diff --git a/.gitignore b/.gitignore index 45b7721..d5a5cc8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ /data/ +/data-copy/ **/.ipynb_checkpoints/ /.virtual_documents/ /stocks_list.csv diff --git a/code/data/index_and_industry.ipynb b/code/data/index_and_industry.ipynb index 3a60e46..ba6435f 100644 --- a/code/data/index_and_industry.ipynb +++ b/code/data/index_and_industry.ipynb @@ -2,15 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:29.087509Z", - "start_time": "2025-04-06T15:33:28.293879Z" + "end_time": "2025-04-08T13:37:01.282302Z", + "start_time": "2025-04-08T13:37:00.318950Z" } }, - "outputs": [], "source": [ "from operator import index\n", "\n", @@ -20,35 +18,19 @@ "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "f448da220816bf98", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:32.756495Z", - "start_time": "2025-04-06T15:33:29.097180Z" + "end_time": "2025-04-08T13:37:02.801052Z", + "start_time": "2025-04-08T13:37:01.292316Z" } }, - "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_26824\\1832869062.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" - ] - } - ], "source": [ "# 定义四个指数\n", "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n", @@ -68,60 +50,78 @@ "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\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "907f732d3c397bf", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:32.795003Z", - "start_time": "2025-04-06T15:33:32.758127Z" - } - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close open high low \\\n", - "0 000905.SH 20250407 5287.0333 5523.9636 5587.8502 5212.6773 \n", - "1 000905.SH 20250403 5845.5045 5842.6167 5906.7057 5817.9662 \n", - "2 000905.SH 20250402 5899.0865 5884.8925 5936.6467 5884.1126 \n", - "3 000905.SH 20250401 5892.8502 5870.9424 5931.5038 5867.8480 \n", - "4 000905.SH 20250331 5857.7721 5886.9560 5908.3026 5802.4187 \n", - "... ... ... ... ... ... ... \n", - "13438 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", - "13439 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", - "13440 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", - "13441 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", - "13442 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", - "\n", - " pre_close change pct_chg vol amount \n", - "0 5845.5045 -558.4712 -9.5539 2.365227e+08 2.673974e+08 \n", - "1 5899.0865 -53.5820 -0.9083 1.349386e+08 1.736621e+08 \n", - "2 5892.8502 6.2363 0.1058 1.121600e+08 1.406421e+08 \n", - "3 5857.7721 35.0781 0.5988 1.364486e+08 1.793280e+08 \n", - "4 5916.0314 -58.2593 -0.9848 1.542561e+08 1.895634e+08 \n", - "... ... ... ... ... ... \n", - "13438 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", - "13439 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", - "13440 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", - "13441 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", - "13442 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", - "\n", - "[13443 rows x 11 columns]\n" + "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_25240\\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": "2025-04-08T13:37:03.042004Z", + "start_time": "2025-04-08T13:37:03.007507Z" + } + }, "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 20250408 5326.9140 5279.7566 5371.1834 5249.2318 \n", + "1 000905.SH 20250407 5287.0333 5523.9636 5587.8502 5212.6773 \n", + "2 000905.SH 20250403 5845.5045 5842.6167 5906.7057 5817.9662 \n", + "3 000905.SH 20250402 5899.0865 5884.8925 5936.6467 5884.1126 \n", + "4 000905.SH 20250401 5892.8502 5870.9424 5931.5038 5867.8480 \n", + "... ... ... ... ... ... ... \n", + "13441 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", + "13442 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", + "13443 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", + "13444 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", + "13445 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "\n", + " pre_close change pct_chg vol amount \n", + "0 5287.0333 39.8807 0.7543 2.238407e+08 2.618753e+08 \n", + "1 5845.5045 -558.4712 -9.5539 2.365227e+08 2.673974e+08 \n", + "2 5899.0865 -53.5820 -0.9083 1.349386e+08 1.736621e+08 \n", + "3 5892.8502 6.2363 0.1058 1.121600e+08 1.406421e+08 \n", + "4 5857.7721 35.0781 0.5988 1.364486e+08 1.793280e+08 \n", + "... ... ... ... ... ... \n", + "13441 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", + "13442 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", + "13443 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", + "13444 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", + "13445 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + "\n", + "[13446 rows x 11 columns]\n" + ] + } + ], + "execution_count": 3 } ], "metadata": { diff --git a/code/data/update/cyq-perf.ipynb b/code/data/update/cyq-perf.ipynb index 3af5515..b0abf17 100644 --- a/code/data/update/cyq-perf.ipynb +++ b/code/data/update/cyq-perf.ipynb @@ -2,55 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:48.836794Z", - "start_time": "2025-04-06T15:33:48.098706Z" + "end_time": "2025-04-08T13:37:05.760051Z", + "start_time": "2025-04-08T13:37:04.957429Z" } }, - "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:55.800360Z", - "start_time": "2025-04-06T15:33:49.011404Z" + "end_time": "2025-04-08T13:37:11.825892Z", + "start_time": "2025-04-08T13:37:05.764874Z" } }, - "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", - "21567 920108.BJ 20250401\n", - "21568 920111.BJ 20250401\n", - "21569 920116.BJ 20250401\n", - "21570 920118.BJ 20250401\n", - "21571 920128.BJ 20250401\n", - "\n", - "[7551938 rows x 2 columns]\n", - "20250403\n", - "start_date: 20250407\n" - ] - } - ], "source": [ "import pandas as pd\n", "import time\n", @@ -69,36 +44,42 @@ "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": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:57.293636Z", - "start_time": "2025-04-06T15:33:55.806283Z" - } - }, + ], "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" + " 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", + "5386 920108.BJ 20250407\n", + "5387 920111.BJ 20250407\n", + "5388 920116.BJ 20250407\n", + "5389 920118.BJ 20250407\n", + "5390 920128.BJ 20250407\n", + "\n", + "[7557329 rows x 2 columns]\n", + "20250407\n", + "start_date: 20250408\n" ] } ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "747acc47-0884-4f76-90fb-276f6494e31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T13:37:13.619472Z", + "start_time": "2025-04-08T13:37:12.062668Z" + } + }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -128,27 +109,35 @@ " 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": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:57.874278Z", - "start_time": "2025-04-06T15:33:57.304371Z" - } - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "所有每日基础数据获取并保存完毕！\n" + "任务 20250417 完成\n", + "任务 20250418 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" ] } ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "c6765638-481f-40d8-a259-2e7b25362618", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T13:37:15.761420Z", + "start_time": "2025-04-08T13:37:13.644113Z" + } + }, "source": [ "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", "\n", @@ -158,7 +147,17 @@ "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": { diff --git a/code/data/update/sw_daily.ipynb b/code/data/update/sw_daily.ipynb index 3201293..f87c614 100644 --- a/code/data/update/sw_daily.ipynb +++ b/code/data/update/sw_daily.ipynb @@ -2,55 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:36.424263Z", - "start_time": "2025-04-06T15:33:36.048334Z" + "end_time": "2025-04-08T13:37:06.782320Z", + "start_time": "2025-04-08T13:37:06.021497Z" } }, - "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:37.871314Z", - "start_time": "2025-04-06T15:33:36.424263Z" + "end_time": "2025-04-08T13:37:08.283747Z", + "start_time": "2025-04-08T13:37:06.788327Z" } }, - "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", - "1751 859811.SI 20250401\n", - "1752 859821.SI 20250401\n", - "1753 859822.SI 20250401\n", - "1754 859852.SI 20250401\n", - "1755 859951.SI 20250401\n", - "\n", - "[1057124 rows x 2 columns]\n", - "20250403\n", - "start_date: 20250407\n" - ] - } - ], "source": [ "import pandas as pd\n", "import time\n", @@ -69,36 +44,42 @@ "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": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:40.123037Z", - "start_time": "2025-04-06T15:33:37.881433Z" - } - }, + ], "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 20250409 完成\n", - "任务 20250410 完成\n", - "任务 20250408 完成\n", - "任务 20250407 完成\n" + " 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", + "434 859811.SI 20250407\n", + "435 859821.SI 20250407\n", + "436 859822.SI 20250407\n", + "437 859852.SI 20250407\n", + "438 859951.SI 20250407\n", + "\n", + "[1057563 rows x 2 columns]\n", + "20250407\n", + "start_date: 20250408\n" ] } ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "747acc47-0884-4f76-90fb-276f6494e31d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T13:37:09.658356Z", + "start_time": "2025-04-08T13:37:08.473145Z" + } + }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -128,27 +109,35 @@ " 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": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:40.286614Z", - "start_time": "2025-04-06T15:33:40.138227Z" - } - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "所有每日基础数据获取并保存完毕！\n" + "任务 20250418 完成\n", + "任务 20250417 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" ] } ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "c6765638-481f-40d8-a259-2e7b25362618", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T13:37:09.826834Z", + "start_time": "2025-04-08T13:37:09.671846Z" + } + }, "source": [ "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", "\n", @@ -158,7 +147,17 @@ "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": { diff --git a/code/data/update/update_daily_basic.ipynb b/code/data/update/update_daily_basic.ipynb index 3077b76..a82c480 100644 --- a/code/data/update/update_daily_basic.ipynb +++ b/code/data/update/update_daily_basic.ipynb @@ -2,32 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:43.537483Z", - "start_time": "2025-04-06T15:33:42.844004Z" + "end_time": "2025-04-08T13:37:08.050676Z", + "start_time": "2025-04-08T13:37:07.328483Z" } }, - "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "14671a7f72de2564", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:45.387772Z", - "start_time": "2025-04-06T15:33:43.537483Z" + "end_time": "2025-04-08T13:37:10.251715Z", + "start_time": "2025-04-08T13:37:08.055681Z" } }, - "outputs": [], "source": [ "from datetime import datetime\n", "import pandas as pd\n", @@ -75,38 +73,19 @@ " st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", " if not st_data.empty:\n", " name_change_dict[ts_code] = filter_rows(st_data)" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 3, "id": "e7f8cce2f80e2f20", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:54.089114Z", - "start_time": "2025-04-06T15:33:45.576286Z" + "end_time": "2025-04-08T13:37:37.727419Z", + "start_time": "2025-04-08T13:37:10.461897Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 8502128 entries, 0 to 21571\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 194.6+ MB\n", - "None\n", - "20250403\n", - "20250407\n" - ] - } - ], "source": [ "import time\n", "from concurrent.futures import ThreadPoolExecutor, as_completed\n", @@ -125,37 +104,39 @@ "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", "start_date = min(trade_dates)\n", "print(start_date)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:57.041254Z", - "start_time": "2025-04-06T15:33:54.103322Z" - }, - "scrolled": true - }, + ], "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" + "\n", + "Index: 8507519 entries, 0 to 5390\n", + "Data columns (total 2 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object\n", + " 1 trade_date object\n", + "dtypes: object(2)\n", + "memory usage: 194.7+ MB\n", + "None\n", + "20250407\n", + "20250408\n" ] } ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-04-08T13:37:39.056144Z", + "start_time": "2025-04-08T13:37:37.770718Z" + } + }, "source": [ "\n", "\n", @@ -205,169 +186,192 @@ " # 重置批次起始时间\n", " batch_start_time = time.time()\n", "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "919023c693d7a47a", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-06T15:33:57.072796Z", - "start_time": "2025-04-06T15:33:57.061670Z" - } - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "0 000059.SZ 20250407 4.54 1.8414 3.4767 \n", - "1 600830.SH 20250407 8.33 2.5217 3.6802 \n", - "2 688061.SH 20250407 24.45 3.1011 3.1011 \n", - "3 600868.SH 20250407 2.79 3.8477 4.1435 \n", - "4 605168.SH 20250407 25.98 1.3857 2.8470 \n", - "... ... ... ... ... ... \n", - "5386 688259.SH 20250407 34.99 5.9799 11.4393 \n", - "5387 301316.SZ 20250407 19.20 7.2272 7.9512 \n", - "5388 601116.SH 20250407 10.37 2.3317 7.1579 \n", - "5389 605016.SH 20250407 17.20 1.4773 3.9134 \n", - "5390 600148.SH 20250407 16.07 2.0776 4.5745 \n", - "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 0.84 103.2927 NaN 0.5851 0.1574 0.1928 0.3084 \n", - "1 0.69 71.1750 71.1750 1.7467 11.2902 11.2902 0.1801 \n", - "2 2.31 292.8121 NaN 1.1504 6.1795 4.9755 NaN \n", - "3 1.16 NaN NaN 2.3425 16.8832 16.0274 0.0000 \n", - "4 1.56 10.3735 14.0394 1.9988 1.0366 1.2218 4.5870 \n", - "... ... ... ... ... ... ... ... \n", - "5386 1.10 66.8795 64.8845 2.6173 5.9119 6.5930 NaN \n", - "5387 1.30 94.0750 110.9182 7.1350 5.7094 4.8530 0.4126 \n", - "5388 1.78 41.2451 36.3656 1.7811 1.4576 1.4350 1.9286 \n", - "5389 1.05 28.7938 22.2858 3.3051 6.4003 4.8254 1.3640 \n", - "5390 2.12 3441.4901 274.8323 4.8916 3.2666 3.3043 0.1867 \n", - "\n", - " dv_ttm total_share float_share free_share total_mv circ_mv \\\n", - "0 0.3084 159944.2537 159944.2537 84712.3362 726146.9118 726146.9118 \n", - "1 0.1801 45432.2747 45432.2747 31131.0133 378450.8483 378450.8483 \n", - "2 NaN 11488.9391 4329.7770 4329.7770 280904.5610 105863.0477 \n", - "3 NaN 189814.8679 189814.8679 176264.8506 529583.4814 529583.4814 \n", - "4 4.5870 21081.6986 21081.6986 10260.7016 547702.5296 547702.5296 \n", - "... ... ... ... ... ... ... \n", - "5386 NaN 11170.0000 11170.0000 5839.1660 390838.3000 390838.3000 \n", - "5387 0.4126 40400.0000 24282.6503 22071.3403 775680.0000 466226.8858 \n", - "5388 1.9286 54767.8400 54767.8400 17840.9208 567942.5008 567942.5008 \n", - "5389 1.3640 32308.6400 32308.6400 12196.5716 555708.6080 555708.6080 \n", - "5390 0.1867 14151.6450 14151.6450 6427.3300 227416.9352 227416.9352 \n", - "\n", - " is_st \n", - "0 False \n", - "1 False \n", - "2 False \n", - "3 False \n", - "4 False \n", - "... ... \n", - "5386 False \n", - "5387 False \n", - "5388 False \n", - "5389 False \n", - "5390 False \n", - "\n", - "[5391 rows x 19 columns]\n" + "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" ] } ], - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "print(all_daily_data_df)" - ] + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 6, - "id": "28cb78d032671b20", + "id": "919023c693d7a47a", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:57.104132Z", - "start_time": "2025-04-06T15:33:57.095010Z" + "end_time": "2025-04-08T13:37:39.072117Z", + "start_time": "2025-04-08T13:37:39.062189Z" } }, + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "print(all_daily_data_df)" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "16 000656.SZ 20250407 1.28 0.9982 1.1644 \n", - "62 002748.SZ 20250407 7.32 0.5503 1.1888 \n", - "114 002490.SZ 20250407 3.49 0.7559 1.3380 \n", - "128 300165.SZ 20250407 2.78 4.0431 4.7932 \n", - "278 600303.SH 20250407 3.22 1.1873 1.4918 \n", + "0 300504.SZ 20250408 12.65 2.5494 4.8465 \n", + "1 002223.SZ 20250408 34.24 0.9832 1.6194 \n", + "2 002036.SZ 20250408 9.13 7.4710 8.1827 \n", + "3 688207.SH 20250408 12.29 4.6144 4.6144 \n", + "4 002401.SZ 20250408 13.88 4.9037 9.6159 \n", "... ... ... ... ... ... \n", - "5263 002217.SZ 20250407 2.07 0.1251 0.1569 \n", - "5267 002808.SZ 20250407 2.99 4.0901 4.7924 \n", - "5290 002602.SZ 20250407 6.44 0.2276 0.2634 \n", - "5315 002501.SZ 20250407 1.92 1.5653 2.0207 \n", - "5375 300376.SZ 20250407 2.96 1.4873 3.4865 \n", + "5387 600610.SH 20250408 7.56 18.8004 29.6937 \n", + "5388 002215.SZ 20250408 8.84 5.7658 6.7838 \n", + "5389 600694.SH 20250408 25.00 3.3101 5.4481 \n", + "5390 600121.SH 20250408 3.66 3.0305 6.3012 \n", + "5391 873167.BJ 20250408 21.56 7.8805 14.2434 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "16 0.44 NaN NaN NaN 0.1081 0.1637 0.0000 \n", - "62 0.61 96.0467 49.7297 1.3328 0.8402 0.8839 1.3661 \n", - "114 0.19 NaN NaN 5.6564 2.0529 2.0529 0.0000 \n", - "128 2.22 NaN NaN 0.9988 1.3542 1.4288 0.0000 \n", - "278 0.77 NaN NaN 1.4997 1.6142 1.6353 0.0000 \n", - "... ... ... ... ... ... ... ... \n", - "5263 0.23 NaN NaN NaN 3.3436 10.3100 0.0000 \n", - "5267 0.79 NaN NaN 2.5039 5.2047 4.8881 0.6689 \n", - "5290 0.20 91.5846 53.4453 1.8455 3.6128 2.5226 0.0000 \n", - "5315 0.58 NaN NaN 7.1559 14.2934 20.0240 0.0000 \n", - "5375 4.52 12.2436 36.2242 0.9837 1.4380 2.0320 1.6554 \n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "0 1.56 34.0479 220.6414 1.5349 1.3422 1.7126 1.5892 \n", + "1 1.07 14.3268 19.7636 2.8291 4.3058 4.6786 3.5030 \n", + "2 2.45 NaN NaN 3.6899 0.9822 0.9210 0.0000 \n", + "3 1.61 NaN NaN 1.5605 12.1348 26.4230 NaN \n", + "4 1.44 40.4258 40.4258 3.0931 2.8715 2.8715 1.2977 \n", + "... ... ... ... ... ... ... ... \n", + "5387 1.18 NaN NaN 122.1550 7.3648 7.3648 0.0000 \n", + "5388 2.49 37.7118 20.0533 2.2997 2.1570 1.7934 1.7092 \n", + "5389 3.51 15.4938 13.3524 0.9057 1.0676 1.1271 3.6364 \n", + "5390 1.13 15.7764 15.7764 2.3738 1.0605 1.0605 0.0000 \n", + "5391 0.79 33.5290 65.6770 3.2183 7.0572 9.9201 NaN \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "16 NaN 533971.5816 531174.3236 455354.2392 6.834836e+05 \n", - "62 1.3661 24000.0000 24000.0000 11108.5000 1.756800e+05 \n", - "114 NaN 79784.8400 54161.3625 30599.6625 2.784491e+05 \n", - "128 NaN 49551.1725 42053.2110 35472.8422 1.377523e+05 \n", - "278 NaN 68360.4211 67560.4211 53770.9211 2.201206e+05 \n", - "... ... ... ... ... ... \n", - "5263 NaN 747939.8928 568036.4278 453036.0995 1.548236e+06 \n", - "5267 0.6689 26880.0000 18638.3713 15907.0731 8.037120e+04 \n", - "5290 NaN 745255.6968 687870.8273 594244.1179 4.799447e+06 \n", - "5315 NaN 355000.0000 354999.9006 274999.9006 6.816000e+05 \n", - "5375 1.6554 232824.0476 232743.4901 99284.6609 6.891592e+05 \n", + " dv_ttm total_share float_share free_share total_mv \\\n", + "0 1.5892 27102.4580 21826.2631 11481.0786 3.428461e+05 \n", + "1 3.5030 100247.6929 93867.3649 56990.4202 3.432481e+06 \n", + "2 NaN 105938.4915 105290.9483 96132.5171 9.672184e+05 \n", + "3 NaN 25897.3147 18867.6306 18867.6306 3.182780e+05 \n", + "4 1.2977 37166.8440 37136.3940 18937.9540 5.158758e+05 \n", + "... ... ... ... ... ... \n", + "5387 NaN 107127.4605 70872.6705 44872.6705 8.098836e+05 \n", + "5388 1.7092 100519.1310 79400.9515 67486.1454 8.885891e+05 \n", + "5389 3.6364 31305.2571 31305.2571 19020.4513 7.826314e+05 \n", + "5390 NaN 121841.2038 121841.2038 58597.2758 4.459388e+05 \n", + "5391 NaN 7086.1250 4178.1867 2311.6822 1.527769e+05 \n", "\n", " circ_mv is_st \n", - "16 6.799031e+05 True \n", - "62 1.756800e+05 True \n", - "114 1.890232e+05 True \n", - "128 1.169079e+05 True \n", - "278 2.175446e+05 True \n", + "0 2.761022e+05 False \n", + "1 3.214019e+06 False \n", + "2 9.613064e+05 False \n", + "3 2.318832e+05 False \n", + "4 5.154531e+05 False \n", "... ... ... \n", - "5263 1.175835e+06 True \n", - "5267 5.572873e+04 True \n", - "5290 4.429888e+06 True \n", - "5315 6.815998e+05 True \n", - "5375 6.889207e+05 True \n", + "5387 5.357974e+05 False \n", + "5388 7.019044e+05 False \n", + "5389 7.826314e+05 False \n", + "5390 4.459388e+05 False \n", + "5391 9.008171e+04 False \n", + "\n", + "[5392 rows x 19 columns]\n" + ] + } + ], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "28cb78d032671b20", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T13:37:39.103515Z", + "start_time": "2025-04-08T13:37:39.093908Z" + } + }, + "source": [ + "print(all_daily_data_df[all_daily_data_df['is_st']])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "20 000488.SZ 20250408 1.74 2.5808 3.5449 \n", + "21 603608.SH 20250408 4.20 0.2313 0.3624 \n", + "88 603363.SH 20250408 3.35 1.2763 1.4156 \n", + "124 000989.SZ 20250408 7.60 2.5216 3.5863 \n", + "136 300965.SZ 20250408 36.20 1.9389 2.6640 \n", + "... ... ... ... ... ... \n", + "5261 603879.SH 20250408 4.13 4.3647 6.8212 \n", + "5273 002024.SZ 20250408 1.76 0.5005 1.3623 \n", + "5298 603828.SH 20250408 4.43 1.3711 2.7554 \n", + "5337 600234.SH 20250408 5.53 0.5518 1.0422 \n", + "5370 300536.SZ 20250408 7.99 2.2037 2.7214 \n", + "\n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "20 0.69 NaN NaN 0.5590 0.2252 0.2252 0.0000 \n", + "21 0.35 NaN NaN 1.5767 1.3841 1.5604 0.0000 \n", + "88 2.09 NaN NaN NaN 0.4481 0.7781 0.0000 \n", + "124 1.71 30.0883 30.0883 1.7332 2.7432 2.7432 5.2053 \n", + "136 1.27 NaN NaN 1.7736 NaN NaN 0.0829 \n", + "... ... ... ... ... ... ... ... \n", + "5261 1.67 NaN NaN 5.6207 4.0072 4.0072 0.0000 \n", + "5273 1.06 26.7044 26.7044 1.3118 0.2871 0.2871 0.0000 \n", + "5298 0.38 NaN NaN 3.5130 1.0396 1.0348 0.0000 \n", + "5337 2.28 NaN NaN 3.2963 20.7089 9.4391 0.0000 \n", + "5370 0.86 NaN NaN 4.2696 32.8078 24.2873 0.0000 \n", + "\n", + " dv_ttm total_share float_share free_share total_mv \\\n", + "20 NaN 294145.6200 167582.4530 122004.3211 5.118134e+05 \n", + "21 NaN 41971.5446 41971.5446 26785.1109 1.762805e+05 \n", + "88 NaN 260296.1826 146776.2912 132325.9245 8.719922e+05 \n", + "124 5.2053 85594.2012 69415.3353 48807.3173 6.505159e+05 \n", + "136 0.0829 6000.0000 2060.9250 1500.0000 2.172000e+05 \n", + "... ... ... ... ... ... \n", + "5261 NaN 35934.4440 35934.4440 22993.7696 1.484093e+05 \n", + "5273 NaN 926476.7618 925444.1318 340007.5385 1.630599e+06 \n", + "5298 NaN 59596.0158 59593.9625 29654.2988 2.640103e+05 \n", + "5337 NaN 26252.0973 26252.0973 13899.8888 1.451741e+05 \n", + "5370 NaN 29328.8133 29325.3240 23747.3240 2.343372e+05 \n", + "\n", + " circ_mv is_st \n", + "20 2.915935e+05 True \n", + "21 1.762805e+05 True \n", + "88 4.917006e+05 True \n", + "124 5.275565e+05 True \n", + "136 7.460549e+04 True \n", + "... ... ... \n", + "5261 1.484093e+05 True \n", + "5273 1.628782e+06 True \n", + "5298 2.640013e+05 True \n", + "5337 1.451741e+05 True \n", + "5370 2.343093e+05 True \n", "\n", "[106 rows x 19 columns]\n" ] } ], - "source": [ - "print(all_daily_data_df[all_daily_data_df['is_st']])" - ] + "execution_count": 6 }, { "cell_type": "code", - "execution_count": 7, "id": "692b58674b7462c9", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:33:57.927188Z", - "start_time": "2025-04-06T15:33:57.127166Z" + "end_time": "2025-04-08T13:37:39.921445Z", + "start_time": "2025-04-08T13:37:39.128232Z" } }, + "source": [ + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key='daily_basic', 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！\")\n" + ], "outputs": [ { "name": "stdout", @@ -377,30 +381,29 @@ ] } ], - "source": [ - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key='daily_basic', 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！\")\n" - ] + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 8, "id": "d7a773fc20293477", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:06.721517Z", - "start_time": "2025-04-06T15:33:57.951119Z" + "end_time": "2025-04-08T13:37:46.393814Z", + "start_time": "2025-04-08T13:37:39.941474Z" } }, + "source": [ + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date', 'is_st']]\n", + " print(df.info())" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Index: 8507519 entries, 0 to 5390\n", + "Index: 8512911 entries, 0 to 5391\n", "Data columns (total 3 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -408,16 +411,12 @@ " 1 trade_date object\n", " 2 is_st bool \n", "dtypes: bool(1), object(2)\n", - "memory usage: 202.8+ MB\n", + "memory usage: 203.0+ MB\n", "None\n" ] } ], - "source": [ - "with pd.HDFStore(h5_filename, mode='r') as store:\n", - " df = store[key][['ts_code', 'trade_date', 'is_st']]\n", - " print(df.info())" - ] + "execution_count": 8 } ], "metadata": { diff --git a/code/data/update/update_daily_data.ipynb b/code/data/update/update_daily_data.ipynb index 528e7f9..05a6e31 100644 --- a/code/data/update/update_daily_data.ipynb +++ b/code/data/update/update_daily_data.ipynb @@ -2,15 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "f294ba92-512a-48e6-bbaa-e19401c691ba", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:31:33.394624Z", - "start_time": "2025-04-06T15:31:32.508688Z" + "end_time": "2025-04-08T13:37:10.056146Z", + "start_time": "2025-04-08T13:37:09.245906Z" } }, - "outputs": [], "source": [ "import tushare as ts\n", "import pandas as pd\n", @@ -20,38 +18,19 @@ "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "d31855fab4f8b1bc", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:32:56.686423Z", - "start_time": "2025-04-06T15:32:49.588940Z" + "end_time": "2025-04-08T13:37:34.909395Z", + "start_time": "2025-04-08T13:37:10.062153Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 8574098 entries, 0 to 21509\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 196.2+ MB\n", - "None\n", - "20250403\n", - "20250407\n" - ] - } - ], "source": [ "h5_filename = '../../../data/daily_data.h5'\n", "key = '/daily_data'\n", @@ -67,19 +46,85 @@ "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", "start_date = min(trade_dates)\n", "print(start_date)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 8579473 entries, 0 to 5374\n", + "Data columns (total 2 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object\n", + " 1 trade_date object\n", + "dtypes: object(2)\n", + "memory usage: 196.4+ MB\n", + "None\n", + "20250407\n", + "20250408\n" + ] + } + ], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 3, "id": "f403cc963e1d39b", "metadata": { + "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-06T15:50:12.427897Z", - "start_time": "2025-04-06T15:33:09.027974Z" - }, - "scrolled": true + "end_time": "2025-04-08T13:50:37.772526Z", + "start_time": "2025-04-08T13:37:35.101668Z" + } }, + "source": [ + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", + "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", + "stocks_df = pd.read_csv('../../../stocks_list.csv', encoding='utf-8-sig')\n", + "\n", + "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", + "daily_data_list = []\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(ts_code):\n", + " daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date=start_date)\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if daily_data is not None and not daily_data.empty:\n", + " return daily_data\n", + "\n", + "\n", + "# 循环遍历每个股票代码并获取数据\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, row['ts_code']): row['ts_code'] for idx, row in stocks_df.iterrows()}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " ts_code = future_to_date[future]\n", + " try:\n", + " result = future.result()\n", + " daily_data_list.append(result)\n", + " print(f\"任务 {ts_code} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\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" + ], "outputs": [ { "name": "stdout", @@ -87,8 +132,8 @@ "text": [ "任务 000001.SZ 完成\n", "任务 000002.SZ 完成\n", - "任务 000006.SZ 完成\n", "任务 000004.SZ 完成\n", + "任务 000006.SZ 完成\n", "任务 000007.SZ 完成\n", "任务 000008.SZ 完成\n", "任务 000009.SZ 完成\n", @@ -101,22 +146,22 @@ "任务 000019.SZ 完成\n", "任务 000020.SZ 完成\n", "任务 000021.SZ 完成\n", - "任务 000025.SZ 完成\n", "任务 000026.SZ 完成\n", + "任务 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"任务 301502.SZ 完成\n", "任务 301503.SZ 完成\n", + "任务 301502.SZ 完成\n", "任务 301505.SZ 完成\n", "任务 301507.SZ 完成\n", "任务 301508.SZ 完成\n", "任务 301509.SZ 完成\n", - "任务 301510.SZ 完成\n", "任务 301511.SZ 完成\n", - "任务 301512.SZ 完成\n", + "任务 301510.SZ 完成\n", "任务 301515.SZ 完成\n", + "任务 301512.SZ 完成\n", "任务 301516.SZ 完成\n", "任务 301517.SZ 完成\n", "任务 301518.SZ 完成\n", @@ -2892,8 +2937,8 @@ "任务 301526.SZ 完成\n", "任务 301528.SZ 完成\n", "任务 301529.SZ 完成\n", - "任务 301533.SZ 完成\n", "任务 301536.SZ 完成\n", + "任务 301533.SZ 完成\n", "任务 301538.SZ 完成\n", "任务 301539.SZ 完成\n", "任务 301548.SZ 完成\n", @@ -2978,14 +3023,14 @@ "任务 600028.SH 完成\n", "任务 600029.SH 完成\n", "任务 600030.SH 完成\n", - "任务 600032.SH 完成\n", "任务 600031.SH 完成\n", - "任务 600035.SH 完成\n", + "任务 600032.SH 完成\n", "任务 600033.SH 完成\n", - "任务 600037.SH 完成\n", + "任务 600035.SH 完成\n", "任务 600036.SH 完成\n", - "任务 600039.SH 完成\n", + "任务 600037.SH 完成\n", "任务 600038.SH 完成\n", + "任务 600039.SH 完成\n", "任务 600048.SH 完成\n", "任务 600050.SH 完成\n", "任务 600051.SH 完成\n", @@ -3057,8 +3102,8 @@ 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"任务 600192.SH 完成\n", "任务 600196.SH 完成\n", + "任务 600195.SH 完成\n", "任务 600197.SH 完成\n", "任务 600198.SH 完成\n", "任务 600199.SH 完成\n", "任务 600200.SH 完成\n", - "任务 600202.SH 完成\n", "任务 600201.SH 完成\n", + "任务 600202.SH 完成\n", "任务 600203.SH 完成\n", "任务 600206.SH 完成\n", "任务 600207.SH 完成\n", @@ -3119,46 +3164,46 @@ "任务 600211.SH 完成\n", "任务 600212.SH 完成\n", "任务 600215.SH 完成\n", - "任务 600216.SH 完成\n", "任务 600217.SH 完成\n", - "任务 600218.SH 完成\n", + "任务 600216.SH 完成\n", "任务 600219.SH 完成\n", - "任务 600221.SH 完成\n", + "任务 600218.SH 完成\n", "任务 600222.SH 完成\n", - "任务 600223.SH 完成\n", + "任务 600221.SH 完成\n", "任务 600225.SH 完成\n", + "任务 600223.SH 完成\n", "任务 600226.SH 完成\n", "任务 600227.SH 完成\n", - "任务 600228.SH 完成\n", "任务 600229.SH 完成\n", + "任务 600228.SH 完成\n", "任务 600230.SH 完成\n", "任务 600231.SH 完成\n", - "任务 600233.SH 完成\n", "任务 600232.SH 完成\n", + "任务 600233.SH 完成\n", "任务 600234.SH 完成\n", "任务 600235.SH 完成\n", - "任务 600237.SH 完成\n", "任务 600236.SH 完成\n", - "任务 600239.SH 完成\n", + "任务 600237.SH 完成\n", "任务 600238.SH 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"任务 600675.SH 完成\n", "任务 600676.SH 完成\n", - "任务 600678.SH 完成\n", "任务 600679.SH 完成\n", + "任务 600678.SH 完成\n", "任务 600681.SH 完成\n", "任务 600682.SH 完成\n", "任务 600683.SH 完成\n", @@ -3484,16 +3529,16 @@ "任务 600691.SH 完成\n", "任务 600692.SH 完成\n", "任务 600693.SH 完成\n", - "任务 600694.SH 完成\n", "任务 600696.SH 完成\n", + "任务 600694.SH 完成\n", "任务 600697.SH 完成\n", "任务 600698.SH 完成\n", - "任务 600699.SH 完成\n", "任务 600702.SH 完成\n", + "任务 600699.SH 完成\n", "任务 600703.SH 完成\n", - "任务 600705.SH 完成\n", "任务 600704.SH 完成\n", "任务 600706.SH 完成\n", + "任务 600705.SH 完成\n", "任务 600707.SH 完成\n", "任务 600708.SH 完成\n", "任务 600710.SH 完成\n", @@ -3571,34 +3616,34 @@ "任务 600792.SH 完成\n", "任务 600793.SH 完成\n", "任务 600794.SH 完成\n", - "任务 600796.SH 完成\n", "任务 600795.SH 完成\n", + "任务 600796.SH 完成\n", "任务 600797.SH 完成\n", "任务 600798.SH 完成\n", - "任务 600801.SH 完成\n", "任务 600800.SH 完成\n", + "任务 600801.SH 完成\n", "任务 600802.SH 完成\n", "任务 600803.SH 完成\n", - "任务 600805.SH 完成\n", "任务 600804.SH 完成\n", - "任务 600808.SH 完成\n", + "任务 600805.SH 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"任务 688565.SH 完成\n", "任务 688566.SH 完成\n", "任务 688567.SH 完成\n", - "任务 688569.SH 完成\n", "任务 688568.SH 完成\n", - "任务 688571.SH 完成\n", + "任务 688569.SH 完成\n", "任务 688570.SH 完成\n", - "任务 688575.SH 完成\n", + "任务 688571.SH 完成\n", "任务 688573.SH 完成\n", + "任务 688575.SH 完成\n", "任务 688576.SH 完成\n", "任务 688577.SH 完成\n", "任务 688578.SH 完成\n", "任务 688579.SH 完成\n", - "任务 688581.SH 完成\n", "任务 688580.SH 完成\n", + "任务 688581.SH 完成\n", "任务 688582.SH 完成\n", "任务 688583.SH 完成\n", "任务 688584.SH 完成\n", @@ -5112,8 +5157,8 @@ "任务 688590.SH 完成\n", "任务 688591.SH 完成\n", "任务 688592.SH 完成\n", - "任务 688595.SH 完成\n", "任务 688593.SH 完成\n", + "任务 688595.SH 完成\n", "任务 688596.SH 完成\n", "任务 688597.SH 完成\n", "任务 688598.SH 完成\n", @@ -5165,8 +5210,8 @@ "任务 688661.SH 完成\n", "任务 688662.SH 完成\n", "任务 688663.SH 完成\n", - "任务 688665.SH 完成\n", "任务 688667.SH 完成\n", + "任务 688665.SH 完成\n", "任务 688668.SH 完成\n", "任务 688669.SH 完成\n", "任务 688670.SH 完成\n", @@ -5183,26 +5228,26 @@ "任务 688686.SH 完成\n", "任务 688687.SH 完成\n", "任务 688689.SH 完成\n", - "任务 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"任务 832000.BJ 完成\n", "任务 832023.BJ 完成\n", "任务 832089.BJ 完成\n", "任务 832110.BJ 完成\n", - "任务 832145.BJ 完成\n", "任务 832149.BJ 完成\n", + "任务 832145.BJ 完成\n", "任务 832171.BJ 完成\n", "任务 832175.BJ 完成\n", "任务 832225.BJ 完成\n", @@ -5283,24 +5328,24 @@ "任务 832491.BJ 完成\n", "任务 832522.BJ 完成\n", "任务 832566.BJ 完成\n", - "任务 832662.BJ 完成\n", "任务 832651.BJ 完成\n", + "任务 832662.BJ 完成\n", "任务 832735.BJ 完成\n", "任务 832786.BJ 完成\n", - "任务 832802.BJ 完成\n", "任务 832876.BJ 完成\n", + "任务 832802.BJ 完成\n", "任务 832885.BJ 完成\n", "任务 832978.BJ 完成\n", - "任务 832982.BJ 完成\n", "任务 833030.BJ 完成\n", - "任务 833171.BJ 完成\n", + "任务 832982.BJ 完成\n", "任务 833075.BJ 完成\n", + "任务 833171.BJ 完成\n", "任务 833230.BJ 完成\n", "任务 833266.BJ 完成\n", - "任务 833346.BJ 完成\n", "任务 833284.BJ 完成\n", - "任务 833427.BJ 完成\n", + "任务 833346.BJ 完成\n", "任务 833394.BJ 完成\n", + "任务 833427.BJ 完成\n", "任务 833429.BJ 完成\n", "任务 833454.BJ 完成\n", "任务 833455.BJ 完成\n", @@ -5323,6 +5368,7 @@ "任务 834261.BJ 完成\n", "任务 834407.BJ 完成\n", "任务 834415.BJ 完成\n", + "任务 834475.BJ 完成\n", "任务 834599.BJ 完成\n", "任务 834639.BJ 完成\n", "任务 834682.BJ 完成\n", @@ -5380,7 +5426,6 @@ "任务 837212.BJ 完成\n", "任务 837242.BJ 完成\n", "任务 837344.BJ 完成\n", - "任务 834475.BJ 完成\n", "任务 837403.BJ 完成\n", "任务 837592.BJ 完成\n", "任务 837663.BJ 完成\n", @@ -5399,8 +5444,8 @@ "任务 838837.BJ 完成\n", "任务 838924.BJ 完成\n", "任务 838971.BJ 完成\n", - "任务 839167.BJ 完成\n", "任务 839273.BJ 完成\n", + "任务 839167.BJ 完成\n", "任务 839371.BJ 完成\n", "任务 839493.BJ 完成\n", "任务 839680.BJ 完成\n", @@ -5419,10 +5464,10 @@ "任务 870656.BJ 完成\n", "任务 870726.BJ 完成\n", "任务 870866.BJ 完成\n", - "任务 870976.BJ 完成\n", "任务 871245.BJ 完成\n", - "任务 871263.BJ 完成\n", + "任务 870976.BJ 完成\n", "任务 871396.BJ 完成\n", + "任务 871263.BJ 完成\n", "任务 871478.BJ 完成\n", "任务 871553.BJ 完成\n", "任务 871634.BJ 完成\n", @@ -5488,14 +5533,14 @@ "任务 000018.SZ 完成\n", "任务 000023.SZ 完成\n", "任务 000024.SZ 完成\n", - "任务 000033.SZ 完成\n", "任务 000038.SZ 完成\n", - "任务 000046.SZ 完成\n", + "任务 000033.SZ 完成\n", "任务 000047.SZ 完成\n", + "任务 000046.SZ 完成\n", "任务 000150.SZ 完成\n", "任务 000405.SZ 完成\n", - "任务 000406.SZ 完成\n", "任务 000412.SZ 完成\n", + "任务 000406.SZ 完成\n", "任务 000413.SZ 完成\n", "任务 000416.SZ 完成\n", "任务 000418.SZ 完成\n", @@ -5506,14 +5551,14 @@ "任务 000522.SZ 完成\n", "任务 000527.SZ 完成\n", "任务 000535.SZ 完成\n", - "任务 000540.SZ 完成\n", "任务 000542.SZ 完成\n", + "任务 000540.SZ 完成\n", "任务 000549.SZ 完成\n", "任务 000556.SZ 完成\n", "任务 000562.SZ 完成\n", "任务 000569.SZ 完成\n", - "任务 000578.SZ 完成\n", "任务 000583.SZ 完成\n", + "任务 000578.SZ 完成\n", "任务 000585.SZ 完成\n", "任务 000587.SZ 完成\n", "任务 000588.SZ 完成\n", @@ -5528,16 +5573,16 @@ "任务 000653.SZ 完成\n", "任务 000658.SZ 完成\n", "任务 000660.SZ 完成\n", - "任务 000662.SZ 完成\n", "任务 000666.SZ 完成\n", - "任务 000667.SZ 完成\n", + "任务 000662.SZ 完成\n", "任务 000671.SZ 完成\n", + "任务 000667.SZ 完成\n", "任务 000673.SZ 完成\n", "任务 000675.SZ 完成\n", - "任务 000687.SZ 完成\n", "任务 000689.SZ 完成\n", - "任务 000693.SZ 完成\n", + "任务 000687.SZ 完成\n", "任务 000699.SZ 完成\n", + "任务 000693.SZ 完成\n", "任务 000730.SZ 完成\n", "任务 000732.SZ 完成\n", "任务 000748.SZ 完成\n", @@ -5546,8 +5591,8 @@ "任务 000765.SZ 完成\n", 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stocks_data.csv）\n", - "stocks_df = pd.read_csv('../../../stocks_list.csv', encoding='utf-8-sig')\n", - "\n", - "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", - "daily_data_list = []\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(ts_code):\n", - " daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date=start_date)\n", - " # 如果返回数据不为空，则添加一列标识股票代码\n", - " if daily_data is not None and not daily_data.empty:\n", - " return daily_data\n", - "\n", - "\n", - "# 循环遍历每个股票代码并获取数据\n", - "with ThreadPoolExecutor(max_workers=2) as executor:\n", - " future_to_date = {executor.submit(get_data, row['ts_code']): row['ts_code'] for idx, row in stocks_df.iterrows()}\n", - "\n", - " for future in as_completed(future_to_date):\n", - " ts_code = future_to_date[future]\n", - " try:\n", - " result = future.result()\n", - " daily_data_list.append(result)\n", - " print(f\"任务 {ts_code} 完成\")\n", - " except Exception as e:\n", - " print(f\"获取 {ts_code} 数据时出错: {e}\")\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" - ] + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 4, "id": "35d605a189a83c1f", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:50:12.620072Z", - "start_time": "2025-04-06T15:50:12.492310Z" + "end_time": "2025-04-08T13:50:37.966866Z", + "start_time": "2025-04-08T13:50:37.846779Z" } }, + "source": [ + "all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n", + "print(all_daily_data)" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date open high low close pre_close \\\n", - "0 000001.SZ 20250407 1405.63 1412.01 1339.18 1367.29 1449.07 \n", - "1 000002.SZ 20250407 1246.49 1259.21 1173.81 1199.25 1297.37 \n", - "2 000006.SZ 20250407 257.52 258.31 249.17 249.17 276.99 \n", - "3 000004.SZ 20250407 37.88 37.88 35.93 35.93 39.91 \n", - "4 000007.SZ 20250407 50.37 50.37 46.22 46.22 51.36 \n", + "0 000001.SZ 20250408 1364.73 1386.46 1362.18 1382.62 1367.29 \n", + "1 000002.SZ 20250408 1199.25 1242.86 1199.25 1235.59 1199.25 \n", + "2 000004.SZ 20250408 34.50 36.58 33.93 35.19 35.93 \n", + "3 000006.SZ 20250408 238.44 244.00 224.13 226.92 249.17 \n", + "4 000007.SZ 20250408 45.06 46.22 42.91 44.49 46.22 \n", "... ... ... ... ... ... ... ... \n", - "5370 920111.BJ 20250407 26.10 26.77 20.83 22.60 28.35 \n", - "5371 920116.BJ 20250407 85.00 85.00 69.50 74.16 89.27 \n", - "5372 920118.BJ 20250407 27.80 27.80 22.04 23.20 29.29 \n", - "5373 920128.BJ 20250407 30.01 31.00 23.61 26.27 33.37 \n", - "5374 689009.SH 20250407 57.91 59.33 50.94 53.66 63.44 \n", + "5370 920111.BJ 20250408 23.05 23.98 22.94 23.73 22.60 \n", + "5371 920116.BJ 20250408 76.77 79.28 74.18 77.15 74.16 \n", + "5372 920118.BJ 20250408 23.78 24.40 23.30 24.33 23.20 \n", + "5373 920128.BJ 20250408 26.21 27.76 26.21 27.13 26.27 \n", + "5374 689009.SH 20250408 54.16 56.87 54.16 54.70 53.66 \n", "\n", " change pct_chg vol amount \n", - "0 -81.78 -5.64 2545560.48 2751055.412 \n", - "1 -98.12 -7.56 1966334.80 1318713.482 \n", - "2 -27.82 -10.04 322745.00 203606.158 \n", - "3 -3.98 -9.97 24554.00 21882.659 \n", - "4 -5.14 -10.01 69638.00 39393.128 \n", + "0 15.33 1.12 1463987.71 1576590.040 \n", + "1 36.34 3.03 1425550.14 962248.248 \n", + "2 -0.74 -2.06 69768.00 60315.630 \n", + "3 -22.25 -8.93 759990.08 438643.481 \n", + "4 -1.73 -3.74 65714.65 35143.987 \n", "... ... ... ... ... \n", - "5370 -5.75 -20.28 46314.03 111024.758 \n", - "5371 -15.11 -16.93 50279.43 391921.986 \n", - "5372 -6.09 -20.79 15283.54 38546.069 \n", - "5373 -7.10 -21.28 24527.55 68372.175 \n", - "5374 -9.78 -15.42 184996.22 1006333.988 \n", + "5370 1.13 5.00 34595.60 81278.233 \n", + "5371 2.99 4.03 36521.19 279699.317 \n", + "5372 1.13 4.87 9287.01 22345.957 \n", + "5373 0.86 3.27 18725.30 50284.791 \n", + "5374 1.04 1.94 118442.74 650396.418 \n", "\n", "[5375 rows x 11 columns]\n" ] } ], - "source": [ - "all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n", - "print(all_daily_data)" - ] + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 5, "id": "446aeba1a846b34", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:50:12.736416Z", - "start_time": "2025-04-06T15:50:12.661610Z" + "end_time": "2025-04-08T13:50:38.034544Z", + "start_time": "2025-04-08T13:50:37.971871Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "所有日线数据已保存到 daily_data.h5\n" - ] - } - ], "source": [ "\n", "# 合并所有获取到的日线数据\n", @@ -5898,7 +5888,17 @@ " print(\"所有日线数据已保存到 daily_data.h5\")\n", "else:\n", " print(\"未获取到任何日线数据。\")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有日线数据已保存到 daily_data.h5\n" + ] + } + ], + "execution_count": 5 } ], "metadata": { diff --git a/code/data/update/update_money_flow.ipynb b/code/data/update/update_money_flow.ipynb index 41a7781..133c4fd 100644 --- a/code/data/update/update_money_flow.ipynb +++ b/code/data/update/update_money_flow.ipynb @@ -2,52 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "b94bb1f2-5332-485e-ae1b-eea01f938106", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:19.686298Z", - "start_time": "2025-04-06T15:34:19.679462Z" + "end_time": "2025-04-08T13:37:11.623192Z", + "start_time": "2025-04-08T13:37:10.611486Z" } }, - "outputs": [], "source": [ "import tushare as ts\n", "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "742c29d453b9bb38", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:29.569406Z", - "start_time": "2025-04-06T15:34:19.711970Z" + "end_time": "2025-04-08T13:37:32.754262Z", + "start_time": "2025-04-08T13:37:11.629198Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 8343458 entries, 0 to 20511\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 191.0+ MB\n", - "None\n", - "20250403\n", - "start_date: 20250407\n" - ] - } - ], "source": [ "import pandas as pd\n", "import time\n", @@ -66,37 +45,39 @@ "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": "679ce40e-8d62-4887-970c-e1d8cbdeee6b", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-06T15:34:32.842166Z", - "start_time": "2025-04-06T15:34:29.601368Z" - }, - "scrolled": true - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "任务 20250417 完成\n", - "任务 20250418 完成\n", - "任务 20250415 完成\n", - "任务 20250416 完成\n", - "任务 20250414 完成\n", - "任务 20250411 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" + "\n", + "Index: 8348584 entries, 0 to 5125\n", + "Data columns (total 2 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object\n", + " 1 trade_date object\n", + "dtypes: object(2)\n", + "memory usage: 191.1+ MB\n", + "None\n", + "20250407\n", + "start_date: 20250408\n" ] } ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-04-08T13:37:34.659267Z", + "start_time": "2025-04-08T13:37:33.094502Z" + } + }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -126,33 +107,59 @@ " 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 20250415 完成\n", + "任务 20250416 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" + ] + } + ], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 4, "id": "9af80516849d4e80", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:32.851075Z", - "start_time": "2025-04-06T15:34:32.844866Z" + "end_time": "2025-04-08T13:37:34.678164Z", + "start_time": "2025-04-08T13:37:34.674804Z" } }, - "outputs": [], "source": [ "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n" - ] + ], + "outputs": [], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": 5, "id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:35.261741Z", - "start_time": "2025-04-06T15:34:32.864789Z" + "end_time": "2025-04-08T13:37:37.285649Z", + "start_time": "2025-04-08T13:37:34.694595Z" } }, + "source": [ + "\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='money_flow', 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", @@ -162,15 +169,7 @@ ] } ], - "source": [ - "\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='money_flow', 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！\")" - ] + "execution_count": 5 } ], "metadata": { diff --git a/code/data/update/update_stk_limit.ipynb b/code/data/update/update_stk_limit.ipynb index 4dbb66e..4a7adba 100644 --- a/code/data/update/update_stk_limit.ipynb +++ b/code/data/update/update_stk_limit.ipynb @@ -2,58 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "500802dc-7a20-48b7-a470-a4bae3ec534b", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:34.020485Z", - "start_time": "2025-04-06T15:34:33.497731Z" + "end_time": "2025-04-08T13:37:12.814092Z", + "start_time": "2025-04-08T13:37:11.953133Z" } }, - "outputs": [], "source": [ "import tushare as ts\n", "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ] + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": 2, "id": "5a84bc9da6d54868", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:46.227924Z", - "start_time": "2025-04-06T15:34:34.042810Z" + "end_time": "2025-04-08T13:37:35.724923Z", + "start_time": "2025-04-08T13:37:12.820096Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ts_code trade_date\n", - "4717 600285.SH 20250403\n", - "4718 600287.SH 20250403\n", - "4719 600288.SH 20250403\n", - "4708 600273.SH 20250403\n", - "5309 601121.SH 20250403\n", - "\n", - "Index: 10301468 entries, 0 to 28272\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 235.8+ MB\n", - "None\n", - "20250403\n", - "20250407\n" - ] - } - ], "source": [ "import pandas as pd\n", "import time\n", @@ -73,37 +46,45 @@ "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", "start_date = min(trade_dates)\n", "print(start_date)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-06T15:34:48.652346Z", - "start_time": "2025-04-06T15:34:46.236695Z" - }, - "scrolled": true - }, + ], "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" + " ts_code trade_date\n", + "4721 600284.SH 20250408\n", + "4722 600285.SH 20250408\n", + "4723 600287.SH 20250408\n", + "4712 600272.SH 20250408\n", + "5 000008.SZ 20250408\n", + "\n", + "Index: 10315620 entries, 0 to 14151\n", + "Data columns (total 2 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object\n", + " 1 trade_date object\n", + "dtypes: object(2)\n", + "memory usage: 236.1+ MB\n", + "None\n", + "20250408\n", + "20250409\n" ] } ], + "execution_count": 2 + }, + { + "cell_type": "code", + "id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-04-08T13:37:36.896959Z", + "start_time": "2025-04-08T13:37:35.931558Z" + } + }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -134,58 +115,63 @@ " 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": "96a81aa5890ea3c3", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-06T15:34:48.680504Z", - "start_time": "2025-04-06T15:34:48.665530Z" - } - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ trade_date ts_code up_limit down_limit\n", - "0 20250408 000001.SZ 11.77 9.63\n", - "1 20250408 000002.SZ 7.26 5.94\n", - "2 20250408 000004.SZ 9.72 7.96\n", - "3 20250408 000006.SZ 6.90 5.64\n", - "4 20250408 000007.SZ 6.14 5.02\n", - "... ... ... ... ...\n", - "7072 20250408 920108.BJ 25.33 13.65\n", - "7073 20250408 920111.BJ 29.38 15.82\n", - "7074 20250408 920116.BJ 96.40 51.92\n", - "7075 20250408 920118.BJ 30.16 16.24\n", - "7076 20250408 920128.BJ 34.15 18.39\n", - "\n", - "[7077 rows x 4 columns], trade_date ts_code up_limit down_limit\n", - "0 20250407 000001.SZ 12.47 10.21\n", - "1 20250407 000002.SZ 7.85 6.43\n", - "2 20250407 000004.SZ 10.80 8.84\n", - "3 20250407 000006.SZ 7.67 6.27\n", - "4 20250407 000007.SZ 6.82 5.58\n", - "... ... ... ... ...\n", - "7070 20250407 920108.BJ 31.72 17.08\n", - "7071 20250407 920111.BJ 36.85 19.85\n", - "7072 20250407 920116.BJ 116.05 62.49\n", - "7073 20250407 920118.BJ 38.07 20.51\n", - "7074 20250407 920128.BJ 43.38 23.36\n", - "\n", - "[7075 rows x 4 columns]]\n" + "任务 20250418 完成\n", + "任务 20250417 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 20250409 完成\n", + "任务 20250410 完成\n" ] } ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "96a81aa5890ea3c3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T13:37:37.699901Z", + "start_time": "2025-04-08T13:37:36.909744Z" + } + }, "source": [ "print(all_daily_data)\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)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + }, + { + "ename": "ValueError", + "evalue": "No objects to concatenate", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[4], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28mprint\u001B[39m(all_daily_data)\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mconcat(all_daily_data, ignore_index\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\\pandas\\core\\reshape\\concat.py:382\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 380\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 382\u001B[0m op \u001B[38;5;241m=\u001B[39m _Concatenator(\n\u001B[0;32m 383\u001B[0m objs,\n\u001B[0;32m 384\u001B[0m axis\u001B[38;5;241m=\u001B[39maxis,\n\u001B[0;32m 385\u001B[0m ignore_index\u001B[38;5;241m=\u001B[39mignore_index,\n\u001B[0;32m 386\u001B[0m join\u001B[38;5;241m=\u001B[39mjoin,\n\u001B[0;32m 387\u001B[0m keys\u001B[38;5;241m=\u001B[39mkeys,\n\u001B[0;32m 388\u001B[0m levels\u001B[38;5;241m=\u001B[39mlevels,\n\u001B[0;32m 389\u001B[0m names\u001B[38;5;241m=\u001B[39mnames,\n\u001B[0;32m 390\u001B[0m verify_integrity\u001B[38;5;241m=\u001B[39mverify_integrity,\n\u001B[0;32m 391\u001B[0m copy\u001B[38;5;241m=\u001B[39mcopy,\n\u001B[0;32m 392\u001B[0m sort\u001B[38;5;241m=\u001B[39msort,\n\u001B[0;32m 393\u001B[0m )\n\u001B[0;32m 395\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:445\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 442\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mverify_integrity \u001B[38;5;241m=\u001B[39m verify_integrity\n\u001B[0;32m 443\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcopy \u001B[38;5;241m=\u001B[39m copy\n\u001B[1;32m--> 445\u001B[0m objs, keys \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_clean_keys_and_objs(objs, keys)\n\u001B[0;32m 447\u001B[0m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[0;32m 448\u001B[0m ndims \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_ndims(objs)\n", + "File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:507\u001B[0m, in \u001B[0;36m_Concatenator._clean_keys_and_objs\u001B[1;34m(self, objs, keys)\u001B[0m\n\u001B[0;32m 504\u001B[0m objs_list \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(objs)\n\u001B[0;32m 506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs_list) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 507\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo objects to concatenate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 509\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m keys \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 510\u001B[0m objs_list \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(com\u001B[38;5;241m.\u001B[39mnot_none(\u001B[38;5;241m*\u001B[39mobjs_list))\n", + "\u001B[1;31mValueError\u001B[0m: No objects to concatenate" + ] + } + ], + "execution_count": 4 }, { "cell_type": "code", @@ -193,7 +179,7 @@ "id": "ad9733a1-2f42-43ee-a98c-0bf699304c21", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:48.966102Z", + "end_time": "2025-04-08T13:37:37.748574900Z", "start_time": "2025-04-06T15:34:48.693158Z" } }, @@ -221,7 +207,7 @@ "id": "7e777f1f-4d54-4a74-b916-691ede6af055", "metadata": { "ExecuteTime": { - "end_time": "2025-04-06T15:34:48.980659Z", + "end_time": "2025-04-08T13:37:37.762102Z", "start_time": "2025-04-06T15:34:48.977771Z" } }, diff --git a/code/train/RollingRank.ipynb b/code/train/RollingRank.ipynb index f37315d..a466b18 100644 --- a/code/train/RollingRank.ipynb +++ b/code/train/RollingRank.ipynb @@ -2,18 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, "id": "79a7758178bafdd3", "metadata": { - "ExecuteTime": { - "end_time": "2025-04-07T14:56:49.543144Z", - "start_time": "2025-04-07T14:56:48.894078Z" - }, "jupyter": { "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-04-09T14:48:04.783264Z", + "start_time": "2025-04-09T14:48:04.779754Z" } }, - "outputs": [], "source": [ "# %load_ext autoreload\n", "# %autoreload 2\n", @@ -24,19 +22,51 @@ "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option('display.max_columns', None)\n" - ] + ], + "outputs": [], + "execution_count": 25 }, { "cell_type": "code", - "execution_count": 2, "id": "a79cafb06a7e0e43", "metadata": { + "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-07T14:57:43.308397Z", - "start_time": "2025-04-07T14:56:49.554151Z" - }, - "scrolled": true + "end_time": "2025-04-09T14:48:50.995967Z", + "start_time": "2025-04-09T14:48:04.834658Z" + } }, + "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", + "df = df[df['trade_date'] >= '2021-01-01']\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", @@ -52,7 +82,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8504242 entries, 0 to 8504241\n", + "RangeIndex: 5113004 entries, 0 to 5113003\n", "Data columns (total 31 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -88,64 +118,22 @@ " 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", + "memory usage: 1.1+ 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', '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())" - ] + "execution_count": 26 }, { "cell_type": "code", - "execution_count": 3, "id": "cac01788dac10678", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T14:57:56.408385Z", - "start_time": "2025-04-07T14:57:43.722790Z" - }, - "jupyter": { - "source_hidden": true + "end_time": "2025-04-09T14:48:56.021834Z", + "start_time": "2025-04-09T14:48:51.037076Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "industry\n" - ] - } - ], "source": [ "print('industry')\n", "industry_df = read_and_merge_h5_data('../../data/industry_data.h5', key='industry_data',\n", @@ -193,19 +181,27 @@ "# 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": 27 }, { "cell_type": "code", - "execution_count": 4, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T14:57:56.676422Z", - "start_time": "2025-04-07T14:57:56.445955Z" + "end_time": "2025-04-09T14:48:56.082954Z", + "start_time": "2025-04-09T14:48:56.026849Z" } }, - "outputs": [], "source": [ "def calculate_indicators(df):\n", " \"\"\"\n", @@ -282,22 +278,22 @@ "h5_filename = '../../data/index_data.h5'\n", "index_data = generate_index_indicators(h5_filename)\n", "index_data = index_data.dropna()\n" - ] + ], + "outputs": [], + "execution_count": 28 }, { "cell_type": "code", - "execution_count": 5, "id": "a735bc02ceb4d872", "metadata": { - "ExecuteTime": { - "end_time": "2025-04-07T14:57:56.777758Z", - "start_time": "2025-04-07T14:57:56.706997Z" - }, "jupyter": { "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-04-09T14:48:56.140563Z", + "start_time": "2025-04-09T14:48:56.110420Z" } }, - "outputs": [], "source": [ "import numpy as np\n", "import talib\n", @@ -387,7 +383,7 @@ " 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", + " df['vol_std_5'] = grouped['vol'].pct_change().rolling(window=5).std()\n", "\n", " # 计算 ATR\n", " df['atr_14'] = grouped.apply(\n", @@ -467,31 +463,36 @@ "\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", + "\n", + " # 计算每只股票的滚动协方差\n", + " def calculate_rolling_cov(group):\n", + " return group['high'].rolling(window_high_volume).cov(group['vol'])\n", + "\n", + " df['cov'] = grouped.apply(calculate_rolling_cov)\n", + "\n", + " # 计算每只股票的协方差差分\n", + " def calculate_delta_cov(group):\n", + " return group['cov'].diff(period_delta)\n", + "\n", + " df['delta_cov'] = grouped.apply(calculate_delta_cov)\n", + "\n", + " # 计算每只股票的滚动标准差\n", + " def calculate_stddev_close(group):\n", + " return group['close'].rolling(window_close_stddev).std()\n", + "\n", + " df['_stddev_close'] = grouped.apply(calculate_stddev_close)\n", + " df['_rank_stddev'] = df.groupby('trade_date')['_stddev_close'].rank(pct=True)\n", " df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']\n", "\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'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol']))\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", @@ -516,7 +517,7 @@ " # 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", "\n", " df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)\n", "\n", @@ -542,20 +543,24 @@ " 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['log(circ_mv)']).reset_index(level=0, drop=True)\n", + " # 7. 市值波动率因子 (使用 grouped)\n", + " df['turnover_std'] = grouped['turnover_rate'].transform(lambda x: x.rolling(window=20).std())\n", + " df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log(circ_mv)'])\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['log(circ_mv)']).reset_index(level=0, drop=True)\n", + " df['volume_growth'] = grouped['vol'].pct_change(periods=20)\n", + " df['mv_growth'] = df['volume_growth'] / df['log(circ_mv)']\n", "\n", - " df[\"ar\"] = df[\"high\"].div(df[\"open\"]).rolling(3).sum() / df[\"open\"].div(df[\"low\"]).rolling(3).sum() * 100\n", - " # 计算 BR 指标\n", - " df[\"pre_close\"] = df[\"close\"].shift(1)\n", + " # AR 指标\n", + " df[\"ar\"] = grouped.apply(lambda x: (x[\"high\"].div(x[\"open\"]).rolling(3).sum()) / (x[\"open\"].div(x[\"low\"]).rolling(3).sum()) * 100)\n", + "\n", + " # BR 指标\n", + " df[\"pre_close\"] = grouped[\"close\"].shift(1)\n", " df[\"br_up\"] = (df[\"high\"] - df[\"pre_close\"]).clip(lower=0)\n", " df[\"br_down\"] = (df[\"pre_close\"] - df[\"low\"]).clip(lower=0)\n", - " df[\"br\"] = df[\"br_up\"].rolling(3).sum() / df[\"br_down\"].rolling(3).sum() * 100\n", + " df[\"br\"] = grouped.apply(lambda x: (x[\"br_up\"].rolling(3).sum()) / (x[\"br_down\"].rolling(3).sum()) * 100)\n", + "\n", + " # ARBR\n", " df['arbr'] = df['ar'] - df['br']\n", " df.drop(columns=[\"pre_close\", \"br_up\", \"br_down\", 'ar', 'br'], inplace=True)\n", "\n", @@ -647,23 +652,20 @@ "\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": 29 }, { "cell_type": "code", - "execution_count": 6, "id": "53f86ddc0677a6d7", "metadata": { + "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-07T14:58:02.350944Z", - "start_time": "2025-04-07T14:57:56.782771Z" - }, - "jupyter": { - "source_hidden": true - }, - "scrolled": true + "end_time": "2025-04-09T14:49:01.535858Z", + "start_time": "2025-04-09T14:48:56.166990Z" + } }, - "outputs": [], "source": [ "from utils.factor import get_act_factor\n", "\n", @@ -689,7 +691,7 @@ "\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", + " #\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", @@ -710,18 +712,27 @@ "\n", "\n", "industry_df = read_industry_data('../../data/sw_daily.h5')\n" - ] + ], + "outputs": [], + "execution_count": 30 }, { "cell_type": "code", - "execution_count": 7, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T14:58:02.391649Z", - "start_time": "2025-04-07T14:58:02.387083Z" + "end_time": "2025-04-09T14:49:01.569403Z", + "start_time": "2025-04-09T14:49:01.565741Z" } }, + "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", @@ -731,53 +742,17 @@ ] } ], - "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)" - ] + "execution_count": 31 }, { "cell_type": "code", - "execution_count": 8, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T15:01:03.522872Z", - "start_time": "2025-04-07T14:58:02.425021Z" + "end_time": "2025-04-09T14:50:18.282461Z", + "start_time": "2025-04-09T14:49:01.600962Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", - " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n", - " 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n", - " 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", - " 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n", - " 'l2_code', '_is_positive', '_is_negative', 'cat_is_positive',\n", - " '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq',\n", - " 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew',\n", - " 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout',\n", - " 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio',\n", - " 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14',\n", - " 'atr_6', 'obv'],\n", - " dtype='object')\n", - "\n", - "Index: 5133637 entries, 0 to 5133636\n", - "Columns: 119 entries, ts_code to mv_momentum\n", - "dtypes: bool(12), datetime64[ns](1), float64(101), int32(2), int64(1), object(2)\n", - "memory usage: 4.1+ GB\n", - "None\n" - ] - } - ], "source": [ "def filter_data(df):\n", " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", @@ -808,22 +783,49 @@ "# df = df.merge(index_data, on='trade_date', how='left')\n", "\n", "print(df.info())" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n", + " 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n", + " 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", + " 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n", + " 'l2_code', '_is_positive', '_is_negative', 'cat_is_positive',\n", + " '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq',\n", + " 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew',\n", + " 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout',\n", + " 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio',\n", + " 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14',\n", + " 'atr_6', 'obv'],\n", + " dtype='object')\n", + "\n", + "Index: 3142516 entries, 0 to 3142515\n", + "Columns: 119 entries, ts_code to mv_momentum\n", + "dtypes: bool(12), datetime64[ns](1), float64(101), int32(2), int64(1), object(2)\n", + "memory usage: 2.5+ GB\n", + "None\n" + ] + } + ], + "execution_count": 32 }, { "cell_type": "code", - "execution_count": 9, "id": "f4f16d63ad18d1bc", "metadata": { - "ExecuteTime": { - "end_time": "2025-04-07T15:01:03.651232Z", - "start_time": "2025-04-07T15:01:03.641507Z" - }, "jupyter": { "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-04-09T14:50:18.967476Z", + "start_time": "2025-04-09T14:50:18.940918Z" } }, - "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", @@ -860,19 +862,19 @@ " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", "\n", " return df, ret_feature_columns\n" - ] + ], + "outputs": [], + "execution_count": 33 }, { "cell_type": "code", - "execution_count": 10, "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T15:01:05.578731Z", - "start_time": "2025-04-07T15:01:03.776079Z" + "end_time": "2025-04-09T14:50:19.189566Z", + "start_time": "2025-04-09T14:50:19.074786Z" } }, - "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -1117,37 +1119,37 @@ " # 应用 clip\n", " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", " return df" - ] + ], + "outputs": [], + "execution_count": 34 }, { "cell_type": "code", - "execution_count": 11, "id": "1c46817a-b5dd-4bec-8bb4-e6e80bfd9d66", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T15:01:05.587296Z", - "start_time": "2025-04-07T15:01:05.583736Z" + "end_time": "2025-04-09T14:50:19.205714Z", + "start_time": "2025-04-09T14:50:19.197581Z" } }, - "outputs": [], "source": [ "# print(test_data.head()[['act_factor1', 'act_factor2', 'ts_code', 'trade_date']])" - ] + ], + "outputs": [], + "execution_count": 35 }, { "cell_type": "code", - "execution_count": 12, "id": "da2bb202843d9275", "metadata": { - "ExecuteTime": { - "end_time": "2025-04-07T15:01:06.207888Z", - "start_time": "2025-04-07T15:01:05.611601Z" - }, "jupyter": { "source_hidden": true + }, + "ExecuteTime": { + "end_time": "2025-04-09T14:50:19.291833Z", + "start_time": "2025-04-09T14:50:19.280710Z" } }, - "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "import lightgbm as lgb\n", @@ -1242,19 +1244,19 @@ " plt.show()\n", "\n", " return model, scaler, None" - ] + ], + "outputs": [], + "execution_count": 36 }, { "cell_type": "code", - "execution_count": 13, "id": "ff19e3f1e051a489", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T15:02:24.864887Z", - "start_time": "2025-04-07T15:01:06.233628Z" + "end_time": "2025-04-09T14:51:32.646948Z", + "start_time": "2025-04-09T14:50:19.359656Z" } }, - "outputs": [], "source": [ "\n", "days = 2\n", @@ -1277,27 +1279,19 @@ "# 0.7 * df['future_return']\n", "# * 0.3 * df['future_volatility']\n", "# )" - ] + ], + "outputs": [], + "execution_count": 37 }, { "cell_type": "code", - "execution_count": 14, "id": "27dba27b2e108316", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:28:54.014475Z", - "start_time": "2025-04-07T16:28:38.494784Z" + "end_time": "2025-04-09T14:51:45.505400Z", + "start_time": "2025-04-09T14:51:32.722125Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-04-07 00:00:00\n" - ] - } - ], "source": [ "def select_pre_zt_stocks_dynamic(stock_df):\n", " def select_stocks(group):\n", @@ -1320,33 +1314,33 @@ " return stock_df\n", "\n", "\n", - "pdf = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-01-01') & (df['trade_date'] <= '2029-03-26')])\n", + "pdf = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-01-01') & (df['trade_date'] <= '2029-04-07')])\n", "print(pdf['trade_date'].max())\n", "\n", "# pdf['label'] = pdf.groupby('trade_date', group_keys=False)['future_score'].transform(\n", "# lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\n", "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ca96fb81e17c4a90", - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-07T16:28:55.029462Z", - "start_time": "2025-04-07T16:28:54.022482Z" - } - }, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', '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', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', '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', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'volume_growth', 'mv_growth', 'arbr', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv-maobv_6', 'std_return_5 / std_return_90', 'std_return_90 - std_return_90_2', '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_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', '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']\n" + "2025-04-07 00:00:00\n" ] } ], + "execution_count": 38 + }, + { + "cell_type": "code", + "id": "ca96fb81e17c4a90", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-09T14:51:46.074673Z", + "start_time": "2025-04-09T14:51:45.512822Z" + } + }, "source": [ "pdf = pdf.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "pdf = pdf.replace([np.inf, -np.inf], np.nan)\n", @@ -1364,6 +1358,7 @@ "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", + "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", "\n", "print(feature_columns)\n", "numeric_columns = pdf.select_dtypes(include=['float64', 'int64']).columns\n", @@ -1372,19 +1367,27 @@ "\n", "# filter_index = pdf['future_volatility'].between(pdf['future_volatility'].quantile(0.01),\n", "# pdf['future_volatility'].quantile(0.99)) | filter_index" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', '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', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', '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', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'volume_growth', 'mv_growth', 'arbr', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv-maobv_6', 'std_return_5 / std_return_90', 'std_return_90 - std_return_90_2', '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_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', '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']\n" + ] + } + ], + "execution_count": 39 }, { "cell_type": "code", - "execution_count": 16, "id": "81d4570663ae21d7", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:29:09.269040Z", - "start_time": "2025-04-07T16:28:55.127230Z" + "end_time": "2025-04-09T14:52:38.849524Z", + "start_time": "2025-04-09T14:51:46.113849Z" } }, - "outputs": [], "source": [ "\n", "# pdf = time_series_quantile_filter(pdf, numeric_columns)\n", @@ -1408,30 +1411,19 @@ "# pdf = cross_sectional_standardization(pdf, numeric_columns)\n", "#\n", "# print(pdf[pdf['trade_date'] == '2025-03-26'][['ts_code', 'trade_date', 'pct_chg']])" - ] + ], + "outputs": [], + "execution_count": 40 }, { "cell_type": "code", - "execution_count": 17, "id": "92428d543f4727ad", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:29:10.824723Z", - "start_time": "2025-04-07T16:29:09.328953Z" + "end_time": "2025-04-09T14:52:38.972693Z", + "start_time": "2025-04-09T14:52:38.887044Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# print('train data size: ', len(train_data))\n", "\n", @@ -1464,19 +1456,30 @@ "evals = {}\n", "\n", "gc.collect()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 41 }, { "cell_type": "code", - "execution_count": 18, "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:29:11.191288Z", - "start_time": "2025-04-07T16:29:10.946199Z" + "end_time": "2025-04-09T14:52:39.087454Z", + "start_time": "2025-04-09T14:52:39.016514Z" } }, - "outputs": [], "source": [ "gc.collect()\n", "\n", @@ -1634,1719 +1637,25 @@ "\n", " final_predictions = pd.concat(predictions_list, ignore_index=True)\n", " return final_predictions\n" - ] + ], + "outputs": [], + "execution_count": 42 }, { "cell_type": "code", - "execution_count": 19, "id": "63235069-dc59-48fb-961a-e80373e41a61", "metadata": { - "ExecuteTime": { - "end_time": "2025-04-07T16:30:26.234303Z", - "start_time": "2025-04-07T16:29:11.196293Z" - }, "editable": true, "scrolled": true, "slideshow": { "slide_type": "" }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "finish\n", - "train_data最大日期: 2022-12-07\n", - "test_data最大日期: 2022-12-08\n", - "划分后的训练集大小: 492, 验证集大小: 93\n", - "train_data最大日期: 2022-12-08\n", - "test_data最大日期: 2022-12-09\n", - "划分后的训练集大小: 499, 验证集大小: 102\n", - "train_data最大日期: 2022-12-09\n", - "test_data最大日期: 2022-12-12\n", - "划分后的训练集大小: 485, 验证集大小: 96\n", - "train_data最大日期: 2022-12-12\n", - "test_data最大日期: 2022-12-13\n", - "划分后的训练集大小: 497, 验证集大小: 102\n", - "train_data最大日期: 2022-12-13\n", - "test_data最大日期: 2022-12-14\n", - "划分后的训练集大小: 531, 验证集大小: 138\n", - "train_data最大日期: 2022-12-14\n", - "test_data最大日期: 2022-12-15\n", - "划分后的训练集大小: 532, 验证集大小: 94\n", - "train_data最大日期: 2022-12-15\n", - "test_data最大日期: 2022-12-16\n", - "划分后的训练集大小: 564, 验证集大小: 134\n", - "train_data最大日期: 2022-12-16\n", - "test_data最大日期: 2022-12-19\n", - "划分后的训练集大小: 561, 验证集大小: 93\n", - "train_data最大日期: 2022-12-19\n", - "test_data最大日期: 2022-12-20\n", - "划分后的训练集大小: 594, 验证集大小: 135\n", - "train_data最大日期: 2022-12-20\n", - "test_data最大日期: 2022-12-21\n", - "划分后的训练集大小: 592, 验证集大小: 136\n", - "train_data最大日期: 2022-12-21\n", - "test_data最大日期: 2022-12-22\n", - "划分后的训练集大小: 588, 验证集大小: 90\n", - "train_data最大日期: 2022-12-22\n", - "test_data最大日期: 2022-12-23\n", - "划分后的训练集大小: 570, 验证集大小: 116\n", - "train_data最大日期: 2022-12-23\n", - "test_data最大日期: 2022-12-26\n", - "划分后的训练集大小: 566, 验证集大小: 89\n", - "train_data最大日期: 2022-12-26\n", - "test_data最大日期: 2022-12-27\n", - "划分后的训练集大小: 522, 验证集大小: 91\n", - "train_data最大日期: 2022-12-27\n", - "test_data最大日期: 2022-12-28\n", - "划分后的训练集大小: 474, 验证集大小: 88\n", - "train_data最大日期: 2022-12-28\n", - 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2025-03-10\n", + "划分后的训练集大小: 682, 验证集大小: 143\n", + "train_data最大日期: 2025-03-10\n", + "test_data最大日期: 2025-03-11\n", + "划分后的训练集大小: 638, 验证集大小: 96\n", + "train_data最大日期: 2025-03-11\n", + "test_data最大日期: 2025-03-12\n", + "划分后的训练集大小: 640, 验证集大小: 147\n", + "train_data最大日期: 2025-03-12\n", + "test_data最大日期: 2025-03-13\n", + "划分后的训练集大小: 674, 验证集大小: 143\n", + "train_data最大日期: 2025-03-13\n", + "test_data最大日期: 2025-03-14\n", + "划分后的训练集大小: 678, 验证集大小: 149\n", + "train_data最大日期: 2025-03-14\n", + "test_data最大日期: 2025-03-17\n", + "划分后的训练集大小: 642, 验证集大小: 107\n", + "train_data最大日期: 2025-03-17\n", + "test_data最大日期: 2025-03-18\n", + "划分后的训练集大小: 691, 验证集大小: 145\n", + "train_data最大日期: 2025-03-18\n", + "test_data最大日期: 2025-03-19\n", + "划分后的训练集大小: 633, 验证集大小: 89\n", + "train_data最大日期: 2025-03-19\n", + "test_data最大日期: 2025-03-20\n", + "划分后的训练集大小: 610, 验证集大小: 120\n", + "train_data最大日期: 2025-03-20\n", + "test_data最大日期: 2025-03-21\n", + "划分后的训练集大小: 601, 验证集大小: 140\n", + "train_data最大日期: 2025-03-21\n", + "test_data最大日期: 2025-03-24\n", + "划分后的训练集大小: 629, 验证集大小: 135\n", + "train_data最大日期: 2025-03-24\n", + "test_data最大日期: 2025-03-25\n", + "划分后的训练集大小: 596, 验证集大小: 112\n", + "train_data最大日期: 2025-03-25\n", + "test_data最大日期: 2025-03-26\n", + "划分后的训练集大小: 651, 验证集大小: 144\n", + "train_data最大日期: 2025-03-26\n", + "test_data最大日期: 2025-03-27\n", + "划分后的训练集大小: 671, 验证集大小: 140\n", + "train_data最大日期: 2025-03-27\n", + "test_data最大日期: 2025-03-28\n", + "划分后的训练集大小: 623, 验证集大小: 92\n", + "train_data最大日期: 2025-03-28\n", + "test_data最大日期: 2025-03-31\n", + "划分后的训练集大小: 633, 验证集大小: 145\n", + "train_data最大日期: 2025-03-31\n", + "test_data最大日期: 2025-04-01\n", + "划分后的训练集大小: 666, 验证集大小: 145\n", + "train_data最大日期: 2025-04-01\n", + "test_data最大日期: 2025-04-02\n", + "划分后的训练集大小: 637, 验证集大小: 115\n", + "train_data最大日期: 2025-04-02\n", + "test_data最大日期: 2025-04-03\n", + "划分后的训练集大小: 620, 验证集大小: 123\n", + "train_data最大日期: 2025-04-02\n", + "test_data最大日期: 2025-04-07\n", + "划分后的训练集大小: 528, 验证集大小: 123\n" + ] + } + ], + "execution_count": 43 }, { "cell_type": "code", - "execution_count": 20, "id": "e01fe33b-e30d-4bc6-bf40-de91e61862b4", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:30:26.382297Z", - "start_time": "2025-04-07T16:30:26.377596Z" + "end_time": "2025-04-09T14:53:53.045759Z", + "start_time": "2025-04-09T14:53:53.040829Z" } }, + "source": [ + "print(final_predictions[['ts_code', 'trade_date', 'score']].tail())" + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date score\n", - "557 002595.SZ 2025-03-31 0.677121\n", - "558 603416.SH 2025-04-01 1.144557\n", - "559 603630.SH 2025-04-02 0.116357\n", - "560 603169.SH 2025-04-03 1.025368\n", - "561 001289.SZ 2025-04-07 0.491444\n" + "557 002851.SZ 2025-03-31 1.547811\n", + "558 002847.SZ 2025-04-01 0.596230\n", + "559 601595.SH 2025-04-02 -0.014094\n", + "560 603353.SH 2025-04-03 1.512002\n", + "561 600600.SH 2025-04-07 0.640985\n" ] } ], - "source": [ - "print(final_predictions[['ts_code', 'trade_date', 'score']].tail())" - ] + "execution_count": 44 }, { "cell_type": "code", - "execution_count": 21, "id": "0dc75517-c857-4f1d-8815-e807400a6d33", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:30:26.392890Z", - "start_time": "2025-04-07T16:30:26.388311Z" + "end_time": "2025-04-09T14:53:53.055527Z", + "start_time": "2025-04-09T14:53:53.049774Z" } }, - "outputs": [], "source": [ "# pdf1 = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-03-26') & (df['trade_date'] <= '2029-03-26')])\n", "# pdf1 = pdf1.replace([np.inf, -np.inf], np.nan)\n", @@ -3423,19 +3426,19 @@ "# # pdf2 = pdf2[pdf2['trade_date'] <= '2025-03-26']\n", "# # pdf2 = pdf2.sort_values(by=['ts_code', 'trade_date'])\n", "# filter_index2 = pdf2['future_return'].between(pdf2['future_return'].quantile(0.01), pdf2['future_return'].quantile(0.99))\n" - ] + ], + "outputs": [], + "execution_count": 45 }, { "cell_type": "code", - "execution_count": 22, "id": "8299a6f461097f14", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:30:26.519826Z", - "start_time": "2025-04-07T16:30:26.513721Z" + "end_time": "2025-04-09T14:53:53.135966Z", + "start_time": "2025-04-09T14:53:53.131344Z" } }, - "outputs": [], "source": [ "# are_equal = pdf1[filter_index1].equals(pdf2[filter_index2])\n", "# print(are_equal) # 输出 True 或 False\n", @@ -3445,19 +3448,19 @@ "#\n", "# are_equal = filter_index1.equals(filter_index2)\n", "# print(are_equal) # 输出 True 或 False" - ] + ], + "outputs": [], + "execution_count": 46 }, { "cell_type": "code", - "execution_count": 23, "id": "3f5079aa2c937c22", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:30:26.533277Z", - "start_time": "2025-04-07T16:30:26.523832Z" + "end_time": "2025-04-09T14:53:53.148195Z", + "start_time": "2025-04-09T14:53:53.138983Z" } }, - "outputs": [], "source": [ "# final_predictions1 = rolling_train_predict(\n", "# pdf1[(pdf1['trade_date'] >= '2024-12-01') & (pdf1['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n", @@ -3468,26 +3471,28 @@ "# pdf2[(pdf2['trade_date'] >= '2024-12-01') & (pdf2['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n", "# days=days, validation_days=0, filter_index=filter_index2, params=light_params)\n", "# final_predictions2.to_csv('test2.tsv', index=False)" - ] + ], + "outputs": [], + "execution_count": 47 }, { "cell_type": "code", - "execution_count": 24, "id": "199b12e7e20e4e6a", "metadata": { "ExecuteTime": { - "end_time": "2025-04-07T16:30:26.595030Z", - "start_time": "2025-04-07T16:30:26.592351Z" + "end_time": "2025-04-09T14:53:53.186822Z", + "start_time": "2025-04-09T14:53:53.184469Z" } }, - "outputs": [], "source": [ "# print(final_predictions1['trade_date'].max())\n", "# print(final_predictions2['trade_date'].max())\n", "#\n", "# are_equal = final_predictions1[(final_predictions1['trade_date'] >= '2022-12-01') & (final_predictions1['trade_date'] <= '2025-03-26')].equals(final_predictions2[(final_predictions2['trade_date'] >= '2022-12-01') & (final_predictions2['trade_date'] <= '2025-03-26')])\n", "# print(are_equal) # 输出 True 或 False" - ] + ], + "outputs": [], + "execution_count": 48 } ], "metadata": { diff --git a/code/train/predictions_test.tsv b/code/train/predictions_test.tsv index 89fbcd2..59e543e 100644 --- a/code/train/predictions_test.tsv +++ b/code/train/predictions_test.tsv @@ -1,563 +1,563 @@ trade_date,score,ts_code -2022-12-08,1.2833561266938238,603833.SH -2022-12-09,1.2815220032893768,603567.SH -2022-12-12,0.7874001245223025,002305.SZ -2022-12-13,1.3536751588061415,603198.SH -2022-12-14,1.1239884897004895,001317.SZ -2022-12-15,0.5667299637183139,600222.SH -2022-12-16,0.2513365967190901,000679.SZ -2022-12-19,1.1592690121179248,002120.SZ -2022-12-20,0.8152364328287158,000978.SZ -2022-12-21,0.25301380554346914,603238.SH -2022-12-22,1.302146057178233,001209.SZ -2022-12-23,1.3047547932897534,002186.SZ -2022-12-26,1.247092628611336,600257.SH -2022-12-27,1.4824384717358212,002095.SZ -2022-12-28,1.983968347819751,000679.SZ -2022-12-29,0.229780091962585,600520.SH -2022-12-30,1.3504357320703664,002357.SZ -2023-01-03,0.7142948002846212,605188.SH -2023-01-04,0.797839822034453,000715.SZ -2023-01-05,0.7570791190146945,002193.SZ -2023-01-06,0.3098976682675483,600521.SH -2023-01-09,1.5041484610786304,000716.SZ -2023-01-10,0.15267679027901798,002518.SZ -2023-01-11,0.5738201293835982,603529.SH -2023-01-12,1.6836291999265451,002441.SZ -2023-01-13,2.108261545479461,002043.SZ -2023-01-16,0.2532429369324145,002195.SZ -2023-01-17,1.6805416632105206,600705.SH -2023-01-18,1.2021336109226464,002670.SZ -2023-01-19,0.7769527512869483,000567.SZ -2023-01-20,1.328094150470525,600705.SH -2023-01-30,0.4965822235665385,002965.SZ -2023-01-31,1.0182152098820885,002235.SZ -2023-02-01,1.4819511286872855,601958.SH -2023-02-02,1.7997160721641545,002576.SZ -2023-02-03,1.2957962247569712,002536.SZ -2023-02-06,1.1349769065669424,002615.SZ -2023-02-07,1.179609151856121,003021.SZ 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