 'min_data_in_leaf': 16,\n", + " 'max_depth': 32,\n", + " 'max_bin': 1024,\n", + " 'nthread': 2,\n", + " 'feature_fraction': 0.7,\n", + " 'bagging_fraction': 0.7,\n", + " 'bagging_freq': 5,\n", + " 'lambda_l1': 80,\n", + " 'lambda_l2': 65,\n", + " 'verbosity': -1\n", + "}" + ], + "id": "4a4542e1ed6afe7d", + "outputs": [], + "execution_count": 15 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:57:25.341222Z", + "start_time": "2025-02-09T14:56:05.640256Z" + } + }, + "cell_type": "code", + "source": [ + "print('train data size: ', len(train_data))\n", + "df = train_data\n", + "\n", + "evals = {}\n", + "light_model = train_light_model(train_data, light_params, feature_columns,\n", + " [lgb.log_evaluation(period=500),\n", + " lgb.callback.record_evaluation(evals),\n", + " lgb.early_stopping(50, first_metric_only=True)\n", + " ], evals,\n", + " num_boost_round=1000, use_optuna=False,\n", + " print_feature_importance=False)" + ], + "id": "beeb098799ecfa6a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 875004\n", + "未发现缓存模型，开始训练新模型。\n", + "Training until validation scores don't improve for 50 rounds\n", + "Early stopping, best iteration is:\n", + "[378]\ttrain's l2: 0.435049\tvalid's l2: 0.589178\n", + "Evaluated only: l2\n" + ] + } + ], + "execution_count": 16 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:57:27.394697Z", + "start_time": "2025-02-09T14:57:25.373274Z" + } + }, + "cell_type": "code", + "source": [ + "test_data['score'] = light_model.predict(test_data[feature_columns])\n", + "predictions = test_data.loc[test_data.groupby('trade_date')['score'].idxmax()]" + ], + "id": "5bb96ca8492e74d", + "outputs": [], + "execution_count": 17 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-09T14:57:27.489570Z", + "start_time": "2025-02-09T14:57:27.397368Z" + } + }, + "cell_type": "code", + "source": "predictions[['trade_date', 'score', 'ts_code']].to_csv('predictions.csv', index=False)", + "id": "5d1522a7538db91b", + "outputs": [], + "execution_count": 18 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/best_model.pth b/code/train/best_model.pth new file mode 100644 index 0000000..69fa32d Binary files /dev/null and b/code/train/best_model.pth differ diff --git a/code/train/catboost_info/catboost_training.json b/code/train/catboost_info/catboost_training.json new file mode 100644 index 0000000..b4e25f8 --- /dev/null +++ b/code/train/catboost_info/catboost_training.json @@ -0,0 +1,5004 @@ +{ 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"metadata": {}, + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "def get_technical_factor(df):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)\n", + " df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)\n", + "\n", + " # 因子 1：短期成交量变化率\n", + " df['volume_change_rate'] = (\n", + " grouped['vol'].rolling(window=2).mean() /\n", + " grouped['vol'].rolling(window=5).mean() - 1\n", + " ).reset_index(level=0, drop=True) # 确保索引对齐\n", + "\n", + " # 因子 2：成交量突破信号\n", + " max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐\n", + " df['cat_volume_breakout'] = (df['vol'] > max_volume)\n", + "\n", + " # 因子 3：换手率均线偏离度\n", + " mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + " std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)\n", + " df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover\n", + "\n", + " # 因子 4：换手率激增信号\n", + " df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)\n", + "\n", + " # 因子 5：量比均值\n", + " df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)\n", + "\n", + " # 因子 6：量比突破信号\n", + " max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)\n", + " df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)\n", + "\n", + " # 因子 7：成交量与换手率的综合动量因子\n", + " alpha = 0.5\n", + " df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']\n", + "\n", + " # 因子 8：量价共振因子\n", + " df['price_change_rate'] = grouped['close'].pct_change()\n", + " df['resonance_factor'] = df['volume_ratio'] * df['price_change_rate']\n", + "\n", + " # 计算 up 和 down\n", + " df['log_close'] = np.log(df['close'])\n", + "\n", + " df['vol_spike'] = grouped.apply(\n", + " lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)\n", + " )\n", + " df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']\n", + " df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()\n", + "\n", + " df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n", + " df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n", + "\n", + " # 计算 ATR\n", + " df['atr_14'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),\n", + " index=x.index)\n", + " )\n", + " df['atr_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),\n", + " index=x.index)\n", + " )\n", + "\n", + " # 计算 OBV 及其均线\n", + " df['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " df['maobv_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n", + "\n", + " # 计算 RSI\n", + " df['rsi_3'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n", + " )\n", + " df['rsi_6'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n", + " )\n", + " df['rsi_9'] = grouped.apply(\n", + " lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n", + " )\n", + "\n", + " # 计算 return_10 和 return_20\n", + " df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n", + " df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n", + "\n", + " # 计算标准差指标\n", + " df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n", + " df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n", + " df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n", + " df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n", + " df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n", + "\n", + " # 计算比值指标\n", + " df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n", + " df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n", + "\n", + " # 计算标准差差值\n", + " df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_act_factor(df, cat=True):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + " # 计算 EMA 指标\n", + " df['_ema_5'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n", + " )\n", + " df['_ema_13'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n", + " )\n", + " df['_ema_20'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n", + " )\n", + " df['_ema_60'] = grouped['close'].apply(\n", + " lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n", + " )\n", + "\n", + " # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n", + " df['act_factor1'] = grouped['_ema_5'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n", + " )\n", + " df['act_factor2'] = grouped['_ema_13'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n", + " )\n", + " df['act_factor3'] = grouped['_ema_20'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n", + " )\n", + " df['act_factor4'] = grouped['_ema_60'].apply(\n", + " lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n", + " )\n", + "\n", + " if cat:\n", + " df['cat_af1'] = df['act_factor1'] > 0\n", + " df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n", + " df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n", + " df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n", + "\n", + " # 计算 act_factor5 和 act_factor6\n", + " df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n", + " df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(\n", + " df['act_factor1'] ** 2 + df['act_factor2'] ** 2)\n", + "\n", + " # 根据 trade_date 截面计算排名\n", + " df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n", + " df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_money_flow_factor(df):\n", + " # 计算资金流相关因子（字段名称见 tushare 数据说明）\n", + " df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n", + " df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n", + "\n", + " df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n", + " df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n", + "\n", + " df['log(circ_mv)'] = np.log(df['circ_mv'])\n", + " return df\n", + "\n", + "\n", + "def get_alpha_factor(df):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code')\n", + "\n", + " # alpha_022: 当前 close 与 5 日前 close 差值\n", + " df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))\n", + "\n", + " # alpha_003: (close - open) / (high - low)\n", + " df['alpha_003'] = np.where(df['high'] != df['low'],\n", + " (df['close'] - df['open']) / (df['high'] - df['low']),\n", + " 0)\n", + "\n", + " # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名\n", + " df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n", + " df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n", + "\n", + " # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名\n", + " df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n", + " df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_limit_factor(df):\n", + " # 按股票和日期排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 分组处理\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # 1. 今日是否涨停/跌停\n", + " df['cat_up_limit'] = (df['close'] == df['up_limit']).astype(int) # 是否涨停（1表示涨停，0表示未涨停）\n", + " df['cat_down_limit'] = (df['close'] == df['down_limit']).astype(int) # 是否跌停（1表示跌停，0表示未跌停）\n", + "\n", + " # 2. 最近涨跌停次数（过去20个交易日）\n", + " df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + " df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,\n", + " drop=True)\n", + "\n", + " # 3. 最近连续涨跌停天数\n", + " def calculate_consecutive_limits(series):\n", + " \"\"\"\n", + " 计算连续涨停/跌停天数。\n", + " \"\"\"\n", + " consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)\n", + " return consecutive_up, consecutive_down\n", + "\n", + " # 连续涨停天数\n", + " df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(\n", + " lambda x: calculate_consecutive_limits(x)[0]\n", + " ).reset_index(level=0, drop=True)\n", + "\n", + " # 连续跌停天数\n", + " # df['consecutive_down_limit'] = grouped['cat_down_limit'].apply(\n", + " # lambda x: calculate_consecutive_limits(x)[1]\n", + " # ).reset_index(level=0, drop=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_cyp_perf_factor(df):\n", + " # 预处理：按股票代码和时间排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 按股票代码分组处理\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])\n", + "\n", + " df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])\n", + "\n", + " df['lock_factor'] = df['turnover_rate'] * (\n", + " 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))\n", + "\n", + " df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n", + "\n", + " df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))\n", + "\n", + " def rolling_corr(group):\n", + " roc_close = group['close'].pct_change()\n", + " roc_weight = group['weight_avg'].pct_change()\n", + " return roc_close.rolling(10).corr(roc_weight)\n", + "\n", + " df['price_cost_divergence'] = grouped.apply(rolling_corr)\n", + "\n", + " def calc_atr(group):\n", + " high, low, close = group['high'], group['low'], group['close']\n", + " tr = np.maximum(high - low,\n", + " np.maximum(abs(high - close.shift()),\n", + " abs(low - close.shift())))\n", + " return tr.rolling(14).mean()\n", + "\n", + " df['atr_14'] = grouped.apply(calc_atr)\n", + " df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']\n", + "\n", + " # 12. 小盘股筹码集中度\n", + " df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])\n", + "\n", + " # 16. 筹码稳定性指数 (20日波动率)\n", + " df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())\n", + " df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())\n", + "\n", + " # 17. 成本区间突破标记\n", + " df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())\n", + "\n", + " # 18. 黄金筹码共振 (复合事件)\n", + " df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &\n", + " (df['volume_ratio'] > 1.5) &\n", + " (df['winner_rate'] > 0.7))\n", + "\n", + " # 20. 筹码-流动性风险\n", + " df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (\n", + " 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))\n", + "\n", + " df.drop(columns=['weight_std20'], inplace=True, errors='ignore')\n", + "\n", + " return df\n", + "\n", + "\n", + "def get_mv_factors(df):\n", + " \"\"\"\n", + " 计算多个因子并生成最终的综合因子。\n", + "\n", + " 参数:\n", + " df (pd.DataFrame): 包含 ts_code, trade_date, turnover_rate, pe_ttm, pb, ps, circ_mv, volume_ratio, vol 等列的数据框。\n", + "\n", + " 返回:\n", + " pd.DataFrame: 包含新增因子和最终综合因子的数据框。\n", + " \"\"\"\n", + " # 按 ts_code 和 trade_date 排序\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 按 ts_code 分组\n", + " grouped = df.groupby('ts_code', group_keys=False)\n", + "\n", + " # 1. 市值流动比因子\n", + " df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']\n", + "\n", + " # 2. 市值调整成交量因子\n", + " df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " # 3. 市值加权换手率因子\n", + " df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])\n", + "\n", + " # 4. 非线性市值成交量因子\n", + " df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']\n", + "\n", + " # 5. 市值量比因子\n", + " df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']\n", + "\n", + " # 6. 市值动量因子\n", + " df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']\n", + "\n", + " # 7. 市值波动率因子\n", + " df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)\n", + " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " # 8. 市值成长性因子\n", + " df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)\n", + " df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)\n", + "\n", + " # # 标准化因子\n", + " # factor_columns = [\n", + " # 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover',\n", + " # 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum',\n", + " # 'mv_volatility', 'mv_growth'\n", + " # ]\n", + " # scaler = StandardScaler()\n", + " # df[factor_columns] = scaler.fit_transform(df[factor_columns])\n", + " #\n", + " # # 加权合成因子\n", + " # weights = [0.2, 0.15, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1] # 各因子权重\n", + " # df['final_combined_factor'] = df[factor_columns].dot(weights)\n", + "\n", + " return df" + ], + "id": "505e825945e4b8cf" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/code/train/predictions.tsv b/code/train/predictions.tsv new file mode 100644 index 0000000..8b9bdff --- /dev/null +++ b/code/train/predictions.tsv @@ -0,0 +1,1359 @@ 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df['cat_volume_breakout'] = (df['vol'] > max_volume) + + # 因子 3：换手率均线偏离度 + mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True) + std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True) + df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover + + # 因子 4：换手率激增信号 + df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover) + + # 因子 5：量比均值 + df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True) + + # 因子 6：量比突破信号 + max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True) + df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio) + + # 因子 7：成交量与换手率的综合动量因子 + alpha = 0.5 + df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation'] + + # 因子 8：量价共振因子 + df['price_change_rate'] = grouped['close'].pct_change() + df['resonance_factor'] = df['volume_ratio'] * df['price_change_rate'] + + # 计算 up 和 down + df['log_close'] = np.log(df['close']) + + df['vol_spike'] = grouped.apply( + lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index) + ) + df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike'] + df['vol_std_5'] = df['vol'].pct_change().rolling(5).std() + + df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close'] + df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close'] + + # 计算 ATR + df['atr_14'] = grouped.apply( + lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), + index=x.index) + ) + df['atr_6'] = grouped.apply( + lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), + index=x.index) + ) + + # 计算 OBV 及其均线 + df['obv'] = grouped.apply( + lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index) + ) + df['maobv_6'] = grouped.apply( + lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index) + ) + df['obv-maobv_6'] = df['obv'] - df['maobv_6'] + + # 计算 RSI + df['rsi_3'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index) + ) + df['rsi_6'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index) + ) + df['rsi_9'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index) + ) + + # 计算 return_10 和 return_20 + df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1) + df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1) + df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1) + + # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x) + + # 计算标准差指标 + df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std()) + df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std()) + df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std()) + df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std()) + df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std()) + + # 计算比值指标 + df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90'] + df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25'] + + # 计算标准差差值 + df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2'] + + return df + + +def get_act_factor(df, cat=True): + # 按股票和日期排序 + df = df.sort_values(by=['ts_code', 'trade_date']) + grouped = df.groupby('ts_code', group_keys=False) + # 计算 EMA 指标 + df['_ema_5'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index) + ) + df['_ema_13'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index) + ) + df['_ema_20'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index) + ) + df['_ema_60'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index) + ) + + # 计算 act_factor1, act_factor2, act_factor3, act_factor4 + df['act_factor1'] = grouped['_ema_5'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50 + ) + df['act_factor2'] = grouped['_ema_13'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40 + ) + df['act_factor3'] = grouped['_ema_20'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21 + ) + df['act_factor4'] = grouped['_ema_60'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10 + ) + + if cat: + df['cat_af1'] = df['act_factor1'] > 0 + df['cat_af2'] = df['act_factor2'] > df['act_factor1'] + df['cat_af3'] = df['act_factor3'] > df['act_factor2'] + df['cat_af4'] = df['act_factor4'] > df['act_factor3'] + + # 计算 act_factor5 和 act_factor6 + df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4'] + df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt( + df['act_factor1'] ** 2 + df['act_factor2'] ** 2) + + # 根据 trade_date 截面计算排名 + df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True) + df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True) + df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True) + + return df + + +def get_money_flow_factor(df): + # 计算资金流相关因子（字段名称见 tushare 数据说明） + df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol'] + df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol'] + df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol'] + + df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol'] + df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol'] + + df['log(circ_mv)'] = np.log(df['circ_mv']) + return df + + +def get_alpha_factor(df): + df = df.sort_values(by=['ts_code', 'trade_date']) + grouped = df.groupby('ts_code') + + # alpha_022: 当前 close 与 5 日前 close 差值 + # df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5)) + def rolling_covariance(x, y, window): + return x.rolling(window).cov(y) + + def delta(series, period): + return series.diff(period) + + def rank(series): + return series.rank(pct=True) + + def stddev(series, window): + return series.rolling(window).std() + + # 计算改进后的 Alpha 22 因子 + window_high_volume = 5 + window_close_stddev = 20 + period_delta = 5 + + df['cov'] = rolling_covariance(df['high'], df['volume'], window_high_volume) + df['delta_cov'] = delta(df['cov'], period_delta) + df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev)) + df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev'] + + # alpha_003: (close - open) / (high - low) + df['alpha_003'] = np.where(df['high'] != df['low'], + (df['close'] - df['open']) / (df['high'] - df['low']), + 0) + + # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名 + df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True) + df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True) + + # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名 + df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum()) + df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True) + + return df + + +def get_limit_factor(df): + # 按股票和日期排序 + df = df.sort_values(by=['ts_code', 'trade_date']) + + # 分组处理 + grouped = df.groupby('ts_code', group_keys=False) + + # 1. 今日是否涨停/跌停 + df['cat_up_limit'] = (df['close'] == df['up_limit']).astype(int) # 是否涨停（1表示涨停，0表示未涨停） + df['cat_down_limit'] = (df['close'] == df['down_limit']).astype(int) # 是否跌停（1表示跌停，0表示未跌停） + + # 2. 最近涨跌停次数（过去20个交易日） + df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, + drop=True) + df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, + drop=True) + + # 3. 最近连续涨跌停天数 + def calculate_consecutive_limits(series): + """ + 计算连续涨停/跌停天数。 + """ + consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) + consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) + return consecutive_up, consecutive_down + + # 连续涨停天数 + df['consecutive_up_limit'] = grouped['cat_up_limit'].apply( + lambda x: calculate_consecutive_limits(x)[0] + ).reset_index(level=0, drop=True) + + # 连续跌停天数 + # df['consecutive_down_limit'] = grouped['cat_down_limit'].apply( + # lambda x: calculate_consecutive_limits(x)[1] + # ).reset_index(level=0, drop=True) + + return df + + +def get_cyp_perf_factor(df): + # 预处理：按股票代码和时间排序 + df = df.sort_values(by=['ts_code', 'trade_date']) + + # 按股票代码分组处理 + grouped = df.groupby('ts_code', group_keys=False) + + df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low']) + + df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct']) + + df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct']) + + df['lock_factor'] = df['turnover_rate'] * ( + 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low'])) + + df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0) + + df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5)) + + def rolling_corr(group): + roc_close = group['close'].pct_change() + roc_weight = group['weight_avg'].pct_change() + return roc_close.rolling(10).corr(roc_weight) + + df['price_cost_divergence'] = grouped.apply(rolling_corr) + + def calc_atr(group): + high, low, close = group['high'], group['low'], group['close'] + tr = np.maximum(high - low, + np.maximum(abs(high - close.shift()), + abs(low - close.shift()))) + return tr.rolling(14).mean() + + df['atr_14'] = grouped.apply(calc_atr) + df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14'] + + # 12. 小盘股筹码集中度 + df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct']) + + # 16. 筹码稳定性指数 (20日波动率) + df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std()) + df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean()) + + # 17. 成本区间突破标记 + df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum()) + + # 18. 黄金筹码共振 (复合事件) + df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) & + (df['volume_ratio'] > 1.5) & + (df['winner_rate'] > 0.7)) + + # 20. 筹码-流动性风险 + df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * ( + 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean())) + + df.drop(columns=['weight_std20'], inplace=True, errors='ignore') + + return df + + +def get_mv_factors(df): + """ + 计算多个因子并生成最终的综合因子。 + + 参数: + df (pd.DataFrame): 包含 ts_code, trade_date, turnover_rate, pe_ttm, pb, ps, circ_mv, volume_ratio, vol 等列的数据框。 + + 返回: + pd.DataFrame: 包含新增因子和最终综合因子的数据框。 + """ + # 按 ts_code 和 trade_date 排序 + df = df.sort_values(by=['ts_code', 'trade_date']) + + # 按 ts_code 分组 + grouped = df.groupby('ts_code', group_keys=False) + + # 1. 市值流动比因子 + df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv'] + + # 2. 市值调整成交量因子 + df['mv_adjusted_volume'] = df['vol'] / df['circ_mv'] + + # 3. 市值加权换手率因子 + df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv']) + + # 4. 非线性市值成交量因子 + df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv'] + + # 5. 市值量比因子 + df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv'] + + # 6. 市值动量因子 + df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv'] + + # 7. 市值波动率因子 + df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True) + df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True) + + # 8. 市值成长性因子 + df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True) + df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True) + + # # 标准化因子 + # factor_columns = [ + # 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', + # 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', + # 'mv_volatility', 'mv_growth' + # ] + # scaler = StandardScaler() + # df[factor_columns] = scaler.fit_transform(df[factor_columns]) + # + # # 加权合成因子 + # weights = [0.2, 0.15, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1] # 各因子权重 + # df['final_combined_factor'] = df[factor_columns].dot(weights) + + return df + + +import numpy as np +import talib + + +def get_rolling_factor(df): + old_columns = df.columns.tolist()[:] + # 按股票和日期排序 + df = df.sort_values(by=['ts_code', 'trade_date']) + grouped = df.groupby('ts_code', group_keys=False) + + df["gap_next_open"] = (df["open"].shift(-1) - df["close"]) / df["close"] + + df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True) + df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True) + + # 因子 1：短期成交量变化率 + df['volume_change_rate'] = ( + grouped['vol'].rolling(window=2).mean() / + grouped['vol'].rolling(window=10).mean() - 1 + ).reset_index(level=0, drop=True) # 确保索引对齐 + + # 因子 2：成交量突破信号 + max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐 + df['cat_volume_breakout'] = (df['vol'] > max_volume) + + # 因子 3：换手率均线偏离度 + mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True) + std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True) + df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover + + # 因子 4：换手率激增信号 + df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover) + + # 因子 5：量比均值 + df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True) + + # 因子 6：量比突破信号 + max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True) + df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio) + + df['vol_spike'] = grouped.apply( + lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index) + ) + df['vol_std_5'] = df['vol'].pct_change().rolling(5).std() + + # 计算 ATR + df['atr_14'] = grouped.apply( + lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), + index=x.index) + ) + df['atr_6'] = grouped.apply( + lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), + index=x.index) + ) + + # 计算 OBV 及其均线 + df['obv'] = grouped.apply( + lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index) + ) + df['maobv_6'] = grouped.apply( + lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index) + ) + + df['rsi_3'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index) + ) + df['rsi_6'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index) + ) + df['rsi_9'] = grouped.apply( + lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index) + ) + + # 计算 return_10 和 return_20 + df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1) + df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1) + df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1) + + # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x) + + # 计算标准差指标 + df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std()) + df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std()) + df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std()) + df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std()) + df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std()) + + # 计算 EMA 指标 + df['_ema_5'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index) + ) + df['_ema_13'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index) + ) + df['_ema_20'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index) + ) + df['_ema_60'] = grouped['close'].apply( + lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index) + ) + + # 计算 act_factor1, act_factor2, act_factor3, act_factor4 + df['act_factor1'] = grouped['_ema_5'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50 + ) + df['act_factor2'] = grouped['_ema_13'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40 + ) + df['act_factor3'] = grouped['_ema_20'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21 + ) + df['act_factor4'] = grouped['_ema_60'].apply( + lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10 + ) + + # 根据 trade_date 截面计算排名 + df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True) + df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True) + df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True) + + df['log(circ_mv)'] = np.log(df['circ_mv']) + + def rolling_covariance(x, y, window): + return x.rolling(window).cov(y) + + def delta(series, period): + return series.diff(period) + + def rank(series): + return series.rank(pct=True) + + def stddev(series, window): + return series.rolling(window).std() + + window_high_volume = 5 + window_close_stddev = 20 + period_delta = 5 + df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume) + df['delta_cov'] = delta(df['cov'], period_delta) + df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev)) + df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev'] + + df['alpha_003'] = np.where(df['high'] != df['low'], + (df['close'] - df['open']) / (df['high'] - df['low']), + 0) + + df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True) + df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True) + + df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum()) + df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True) + + df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停） + df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停） + df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, + drop=True) + df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, + drop=True) + + # 3. 最近连续涨跌停天数 + def calculate_consecutive_limits(series): + """ + 计算连续涨停/跌停天数。 + """ + consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) + consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) + return consecutive_up, consecutive_down + + # 连续涨停天数 + df['consecutive_up_limit'] = grouped['cat_up_limit'].apply( + lambda x: calculate_consecutive_limits(x)[0] + ).reset_index(level=0, drop=True) + + df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0) + + df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5)) + + def rolling_corr(group): + roc_close = group['close'].pct_change() + roc_weight = group['weight_avg'].pct_change() + return roc_close.rolling(10).corr(roc_weight) + + df['price_cost_divergence'] = grouped.apply(rolling_corr) + + df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct']) + + # 16. 筹码稳定性指数 (20日波动率) + df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std()) + df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean()) + + # 17. 成本区间突破标记 + df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum()) + + # 20. 筹码-流动性风险 + df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * ( + 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean())) + + # 7. 市值波动率因子 + df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True) + df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True) + + # 8. 市值成长性因子 + df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True) + df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True) + + df.drop(columns=['weight_std20'], inplace=True, errors='ignore') + new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns] + + return df, new_columns + + +def get_simple_factor(df): + old_columns = df.columns.tolist()[:] + df = df.sort_values(by=['ts_code', 'trade_date']) + + alpha = 0.5 + df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation'] + df['resonance_factor'] = df['volume_ratio'] * df['pct_chg'] + df['log_close'] = np.log(df['close']) + + df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike'] + + df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close'] + df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close'] + + df['obv-maobv_6'] = df['obv'] - df['maobv_6'] + + # 计算比值指标 + df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90'] + df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25'] + + # 计算标准差差值 + df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2'] + + df['cat_af1'] = df['act_factor1'] > 0 + df['cat_af2'] = df['act_factor2'] > df['act_factor1'] + df['cat_af3'] = df['act_factor3'] > df['act_factor2'] + df['cat_af4'] = df['act_factor4'] > df['act_factor3'] + + # 计算 act_factor5 和 act_factor6 + df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4'] + df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt( + df['act_factor1'] ** 2 + df['act_factor2'] ** 2) + + df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol'] + df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol'] + df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol'] + + df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol'] + df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol'] + + df['log(circ_mv)'] = np.log(df['circ_mv']) + + df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low']) + + df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct']) + + df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct']) + + df['lock_factor'] = df['turnover_rate'] * ( + 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low'])) + + df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2) + + df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14'] + + # 12. 小盘股筹码集中度 + df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct']) + + df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) & + (df['volume_ratio'] > 1.5) & + (df['winner_rate'] > 0.7)) + + df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv'] + + df['mv_adjusted_volume'] = df['vol'] / df['circ_mv'] + + df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv']) + + df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv'] + + df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv'] + + df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv'] + + drop_columns = [col for col in df.columns if col.startswith('_')] + df.drop(columns=drop_columns, inplace=True, errors='ignore') + + new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns] + return df, new_columns + + +def calculate_indicators(df): + """ + 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。 + """ + df = df.sort_values('trade_date') + df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100 + # df['5_day_ma'] = df['close'].rolling(window=5).mean() + delta = df['close'].diff() + gain = delta.where(delta > 0, 0) + loss = -delta.where(delta < 0, 0) + avg_gain = gain.rolling(window=14).mean() + avg_loss = loss.rolling(window=14).mean() + rs = avg_gain / avg_loss + df['RSI'] = 100 - (100 / (1 + rs)) + + # 计算MACD + ema12 = df['close'].ewm(span=12, adjust=False).mean() + ema26 = df['close'].ewm(span=26, adjust=False).mean() + df['MACD'] = ema12 - ema26 + df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean() + df['MACD_hist'] = df['MACD'] - df['Signal_line'] + + # 4. 情绪因子1：市场上涨比例（Up Ratio） + df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0) + df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例 + + # 5. 情绪因子2：成交量变化率（Volume Change Rate） + df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量 + df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率 + + # 6. 情绪因子3：波动率（Volatility） + df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差 + + # 7. 情绪因子4：成交额变化率（Amount Change Rate） + df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额 + df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率 + + return df + + +def generate_index_indicators(h5_filename): + df = pd.read_hdf(h5_filename, key='index_data') + df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + df = df.sort_values('trade_date') + + # 计算每个ts_code的相关指标 + df_indicators = [] + for ts_code in df['ts_code'].unique(): + df_index = df[df['ts_code'] == ts_code].copy() + df_index = calculate_indicators(df_index) + df_indicators.append(df_index) + + # 合并所有指数的结果 + df_all_indicators = pd.concat(df_indicators, ignore_index=True) + + # 保留trade_date列，并将同一天的数据按ts_code合并成一行 + df_final = df_all_indicators.pivot_table( + index='trade_date', + columns='ts_code', + values=['daily_return', 'RSI', 'MACD', 'Signal_line', + 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility', + 'amount_change_rate', 'amount_mean'], + aggfunc='last' + ) + + df_final.columns = [f"{col[1]}_{col[0]}" for col in df_final.columns] + df_final = df_final.reset_index() + + return df_final \ No newline at end of file