From dc1e62c77c2b949454011a7da05e65982986c948 Mon Sep 17 00:00:00 2001 From: liaozhaorun Date: Tue, 8 Apr 2025 20:32:51 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A4=9A=E7=BA=BF=E7=A8=8Brank6.0=EF=BC=8C?= =?UTF-8?q?=E8=B5=9A=E9=92=B1=EF=BC=8C=E5=9B=9E=E6=92=A4=E7=95=A5=E5=BE=AE?= =?UTF-8?q?=E5=87=8F=E5=B0=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/data/index_and_industry.ipynb | 118 +- code/data/update/cyq-perf.ipynb | 145 +- code/data/update/sw_daily.ipynb | 145 +- code/data/update/update_daily_basic.ipynb | 401 +- code/data/update/update_daily_data.ipynb | 1036 ++--- code/data/update/update_money_flow.ipynb | 147 +- code/data/update/update_stk_limit.ipynb | 254 +- code/train/RollingRank.ipynb | 5062 +++++++++------------ code/train/predictions_test.tsv | 1097 ++--- 9 files changed, 3737 insertions(+), 4668 deletions(-) diff --git a/code/data/index_and_industry.ipynb b/code/data/index_and_industry.ipynb index 35613dd..3a60e46 100644 --- a/code/data/index_and_industry.ipynb +++ b/code/data/index_and_industry.ipynb @@ -2,13 +2,15 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:23.864275Z", - "start_time": "2025-03-30T16:42:22.963221Z" + "end_time": "2025-04-06T15:33:29.087509Z", + "start_time": "2025-04-06T15:33:28.293879Z" } }, + "outputs": [], "source": [ "from operator import index\n", "\n", @@ -18,19 +20,35 @@ "\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": "2025-03-30T16:42:25.559047Z", - "start_time": "2025-03-30T16:42:23.868783Z" + "end_time": "2025-04-06T15:33:32.756495Z", + "start_time": "2025-04-06T15:33:29.097180Z" } }, + "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", @@ -50,78 +68,60 @@ "final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n", "\n", "print(\"数据已经成功存储到index_data.h5文件中\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "数据已经成功存储到index_data.h5文件中\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6192\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " final_df = pd.concat(all_data, ignore_index=True)\n" - ] - } - ], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "907f732d3c397bf", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:25.802535Z", - "start_time": "2025-03-30T16:42:25.766399Z" + "end_time": "2025-04-06T15:33:32.795003Z", + "start_time": "2025-04-06T15:33:32.758127Z" } }, - "source": [ - "h5_filename = '../../data/index_data.h5'\n", - "key = '/index_data'\n", - "with pd.HDFStore(h5_filename, mode='r') as store:\n", - " df = store[key]\n", - " print(df)\n" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date close open high low \\\n", - "0 000905.SH 20250328 5916.0314 5954.7297 5973.8015 5904.9159 \n", - "1 000905.SH 20250327 5957.6017 5932.5165 6000.6615 5891.7664 \n", - "2 000905.SH 20250326 5948.4986 5935.8537 5983.4739 5935.8537 \n", - "3 000905.SH 20250325 5946.9510 5969.4164 5993.9312 5929.6734 \n", - "4 000905.SH 20250324 5969.0789 5973.0466 5987.0606 5882.8780 \n", + "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", - "13423 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", - "13424 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", - "13425 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", - "13426 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", - "13427 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "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 5957.6017 -41.5703 -0.6978 1.342619e+08 1.688995e+08 \n", - "1 5948.4986 9.1031 0.1530 1.347089e+08 1.765905e+08 \n", - "2 5946.9510 1.5476 0.0260 1.367021e+08 1.716958e+08 \n", - "3 5969.0789 -22.1279 -0.3707 1.474839e+08 1.922270e+08 \n", - "4 5971.9302 -2.8513 -0.0477 1.691924e+08 2.200943e+08 \n", - "... ... ... ... ... ... \n", - "13423 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", - "13424 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", - "13425 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", - "13426 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", - "13427 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + " 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", - "[13428 rows x 11 columns]\n" + "[13443 rows x 11 columns]\n" ] } ], - "execution_count": 3 + "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" + ] } ], "metadata": { diff --git a/code/data/update/cyq-perf.ipynb b/code/data/update/cyq-perf.ipynb index 499cbf1..3af5515 100644 --- a/code/data/update/cyq-perf.ipynb +++ b/code/data/update/cyq-perf.ipynb @@ -2,30 +2,55 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:31.596637Z", - "start_time": "2025-03-30T16:42:30.883319Z" + "end_time": "2025-04-06T15:33:48.836794Z", + "start_time": "2025-04-06T15:33:48.098706Z" } }, + "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": "2025-03-30T16:42:37.590148Z", - "start_time": "2025-03-30T16:42:31.596637Z" + "end_time": "2025-04-06T15:33:55.800360Z", + "start_time": "2025-04-06T15:33:49.011404Z" } }, + "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", @@ -44,42 +69,36 @@ "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": [ - " ts_code trade_date\n", - "0 000001.SZ 20250312\n", - "1 000002.SZ 20250312\n", - "2 000004.SZ 20250312\n", - "3 000006.SZ 20250312\n", - "4 000007.SZ 20250312\n", - "... ... ...\n", - "32304 920108.BJ 20250314\n", - "32305 920111.BJ 20250314\n", - "32306 920116.BJ 20250314\n", - "32307 920118.BJ 20250314\n", - "32308 920128.BJ 20250314\n", - "\n", - "[7503415 rows x 2 columns]\n", - "20250321\n", - "start_date: 20250324\n" + "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" ] } ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "747acc47-0884-4f76-90fb-276f6494e31d", - "metadata": { - "ExecuteTime": { - "end_time": "2025-03-30T16:43:29.275885Z", - "start_time": "2025-03-30T16:42:37.858763Z" - } - }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -109,55 +128,18 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250418 完成\n", - "任务 20250417 完成\n", - "任务 20250415 完成\n", - "任务 20250416 完成\n", - "任务 20250411 完成\n", - "任务 20250414 完成\n", - "任务 20250409 完成\n", - "任务 20250410 完成\n", - "任务 20250408 完成\n", - "任务 20250407 完成\n", - "任务 20250403 完成\n", - "任务 20250402 完成\n", - "任务 20250401 完成\n", - "任务 20250331 完成\n", - "任务 20250328 完成\n", - "任务 20250327 完成\n", - "任务 20250326 完成\n", - "任务 20250325 完成\n", - "任务 20250324 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "c6765638-481f-40d8-a259-2e7b25362618", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:30.100678Z", - "start_time": "2025-03-30T16:43:29.311710Z" + "end_time": "2025-04-06T15:33:57.874278Z", + "start_time": "2025-04-06T15:33:57.304371Z" } }, - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "\n", - "# 将所有数据合并为一个 DataFrame\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -167,7 +149,16 @@ ] } ], - "execution_count": 4 + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] } ], "metadata": { diff --git a/code/data/update/sw_daily.ipynb b/code/data/update/sw_daily.ipynb index 1ce7b7f..3201293 100644 --- a/code/data/update/sw_daily.ipynb +++ b/code/data/update/sw_daily.ipynb @@ -2,30 +2,55 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:32.996500Z", - "start_time": "2025-03-30T16:42:32.209631Z" + "end_time": "2025-04-06T15:33:36.424263Z", + "start_time": "2025-04-06T15:33:36.048334Z" } }, + "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": "2025-03-30T16:42:34.591433Z", - "start_time": "2025-03-30T16:42:32.996500Z" + "end_time": "2025-04-06T15:33:37.871314Z", + "start_time": "2025-04-06T15:33:36.424263Z" } }, + "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", @@ -44,42 +69,36 @@ "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": [ - " ts_code trade_date\n", - "0 801001.SI 20250221\n", - "1 801002.SI 20250221\n", - "2 801003.SI 20250221\n", - "3 801005.SI 20250221\n", - "4 801010.SI 20250221\n", - "... ... ...\n", - "2629 859811.SI 20250314\n", - "2630 859821.SI 20250314\n", - "2631 859822.SI 20250314\n", - "2632 859852.SI 20250314\n", - "2633 859951.SI 20250314\n", - "\n", - "[1053173 rows x 2 columns]\n", - "20250321\n", - "start_date: 20250324\n" + "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" ] } ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "747acc47-0884-4f76-90fb-276f6494e31d", - "metadata": { - "ExecuteTime": { - "end_time": "2025-03-30T16:42:37.718270Z", - "start_time": "2025-03-30T16:42:34.817305Z" - } - }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -109,55 +128,18 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250417 完成\n", - "任务 20250418 完成\n", - "任务 20250416 完成\n", - "任务 20250415 完成\n", - "任务 20250414 完成\n", - "任务 20250411 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n", - "任务 20250407 完成\n", - "任务 20250403 完成\n", - "任务 20250402 完成\n", - "任务 20250401 完成\n", - "任务 20250331 完成\n", - "任务 20250328 完成\n", - "任务 20250327 完成\n", - "任务 20250326 完成\n", - "任务 20250325 完成\n", - "任务 20250324 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "c6765638-481f-40d8-a259-2e7b25362618", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:37.922827Z", - "start_time": "2025-03-30T16:42:37.739040Z" + "end_time": "2025-04-06T15:33:40.286614Z", + "start_time": "2025-04-06T15:33:40.138227Z" } }, - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "\n", - "# 将所有数据合并为一个 DataFrame\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -167,7 +149,16 @@ ] } ], - "execution_count": 4 + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] } ], "metadata": { diff --git a/code/data/update/update_daily_basic.ipynb b/code/data/update/update_daily_basic.ipynb index db50192..3077b76 100644 --- a/code/data/update/update_daily_basic.ipynb +++ b/code/data/update/update_daily_basic.ipynb @@ -2,30 +2,32 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:34.194992Z", - "start_time": "2025-03-30T16:42:33.440178Z" + "end_time": "2025-04-06T15:33:43.537483Z", + "start_time": "2025-04-06T15:33:42.844004Z" } }, + "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": "2025-03-30T16:42:36.432691Z", - "start_time": "2025-03-30T16:42:34.197998Z" + "end_time": "2025-04-06T15:33:45.387772Z", + "start_time": "2025-04-06T15:33:43.537483Z" } }, + "outputs": [], "source": [ "from datetime import datetime\n", "import pandas as pd\n", @@ -73,19 +75,38 @@ " 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": "2025-03-30T16:43:03.790361Z", - "start_time": "2025-03-30T16:42:36.633554Z" + "end_time": "2025-04-06T15:33:54.089114Z", + "start_time": "2025-04-06T15:33:45.576286Z" } }, + "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", @@ -104,39 +125,37 @@ "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": [ - "\n", - "Index: 8453605 entries, 0 to 32308\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: 193.5+ MB\n", - "None\n", - "20250321\n", - "20250324\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 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" ] } ], - "execution_count": 3 - }, - { - "cell_type": "code", - "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", - "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-03-30T16:43:07.947442Z", - "start_time": "2025-03-30T16:43:03.827519Z" - } - }, "source": [ "\n", "\n", @@ -186,202 +205,169 @@ " # 重置批次起始时间\n", " batch_start_time = time.time()\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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 20250411 完成\n", - "任务 20250414 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n", - "任务 20250407 完成\n", - "任务 20250403 完成\n", - "任务 20250402 完成\n", - "任务 20250331 完成\n", - "任务 20250401 完成\n", - "任务 20250327 完成\n", - "任务 20250328 完成\n", - "任务 20250326 完成\n", - "任务 20250324 完成\n", - "任务 20250325 完成\n" - ] - } - ], - "execution_count": 4 + ] }, { "cell_type": "code", + "execution_count": 5, "id": "919023c693d7a47a", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:07.962318Z", - "start_time": "2025-03-30T16:43:07.951757Z" + "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" + ] + } + ], "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", - "0 603328.SH 20250327 10.44 1.0910 2.6596 \n", - "1 603989.SH 20250327 15.66 0.9036 2.6145 \n", - "2 603194.SH 20250327 38.03 14.0348 14.0348 \n", - "3 600884.SH 20250327 7.13 1.9769 2.1153 \n", - "4 688325.SH 20250327 47.26 1.5250 1.8078 \n", - "... ... ... ... ... ... \n", - "26946 688539.SH 20250325 26.70 1.0257 1.3011 \n", - "26947 688479.SH 20250325 18.73 0.9840 1.2588 \n", - "26948 000552.SZ 20250325 2.63 1.8147 3.0665 \n", - "26949 688719.SH 20250325 31.64 4.2998 5.1737 \n", - "26950 002709.SZ 20250325 19.50 1.2468 1.4268 \n", - "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 0.79 29.3625 23.3887 2.5786 3.2807 2.9727 1.8582 \n", - "1 0.79 17.8968 27.7940 1.7060 1.8591 1.6666 1.6823 \n", - "2 1.87 18.9266 18.3213 3.2891 2.5755 2.4322 NaN \n", - "3 0.52 20.9930 NaN 0.7305 0.8425 0.9106 2.7224 \n", - "4 0.93 67.1638 50.1073 2.3433 16.1029 10.2149 NaN \n", - "... ... ... ... ... ... ... ... \n", - "26946 0.56 51.5254 83.3548 2.8475 14.5500 13.9718 NaN \n", - "26947 0.61 23.5448 33.4921 1.4043 3.6736 4.5444 NaN \n", - "26948 1.42 8.0989 11.6324 0.8431 1.2501 1.3463 3.8023 \n", - "26949 1.64 26.3323 49.9921 2.0474 4.4195 3.6954 NaN \n", - "26950 0.76 19.7447 78.2248 2.9106 2.4233 3.0741 1.5444 \n", - "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 1.8582 99844.2611 99844.2611 40955.5563 1.042374e+06 \n", - "1 1.6823 40113.0603 40113.0603 13863.2102 6.281705e+05 \n", - "2 NaN 40100.0000 4982.8436 4982.8436 1.525003e+06 \n", - "3 2.7224 225339.6168 175723.6492 164220.4548 1.606671e+06 \n", - "4 NaN 8494.7740 3830.4117 3231.0886 4.014630e+05 \n", - "... ... ... ... ... ... \n", - "26946 NaN 18592.0000 10286.0800 8109.0800 4.964064e+05 \n", - "26947 NaN 14431.7400 6087.4224 4758.2224 2.703065e+05 \n", - "26948 3.8023 535180.1936 372577.7383 220477.9354 1.407524e+06 \n", - "26949 NaN 11538.5418 7349.9938 6108.5305 3.650795e+05 \n", - "26950 1.5444 191434.3762 138501.6891 121034.9868 3.732970e+06 \n", - "\n", - " circ_mv is_st \n", - "0 1.042374e+06 False \n", - "1 6.281705e+05 False \n", - "2 1.894975e+05 False \n", - "3 1.252910e+06 False \n", - "4 1.810253e+05 False \n", - "... ... ... \n", - "26946 2.746383e+05 False \n", - "26947 1.140174e+05 False \n", - "26948 9.798795e+05 False \n", - "26949 2.325538e+05 False \n", - "26950 2.700783e+06 False \n", - "\n", - "[26951 rows x 19 columns]\n" - ] - } - ], - "execution_count": 5 + ] }, { "cell_type": "code", + "execution_count": 6, "id": "28cb78d032671b20", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:08.000073Z", - "start_time": "2025-03-30T16:43:07.984082Z" + "end_time": "2025-04-06T15:33:57.104132Z", + "start_time": "2025-04-06T15:33:57.095010Z" } }, - "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", - "100 002528.SZ 20250327 2.53 0.6855 1.4642 \n", - "128 300163.SZ 20250327 3.15 3.0563 3.2999 \n", - "129 300205.SZ 20250327 4.34 0.9211 1.5246 \n", - "147 000851.SZ 20250327 2.53 2.2990 2.6472 \n", - "299 300097.SZ 20250327 4.88 3.1648 3.6912 \n", - "... ... ... ... ... ... \n", - "26750 000506.SZ 20250325 5.21 1.2689 1.8939 \n", - "26770 002592.SZ 20250325 5.22 1.0547 1.6712 \n", - "26786 600603.SH 20250325 7.63 0.4610 1.0776 \n", - "26828 002528.SZ 20250325 2.51 0.9799 2.0928 \n", - "26906 300097.SZ 20250325 4.92 3.2717 3.8159 \n", + " 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", + "... ... ... ... ... ... \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", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "100 0.43 NaN NaN 7.3528 2.1714 2.7257 0.0000 \n", - "128 0.87 NaN NaN 3.0547 5.9187 5.8999 0.0000 \n", - "129 0.63 94.7108 NaN 1.3743 1.0976 1.5538 0.4608 \n", - "147 0.64 NaN NaN 1.0360 0.4939 0.8666 0.0000 \n", - "299 0.70 10.0614 NaN 2.2055 2.9549 3.1999 0.0000 \n", - "... ... ... ... ... ... ... ... \n", - "26750 0.37 725.4828 NaN 8.2869 17.0204 21.9262 0.0000 \n", - "26770 0.94 14.0192 61.1217 1.6387 2.7253 2.3121 0.0000 \n", - "26786 0.56 15.6086 24.2223 1.3160 1.8461 2.4398 0.0000 \n", - "26828 0.58 NaN NaN 7.2947 2.1542 2.7042 0.0000 \n", - "26906 0.53 10.1438 NaN 2.2236 2.9791 3.2261 0.0000 \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", "\n", - " dv_ttm total_share float_share free_share total_mv circ_mv \\\n", - "100 NaN 119867.5082 105021.9577 49171.2582 303264.7957 265705.5530 \n", - "128 NaN 47400.0000 41596.4553 38525.5904 149310.0000 131028.8342 \n", - "129 0.4608 43005.6000 42599.1218 25737.4813 186644.3040 184880.1886 \n", - "147 NaN 115786.0020 113197.7266 98311.5254 292938.5851 286390.2483 \n", - "299 NaN 28854.9669 27000.9948 23150.5534 140812.2385 131764.8546 \n", - "... ... ... ... ... ... ... \n", - "26750 NaN 92901.7761 92867.0961 62218.8027 484018.2535 483837.5707 \n", - "26770 NaN 28333.1157 26271.6370 16580.1814 147898.8640 137137.9451 \n", - "26786 NaN 119332.9151 119332.9151 51048.6002 910510.1422 910510.1422 \n", - "26828 NaN 119867.5082 105021.9577 49171.2582 300867.4456 263605.1138 \n", - "26906 NaN 28854.9669 27000.9948 23150.5534 141966.4371 132844.8944 \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", "\n", - " is_st \n", - "100 True \n", - "128 True \n", - "129 True \n", - "147 True \n", - "299 True \n", - "... ... \n", - "26750 True \n", - "26770 True \n", - "26786 True \n", - "26828 True \n", - "26906 True \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", + "... ... ... \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", "\n", - "[540 rows x 19 columns]\n" + "[106 rows x 19 columns]\n" ] } ], - "execution_count": 6 + "source": [ + "print(all_daily_data_df[all_daily_data_df['is_st']])" + ] }, { "cell_type": "code", + "execution_count": 7, "id": "692b58674b7462c9", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:08.703938Z", - "start_time": "2025-03-30T16:43:08.021067Z" + "end_time": "2025-04-06T15:33:57.927188Z", + "start_time": "2025-04-06T15:33:57.127166Z" } }, - "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", @@ -391,29 +377,30 @@ ] } ], - "execution_count": 7 + "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" + ] }, { "cell_type": "code", + "execution_count": 8, "id": "d7a773fc20293477", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:15.188800Z", - "start_time": "2025-03-30T16:43:08.725449Z" + "end_time": "2025-04-06T15:34:06.721517Z", + "start_time": "2025-04-06T15:33:57.951119Z" } }, - "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: 8480556 entries, 0 to 26950\n", + "Index: 8507519 entries, 0 to 5390\n", "Data columns (total 3 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -421,12 +408,16 @@ " 1 trade_date object\n", " 2 is_st bool \n", "dtypes: bool(1), object(2)\n", - "memory usage: 202.2+ MB\n", + "memory usage: 202.8+ MB\n", "None\n" ] } ], - "execution_count": 8 + "source": [ + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date', 'is_st']]\n", + " print(df.info())" + ] } ], "metadata": { diff --git a/code/data/update/update_daily_data.ipynb b/code/data/update/update_daily_data.ipynb index b1b40c0..528e7f9 100644 --- a/code/data/update/update_daily_data.ipynb +++ b/code/data/update/update_daily_data.ipynb @@ -2,13 +2,15 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "f294ba92-512a-48e6-bbaa-e19401c691ba", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:44:16.291227Z", - "start_time": "2025-03-30T16:44:15.451043Z" + "end_time": "2025-04-06T15:31:33.394624Z", + "start_time": "2025-04-06T15:31:32.508688Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "import pandas as pd\n", @@ -18,19 +20,38 @@ "\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": "2025-03-30T16:44:23.242175Z", - "start_time": "2025-03-30T16:44:16.454498Z" + "end_time": "2025-04-06T15:32:56.686423Z", + "start_time": "2025-04-06T15:32:49.588940Z" } }, + "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", @@ -46,94 +67,28 @@ "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: 8525701 entries, 0 to 32262\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: 195.1+ MB\n", - "None\n", - "20250321\n", - "20250324\n" - ] - } - ], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "f403cc963e1d39b", "metadata": { - "scrolled": true, "ExecuteTime": { - "end_time": "2025-03-30T16:56:34.154056Z", - "start_time": "2025-03-30T16:44:23.242175Z" - } + "end_time": "2025-04-06T15:50:12.427897Z", + "start_time": "2025-04-06T15:33:09.027974Z" + }, + "scrolled": true }, - "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", "output_type": "stream", "text": [ - "任务 000002.SZ 完成\n", "任务 000001.SZ 完成\n", - "任务 000004.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 000007.SZ 完成\n", "任务 000008.SZ 完成\n", "任务 000009.SZ 完成\n", @@ -152,16 +107,16 @@ "任务 000028.SZ 完成\n", "任务 000029.SZ 完成\n", "任务 000030.SZ 完成\n", - "任务 000031.SZ 完成\n", "任务 000032.SZ 完成\n", + "任务 000031.SZ 完成\n", "任务 000034.SZ 完成\n", "任务 000035.SZ 完成\n", "任务 000036.SZ 完成\n", "任务 000037.SZ 完成\n", "任务 000039.SZ 完成\n", "任务 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+ "任务 002931.SZ 完成\n", "任务 002933.SZ 完成\n", "任务 002935.SZ 完成\n", "任务 002936.SZ 完成\n", @@ -1521,30 +1476,30 @@ "任务 002941.SZ 完成\n", "任务 002942.SZ 完成\n", "任务 002943.SZ 完成\n", - "任务 002945.SZ 完成\n", "任务 002946.SZ 完成\n", + "任务 002945.SZ 完成\n", "任务 002947.SZ 完成\n", "任务 002948.SZ 完成\n", - "任务 002949.SZ 完成\n", "任务 002950.SZ 完成\n", + "任务 002949.SZ 完成\n", "任务 002951.SZ 完成\n", "任务 002952.SZ 完成\n", "任务 002953.SZ 完成\n", "任务 002955.SZ 完成\n", - "任务 002956.SZ 完成\n", "任务 002957.SZ 完成\n", + "任务 002956.SZ 完成\n", "任务 002958.SZ 完成\n", "任务 002959.SZ 完成\n", "任务 002960.SZ 完成\n", "任务 002961.SZ 完成\n", - "任务 002962.SZ 完成\n", "任务 002963.SZ 完成\n", - "任务 002965.SZ 完成\n", + "任务 002962.SZ 完成\n", "任务 002966.SZ 完成\n", - "任务 002967.SZ 完成\n", + "任务 002965.SZ 完成\n", "任务 002968.SZ 完成\n", - "任务 002969.SZ 完成\n", + "任务 002967.SZ 完成\n", "任务 002970.SZ 完成\n", + "任务 002969.SZ 完成\n", "任务 002971.SZ 完成\n", "任务 002972.SZ 完成\n", "任务 002973.SZ 完成\n", @@ -1553,14 +1508,14 @@ "任务 002977.SZ 完成\n", "任务 002978.SZ 完成\n", "任务 002979.SZ 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完成\n", "任务 300035.SZ 完成\n", "任务 300036.SZ 完成\n", - "任务 300037.SZ 完成\n", "任务 300039.SZ 完成\n", + "任务 300037.SZ 完成\n", "任务 300040.SZ 完成\n", "任务 300041.SZ 完成\n", "任务 300042.SZ 完成\n", @@ -1666,10 +1621,10 @@ "任务 300053.SZ 完成\n", "任务 300054.SZ 完成\n", "任务 300055.SZ 完成\n", - "任务 300056.SZ 完成\n", "任务 300057.SZ 完成\n", - "任务 300058.SZ 完成\n", + "任务 300056.SZ 完成\n", "任务 300059.SZ 完成\n", + "任务 300058.SZ 完成\n", "任务 300061.SZ 完成\n", "任务 300062.SZ 完成\n", "任务 300063.SZ 完成\n", @@ -1736,72 +1691,72 @@ "任务 300129.SZ 完成\n", "任务 300130.SZ 完成\n", "任务 300131.SZ 完成\n", - "任务 300132.SZ 完成\n", "任务 300133.SZ 完成\n", - "任务 300134.SZ 完成\n", + "任务 300132.SZ 完成\n", "任务 300135.SZ 完成\n", + "任务 300134.SZ 完成\n", "任务 300136.SZ 完成\n", "任务 300137.SZ 完成\n", "任务 300138.SZ 完成\n", "任务 300139.SZ 完成\n", - "任务 300140.SZ 完成\n", "任务 300141.SZ 完成\n", - "任务 300142.SZ 完成\n", + "任务 300140.SZ 完成\n", "任务 300143.SZ 完成\n", - "任务 300145.SZ 完成\n", + "任务 300142.SZ 完成\n", "任务 300144.SZ 完成\n", - "任务 300147.SZ 完成\n", + "任务 300145.SZ 完成\n", "任务 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完成\n", "任务 300183.SZ 完成\n", - "任务 300184.SZ 完成\n", "任务 300185.SZ 完成\n", + "任务 300184.SZ 完成\n", "任务 300187.SZ 完成\n", "任务 300188.SZ 完成\n", "任务 300189.SZ 完成\n", "任务 300190.SZ 完成\n", - "任务 300191.SZ 完成\n", "任务 300192.SZ 完成\n", + "任务 300191.SZ 完成\n", "任务 300193.SZ 完成\n", "任务 300194.SZ 完成\n", "任务 300195.SZ 完成\n", "任务 300196.SZ 完成\n", - "任务 300197.SZ 完成\n", "任务 300198.SZ 完成\n", - "任务 300199.SZ 完成\n", + "任务 300197.SZ 完成\n", "任务 300200.SZ 完成\n", + "任务 300199.SZ 完成\n", "任务 300201.SZ 完成\n", "任务 300203.SZ 完成\n", "任务 300204.SZ 完成\n", @@ -1810,18 +1765,18 @@ "任务 300207.SZ 完成\n", "任务 300208.SZ 完成\n", "任务 300209.SZ 完成\n", - "任务 300210.SZ 完成\n", "任务 300211.SZ 完成\n", - "任务 300212.SZ 完成\n", + "任务 300210.SZ 完成\n", "任务 300213.SZ 完成\n", + "任务 300212.SZ 完成\n", "任务 300214.SZ 完成\n", "任务 300215.SZ 完成\n", "任务 300217.SZ 完成\n", "任务 300218.SZ 完成\n", "任务 300219.SZ 完成\n", "任务 300220.SZ 完成\n", - "任务 300221.SZ 完成\n", "任务 300222.SZ 完成\n", + "任务 300221.SZ 完成\n", "任务 300223.SZ 完成\n", "任务 300224.SZ 完成\n", "任务 300225.SZ 完成\n", @@ -1986,8 +1941,8 @@ "任务 300399.SZ 完成\n", "任务 300400.SZ 完成\n", "任务 300401.SZ 完成\n", - "任务 300403.SZ 完成\n", "任务 300402.SZ 完成\n", + "任务 300403.SZ 完成\n", "任务 300404.SZ 完成\n", "任务 300405.SZ 完成\n", "任务 300406.SZ 完成\n", @@ -1998,8 +1953,8 @@ "任务 300411.SZ 完成\n", "任务 300412.SZ 完成\n", "任务 300413.SZ 完成\n", - "任务 300415.SZ 完成\n", "任务 300414.SZ 完成\n", + "任务 300415.SZ 完成\n", "任务 300416.SZ 完成\n", "任务 300417.SZ 完成\n", "任务 300418.SZ 完成\n", @@ -2025,8 +1980,8 @@ "任务 300439.SZ 完成\n", "任务 300440.SZ 完成\n", "任务 300441.SZ 完成\n", - "任务 300442.SZ 完成\n", "任务 300443.SZ 完成\n", + "任务 300442.SZ 完成\n", "任务 300444.SZ 完成\n", "任务 300445.SZ 完成\n", "任务 300446.SZ 完成\n", @@ -2037,12 +1992,12 @@ "任务 300451.SZ 完成\n", "任务 300452.SZ 完成\n", "任务 300453.SZ 完成\n", - "任务 300454.SZ 完成\n", "任务 300455.SZ 完成\n", - "任务 300456.SZ 完成\n", + "任务 300454.SZ 完成\n", "任务 300457.SZ 完成\n", - "任务 300458.SZ 完成\n", + "任务 300456.SZ 完成\n", "任务 300459.SZ 完成\n", + "任务 300458.SZ 完成\n", "任务 300460.SZ 完成\n", "任务 300461.SZ 完成\n", "任务 300462.SZ 完成\n", @@ -2051,16 +2006,16 @@ "任务 300465.SZ 完成\n", "任务 300466.SZ 完成\n", "任务 300467.SZ 完成\n", - "任务 300468.SZ 完成\n", "任务 300469.SZ 完成\n", + "任务 300468.SZ 完成\n", "任务 300470.SZ 完成\n", "任务 300471.SZ 完成\n", "任务 300472.SZ 完成\n", "任务 300473.SZ 完成\n", - "任务 300475.SZ 完成\n", "任务 300474.SZ 完成\n", - "任务 300476.SZ 完成\n", + "任务 300475.SZ 完成\n", "任务 300477.SZ 完成\n", + "任务 300476.SZ 完成\n", "任务 300479.SZ 完成\n", "任务 300478.SZ 完成\n", "任务 300480.SZ 完成\n", @@ -2245,8 +2200,8 @@ "任务 300665.SZ 完成\n", "任务 300666.SZ 完成\n", "任务 300667.SZ 完成\n", - "任务 300669.SZ 完成\n", "任务 300668.SZ 完成\n", + "任务 300669.SZ 完成\n", "任务 300670.SZ 完成\n", "任务 300671.SZ 完成\n", "任务 300672.SZ 完成\n", @@ -2329,8 +2284,8 @@ "任务 300756.SZ 完成\n", "任务 300757.SZ 完成\n", "任务 300758.SZ 完成\n", - "任务 300759.SZ 完成\n", "任务 300760.SZ 完成\n", + "任务 300759.SZ 完成\n", "任务 300761.SZ 完成\n", "任务 300762.SZ 完成\n", "任务 300763.SZ 完成\n", @@ -2341,8 +2296,8 @@ "任务 300769.SZ 完成\n", "任务 300770.SZ 完成\n", "任务 300771.SZ 完成\n", - "任务 300772.SZ 完成\n", "任务 300773.SZ 完成\n", + "任务 300772.SZ 完成\n", "任务 300774.SZ 完成\n", "任务 300775.SZ 完成\n", "任务 300776.SZ 完成\n", @@ -2371,24 +2326,24 @@ "任务 300801.SZ 完成\n", "任务 300802.SZ 完成\n", "任务 300803.SZ 完成\n", - "任务 300805.SZ 完成\n", "任务 300804.SZ 完成\n", + "任务 300805.SZ 完成\n", "任务 300806.SZ 完成\n", "任务 300807.SZ 完成\n", "任务 300808.SZ 完成\n", "任务 300809.SZ 完成\n", - "任务 300811.SZ 完成\n", "任务 300810.SZ 完成\n", + "任务 300811.SZ 完成\n", "任务 300812.SZ 完成\n", "任务 300813.SZ 完成\n", "任务 300814.SZ 完成\n", "任务 300815.SZ 完成\n", "任务 300816.SZ 完成\n", "任务 300817.SZ 完成\n", - "任务 300819.SZ 完成\n", "任务 300818.SZ 完成\n", - "任务 300821.SZ 完成\n", + "任务 300819.SZ 完成\n", "任务 300820.SZ 完成\n", + "任务 300821.SZ 完成\n", "任务 300822.SZ 完成\n", "任务 300823.SZ 完成\n", "任务 300824.SZ 完成\n", @@ -2425,8 +2380,8 @@ "任务 300855.SZ 完成\n", "任务 300856.SZ 完成\n", "任务 300857.SZ 完成\n", - "任务 300859.SZ 完成\n", "任务 300858.SZ 完成\n", + "任务 300859.SZ 完成\n", "任务 300860.SZ 完成\n", "任务 300861.SZ 完成\n", "任务 300862.SZ 完成\n", @@ -2435,8 +2390,8 @@ "任务 300865.SZ 完成\n", "任务 300866.SZ 完成\n", "任务 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300911.SZ 完成\n", @@ -2487,12 +2442,12 @@ "任务 300919.SZ 完成\n", "任务 300920.SZ 完成\n", "任务 300921.SZ 完成\n", - "任务 300923.SZ 完成\n", "任务 300922.SZ 完成\n", + "任务 300923.SZ 完成\n", "任务 300925.SZ 完成\n", "任务 300926.SZ 完成\n", - "任务 300927.SZ 完成\n", "任务 300928.SZ 完成\n", + "任务 300927.SZ 完成\n", "任务 300929.SZ 完成\n", "任务 300930.SZ 完成\n", "任务 300931.SZ 完成\n", @@ -2553,14 +2508,14 @@ "任务 300990.SZ 完成\n", "任务 300991.SZ 完成\n", "任务 300992.SZ 完成\n", - "任务 300993.SZ 完成\n", "任务 300994.SZ 完成\n", - "任务 300995.SZ 完成\n", + "任务 300993.SZ 完成\n", "任务 300996.SZ 完成\n", - "任务 300997.SZ 完成\n", + "任务 300995.SZ 完成\n", "任务 300998.SZ 完成\n", - "任务 300999.SZ 完成\n", + "任务 300997.SZ 完成\n", "任务 301000.SZ 完成\n", + "任务 300999.SZ 完成\n", "任务 301001.SZ 完成\n", "任务 301002.SZ 完成\n", "任务 301003.SZ 完成\n", @@ -2572,10 +2527,10 @@ "任务 301009.SZ 完成\n", "任务 301010.SZ 完成\n", "任务 301011.SZ 完成\n", - "任务 301013.SZ 完成\n", "任务 301012.SZ 完成\n", - "任务 301016.SZ 完成\n", + "任务 301013.SZ 完成\n", "任务 301015.SZ 完成\n", + "任务 301016.SZ 完成\n", "任务 301017.SZ 完成\n", "任务 301018.SZ 完成\n", "任务 301019.SZ 完成\n", @@ -2661,8 +2616,8 @@ "任务 301107.SZ 完成\n", "任务 301108.SZ 完成\n", "任务 301109.SZ 完成\n", - "任务 301110.SZ 完成\n", "任务 301111.SZ 完成\n", + "任务 301110.SZ 完成\n", "任务 301112.SZ 完成\n", "任务 301113.SZ 完成\n", "任务 301115.SZ 完成\n", @@ -2745,8 +2700,8 @@ "任务 301208.SZ 完成\n", "任务 301209.SZ 完成\n", "任务 301210.SZ 完成\n", - "任务 301212.SZ 完成\n", "任务 301211.SZ 完成\n", + "任务 301212.SZ 完成\n", "任务 301213.SZ 完成\n", "任务 301215.SZ 完成\n", "任务 301216.SZ 完成\n", @@ -3023,14 +2978,14 @@ "任务 600028.SH 完成\n", "任务 600029.SH 完成\n", "任务 600030.SH 完成\n", - "任务 600031.SH 完成\n", "任务 600032.SH 完成\n", - "任务 600033.SH 完成\n", + "任务 600031.SH 完成\n", "任务 600035.SH 完成\n", - "任务 600036.SH 完成\n", + "任务 600033.SH 完成\n", "任务 600037.SH 完成\n", - "任务 600038.SH 完成\n", + "任务 600036.SH 完成\n", "任务 600039.SH 完成\n", + "任务 600038.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", @@ -3113,11 +3068,11 @@ "任务 600151.SH 完成\n", "任务 600152.SH 完成\n", "任务 600153.SH 完成\n", - "任务 600155.SH 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"任务 600236.SH 完成\n", "任务 600237.SH 完成\n", - "任务 600238.SH 完成\n", + "任务 600236.SH 完成\n", "任务 600239.SH 完成\n", + "任务 600238.SH 完成\n", "任务 600241.SH 完成\n", "任务 600243.SH 完成\n", - "任务 600246.SH 完成\n", "任务 600248.SH 完成\n", + "任务 600246.SH 完成\n", "任务 600249.SH 完成\n", "任务 600250.SH 完成\n", "任务 600251.SH 完成\n", "任务 600252.SH 完成\n", "任务 600255.SH 完成\n", "任务 600256.SH 完成\n", - "任务 600257.SH 完成\n", "任务 600258.SH 完成\n", + "任务 600257.SH 完成\n", "任务 600259.SH 完成\n", "任务 600261.SH 完成\n", - "任务 600262.SH 完成\n", "任务 600265.SH 完成\n", - "任务 600266.SH 完成\n", + "任务 600262.SH 完成\n", "任务 600267.SH 完成\n", + "任务 600266.SH 完成\n", "任务 600268.SH 完成\n", "任务 600269.SH 完成\n", "任务 600271.SH 完成\n", @@ -3291,36 +3246,36 @@ "任务 600378.SH 完成\n", "任务 600379.SH 完成\n", "任务 600380.SH 完成\n", - "任务 600382.SH 完成\n", "任务 600381.SH 完成\n", - "任务 600386.SH 完成\n", + "任务 600382.SH 完成\n", "任务 600383.SH 完成\n", - "任务 600388.SH 完成\n", + "任务 600386.SH 完成\n", "任务 600387.SH 完成\n", + "任务 600388.SH 完成\n", "任务 600389.SH 完成\n", "任务 600390.SH 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完成\n", - "任务 688573.SH 完成\n", + "任务 688570.SH 完成\n", "任务 688575.SH 完成\n", + "任务 688573.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 688580.SH 完成\n", "任务 688581.SH 完成\n", + "任务 688580.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", @@ -5157,8 +5112,8 @@ "任务 688590.SH 完成\n", "任务 688591.SH 完成\n", "任务 688592.SH 完成\n", - "任务 688593.SH 完成\n", "任务 688595.SH 完成\n", + "任务 688593.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", @@ -5173,12 +5128,12 @@ "任务 688608.SH 完成\n", "任务 688609.SH 完成\n", "任务 688610.SH 完成\n", - "任务 688612.SH 完成\n", "任务 688611.SH 完成\n", + "任务 688612.SH 完成\n", "任务 688613.SH 完成\n", "任务 688615.SH 完成\n", - "任务 688617.SH 完成\n", "任务 688616.SH 完成\n", + "任务 688617.SH 完成\n", "任务 688618.SH 完成\n", "任务 688619.SH 完成\n", "任务 688620.SH 完成\n", @@ -5233,10 +5188,10 @@ "任务 688692.SH 完成\n", "任务 688693.SH 完成\n", "任务 688695.SH 完成\n", - "任务 688696.SH 完成\n", "任务 688697.SH 完成\n", - "任务 688699.SH 完成\n", + "任务 688696.SH 完成\n", "任务 688698.SH 完成\n", + "任务 688699.SH 完成\n", "任务 688700.SH 完成\n", "任务 688701.SH 完成\n", "任务 688702.SH 完成\n", @@ -5245,20 +5200,20 @@ "任务 688709.SH 完成\n", "任务 688710.SH 完成\n", "任务 688711.SH 完成\n", - "任务 688717.SH 完成\n", "任务 688716.SH 完成\n", + "任务 688717.SH 完成\n", "任务 688718.SH 完成\n", "任务 688719.SH 完成\n", "任务 688720.SH 完成\n", "任务 688721.SH 完成\n", - "任务 688726.SH 完成\n", "任务 688722.SH 完成\n", + "任务 688726.SH 完成\n", "任务 688728.SH 完成\n", "任务 688733.SH 完成\n", - "任务 688739.SH 完成\n", "任务 688737.SH 完成\n", - "任务 688758.SH 完成\n", + "任务 688739.SH 完成\n", "任务 688750.SH 完成\n", + "任务 688758.SH 完成\n", "任务 688766.SH 完成\n", "任务 688767.SH 完成\n", "任务 688768.SH 完成\n", @@ -5269,8 +5224,8 @@ "任务 688779.SH 完成\n", "任务 688786.SH 完成\n", "任务 688787.SH 完成\n", - "任务 688789.SH 完成\n", "任务 688788.SH 完成\n", + "任务 688789.SH 完成\n", "任务 688793.SH 完成\n", "任务 688798.SH 完成\n", "任务 688799.SH 完成\n", @@ -5286,9 +5241,9 @@ "任务 830896.BJ 完成\n", "任务 830946.BJ 完成\n", "任务 830964.BJ 完成\n", + "任务 830974.BJ 完成\n", "任务 831010.BJ 完成\n", "任务 831039.BJ 完成\n", - "任务 830974.BJ 完成\n", "任务 831087.BJ 完成\n", "任务 831152.BJ 完成\n", "任务 831167.BJ 完成\n", @@ -5328,8 +5283,8 @@ "任务 832491.BJ 完成\n", "任务 832522.BJ 完成\n", "任务 832566.BJ 完成\n", - "任务 832651.BJ 完成\n", "任务 832662.BJ 完成\n", + "任务 832651.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", @@ -5338,17 +5293,17 @@ "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 833075.BJ 完成\n", "任务 833230.BJ 完成\n", - "任务 833171.BJ 完成\n", - "任务 833284.BJ 完成\n", "任务 833266.BJ 完成\n", - "任务 833394.BJ 完成\n", "任务 833346.BJ 完成\n", - "任务 833429.BJ 完成\n", + "任务 833284.BJ 完成\n", "任务 833427.BJ 完成\n", - "任务 833455.BJ 完成\n", + "任务 833394.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", "任务 833509.BJ 完成\n", "任务 833523.BJ 完成\n", "任务 833533.BJ 完成\n", @@ -5368,7 +5323,6 @@ "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", @@ -5390,8 +5344,8 @@ "任务 835670.BJ 完成\n", "任务 835857.BJ 完成\n", "任务 835892.BJ 完成\n", - "任务 836077.BJ 完成\n", "任务 835985.BJ 完成\n", + "任务 836077.BJ 完成\n", "任务 836149.BJ 完成\n", "任务 836208.BJ 完成\n", "任务 836221.BJ 完成\n", @@ -5426,6 +5380,7 @@ "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", @@ -5455,24 +5410,24 @@ "任务 839790.BJ 完成\n", "任务 839792.BJ 完成\n", "任务 839946.BJ 完成\n", - "任务 870204.BJ 完成\n", "任务 870199.BJ 完成\n", + "任务 870204.BJ 完成\n", "任务 870299.BJ 完成\n", "任务 870357.BJ 完成\n", "任务 870436.BJ 完成\n", "任务 870508.BJ 完成\n", - "任务 870726.BJ 完成\n", "任务 870656.BJ 完成\n", - "任务 870976.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 871396.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", - "任务 871694.BJ 完成\n", "任务 871642.BJ 完成\n", + "任务 871694.BJ 完成\n", "任务 871753.BJ 完成\n", "任务 871857.BJ 完成\n", "任务 871970.BJ 完成\n", @@ -5501,12 +5456,12 @@ "任务 873576.BJ 完成\n", "任务 873593.BJ 完成\n", "任务 873665.BJ 完成\n", - "任务 873690.BJ 完成\n", "任务 873679.BJ 完成\n", - "任务 873703.BJ 完成\n", + "任务 873690.BJ 完成\n", "任务 873693.BJ 完成\n", - "任务 873726.BJ 完成\n", + "任务 873703.BJ 完成\n", "任务 873706.BJ 完成\n", + "任务 873726.BJ 完成\n", "任务 873806.BJ 完成\n", "任务 873833.BJ 完成\n", "任务 920002.BJ 完成\n", @@ -5517,12 +5472,12 @@ "任务 920066.BJ 完成\n", "任务 920082.BJ 完成\n", "任务 920088.BJ 完成\n", - "任务 920099.BJ 完成\n", "任务 920098.BJ 完成\n", - "任务 920108.BJ 完成\n", + "任务 920099.BJ 完成\n", "任务 920106.BJ 完成\n", - "任务 920116.BJ 完成\n", + "任务 920108.BJ 完成\n", "任务 920111.BJ 完成\n", + "任务 920116.BJ 完成\n", "任务 920118.BJ 完成\n", "任务 920128.BJ 完成\n", "任务 689009.SH 完成\n", @@ -5541,26 +5496,26 @@ "任务 000405.SZ 完成\n", "任务 000406.SZ 完成\n", "任务 000412.SZ 完成\n", - "任务 000416.SZ 完成\n", "任务 000413.SZ 完成\n", - "任务 000502.SZ 完成\n", + "任务 000416.SZ 完成\n", "任务 000418.SZ 完成\n", - "任务 000511.SZ 完成\n", + "任务 000502.SZ 完成\n", "任务 000508.SZ 完成\n", - "任务 000522.SZ 完成\n", + "任务 000511.SZ 完成\n", "任务 000515.SZ 完成\n", - "任务 000535.SZ 完成\n", + "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 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 000583.SZ 完成\n", "任务 000578.SZ 完成\n", - "任务 000587.SZ 完成\n", + "任务 000583.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", "任务 000594.SZ 完成\n", "任务 000602.SZ 完成\n", @@ -5628,8 +5583,8 @@ "任务 002288.SZ 完成\n", "任务 002308.SZ 完成\n", "任务 002325.SZ 完成\n", - "任务 002341.SZ 完成\n", "任务 002359.SZ 完成\n", + "任务 002341.SZ 完成\n", "任务 002411.SZ 完成\n", "任务 002417.SZ 完成\n", "任务 002433.SZ 完成\n", @@ -5696,24 +5651,24 @@ "任务 600002.SH 完成\n", "任务 600003.SH 完成\n", "任务 600005.SH 完成\n", - "任务 600065.SH 完成\n", "任务 600068.SH 完成\n", + "任务 600065.SH 完成\n", "任务 600069.SH 完成\n", "任务 600074.SH 完成\n", - "任务 600077.SH 完成\n", "任务 600086.SH 完成\n", + "任务 600077.SH 完成\n", "任务 600087.SH 完成\n", "任务 600090.SH 完成\n", "任务 600091.SH 完成\n", "任务 600092.SH 完成\n", - "任务 600093.SH 完成\n", "任务 600102.SH 完成\n", - "任务 600112.SH 完成\n", + "任务 600093.SH 完成\n", "任务 600122.SH 完成\n", + "任务 600112.SH 完成\n", "任务 600139.SH 完成\n", "任务 600145.SH 完成\n", - "任务 600146.SH 完成\n", "任务 600175.SH 完成\n", + "任务 600146.SH 完成\n", "任务 600181.SH 完成\n", "任务 600205.SH 完成\n", "任务 600209.SH 完成\n", @@ -5728,8 +5683,8 @@ "任务 600270.SH 完成\n", "任务 600275.SH 完成\n", "任务 600277.SH 完成\n", - "任务 600290.SH 完成\n", "任务 600286.SH 完成\n", + "任务 600290.SH 完成\n", "任务 600291.SH 完成\n", "任务 600296.SH 完成\n", "任务 600297.SH 完成\n", @@ -5818,77 +5773,113 @@ ] } ], - "execution_count": 3 + "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" + ] }, { "cell_type": "code", + "execution_count": 4, "id": "35d605a189a83c1f", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:56:34.379208Z", - "start_time": "2025-03-30T16:56:34.194725Z" + "end_time": "2025-04-06T15:50:12.620072Z", + "start_time": "2025-04-06T15:50:12.492310Z" } }, - "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 000002.SZ 20250328 1302.82 1310.09 1291.92 1293.73 1302.82 \n", - "1 000002.SZ 20250327 1306.45 1311.90 1291.92 1302.82 1308.27 \n", - "2 000002.SZ 20250326 1306.45 1319.17 1304.63 1308.27 1306.45 \n", - "3 000002.SZ 20250325 1302.82 1313.72 1297.37 1306.45 1304.63 \n", - "4 000002.SZ 20250324 1335.52 1335.52 1299.18 1304.63 1337.34 \n", - "... ... ... ... ... ... ... ... \n", - "26882 689009.SH 20250328 65.05 65.96 64.68 65.61 65.76 \n", - "26883 689009.SH 20250327 62.39 66.02 62.20 65.76 62.43 \n", - "26884 689009.SH 20250326 62.94 62.94 61.95 62.43 62.44 \n", - "26885 689009.SH 20250325 63.33 63.44 61.87 62.44 63.04 \n", - "26886 689009.SH 20250324 62.06 64.05 61.63 63.04 62.13 \n", + " 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", + "... ... ... ... ... ... ... ... \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", "\n", - " change pct_chg vol amount \n", - "0 -9.09 -0.70 491441.40 351056.751 \n", - "1 -5.45 -0.42 562048.15 402664.947 \n", - "2 1.82 0.14 709377.47 511845.528 \n", - "3 1.82 0.14 655037.31 470444.410 \n", - "4 -32.71 -2.45 1050612.50 759032.562 \n", - "... ... ... ... ... \n", - "26882 -0.15 -0.23 57387.12 372490.242 \n", - "26883 3.33 5.33 108086.41 697534.473 \n", - "26884 -0.01 -0.02 45707.65 283488.125 \n", - "26885 -0.60 -0.95 47613.79 296056.671 \n", - "26886 0.91 1.46 101119.65 636305.468 \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", + "... ... ... ... ... \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", "\n", - "[26887 rows x 11 columns]\n" + "[5375 rows x 11 columns]\n" ] } ], - "execution_count": 4 + "source": [ + "all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n", + "print(all_daily_data)" + ] }, { "cell_type": "code", + "execution_count": 5, "id": "446aeba1a846b34", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:56:34.449212Z", - "start_time": "2025-03-30T16:56:34.379208Z" + "end_time": "2025-04-06T15:50:12.736416Z", + "start_time": "2025-04-06T15:50:12.661610Z" } }, - "source": [ - "\n", - "# 合并所有获取到的日线数据\n", - "if daily_data_list:\n", - " # 使用 HDF5 格式保存到本地文件（文件名：daily_data.h5，key设为 'daily_data'）\n", - " all_daily_data.to_hdf(h5_filename, key='daily_data', mode='a', append=True, data_columns=True, format='table')\n", - " 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", @@ -5898,7 +5889,16 @@ ] } ], - "execution_count": 5 + "source": [ + "\n", + "# 合并所有获取到的日线数据\n", + "if daily_data_list:\n", + " # 使用 HDF5 格式保存到本地文件（文件名：daily_data.h5，key设为 'daily_data'）\n", + " all_daily_data.to_hdf(h5_filename, key='daily_data', mode='a', append=True, data_columns=True, format='table')\n", + " 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。\")" + ] } ], "metadata": { diff --git a/code/data/update/update_money_flow.ipynb b/code/data/update/update_money_flow.ipynb index 7c5ea99..41a7781 100644 --- a/code/data/update/update_money_flow.ipynb +++ b/code/data/update/update_money_flow.ipynb @@ -2,31 +2,52 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "b94bb1f2-5332-485e-ae1b-eea01f938106", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:37.847407Z", - "start_time": "2025-03-30T16:42:36.773187Z" + "end_time": "2025-04-06T15:34:19.686298Z", + "start_time": "2025-04-06T15:34:19.679462Z" } }, + "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": "2025-03-30T16:42:59.016187Z", - "start_time": "2025-03-30T16:42:37.850022Z" + "end_time": "2025-04-06T15:34:29.569406Z", + "start_time": "2025-04-06T15:34:19.711970Z" } }, + "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", @@ -45,39 +66,37 @@ "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": [ - "\n", - "Index: 8297316 entries, 0 to 30724\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: 189.9+ MB\n", - "None\n", - "20250321\n", - "start_date: 20250324\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 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" ] } ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b", - "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-03-30T16:43:03.168764Z", - "start_time": "2025-03-30T16:42:59.422934Z" - } - }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -107,69 +126,33 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250418 完成\n", - "任务 20250417 完成\n", - "任务 20250415 完成\n", - "任务 20250416 完成\n", - "任务 20250414 完成\n", - "任务 20250411 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n", - "任务 20250407 完成\n", - "任务 20250403 完成\n", - "任务 20250402 完成\n", - "任务 20250401 完成\n", - "任务 20250331 完成\n", - "任务 20250328 完成\n", - "任务 20250327 完成\n", - "任务 20250326 完成\n", - "任务 20250325 完成\n", - "任务 20250324 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "9af80516849d4e80", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:03.181032Z", - "start_time": "2025-03-30T16:43:03.173867Z" + "end_time": "2025-04-06T15:34:32.851075Z", + "start_time": "2025-04-06T15:34:32.844866Z" } }, + "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": "2025-03-30T16:43:05.401668Z", - "start_time": "2025-03-30T16:43:03.197033Z" + "end_time": "2025-04-06T15:34:35.261741Z", + "start_time": "2025-04-06T15:34:32.864789Z" } }, - "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", @@ -179,7 +162,15 @@ ] } ], - "execution_count": 5 + "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！\")" + ] } ], "metadata": { diff --git a/code/data/update/update_stk_limit.ipynb b/code/data/update/update_stk_limit.ipynb index 7dc3221..4dbb66e 100644 --- a/code/data/update/update_stk_limit.ipynb +++ b/code/data/update/update_stk_limit.ipynb @@ -2,31 +2,58 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "500802dc-7a20-48b7-a470-a4bae3ec534b", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:42:39.056767Z", - "start_time": "2025-03-30T16:42:37.817887Z" + "end_time": "2025-04-06T15:34:34.020485Z", + "start_time": "2025-04-06T15:34:33.497731Z" } }, + "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": "2025-03-30T16:42:59.784780Z", - "start_time": "2025-03-30T16:42:39.056767Z" + "end_time": "2025-04-06T15:34:46.227924Z", + "start_time": "2025-04-06T15:34:34.042810Z" } }, + "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", @@ -46,45 +73,37 @@ "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": [ - " ts_code trade_date\n", - "4705 600289.SH 20250321\n", - "4706 600292.SH 20250321\n", - "4707 600293.SH 20250321\n", - "4696 600279.SH 20250321\n", - "7051 920116.BJ 20250321\n", - "\n", - "Index: 10237887 entries, 0 to 35266\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: 234.3+ MB\n", - "None\n", - "20250321\n", - "20250324\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 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" ] } ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f", - "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-03-30T16:43:03.372001Z", - "start_time": "2025-03-30T16:43:00.012140Z" - } - }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -115,143 +134,69 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250418 完成\n", - "任务 20250417 完成\n", - "任务 20250416 完成\n", - "任务 20250415 完成\n", - "任务 20250411 完成\n", - "任务 20250414 完成\n", - "任务 20250409 完成\n", - "任务 20250410 完成\n", - "任务 20250408 完成\n", - "任务 20250407 完成\n", - "任务 20250403 完成\n", - "任务 20250402 完成\n", - "任务 20250401 完成\n", - "任务 20250331 完成\n", - "任务 20250327 完成\n", - "任务 20250328 完成\n", - "任务 20250326 完成\n", - "任务 20250325 完成\n", - "任务 20250324 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "96a81aa5890ea3c3", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:03.397757Z", - "start_time": "2025-03-30T16:43:03.384786Z" + "end_time": "2025-04-06T15:34:48.680504Z", + "start_time": "2025-04-06T15:34:48.665530Z" } }, - "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": [ "[ trade_date ts_code up_limit down_limit\n", - "0 20250327 000001.SZ 12.52 10.24\n", - "1 20250327 000002.SZ 7.92 6.48\n", - "2 20250327 000004.SZ 11.40 9.32\n", - "3 20250327 000006.SZ 7.44 6.08\n", - "4 20250327 000007.SZ 7.00 5.72\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", - "7059 20250327 920108.BJ 33.56 18.08\n", - "7060 20250327 920111.BJ 40.57 21.85\n", - "7061 20250327 920116.BJ 126.29 68.01\n", - "7062 20250327 920118.BJ 44.14 23.78\n", - "7063 20250327 920128.BJ 47.35 25.51\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", - "[7064 rows x 4 columns], trade_date ts_code up_limit down_limit\n", - "0 20250328 000001.SZ 12.53 10.25\n", - "1 20250328 000002.SZ 7.89 6.45\n", - "2 20250328 000004.SZ 11.19 9.15\n", - "3 20250328 000006.SZ 8.18 6.70\n", - "4 20250328 000007.SZ 6.99 5.72\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", - "7060 20250328 920108.BJ 31.03 16.71\n", - "7061 20250328 920111.BJ 39.65 21.35\n", - "7062 20250328 920116.BJ 115.67 62.29\n", - "7063 20250328 920118.BJ 41.00 22.08\n", - "7064 20250328 920128.BJ 44.83 24.15\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", - "[7065 rows x 4 columns], trade_date ts_code up_limit down_limit\n", - "0 20250326 000001.SZ 12.57 10.29\n", - "1 20250326 000002.SZ 7.91 6.47\n", - "2 20250326 000004.SZ 11.28 9.23\n", - "3 20250326 000006.SZ 7.17 5.87\n", - "4 20250326 000007.SZ 6.67 5.45\n", - "... ... ... ... ...\n", - "7056 20250326 920108.BJ 33.96 18.30\n", - "7057 20250326 920111.BJ 41.92 22.58\n", - "7058 20250326 920116.BJ 133.64 71.96\n", - "7059 20250326 920118.BJ 41.93 22.59\n", - "7060 20250326 920128.BJ 49.40 26.60\n", - "\n", - "[7061 rows x 4 columns], trade_date ts_code up_limit down_limit\n", - "0 20250325 000001.SZ 12.52 10.24\n", - "1 20250325 000002.SZ 7.90 6.46\n", - "2 20250325 000004.SZ 11.55 9.45\n", - "3 20250325 000006.SZ 7.13 5.83\n", - "4 20250325 000007.SZ 6.60 5.40\n", - "... ... ... ... ...\n", - "7055 20250325 920108.BJ 33.30 17.94\n", - "7056 20250325 920111.BJ 39.97 21.53\n", - "7057 20250325 920116.BJ 137.78 74.20\n", - "7058 20250325 920118.BJ 39.52 21.28\n", - "7059 20250325 920128.BJ 46.22 24.90\n", - "\n", - "[7060 rows x 4 columns], trade_date ts_code up_limit down_limit\n", - "0 20250324 000001.SZ 12.56 10.28\n", - "1 20250324 000002.SZ 8.10 6.62\n", - "2 20250324 000004.SZ 12.82 10.49\n", - "3 20250324 000006.SZ 7.44 6.08\n", - "4 20250324 000007.SZ 6.89 5.63\n", - "... ... ... ... ...\n", - "7053 20250324 920108.BJ 34.84 18.76\n", - "7054 20250324 920111.BJ 40.41 21.77\n", - "7055 20250324 920116.BJ 134.55 72.45\n", - "7056 20250324 920118.BJ 38.67 20.83\n", - "7057 20250324 920128.BJ 45.86 24.70\n", - "\n", - "[7058 rows x 4 columns]]\n" + "[7075 rows x 4 columns]]\n" ] } ], - "execution_count": 4 + "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)" + ] }, { "cell_type": "code", + "execution_count": 5, "id": "ad9733a1-2f42-43ee-a98c-0bf699304c21", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:03.696614Z", - "start_time": "2025-03-30T16:43:03.411036Z" + "end_time": "2025-04-06T15:34:48.966102Z", + "start_time": "2025-04-06T15:34:48.693158Z" } }, - "source": [ - "\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', 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", @@ -261,20 +206,27 @@ ] } ], - "execution_count": 5 + "source": [ + "\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', 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！\")" + ] }, { "cell_type": "code", + "execution_count": null, "id": "7e777f1f-4d54-4a74-b916-691ede6af055", "metadata": { "ExecuteTime": { - "end_time": "2025-03-30T16:43:03.713628Z", - "start_time": "2025-03-30T16:43:03.711521Z" + "end_time": "2025-04-06T15:34:48.980659Z", + "start_time": "2025-04-06T15:34:48.977771Z" } }, - "source": [], "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { diff --git a/code/train/RollingRank.ipynb b/code/train/RollingRank.ipynb index f4e2f57..f37315d 100644 --- a/code/train/RollingRank.ipynb +++ b/code/train/RollingRank.ipynb @@ -2,16 +2,18 @@ "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": "2025-04-05T17:29:47.416680Z", - "start_time": "2025-04-05T17:29:46.949355Z" } }, + "outputs": [], "source": [ "# %load_ext autoreload\n", "# %autoreload 2\n", @@ -22,50 +24,19 @@ "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option('display.max_columns', None)\n" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "a79cafb06a7e0e43", "metadata": { - "scrolled": true, "ExecuteTime": { - "end_time": "2025-04-05T17:30:40.600943Z", - "start_time": "2025-04-05T17:29:47.418440Z" - } + "end_time": "2025-04-07T14:57:43.308397Z", + "start_time": "2025-04-07T14:56:49.554151Z" + }, + "scrolled": true }, - "source": [ - "from utils.utils import read_and_merge_h5_data\n", - "\n", - "print('daily data')\n", - "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n", - " columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg'],\n", - " df=None)\n", - "\n", - "print('daily basic')\n", - "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", - " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st'], df=df, join='inner')\n", - "\n", - "print('stk limit')\n", - "df = read_and_merge_h5_data('../../data/stk_limit.h5', key='stk_limit',\n", - " columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n", - " df=df)\n", - "print('money flow')\n", - "df = read_and_merge_h5_data('../../data/money_flow.h5', key='money_flow',\n", - " columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n", - " 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n", - " df=df)\n", - "print('cyq perf')\n", - "df = read_and_merge_h5_data('../../data/cyq_perf.h5', key='cyq_perf',\n", - " columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", - " 'cost_50pct',\n", - " 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n", - " df=df)\n", - "print(df.info())" - ], "outputs": [ { "name": "stdout", @@ -81,7 +52,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8477357 entries, 0 to 8477356\n", + "RangeIndex: 8504242 entries, 0 to 8504241\n", "Data columns (total 31 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -122,20 +93,59 @@ ] } ], - "execution_count": 2 + "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())" + ] }, { "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 - }, - "ExecuteTime": { - "end_time": "2025-04-05T17:30:55.594572Z", - "start_time": "2025-04-05T17:30:40.882850Z" } }, + "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", @@ -183,27 +193,19 @@ "# 使用示例\n", "df = merge_with_industry_data(df, industry_df)\n", "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "industry\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:30:55.846316Z", - "start_time": "2025-04-05T17:30:55.635945Z" + "end_time": "2025-04-07T14:57:56.676422Z", + "start_time": "2025-04-07T14:57:56.445955Z" } }, + "outputs": [], "source": [ "def calculate_indicators(df):\n", " \"\"\"\n", @@ -280,22 +282,22 @@ "h5_filename = '../../data/index_data.h5'\n", "index_data = generate_index_indicators(h5_filename)\n", "index_data = index_data.dropna()\n" - ], - "outputs": [], - "execution_count": 4 + ] }, { "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": "2025-04-05T17:30:55.912328Z", - "start_time": "2025-04-05T17:30:55.863571Z" } }, + "outputs": [], "source": [ "import numpy as np\n", "import talib\n", @@ -645,23 +647,23 @@ "\n", " new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]\n", " return df, new_columns\n" - ], - "outputs": [], - "execution_count": 5 + ] }, { "cell_type": "code", + "execution_count": 6, "id": "53f86ddc0677a6d7", "metadata": { + "ExecuteTime": { + "end_time": "2025-04-07T14:58:02.350944Z", + "start_time": "2025-04-07T14:57:56.782771Z" + }, "jupyter": { "source_hidden": true }, - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-04-05T17:31:01.075820Z", - "start_time": "2025-04-05T17:30:55.916344Z" - } + "scrolled": true }, + "outputs": [], "source": [ "from utils.factor import get_act_factor\n", "\n", @@ -708,27 +710,18 @@ "\n", "\n", "industry_df = read_industry_data('../../data/sw_daily.h5')\n" - ], - "outputs": [], - "execution_count": 6 + ] }, { "cell_type": "code", + "execution_count": 7, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:31:01.095475Z", - "start_time": "2025-04-05T17:31:01.090126Z" + "end_time": "2025-04-07T14:58:02.391649Z", + "start_time": "2025-04-07T14:58:02.387083Z" } }, - "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", @@ -738,17 +731,53 @@ ] } ], - "execution_count": 7 + "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)" + ] }, { "cell_type": "code", + "execution_count": 8, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:33:15.657140Z", - "start_time": "2025-04-05T17:31:01.114838Z" + "end_time": "2025-04-07T15:01:03.522872Z", + "start_time": "2025-04-07T14:58:02.425021Z" } }, + "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", @@ -779,46 +808,22 @@ "# 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: 5118212 entries, 0 to 5118211\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" - ] - } - ], - "execution_count": 8 + ] }, { "cell_type": "code", + "execution_count": 9, "id": "f4f16d63ad18d1bc", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:33:16.052975Z", - "start_time": "2025-04-05T17:33:16.039150Z" + "end_time": "2025-04-07T15:01:03.651232Z", + "start_time": "2025-04-07T15:01:03.641507Z" + }, + "jupyter": { + "source_hidden": true } }, + "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", @@ -855,19 +860,19 @@ " # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n", "\n", " return df, ret_feature_columns\n" - ], - "outputs": [], - "execution_count": 9 + ] }, { "cell_type": "code", + "execution_count": 10, "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:33:18.110752Z", - "start_time": "2025-04-05T17:33:16.243912Z" + "end_time": "2025-04-07T15:01:05.578731Z", + "start_time": "2025-04-07T15:01:03.776079Z" } }, + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -1084,35 +1089,65 @@ " # 对数据进行裁剪\n", " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", "\n", - " return df\n" - ], - "outputs": [], - "execution_count": 10 + " return df\n", + "\n", + "def time_series_quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", + " df = df.copy()\n", + " # 确保按股票和时间排序\n", + " df = df.sort_values(['ts_code', 'trade_date'])\n", + " grouped = df.groupby('ts_code')\n", + " for col in features:\n", + " # 对每个股票的时间序列计算滚动分位数\n", + " rolling_lower = grouped[col].rolling(window=window, min_periods=window // 2).quantile(lower_quantile)\n", + " rolling_upper = grouped[col].rolling(window=window, min_periods=window // 2).quantile(upper_quantile)\n", + " # rolling结果带有多重索引，需要对齐\n", + " rolling_lower = rolling_lower.reset_index(level=0, drop=True)\n", + " rolling_upper = rolling_upper.reset_index(level=0, drop=True)\n", + " # 应用 clip\n", + " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", + " return df\n", + "\n", + "def cross_sectional_quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99):\n", + " df = df.copy()\n", + " grouped = df.groupby('trade_date')\n", + " for col in features:\n", + " # 计算每日截面的分位数边界\n", + " lower_bound = grouped[col].transform(lambda x: x.quantile(lower_quantile))\n", + " upper_bound = grouped[col].transform(lambda x: x.quantile(upper_quantile))\n", + " # 应用 clip\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + " return df" + ] }, { "cell_type": "code", + "execution_count": 11, "id": "1c46817a-b5dd-4bec-8bb4-e6e80bfd9d66", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:33:18.119789Z", - "start_time": "2025-04-05T17:33:18.111760Z" + "end_time": "2025-04-07T15:01:05.587296Z", + "start_time": "2025-04-07T15:01:05.583736Z" } }, + "outputs": [], "source": [ "# print(test_data.head()[['act_factor1', 'act_factor2', 'ts_code', 'trade_date']])" - ], - "outputs": [], - "execution_count": 11 + ] }, { "cell_type": "code", + "execution_count": 12, "id": "da2bb202843d9275", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:33:18.721857Z", - "start_time": "2025-04-05T17:33:18.147528Z" + "end_time": "2025-04-07T15:01:06.207888Z", + "start_time": "2025-04-07T15:01:05.611601Z" + }, + "jupyter": { + "source_hidden": true } }, + "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "import lightgbm as lgb\n", @@ -1156,9 +1191,6 @@ " X_train = train_data_split[feature_columns]\n", " y_train = train_data_split['label']\n", "\n", - " X_val = val_data_split[feature_columns]\n", - " y_val = val_data_split['label']\n", - "\n", " # 标准化数值特征\n", " scaler = StandardScaler()\n", "\n", @@ -1169,16 +1201,6 @@ " # 处理类别特征\n", " categorical_feature = [col for col in feature_columns if 'cat' in col]\n", "\n", - " pca = None\n", - " if use_pca:\n", - " pca = PCA(n_components=0.95) # 或指定 n_components=固定值（如 10）\n", - " numeric_features = [col for col in feature_columns if col not in categorical_feature]\n", - " numeric_pca = pca.fit_transform(X_train[numeric_features])\n", - " X_train = pd.concat([pd.DataFrame(numeric_pca, index=X_train.index), X_train[categorical_feature]], axis=1)\n", - "\n", - " numeric_pca = pca.transform(X_val[numeric_features])\n", - " X_val = pd.concat([pd.DataFrame(numeric_pca, index=X_val.index), X_val[categorical_feature]], axis=1)\n", - "\n", " # 计算权重（基于时间）\n", " # trade_date = train_data_split['trade_date'] # 交易日期\n", " # weights = (trade_date - trade_date.min()).dt.days / (trade_date.max() - trade_date.min()).days + 1\n", @@ -1219,19 +1241,20 @@ " lgb.plot_importance(model, importance_type='split', max_num_features=20)\n", " plt.show()\n", "\n", - " return model, scaler, pca" - ], - "outputs": [], - "execution_count": 12 + " return model, scaler, None" + ] }, { + "cell_type": "code", + "execution_count": 13, + "id": "ff19e3f1e051a489", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:34:29.738328Z", - "start_time": "2025-04-05T17:33:18.744104Z" + "end_time": "2025-04-07T15:02:24.864887Z", + "start_time": "2025-04-07T15:01:06.233628Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "\n", "days = 2\n", @@ -1254,67 +1277,78 @@ "# 0.7 * df['future_return']\n", "# * 0.3 * df['future_volatility']\n", "# )" - ], - "id": "ff19e3f1e051a489", - "outputs": [], - "execution_count": 13 + ] }, { + "cell_type": "code", + "execution_count": 14, + "id": "27dba27b2e108316", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:56:23.772145Z", - "start_time": "2025-04-05T17:55:42.635301Z" + "end_time": "2025-04-07T16:28:54.014475Z", + "start_time": "2025-04-07T16:28:38.494784Z" } }, - "cell_type": "code", - "source": [ - "def select_pre_zt_stocks_dynamic(stock_df):\n", - " def select_stocks(group):\n", - " data = group.nlargest(100, 'return_20')\n", - " unique_labels = data['label'].nunique()\n", - " if unique_labels >= 20 or unique_labels == 0:\n", - " return data\n", - " else:\n", - " for i in range(100, 1000, 10):\n", - " data = group.nlargest(i, 'return_20')\n", - " unique_labels = data['label'].nunique()\n", - " if unique_labels >= 20:\n", - " return data\n", - "\n", - " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(select_stocks)\n", - " return stock_df\n", - "\n", - "\n", - "pdf = select_pre_zt_stocks_dynamic(df)\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", - "# )" - ], - "id": "27dba27b2e108316", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2025-03-28 00:00:00\n" + "2025-04-07 00:00:00\n" ] } ], - "execution_count": 55 + "source": [ + "def select_pre_zt_stocks_dynamic(stock_df):\n", + " def select_stocks(group):\n", + " max_stocks = 150\n", + " initial_data = group.nlargest(100, 'return_20')\n", + " unique_labels = initial_data['label'].nunique()\n", + "\n", + " if unique_labels >= 20 or unique_labels == 0: # 包含标签种类为0的情况\n", + " return initial_data\n", + "\n", + " for i in range(110, max_stocks + 1, 10):\n", + " data = group.nlargest(i, 'return_20')\n", + " unique_labels = data['label'].nunique()\n", + " if unique_labels >= 20:\n", + " return data\n", + "\n", + " return group.nlargest(max_stocks, 'return_20') # 如果循环结束仍未找到足够标签，则返回最大数量的股票\n", + "\n", + " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(select_stocks)\n", + " 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", + "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": "2025-04-05T17:56:24.793211Z", - "start_time": "2025-04-05T17:56:23.826074Z" + "end_time": "2025-04-07T16:28:55.029462Z", + "start_time": "2025-04-07T16:28:54.022482Z" } }, - "cell_type": "code", + "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" + ] + } + ], "source": [ "pdf = pdf.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", - "pdf = pdf.sort_values(['trade_date'])\n", "pdf = pdf.replace([np.inf, -np.inf], np.nan)\n", "\n", "feature_columns = [col for col in pdf.columns if col in pdf.columns]\n", @@ -1335,61 +1369,69 @@ "numeric_columns = pdf.select_dtypes(include=['float64', 'int64']).columns\n", "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", "\n", - "filter_index = pdf['future_return'].between(pdf['future_return'].quantile(0.01), pdf['future_return'].quantile(0.99))\n", "\n", "# filter_index = pdf['future_volatility'].between(pdf['future_volatility'].quantile(0.01),\n", "# pdf['future_volatility'].quantile(0.99)) | filter_index" - ], - "id": "ca96fb81e17c4a90", - "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": 56 + ] }, { + "cell_type": "code", + "execution_count": 16, + "id": "81d4570663ae21d7", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:57:54.876778Z", - "start_time": "2025-04-05T17:56:24.836264Z" + "end_time": "2025-04-07T16:29:09.269040Z", + "start_time": "2025-04-07T16:28:55.127230Z" } }, - "cell_type": "code", + "outputs": [], "source": [ - "pdf = quantile_filter(pdf, numeric_columns)\n", "\n", + "# pdf = time_series_quantile_filter(pdf, numeric_columns)\n", + "pdf = cross_sectional_quantile_filter(pdf, numeric_columns)\n", "pdf = cross_sectional_standardization(pdf, numeric_columns)\n", "\n", - "feature_columns = remove_highly_correlated_features(pdf,\n", - " feature_columns)\n", - "print(len(pdf))" - ], - "id": "81d4570663ae21d7", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "253290\n" - ] - } - ], - "execution_count": 57 + "pdf = pdf.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + "filter_index = pdf['future_return'].between(pdf['future_return'].quantile(0.01), pdf['future_return'].quantile(0.99))\n", + "\n", + "feature_columns = remove_highly_correlated_features(pdf, feature_columns)\n", + "\n", + "# print(pdf[pdf['trade_date'] == '2025-03-26'][['ts_code', 'trade_date', 'pct_chg']])\n", + "#\n", + "# pdf = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-03-26') & (df['trade_date'] <= '2029-03-26')])\n", + "# pdf = pdf.sort_values(['ts_code', 'trade_date'])\n", + "# pdf = pdf.replace([np.inf, -np.inf], np.nan)\n", + "#\n", + "# # pdf = time_series_quantile_filter(pdf, numeric_columns)\n", + "#\n", + "# pdf = cross_sectional_standardization(pdf, numeric_columns)\n", + "#\n", + "# print(pdf[pdf['trade_date'] == '2025-03-26'][['ts_code', 'trade_date', 'pct_chg']])" + ] }, { "cell_type": "code", + "execution_count": 17, "id": "92428d543f4727ad", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:57:55.077967Z", - "start_time": "2025-04-05T17:57:54.949402Z" + "end_time": "2025-04-07T16:29:10.824723Z", + "start_time": "2025-04-07T16:29:09.328953Z" } }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# print('train data size: ', len(train_data))\n", "\n", @@ -1422,36 +1464,25 @@ "evals = {}\n", "\n", "gc.collect()" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 58 + ] }, { "cell_type": "code", + "execution_count": 18, "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T17:57:55.223213Z", - "start_time": "2025-04-05T17:57:55.122064Z" + "end_time": "2025-04-07T16:29:11.191288Z", + "start_time": "2025-04-07T16:29:10.946199Z" } }, + "outputs": [], "source": [ "gc.collect()\n", "\n", "\n", "def rolling_train_predict(df, train_days, test_days, feature_columns_origin, days=5, use_pca=False, validation_days=60,\n", - " filter_index=None):\n", + " filter_index=None, params=None):\n", " # 1. 按照交易日期排序\n", " unique_dates = df[df['trade_date'] >= '2020-01-01']['trade_date'].unique().tolist()\n", " unique_dates = sorted(unique_dates)\n", @@ -1469,6 +1500,7 @@ " test_dates = unique_dates[start + train_days: start + train_days + test_days]\n", "\n", " # 根据日期筛选数据\n", + " # train_data = df[df['trade_date'].isin(train_dates)]\n", " train_data = df[filter_index & df['trade_date'].isin(train_dates)]\n", " test_data = df[df['trade_date'].isin(test_dates)]\n", "\n", @@ -1488,10 +1520,10 @@ "\n", " # print(len(train_data))\n", " # print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", - " # print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + " print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", " # # print(len(test_data))\n", " # print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n", - " print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + " print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n", "\n", " cat_columns = [col for col in df.columns if col.startswith('cat')]\n", " for col in cat_columns:\n", @@ -1500,17 +1532,17 @@ "\n", " label_gain = list(range(len(train_data['label'].unique())))\n", " label_gain = [(gain + 1) * (gain + 1) for gain in label_gain]\n", - " light_params['label_gain'] = label_gain\n", + " params['label_gain'] = label_gain\n", "\n", " # ud = train_data[\"trade_date\"].unique()\n", " # date_weights = {date: weight for date, weight in zip(ud, np.linspace(1, 2, len(unique_dates)))}\n", - " # light_params['weight'] = train_data[\"trade_date\"].map(date_weights).tolist()\n", + " # params['weight'] = train_data[\"trade_date\"].map(date_weights).tolist()\n", "\n", " # print(f'feature_columns: {feature_columns}')\n", " # feature_contri = [2 if feat.startswith('act_factor') else 1 for feat in feature_columns]\n", - " # light_params['feature_contri'] = feature_contri\n", + " # params['feature_contri'] = feature_contri\n", " model, _, _ = train_light_model(train_data.dropna(subset=['label']),\n", - " light_params, feature_columns,\n", + " params, feature_columns,\n", " [lgb.log_evaluation(period=100),\n", " lgb.callback.record_evaluation(evals),\n", " # lgb.early_stopping(100, first_metric_only=True)\n", @@ -1532,11 +1564,11 @@ "\n", "\n", "def worker(train_data, test_data, train_days, test_days, feature_columns_origin, unique_dates, start, filter_index,\n", - " validation_days):\n", + " validation_days, params=None):\n", " train_data = train_data.sort_values('trade_date')\n", " test_data = test_data.sort_values('trade_date')\n", "\n", - " feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False)\n", + " # feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False)\n", "\n", " train_data = train_data.dropna(subset=feature_columns + ['label'])\n", " train_data = train_data.reset_index(drop=True)\n", @@ -1553,11 +1585,11 @@ "\n", " label_gain = list(range(len(train_data['label'].unique())))\n", " label_gain = [(gain + 1) * (gain + 1) for gain in label_gain]\n", - " current_light_params = light_params.copy()\n", + " current_light_params = params.copy()\n", " current_light_params['label_gain'] = label_gain\n", "\n", " model, _, _ = train_light_model(train_data.dropna(subset=['label']),\n", - " light_params, feature_columns,\n", + " current_light_params, feature_columns,\n", " [lgb.log_evaluation(period=100),\n", " lgb.callback.record_evaluation(evals),\n", " # lgb.early_stopping(100, first_metric_only=True)\n", @@ -1602,2766 +1634,1860 @@ "\n", " final_predictions = pd.concat(predictions_list, ignore_index=True)\n", " return final_predictions\n" - ], - "outputs": [], - "execution_count": 59 + ] }, { "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": [], - "ExecuteTime": { - "end_time": "2025-04-05T18:07:51.160629Z", - "start_time": "2025-04-05T18:04:17.429621Z" - } + "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: 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"test_data最大日期: 2025-01-07\n", + "划分后的训练集大小: 493, 验证集大小: 87\n", + "train_data最大日期: 2025-01-07\n", + "test_data最大日期: 2025-01-08\n", + "划分后的训练集大小: 539, 验证集大小: 145\n", + "train_data最大日期: 2025-01-08\n", + "test_data最大日期: 2025-01-09\n", + "划分后的训练集大小: 557, 验证集大小: 109\n", + "train_data最大日期: 2025-01-09\n", + "test_data最大日期: 2025-01-10\n", + "划分后的训练集大小: 592, 验证集大小: 130\n", + "train_data最大日期: 2025-01-10\n", + "test_data最大日期: 2025-01-13\n", + "划分后的训练集大小: 607, 验证集大小: 136\n", + "train_data最大日期: 2025-01-13\n", + "test_data最大日期: 2025-01-14\n", + "划分后的训练集大小: 617, 验证集大小: 97\n", + "train_data最大日期: 2025-01-14\n", + "test_data最大日期: 2025-01-15\n", + "划分后的训练集大小: 616, 验证集大小: 144\n", + "train_data最大日期: 2025-01-15\n", + "test_data最大日期: 2025-01-16\n", + "划分后的训练集大小: 651, 验证集大小: 144\n", + "train_data最大日期: 2025-01-16\n", + "test_data最大日期: 2025-01-17\n", + "划分后的训练集大小: 608, 验证集大小: 87\n", + "train_data最大日期: 2025-01-17\n", + "test_data最大日期: 2025-01-20\n", + "划分后的训练集大小: 602, 验证集大小: 130\n", + "train_data最大日期: 2025-01-20\n", + "test_data最大日期: 2025-01-21\n", + "划分后的训练集大小: 652, 验证集大小: 147\n", + "train_data最大日期: 2025-01-21\n", + "test_data最大日期: 2025-01-22\n", + "划分后的训练集大小: 648, 验证集大小: 140\n", + "train_data最大日期: 2025-01-22\n", + "test_data最大日期: 2025-01-23\n", + "划分后的训练集大小: 644, 验证集大小: 140\n", + "train_data最大日期: 2025-01-23\n", + "test_data最大日期: 2025-01-24\n", + "划分后的训练集大小: 697, 验证集大小: 140\n", + "train_data最大日期: 2025-01-24\n", + "test_data最大日期: 2025-01-27\n", + "划分后的训练集大小: 706, 验证集大小: 139\n", + "train_data最大日期: 2025-01-27\n", + "test_data最大日期: 2025-02-05\n", + "划分后的训练集大小: 651, 验证集大小: 92\n", + "train_data最大日期: 2025-02-05\n", + "test_data最大日期: 2025-02-06\n", + "划分后的训练集大小: 636, 验证集大小: 125\n", + "train_data最大日期: 2025-02-06\n", + "test_data最大日期: 2025-02-07\n", + "划分后的训练集大小: 588, 验证集大小: 92\n", + "train_data最大日期: 2025-02-07\n", + "test_data最大日期: 2025-02-10\n", + "划分后的训练集大小: 565, 验证集大小: 117\n", + "train_data最大日期: 2025-02-10\n", + "test_data最大日期: 2025-02-11\n", + "划分后的训练集大小: 565, 验证集大小: 139\n", + "train_data最大日期: 2025-02-11\n", + "test_data最大日期: 2025-02-12\n", + "划分后的训练集大小: 595, 验证集大小: 122\n", + "train_data最大日期: 2025-02-12\n", + "test_data最大日期: 2025-02-13\n", + "划分后的训练集大小: 608, 验证集大小: 138\n", + "train_data最大日期: 2025-02-13\n", + "test_data最大日期: 2025-02-14\n", + "划分后的训练集大小: 619, 验证集大小: 103\n", + "train_data最大日期: 2025-02-14\n", + "test_data最大日期: 2025-02-17\n", + "划分后的训练集大小: 640, 验证集大小: 138\n", + "train_data最大日期: 2025-02-17\n", + "test_data最大日期: 2025-02-18\n", + "划分后的训练集大小: 637, 验证集大小: 136\n", + "train_data最大日期: 2025-02-18\n", + "test_data最大日期: 2025-02-19\n", + "划分后的训练集大小: 626, 验证集大小: 111\n", + "train_data最大日期: 2025-02-19\n", + "test_data最大日期: 2025-02-20\n", + "划分后的训练集大小: 577, 验证集大小: 89\n", + "train_data最大日期: 2025-02-20\n", + "test_data最大日期: 2025-02-21\n", + "划分后的训练集大小: 613, 验证集大小: 139\n", + "train_data最大日期: 2025-02-21\n", + "test_data最大日期: 2025-02-24\n", + "划分后的训练集大小: 614, 验证集大小: 139\n", + "train_data最大日期: 2025-02-24\n", + "test_data最大日期: 2025-02-25\n", + "划分后的训练集大小: 612, 验证集大小: 134\n", + "train_data最大日期: 2025-02-25\n", + "test_data最大日期: 2025-02-26\n", + "划分后的训练集大小: 643, 验证集大小: 142\n", + "train_data最大日期: 2025-02-26\n", + "test_data最大日期: 2025-02-27\n", + "划分后的训练集大小: 685, 验证集大小: 131\n", + "train_data最大日期: 2025-02-27\n", + "test_data最大日期: 2025-02-28\n", + "划分后的训练集大小: 679, 验证集大小: 133\n", + "train_data最大日期: 2025-02-28\n", + "test_data最大日期: 2025-03-03\n", + "划分后的训练集大小: 656, 验证集大小: 116\n", + "train_data最大日期: 2025-03-03\n", + "test_data最大日期: 2025-03-04\n", + "划分后的训练集大小: 656, 验证集大小: 134\n", + "train_data最大日期: 2025-03-04\n", + "test_data最大日期: 2025-03-05\n", + "划分后的训练集大小: 656, 验证集大小: 142\n", + "train_data最大日期: 2025-03-05\n", + "test_data最大日期: 2025-03-06\n", + "划分后的训练集大小: 631, 验证集大小: 106\n", + "train_data最大日期: 2025-03-06\n", + "test_data最大日期: 2025-03-07\n", + "划分后的训练集大小: 635, 验证集大小: 137\n", + "train_data最大日期: 2025-03-07\n", + "test_data最大日期: 2025-03-10\n", + "划分后的训练集大小: 661, 验证集大小: 142\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: 620, 验证集大小: 93\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: 623, 验证集大小: 145\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: 657, 验证集大小: 140\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: 663, 验证集大小: 143\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: 622, 验证集大小: 101\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: 666, 验证集大小: 137\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: 608, 验证集大小: 87\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: 585, 验证集大小: 117\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: 578, 验证集大小: 136\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: 608, 验证集大小: 131\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: 579, 验证集大小: 108\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: 629, 验证集大小: 137\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: 645, 验证集大小: 133\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: 597, 验证集大小: 88\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: 609, 验证集大小: 143\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: 642, 验证集大小: 141\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: 616, 验证集大小: 111\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: 602, 验证集大小: 119\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: 514, 验证集大小: 119\n" + ] + } + ], "source": [ "\n", "gc.collect()\n", "\n", - "print(df[df['ts_code'] == '000001.SZ'].tail(1)[['act_factor1', 'act_factor2']])\n", "print('finish')\n", "# qdf = qdf[qdf['trade_date'] >= '2022-01-01']\n", "\n", - "final_predictions = rolling_train_predict(pdf[(pdf['trade_date'] >= '2020-01-01') & (pdf['trade_date'] <= '2025-03-26')], 5, 1, feature_columns,\n", - " days=days, validation_days=0, filter_index=filter_index)\n", + "final_predictions = rolling_train_predict(\n", + " pdf[(pdf['trade_date'] >= '2022-12-01') & (pdf['trade_date'] <= '2029-03-26')], 5, 1, feature_columns,\n", + " days=days, validation_days=0, filter_index=filter_index, params=light_params)\n", "final_predictions.to_csv('predictions_test.tsv', index=False)\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " act_factor1 act_factor2\n", - "5115129 -0.222482 -0.256946\n", - "finish\n", - "最大日期: 2020-01-09\n", - "划分后的训练集大小: 636, 验证集大小: 167\n", - "最大日期: 2020-01-10\n", - "划分后的训练集大小: 648, 验证集大小: 132\n", - "最大日期: 2020-01-13\n", - "划分后的训练集大小: 702, 验证集大小: 145\n", - "最大日期: 2020-01-14\n", - "划分后的训练集大小: 708, 验证集大小: 145\n", - "最大日期: 2020-01-15\n", - "划分后的训练集大小: 793, 验证集大小: 204\n", - "最大日期: 2020-01-16\n", - "划分后的训练集大小: 816, 验证集大小: 190\n", - "最大日期: 2020-01-17\n", - "划分后的训练集大小: 774, 验证集大小: 90\n", - "最大日期: 2020-01-20\n", - "划分后的训练集大小: 718, 验证集大小: 89\n", - "最大日期: 2020-01-21\n", - "划分后的训练集大小: 783, 验证集大小: 210\n", - "最大日期: 2020-01-22\n", - "划分后的训练集大小: 684, 验证集大小: 105\n", - "最大日期: 2020-01-23\n", - "划分后的训练集大小: 578, 验证集大小: 84\n", - "最大日期: 2020-02-03\n", - "划分后的训练集大小: 829, 验证集大小: 341\n", - "最大日期: 2020-02-04\n", - "划分后的训练集大小: 887, 验证集大小: 147\n", - "最大日期: 2020-02-05\n", - "划分后的训练集大小: 779, 验证集大小: 102\n", - "最大日期: 2020-02-06\n", - "划分后的训练集大小: 827, 验证集大小: 153\n", - "最大日期: 2020-02-07\n", - "划分后的训练集大小: 942, 验证集大小: 199\n", - "最大日期: 2020-02-10\n", - "划分后的训练集大小: 747, 验证集大小: 146\n", - "最大日期: 2020-02-11\n", - "划分后的训练集大小: 692, 验证集大小: 92\n", - "最大日期: 2020-02-12\n", - "划分后的训练集大小: 681, 验证集大小: 91\n", - "最大日期: 2020-02-13\n", - "划分后的训练集大小: 706, 验证集大小: 178\n", - "最大日期: 2020-02-14\n", - "划分后的训练集大小: 629, 验证集大小: 122\n", - "最大日期: 2020-02-17\n", - "划分后的训练集大小: 587, 验证集大小: 104\n", - "最大日期: 2020-02-18\n", - "划分后的训练集大小: 622, 验证集大小: 127\n", - "最大日期: 2020-02-19\n", - "划分后的训练集大小: 711, 验证集大小: 180\n", - "最大日期: 2020-02-20\n", - "划分后的训练集大小: 621, 验证集大小: 88\n", - "最大日期: 2020-02-21\n", - "划分后的训练集大小: 671, 验证集大小: 172\n", - "最大日期: 2020-02-24\n", - "划分后的训练集大小: 831, 验证集大小: 264\n", - "最大日期: 2020-02-25\n", - "划分后的训练集大小: 896, 验证集大小: 192\n", - "最大日期: 2020-02-26\n", - "划分后的训练集大小: 865, 验证集大小: 149\n", - "最大日期: 2020-02-27\n", - "划分后的训练集大小: 901, 验证集大小: 124\n", - "最大日期: 2020-02-28\n", - "划分后的训练集大小: 861, 验证集大小: 132\n", - "最大日期: 2020-03-02\n", - "划分后的训练集大小: 687, 验证集大小: 90\n", - "最大日期: 2020-03-03\n", - "划分后的训练集大小: 599, 验证集大小: 104\n", - "最大日期: 2020-03-04\n", - "划分后的训练集大小: 544, 验证集大小: 94\n", - "最大日期: 2020-03-05\n", - "划分后的训练集大小: 574, 验证集大小: 154\n", - "最大日期: 2020-03-06\n", - "划分后的训练集大小: 544, 验证集大小: 102\n", - "最大日期: 2020-03-09\n", - "划分后的训练集大小: 533, 验证集大小: 79\n", - "最大日期: 2020-03-10\n", - "划分后的训练集大小: 519, 验证集大小: 90\n", - "最大日期: 2020-03-11\n", - "划分后的训练集大小: 570, 验证集大小: 145\n", - "最大日期: 2020-03-12\n", - "划分后的训练集大小: 578, 验证集大小: 162\n", - "最大日期: 2020-03-13\n", - "划分后的训练集大小: 607, 验证集大小: 131\n", - "最大日期: 2020-03-16\n", - "划分后的训练集大小: 672, 验证集大小: 144\n", - "最大日期: 2020-03-17\n", - "划分后的训练集大小: 673, 验证集大小: 91\n", - "最大日期: 2020-03-18\n", - "划分后的训练集大小: 613, 验证集大小: 85\n", - "最大日期: 2020-03-19\n", - "划分后的训练集大小: 574, 验证集大小: 123\n", - "最大日期: 2020-03-20\n", - "划分后的训练集大小: 587, 验证集大小: 144\n", - "最大日期: 2020-03-23\n", - "划分后的训练集大小: 544, 验证集大小: 101\n", - "最大日期: 2020-03-24\n", - "划分后的训练集大小: 557, 验证集大小: 104\n", - "最大日期: 2020-03-25\n", - "划分后的训练集大小: 602, 验证集大小: 130\n", - "最大日期: 2020-03-26\n", - "划分后的训练集大小: 665, 验证集大小: 186\n", - "最大日期: 2020-03-27\n", - "划分后的训练集大小: 603, 验证集大小: 82\n", - "最大日期: 2020-03-30\n", - "划分后的训练集大小: 595, 验证集大小: 93\n", - "最大日期: 2020-03-31\n", - "划分后的训练集大小: 589, 验证集大小: 98\n", - "最大日期: 2020-04-01\n", - "划分后的训练集大小: 642, 验证集大小: 183\n", - "最大日期: 2020-04-02\n", - "划分后的训练集大小: 553, 验证集大小: 97\n", - "最大日期: 2020-04-03\n", - "划分后的训练集大小: 603, 验证集大小: 132\n", - "最大日期: 2020-04-07\n", - "划分后的训练集大小: 604, 验证集大小: 94\n", - "最大日期: 2020-04-08\n", - "划分后的训练集大小: 611, 验证集大小: 105\n", - "最大日期: 2020-04-09\n", - "划分后的训练集大小: 621, 验证集大小: 193\n", - "最大日期: 2020-04-10\n", - "划分后的训练集大小: 642, 验证集大小: 118\n", - "最大日期: 2020-04-13\n", - "划分后的训练集大小: 667, 验证集大小: 157\n", - "最大日期: 2020-04-14\n", - "划分后的训练集大小: 759, 验证集大小: 186\n", - "最大日期: 2020-04-15\n", - "划分后的训练集大小: 784, 验证集大小: 130\n", - "最大日期: 2020-04-16\n", - "划分后的训练集大小: 744, 验证集大小: 153\n", - "最大日期: 2020-04-17\n", - "划分后的训练集大小: 723, 验证集大小: 97\n", - "最大日期: 2020-04-20\n", - "划分后的训练集大小: 683, 验证集大小: 117\n", - "最大日期: 2020-04-21\n", - "划分后的训练集大小: 585, 验证集大小: 88\n", - "最大日期: 2020-04-22\n", - "划分后的训练集大小: 538, 验证集大小: 83\n", - "最大日期: 2020-04-23\n", - "划分后的训练集大小: 490, 验证集大小: 105\n", - "最大日期: 2020-04-24\n", - "划分后的训练集大小: 490, 验证集大小: 97\n", - "最大日期: 2020-04-27\n", - "划分后的训练集大小: 458, 验证集大小: 85\n", - "最大日期: 2020-04-28\n", - "划分后的训练集大小: 475, 验证集大小: 105\n", - "最大日期: 2020-04-29\n", - "划分后的训练集大小: 525, 验证集大小: 133\n", - "最大日期: 2020-04-30\n", - "划分后的训练集大小: 584, 验证集大小: 164\n", - "最大日期: 2020-05-06\n", - "划分后的训练集大小: 577, 验证集大小: 90\n", - "最大日期: 2020-05-07\n", - "划分后的训练集大小: 576, 验证集大小: 84\n", - "最大日期: 2020-05-08\n", - "划分后的训练集大小: 562, 验证集大小: 91\n", - "最大日期: 2020-05-11\n", - "划分后的训练集大小: 551, 验证集大小: 122\n", - "最大日期: 2020-05-12\n", - "划分后的训练集大小: 494, 验证集大小: 107\n", - "最大日期: 2020-05-13\n", - "划分后的训练集大小: 613, 验证集大小: 209\n", - "最大日期: 2020-05-14\n", - "划分后的训练集大小: 648, 验证集大小: 119\n", - "最大日期: 2020-05-15\n", - "划分后的训练集大小: 784, 验证集大小: 227\n", - "最大日期: 2020-05-18\n", - "划分后的训练集大小: 841, 验证集大小: 179\n", - "最大日期: 2020-05-19\n", - "划分后的训练集大小: 820, 验证集大小: 86\n", - "最大日期: 2020-05-20\n", - "划分后的训练集大小: 764, 验证集大小: 153\n", - "最大日期: 2020-05-21\n", - "划分后的训练集大小: 727, 验证集大小: 82\n", - "最大日期: 2020-05-22\n", - "划分后的训练集大小: 584, 验证集大小: 84\n", - "最大日期: 2020-05-25\n", - "划分后的训练集大小: 489, 验证集大小: 84\n", - "最大日期: 2020-05-26\n", - "划分后的训练集大小: 571, 验证集大小: 168\n", - "最大日期: 2020-05-27\n", - "划分后的训练集大小: 509, 验证集大小: 91\n", - "最大日期: 2020-05-28\n", - "划分后的训练集大小: 577, 验证集大小: 150\n", - "最大日期: 2020-05-29\n", - "划分后的训练集大小: 678, 验证集大小: 185\n", - "最大日期: 2020-06-01\n", - "划分后的训练集大小: 791, 验证集大小: 197\n", - "最大日期: 2020-06-02\n", - "划分后的训练集大小: 719, 验证集大小: 96\n", - "最大日期: 2020-06-03\n", - "划分后的训练集大小: 760, 验证集大小: 132\n", - "最大日期: 2020-06-04\n", - "划分后的训练集大小: 745, 验证集大小: 135\n", - "最大日期: 2020-06-05\n", - "划分后的训练集大小: 741, 验证集大小: 181\n", - "最大日期: 2020-06-08\n", - "划分后的训练集大小: 627, 验证集大小: 83\n", - "最大日期: 2020-06-09\n", - "划分后的训练集大小: 613, 验证集大小: 82\n", - "最大日期: 2020-06-10\n", - "划分后的训练集大小: 627, 验证集大小: 146\n", - "最大日期: 2020-06-11\n", - "划分后的训练集大小: 585, 验证集大小: 93\n", - "最大日期: 2020-06-12\n", - "划分后的训练集大小: 589, 验证集大小: 185\n", - "最大日期: 2020-06-15\n", - "划分后的训练集大小: 634, 验证集大小: 128\n", - "最大日期: 2020-06-16\n", - "划分后的训练集大小: 638, 验证集大小: 86\n", - "最大日期: 2020-06-17\n", - "划分后的训练集大小: 796, 验证集大小: 304\n", - "最大日期: 2020-06-18\n", - "划分后的训练集大小: 842, 验证集大小: 139\n", - "最大日期: 2020-06-19\n", - "划分后的训练集大小: 751, 验证集大小: 94\n", - "最大日期: 2020-06-22\n", - "划分后的训练集大小: 714, 验证集大小: 91\n", - "最大日期: 2020-06-23\n", - "划分后的训练集大小: 715, 验证集大小: 87\n", - "最大日期: 2020-06-24\n", - "划分后的训练集大小: 519, 验证集大小: 108\n", - "最大日期: 2020-06-29\n", - "划分后的训练集大小: 552, 验证集大小: 172\n", - "最大日期: 2020-06-30\n", - "划分后的训练集大小: 606, 验证集大小: 148\n", - "最大日期: 2020-07-01\n", - "划分后的训练集大小: 610, 验证集大小: 95\n", - "最大日期: 2020-07-02\n", - "划分后的训练集大小: 646, 验证集大小: 123\n", - "最大日期: 2020-07-03\n", - "划分后的训练集大小: 657, 验证集大小: 119\n", - "最大日期: 2020-07-06\n", - "划分后的训练集大小: 690, 验证集大小: 205\n", - "最大日期: 2020-07-07\n", - "划分后的训练集大小: 620, 验证集大小: 78\n", - "最大日期: 2020-07-08\n", - "划分后的训练集大小: 620, 验证集大小: 95\n", - "最大日期: 2020-07-09\n", - "划分后的训练集大小: 575, 验证集大小: 78\n", - "最大日期: 2020-07-10\n", - "划分后的训练集大小: 531, 验证集大小: 75\n", - "最大日期: 2020-07-13\n", - "划分后的训练集大小: 472, 验证集大小: 146\n", - "最大日期: 2020-07-14\n", - "划分后的训练集大小: 492, 验证集大小: 98\n", - "最大日期: 2020-07-15\n", - "划分后的训练集大小: 508, 验证集大小: 111\n", - "最大日期: 2020-07-16\n", - "划分后的训练集大小: 503, 验证集大小: 73\n", - "最大日期: 2020-07-17\n", - "划分后的训练集大小: 618, 验证集大小: 190\n", - "最大日期: 2020-07-20\n", - "划分后的训练集大小: 679, 验证集大小: 207\n", - "最大日期: 2020-07-21\n", - "划分后的训练集大小: 738, 验证集大小: 157\n", - "最大日期: 2020-07-22\n", - "划分后的训练集大小: 741, 验证集大小: 114\n", - "最大日期: 2020-07-23\n", - "划分后的训练集大小: 870, 验证集大小: 202\n", - "最大日期: 2020-07-24\n", - "划分后的训练集大小: 765, 验证集大小: 85\n", - "最大日期: 2020-07-27\n", - "划分后的训练集大小: 647, 验证集大小: 89\n", - "最大日期: 2020-07-28\n", - "划分后的训练集大小: 651, 验证集大小: 161\n", - "最大日期: 2020-07-29\n", - "划分后的训练集大小: 643, 验证集大小: 106\n", - "最大日期: 2020-07-30\n", - "划分后的训练集大小: 566, 验证集大小: 125\n", - "最大日期: 2020-07-31\n", - "划分后的训练集大小: 614, 验证集大小: 133\n", - "最大日期: 2020-08-03\n", - "划分后的训练集大小: 705, 验证集大小: 180\n", - "最大日期: 2020-08-04\n", - "划分后的训练集大小: 626, 验证集大小: 82\n", - "最大日期: 2020-08-05\n", - "划分后的训练集大小: 628, 验证集大小: 108\n", - "最大日期: 2020-08-06\n", - "划分后的训练集大小: 585, 验证集大小: 82\n", - "最大日期: 2020-08-07\n", - "划分后的训练集大小: 584, 验证集大小: 132\n", - "最大日期: 2020-08-10\n", - "划分后的训练集大小: 564, 验证集大小: 160\n", - "最大日期: 2020-08-11\n", - "划分后的训练集大小: 676, 验证集大小: 194\n", - "最大日期: 2020-08-12\n", - "划分后的训练集大小: 767, 验证集大小: 199\n", - "最大日期: 2020-08-13\n", - "划分后的训练集大小: 932, 验证集大小: 247\n", - "最大日期: 2020-08-14\n", - "划分后的训练集大小: 924, 验证集大小: 124\n", - "最大日期: 2020-08-17\n", - "划分后的训练集大小: 863, 验证集大小: 99\n", - "最大日期: 2020-08-18\n", - "划分后的训练集大小: 812, 验证集大小: 143\n", - "最大日期: 2020-08-19\n", - "划分后的训练集大小: 727, 验证集大小: 114\n", - "最大日期: 2020-08-20\n", - "划分后的训练集大小: 672, 验证集大小: 192\n", - "最大日期: 2020-08-21\n", - "划分后的训练集大小: 716, 验证集大小: 168\n", - "最大日期: 2020-08-24\n", - "划分后的训练集大小: 738, 验证集大小: 121\n", - "最大日期: 2020-08-25\n", - "划分后的训练集大小: 708, 验证集大小: 113\n", - "最大日期: 2020-08-26\n", - "划分后的训练集大小: 719, 验证集大小: 125\n", - "最大日期: 2020-08-27\n", - "划分后的训练集大小: 653, 验证集大小: 126\n", - "最大日期: 2020-08-28\n", - "划分后的训练集大小: 606, 验证集大小: 121\n", - "最大日期: 2020-08-31\n", - "划分后的训练集大小: 595, 验证集大小: 110\n", - "最大日期: 2020-09-01\n", - "划分后的训练集大小: 704, 验证集大小: 222\n", - "最大日期: 2020-09-02\n", - "划分后的训练集大小: 664, 验证集大小: 85\n", - "最大日期: 2020-09-03\n", - "划分后的训练集大小: 638, 验证集大小: 100\n", - "最大日期: 2020-09-04\n", - "划分后的训练集大小: 687, 验证集大小: 170\n", - "最大日期: 2020-09-07\n", - "划分后的训练集大小: 805, 验证集大小: 228\n", - "最大日期: 2020-09-08\n", - "划分后的训练集大小: 677, 验证集大小: 94\n", - "最大日期: 2020-09-09\n", - "划分后的训练集大小: 781, 验证集大小: 189\n", - "最大日期: 2020-09-10\n", - "划分后的训练集大小: 772, 验证集大小: 91\n", - "最大日期: 2020-09-11\n", - "划分后的训练集大小: 691, 验证集大小: 89\n", - "最大日期: 2020-09-14\n", - "划分后的训练集大小: 556, 验证集大小: 93\n", - "最大日期: 2020-09-15\n", - "划分后的训练集大小: 552, 验证集大小: 90\n", - "最大日期: 2020-09-16\n", - "划分后的训练集大小: 514, 验证集大小: 151\n", - "最大日期: 2020-09-17\n", - "划分后的训练集大小: 509, 验证集大小: 86\n", - "最大日期: 2020-09-18\n", - "划分后的训练集大小: 545, 验证集大小: 125\n", - "最大日期: 2020-09-21\n", - "划分后的训练集大小: 540, 验证集大小: 88\n", - "最大日期: 2020-09-22\n", - "划分后的训练集大小: 536, 验证集大小: 86\n", - "最大日期: 2020-09-23\n", - "划分后的训练集大小: 466, 验证集大小: 81\n", - "最大日期: 2020-09-24\n", - "划分后的训练集大小: 460, 验证集大小: 80\n", - "最大日期: 2020-09-25\n", - "划分后的训练集大小: 453, 验证集大小: 118\n", - "最大日期: 2020-09-28\n", - "划分后的训练集大小: 519, 验证集大小: 154\n", - "最大日期: 2020-09-29\n", - "划分后的训练集大小: 558, 验证集大小: 125\n", - "最大日期: 2020-09-30\n", - "划分后的训练集大小: 598, 验证集大小: 121\n", - "最大日期: 2020-10-09\n", - "划分后的训练集大小: 675, 验证集大小: 157\n", - "最大日期: 2020-10-12\n", - "划分后的训练集大小: 669, 验证集大小: 112\n", - "最大日期: 2020-10-13\n", - "划分后的训练集大小: 738, 验证集大小: 223\n", - "最大日期: 2020-10-14\n", - "划分后的训练集大小: 698, 验证集大小: 85\n", - "最大日期: 2020-10-15\n", - "划分后的训练集大小: 668, 验证集大小: 91\n", - "最大日期: 2020-10-16\n", - "划分后的训练集大小: 654, 验证集大小: 143\n", - "最大日期: 2020-10-19\n", - "划分后的训练集大小: 635, 验证集大小: 93\n", - "最大日期: 2020-10-20\n", - "划分后的训练集大小: 515, 验证集大小: 103\n", - "最大日期: 2020-10-21\n", - "划分后的训练集大小: 574, 验证集大小: 144\n", - "最大日期: 2020-10-22\n", - "划分后的训练集大小: 621, 验证集大小: 138\n", - "最大日期: 2020-10-23\n", - "划分后的训练集大小: 567, 验证集大小: 89\n", - "最大日期: 2020-10-26\n", - "划分后的训练集大小: 560, 验证集大小: 86\n", - "最大日期: 2020-10-27\n", - "划分后的训练集大小: 572, 验证集大小: 115\n", - "最大日期: 2020-10-28\n", - "划分后的训练集大小: 533, 验证集大小: 105\n", - "最大日期: 2020-10-29\n", - "划分后的训练集大小: 482, 验证集大小: 87\n", - "最大日期: 2020-10-30\n", - "划分后的训练集大小: 480, 验证集大小: 87\n", - "最大日期: 2020-11-02\n", - "划分后的训练集大小: 485, 验证集大小: 91\n", - "最大日期: 2020-11-03\n", - "划分后的训练集大小: 455, 验证集大小: 85\n", - "最大日期: 2020-11-04\n", - "划分后的训练集大小: 449, 验证集大小: 99\n", - "最大日期: 2020-11-05\n", - "划分后的训练集大小: 488, 验证集大小: 126\n", - "最大日期: 2020-11-06\n", - "划分后的训练集大小: 624, 验证集大小: 223\n", - "最大日期: 2020-11-09\n", - "划分后的训练集大小: 672, 验证集大小: 139\n", - "最大日期: 2020-11-10\n", - "划分后的训练集大小: 760, 验证集大小: 173\n", - "最大日期: 2020-11-11\n", - "划分后的训练集大小: 767, 验证集大小: 106\n", - "最大日期: 2020-11-12\n", - "划分后的训练集大小: 736, 验证集大小: 95\n", - "最大日期: 2020-11-13\n", - "划分后的训练集大小: 690, 验证集大小: 177\n", - "最大日期: 2020-11-16\n", - "划分后的训练集大小: 639, 验证集大小: 88\n", - "最大日期: 2020-11-17\n", - "划分后的训练集大小: 685, 验证集大小: 219\n", - "最大日期: 2020-11-18\n", - "划分后的训练集大小: 688, 验证集大小: 109\n", - "最大日期: 2020-11-19\n", - "划分后的训练集大小: 703, 验证集大小: 110\n", - "最大日期: 2020-11-20\n", - "划分后的训练集大小: 612, 验证集大小: 86\n", - "最大日期: 2020-11-23\n", - "划分后的训练集大小: 613, 验证集大小: 89\n", - "最大日期: 2020-11-24\n", - "划分后的训练集大小: 551, 验证集大小: 157\n", - "最大日期: 2020-11-25\n", - "划分后的训练集大小: 532, 验证集大小: 90\n", - "最大日期: 2020-11-26\n", - "划分后的训练集大小: 511, 验证集大小: 89\n", - "最大日期: 2020-11-27\n", - "划分后的训练集大小: 552, 验证集大小: 127\n", - "最大日期: 2020-11-30\n", - "划分后的训练集大小: 569, 验证集大小: 106\n", - "最大日期: 2020-12-01\n", - "划分后的训练集大小: 493, 验证集大小: 81\n", - "最大日期: 2020-12-02\n", - "划分后的训练集大小: 544, 验证集大小: 141\n", - "最大日期: 2020-12-03\n", - "划分后的训练集大小: 535, 验证集大小: 80\n", - "最大日期: 2020-12-04\n", - "划分后的训练集大小: 486, 验证集大小: 78\n", - "最大日期: 2020-12-07\n", - "划分后的训练集大小: 481, 验证集大小: 101\n", - "最大日期: 2020-12-08\n", - "划分后的训练集大小: 484, 验证集大小: 84\n", - "最大日期: 2020-12-09\n", - "划分后的训练集大小: 429, 验证集大小: 86\n", - "最大日期: 2020-12-10\n", - "划分后的训练集大小: 438, 验证集大小: 89\n", - "最大日期: 2020-12-11\n", - "划分后的训练集大小: 487, 验证集大小: 127\n", - "最大日期: 2020-12-14\n", - "划分后的训练集大小: 480, 验证集大小: 94\n", - "最大日期: 2020-12-15\n", - "划分后的训练集大小: 481, 验证集大小: 85\n", - "最大日期: 2020-12-16\n", - "划分后的训练集大小: 480, 验证集大小: 85\n", - "最大日期: 2020-12-17\n", - "划分后的训练集大小: 477, 验证集大小: 86\n", - "最大日期: 2020-12-18\n", - "划分后的训练集大小: 535, 验证集大小: 185\n", - "最大日期: 2020-12-21\n", - "划分后的训练集大小: 562, 验证集大小: 121\n", - "最大日期: 2020-12-22\n", - "划分后的训练集大小: 605, 验证集大小: 128\n", - "最大日期: 2020-12-23\n", - "划分后的训练集大小: 666, 验证集大小: 146\n", - "最大日期: 2020-12-24\n", - "划分后的训练集大小: 664, 验证集大小: 84\n", - "最大日期: 2020-12-25\n", - "划分后的训练集大小: 624, 验证集大小: 145\n", - "最大日期: 2020-12-28\n", - "划分后的训练集大小: 653, 验证集大小: 150\n", - "最大日期: 2020-12-29\n", - "划分后的训练集大小: 633, 验证集大小: 108\n", - "最大日期: 2020-12-30\n", - "划分后的训练集大小: 605, 验证集大小: 118\n", - "最大日期: 2020-12-31\n", - "划分后的训练集大小: 647, 验证集大小: 126\n", - "最大日期: 2021-01-04\n", - "划分后的训练集大小: 612, 验证集大小: 110\n", - "最大日期: 2021-01-05\n", - "划分后的训练集大小: 584, 验证集大小: 122\n", - "最大日期: 2021-01-06\n", - "划分后的训练集大小: 597, 验证集大小: 121\n", - "最大日期: 2021-01-07\n", - "划分后的训练集大小: 589, 验证集大小: 110\n", - "最大日期: 2021-01-08\n", - "划分后的训练集大小: 598, 验证集大小: 135\n", - "最大日期: 2021-01-11\n", - "划分后的训练集大小: 614, 验证集大小: 126\n", - "最大日期: 2021-01-12\n", - "划分后的训练集大小: 595, 验证集大小: 103\n", - "最大日期: 2021-01-13\n", - "划分后的训练集大小: 719, 验证集大小: 245\n", - "最大日期: 2021-01-14\n", - "划分后的训练集大小: 917, 验证集大小: 308\n", - "最大日期: 2021-01-15\n", - "划分后的训练集大小: 899, 验证集大小: 117\n", - "最大日期: 2021-01-18\n", - "划分后的训练集大小: 955, 验证集大小: 182\n", - "最大日期: 2021-01-19\n", - "划分后的训练集大小: 944, 验证集大小: 92\n", - "最大日期: 2021-01-20\n", - "划分后的训练集大小: 890, 验证集大小: 191\n", - "最大日期: 2021-01-21\n", - "划分后的训练集大小: 673, 验证集大小: 91\n", - "最大日期: 2021-01-22\n", - "划分后的训练集大小: 643, 验证集大小: 87\n", - "最大日期: 2021-01-25\n", - "划分后的训练集大小: 593, 验证集大小: 132\n", - "最大日期: 2021-01-26\n", - "划分后的训练集大小: 615, 验证集大小: 114\n", - "最大日期: 2021-01-27\n", - "划分后的训练集大小: 589, 验证集大小: 165\n", - "最大日期: 2021-01-28\n", - "划分后的训练集大小: 595, 验证集大小: 97\n", - "最大日期: 2021-01-29\n", - "划分后的训练集大小: 671, 验证集大小: 163\n", - "最大日期: 2021-02-01\n", - "划分后的训练集大小: 630, 验证集大小: 91\n", - "最大日期: 2021-02-02\n", - "划分后的训练集大小: 604, 验证集大小: 88\n", - "最大日期: 2021-02-03\n", - "划分后的训练集大小: 545, 验证集大小: 106\n", - "最大日期: 2021-02-04\n", - "划分后的训练集大小: 530, 验证集大小: 82\n", - "最大日期: 2021-02-05\n", - "划分后的训练集大小: 459, 验证集大小: 92\n", - "最大日期: 2021-02-08\n", - "划分后的训练集大小: 460, 验证集大小: 92\n", - "最大日期: 2021-02-09\n", - "划分后的训练集大小: 475, 验证集大小: 103\n", - "最大日期: 2021-02-10\n", - "划分后的训练集大小: 454, 验证集大小: 85\n", - "最大日期: 2021-02-18\n", - "划分后的训练集大小: 541, 验证集大小: 169\n", - "最大日期: 2021-02-19\n", - "划分后的训练集大小: 552, 验证集大小: 103\n", - "最大日期: 2021-02-22\n", - "划分后的训练集大小: 580, 验证集大小: 120\n", - "最大日期: 2021-02-23\n", - "划分后的训练集大小: 586, 验证集大小: 109\n", - "最大日期: 2021-02-24\n", - "划分后的训练集大小: 580, 验证集大小: 79\n", - "最大日期: 2021-02-25\n", - "划分后的训练集大小: 622, 验证集大小: 211\n", - "最大日期: 2021-02-26\n", - "划分后的训练集大小: 607, 验证集大小: 88\n", - "最大日期: 2021-03-01\n", - "划分后的训练集大小: 603, 验证集大小: 116\n", - "最大日期: 2021-03-02\n", - "划分后的训练集大小: 585, 验证集大小: 91\n", - "最大日期: 2021-03-03\n", - "划分后的训练集大小: 591, 验证集大小: 85\n", - "最大日期: 2021-03-04\n", - "划分后的训练集大小: 484, 验证集大小: 104\n", - "最大日期: 2021-03-05\n", - "划分后的训练集大小: 525, 验证集大小: 129\n", - "最大日期: 2021-03-08\n", - "划分后的训练集大小: 547, 验证集大小: 138\n", - "最大日期: 2021-03-09\n", - "划分后的训练集大小: 538, 验证集大小: 82\n", - "最大日期: 2021-03-10\n", - "划分后的训练集大小: 548, 验证集大小: 95\n", - "最大日期: 2021-03-11\n", - "划分后的训练集大小: 528, 验证集大小: 84\n", - "最大日期: 2021-03-12\n", - "划分后的训练集大小: 487, 验证集大小: 88\n", - "最大日期: 2021-03-15\n", - "划分后的训练集大小: 458, 验证集大小: 109\n", - "最大日期: 2021-03-16\n", - "划分后的训练集大小: 480, 验证集大小: 104\n", - "最大日期: 2021-03-17\n", - "划分后的训练集大小: 477, 验证集大小: 92\n", - "最大日期: 2021-03-18\n", - "划分后的训练集大小: 473, 验证集大小: 80\n", - "最大日期: 2021-03-19\n", - "划分后的训练集大小: 505, 验证集大小: 120\n", - "最大日期: 2021-03-22\n", - "划分后的训练集大小: 636, 验证集大小: 240\n", - "最大日期: 2021-03-23\n", - "划分后的训练集大小: 692, 验证集大小: 160\n", - "最大日期: 2021-03-24\n", - "划分后的训练集大小: 699, 验证集大小: 99\n", - "最大日期: 2021-03-25\n", - "划分后的训练集大小: 770, 验证集大小: 151\n", - "最大日期: 2021-03-26\n", - "划分后的训练集大小: 746, 验证集大小: 96\n", - "最大日期: 2021-03-29\n", - "划分后的训练集大小: 607, 验证集大小: 101\n", - "最大日期: 2021-03-30\n", - "划分后的训练集大小: 554, 验证集大小: 107\n", - "最大日期: 2021-03-31\n", - "划分后的训练集大小: 568, 验证集大小: 113\n", - "最大日期: 2021-04-01\n", - "划分后的训练集大小: 607, 验证集大小: 190\n", - "最大日期: 2021-04-02\n", - "划分后的训练集大小: 691, 验证集大小: 180\n", - "最大日期: 2021-04-06\n", - "划分后的训练集大小: 702, 验证集大小: 112\n", - "最大日期: 2021-04-07\n", - "划分后的训练集大小: 693, 验证集大小: 98\n", - "最大日期: 2021-04-08\n", - "划分后的训练集大小: 735, 验证集大小: 155\n", - "最大日期: 2021-04-09\n", - "划分后的训练集大小: 652, 验证集大小: 107\n", - "最大日期: 2021-04-12\n", - "划分后的训练集大小: 598, 验证集大小: 126\n", - "最大日期: 2021-04-13\n", - "划分后的训练集大小: 594, 验证集大小: 108\n", - "最大日期: 2021-04-14\n", - "划分后的训练集大小: 582, 验证集大小: 86\n", - "最大日期: 2021-04-15\n", - "划分后的训练集大小: 547, 验证集大小: 120\n", - "最大日期: 2021-04-16\n", - "划分后的训练集大小: 561, 验证集大小: 121\n", - "最大日期: 2021-04-19\n", - "划分后的训练集大小: 582, 验证集大小: 147\n", - "最大日期: 2021-04-20\n", - "划分后的训练集大小: 628, 验证集大小: 154\n", - "最大日期: 2021-04-21\n", - "划分后的训练集大小: 736, 验证集大小: 194\n", - "最大日期: 2021-04-22\n", - "划分后的训练集大小: 719, 验证集大小: 103\n", - "最大日期: 2021-04-23\n", - "划分后的训练集大小: 682, 验证集大小: 84\n", - "最大日期: 2021-04-26\n", - "划分后的训练集大小: 642, 验证集大小: 107\n", - "最大日期: 2021-04-27\n", - "划分后的训练集大小: 591, 验证集大小: 103\n", - "最大日期: 2021-04-28\n", - "划分后的训练集大小: 500, 验证集大小: 103\n", - "最大日期: 2021-04-29\n", - "划分后的训练集大小: 558, 验证集大小: 161\n", - "最大日期: 2021-04-30\n", - "划分后的训练集大小: 573, 验证集大小: 99\n", - "最大日期: 2021-05-06\n", - "划分后的训练集大小: 766, 验证集大小: 300\n", - "最大日期: 2021-05-07\n", - "划分后的训练集大小: 765, 验证集大小: 102\n", - "最大日期: 2021-05-10\n", - "划分后的训练集大小: 753, 验证集大小: 91\n", - "最大日期: 2021-05-11\n", - "划分后的训练集大小: 704, 验证集大小: 112\n", - "最大日期: 2021-05-12\n", - "划分后的训练集大小: 690, 验证集大小: 85\n", - "最大日期: 2021-05-13\n", - "划分后的训练集大小: 519, 验证集大小: 129\n", - "最大日期: 2021-05-14\n", - "划分后的训练集大小: 540, 验证集大小: 123\n", - "最大日期: 2021-05-17\n", - "划分后的训练集大小: 552, 验证集大小: 103\n", - "最大日期: 2021-05-18\n", - "划分后的训练集大小: 532, 验证集大小: 92\n", - "最大日期: 2021-05-19\n", - "划分后的训练集大小: 579, 验证集大小: 132\n", - "最大日期: 2021-05-20\n", - "划分后的训练集大小: 538, 验证集大小: 88\n", - "最大日期: 2021-05-21\n", - "划分后的训练集大小: 520, 验证集大小: 105\n", - "最大日期: 2021-05-24\n", - "划分后的训练集大小: 547, 验证集大小: 130\n", - "最大日期: 2021-05-25\n", - "划分后的训练集大小: 578, 验证集大小: 123\n", - "最大日期: 2021-05-26\n", - "划分后的训练集大小: 545, 验证集大小: 99\n", - "最大日期: 2021-05-27\n", - "划分后的训练集大小: 587, 验证集大小: 130\n", - "最大日期: 2021-05-28\n", - "划分后的训练集大小: 677, 验证集大小: 195\n", - "最大日期: 2021-05-31\n", - "划分后的训练集大小: 754, 验证集大小: 207\n", - "最大日期: 2021-06-01\n", - "划分后的训练集大小: 740, 验证集大小: 109\n", - "最大日期: 2021-06-02\n", - "划分后的训练集大小: 761, 验证集大小: 120\n", - "最大日期: 2021-06-03\n", - "划分后的训练集大小: 813, 验证集大小: 182\n", - "最大日期: 2021-06-04\n", - "划分后的训练集大小: 758, 验证集大小: 140\n", - "最大日期: 2021-06-07\n", - "划分后的训练集大小: 684, 验证集大小: 133\n", - "最大日期: 2021-06-08\n", - "划分后的训练集大小: 758, 验证集大小: 183\n", - "最大日期: 2021-06-09\n", - "划分后的训练集大小: 771, 验证集大小: 133\n", - "最大日期: 2021-06-10\n", - "划分后的训练集大小: 684, 验证集大小: 95\n", - "最大日期: 2021-06-11\n", - "划分后的训练集大小: 659, 验证集大小: 115\n", - "最大日期: 2021-06-15\n", - "划分后的训练集大小: 796, 验证集大小: 270\n", - "最大日期: 2021-06-16\n", - "划分后的训练集大小: 857, 验证集大小: 244\n", - "最大日期: 2021-06-17\n", - "划分后的训练集大小: 837, 验证集大小: 113\n", - "最大日期: 2021-06-18\n", - "划分后的训练集大小: 847, 验证集大小: 105\n", - "最大日期: 2021-06-21\n", - "划分后的训练集大小: 873, 验证集大小: 141\n", - "最大日期: 2021-06-22\n", - "划分后的训练集大小: 771, 验证集大小: 168\n", - "最大日期: 2021-06-23\n", - "划分后的训练集大小: 619, 验证集大小: 92\n", - "最大日期: 2021-06-24\n", - "划分后的训练集大小: 619, 验证集大小: 113\n", - "最大日期: 2021-06-25\n", - "划分后的训练集大小: 617, 验证集大小: 103\n", - "最大日期: 2021-06-28\n", - "划分后的训练集大小: 609, 验证集大小: 133\n", - "最大日期: 2021-06-29\n", - "划分后的训练集大小: 548, 验证集大小: 107\n", - "最大日期: 2021-06-30\n", - "划分后的训练集大小: 551, 验证集大小: 95\n", - "最大日期: 2021-07-01\n", - "划分后的训练集大小: 524, 验证集大小: 86\n", - "最大日期: 2021-07-02\n", - "划分后的训练集大小: 504, 验证集大小: 83\n", - "最大日期: 2021-07-05\n", - "划分后的训练集大小: 596, 验证集大小: 225\n", - "最大日期: 2021-07-06\n", - "划分后的训练集大小: 600, 验证集大小: 111\n", - "最大日期: 2021-07-07\n", - "划分后的训练集大小: 677, 验证集大小: 172\n", - "最大日期: 2021-07-08\n", - "划分后的训练集大小: 697, 验证集大小: 106\n", - "最大日期: 2021-07-09\n", - "划分后的训练集大小: 713, 验证集大小: 99\n", - "最大日期: 2021-07-12\n", - "划分后的训练集大小: 590, 验证集大小: 102\n", - "最大日期: 2021-07-13\n", - "划分后的训练集大小: 579, 验证集大小: 100\n", - "最大日期: 2021-07-14\n", - "划分后的训练集大小: 496, 验证集大小: 89\n", - "最大日期: 2021-07-15\n", - "划分后的训练集大小: 589, 验证集大小: 199\n", - "最大日期: 2021-07-16\n", - "划分后的训练集大小: 605, 验证集大小: 115\n", - "最大日期: 2021-07-19\n", - "划分后的训练集大小: 660, 验证集大小: 157\n", - "最大日期: 2021-07-20\n", - "划分后的训练集大小: 749, 验证集大小: 189\n", - "最大日期: 2021-07-21\n", - "划分后的训练集大小: 875, 验证集大小: 215\n", - "最大日期: 2021-07-22\n", - "划分后的训练集大小: 778, 验证集大小: 102\n", - "最大日期: 2021-07-23\n", - "划分后的训练集大小: 808, 验证集大小: 145\n", - "最大日期: 2021-07-26\n", - "划分后的训练集大小: 862, 验证集大小: 211\n", - "最大日期: 2021-07-27\n", - "划分后的训练集大小: 754, 验证集大小: 81\n", - "最大日期: 2021-07-28\n", - "划分后的训练集大小: 660, 验证集大小: 121\n", - "最大日期: 2021-07-29\n", - "划分后的训练集大小: 665, 验证集大小: 107\n", - "最大日期: 2021-07-30\n", - "划分后的训练集大小: 656, 验证集大小: 136\n", - "最大日期: 2021-08-02\n", - "划分后的训练集大小: 891, 验证集大小: 446\n", - "最大日期: 2021-08-03\n", - "划分后的训练集大小: 956, 验证集大小: 146\n", - "最大日期: 2021-08-04\n", - "划分后的训练集大小: 980, 验证集大小: 145\n", - "最大日期: 2021-08-05\n", - "划分后的训练集大小: 1036, 验证集大小: 163\n", - "最大日期: 2021-08-06\n", - "划分后的训练集大小: 1000, 验证集大小: 100\n", - "最大日期: 2021-08-09\n", - "划分后的训练集大小: 716, 验证集大小: 162\n", - "最大日期: 2021-08-10\n", - "划分后的训练集大小: 694, 验证集大小: 124\n", - "最大日期: 2021-08-11\n", - "划分后的训练集大小: 708, 验证集大小: 159\n", - "最大日期: 2021-08-12\n", - "划分后的训练集大小: 651, 验证集大小: 106\n", - "最大日期: 2021-08-13\n", - "划分后的训练集大小: 734, 验证集大小: 183\n", - "最大日期: 2021-08-16\n", - "划分后的训练集大小: 715, 验证集大小: 143\n", - "最大日期: 2021-08-17\n", - "划分后的训练集大小: 677, 验证集大小: 86\n", - "最大日期: 2021-08-18\n", - "划分后的训练集大小: 606, 验证集大小: 88\n", - "最大日期: 2021-08-19\n", - "划分后的训练集大小: 588, 验证集大小: 88\n", - "最大日期: 2021-08-20\n", - "划分后的训练集大小: 492, 验证集大小: 87\n", - "最大日期: 2021-08-23\n", - "划分后的训练集大小: 469, 验证集大小: 120\n", - "最大日期: 2021-08-24\n", - "划分后的训练集大小: 544, 验证集大小: 161\n", - "最大日期: 2021-08-25\n", - "划分后的训练集大小: 544, 验证集大小: 88\n", - "最大日期: 2021-08-26\n", - "划分后的训练集大小: 545, 验证集大小: 89\n", - "最大日期: 2021-08-27\n", - "划分后的训练集大小: 579, 验证集大小: 121\n", - "最大日期: 2021-08-30\n", - "划分后的训练集大小: 675, 验证集大小: 216\n", - "最大日期: 2021-08-31\n", - "划分后的训练集大小: 712, 验证集大小: 198\n", - "最大日期: 2021-09-01\n", - "划分后的训练集大小: 741, 验证集大小: 117\n", - "最大日期: 2021-09-02\n", - "划分后的训练集大小: 776, 验证集大小: 124\n", - "最大日期: 2021-09-03\n", - "划分后的训练集大小: 904, 验证集大小: 249\n", - "最大日期: 2021-09-06\n", - "划分后的训练集大小: 871, 验证集大小: 183\n", - "最大日期: 2021-09-07\n", - "划分后的训练集大小: 790, 验证集大小: 117\n", - "最大日期: 2021-09-08\n", - "划分后的训练集大小: 772, 验证集大小: 99\n", - "最大日期: 2021-09-09\n", - "划分后的训练集大小: 764, 验证集大小: 116\n", - "最大日期: 2021-09-10\n", - "划分后的训练集大小: 674, 验证集大小: 159\n", - "最大日期: 2021-09-13\n", - "划分后的训练集大小: 632, 验证集大小: 141\n", - "最大日期: 2021-09-14\n", - "划分后的训练集大小: 751, 验证集大小: 236\n", - "最大日期: 2021-09-15\n", - "划分后的训练集大小: 786, 验证集大小: 134\n", - "最大日期: 2021-09-16\n", - "划分后的训练集大小: 893, 验证集大小: 223\n", - "最大日期: 2021-09-17\n", - "划分后的训练集大小: 853, 验证集大小: 119\n", - "最大日期: 2021-09-22\n", - "划分后的训练集大小: 799, 验证集大小: 87\n", - "最大日期: 2021-09-23\n", - "划分后的训练集大小: 723, 验证集大小: 160\n", - "最大日期: 2021-09-24\n", - "划分后的训练集大小: 682, 验证集大小: 93\n", - "最大日期: 2021-09-27\n", - "划分后的训练集大小: 679, 验证集大小: 220\n", - "最大日期: 2021-09-28\n", - "划分后的训练集大小: 730, 验证集大小: 170\n", - "最大日期: 2021-09-29\n", - "划分后的训练集大小: 776, 验证集大小: 133\n", - "最大日期: 2021-09-30\n", - "划分后的训练集大小: 782, 验证集大小: 166\n", - "最大日期: 2021-10-08\n", - "划分后的训练集大小: 869, 验证集大小: 180\n", - "最大日期: 2021-10-11\n", - "划分后的训练集大小: 824, 验证集大小: 175\n", - "最大日期: 2021-10-12\n", - "划分后的训练集大小: 830, 验证集大小: 176\n", - "最大日期: 2021-10-13\n", - "划分后的训练集大小: 861, 验证集大小: 164\n", - "最大日期: 2021-10-14\n", - "划分后的训练集大小: 819, 验证集大小: 124\n", - "最大日期: 2021-10-15\n", - "划分后的训练集大小: 769, 验证集大小: 130\n", - "最大日期: 2021-10-18\n", - "划分后的训练集大小: 685, 验证集大小: 91\n", - "最大日期: 2021-10-19\n", - "划分后的训练集大小: 624, 验证集大小: 115\n", - "最大日期: 2021-10-20\n", - "划分后的训练集大小: 696, 验证集大小: 236\n", - "最大日期: 2021-10-21\n", - "划分后的训练集大小: 660, 验证集大小: 88\n", - "最大日期: 2021-10-22\n", - "划分后的训练集大小: 616, 验证集大小: 86\n", - "最大日期: 2021-10-25\n", - "划分后的训练集大小: 643, 验证集大小: 118\n", - "最大日期: 2021-10-26\n", - "划分后的训练集大小: 622, 验证集大小: 94\n", - "最大日期: 2021-10-27\n", - "划分后的训练集大小: 477, 验证集大小: 91\n", - "最大日期: 2021-10-28\n", - "划分后的训练集大小: 490, 验证集大小: 101\n", - "最大日期: 2021-10-29\n", - "划分后的训练集大小: 532, 验证集大小: 128\n", - "最大日期: 2021-11-01\n", - "划分后的训练集大小: 508, 验证集大小: 94\n", - "最大日期: 2021-11-02\n", - "划分后的训练集大小: 622, 验证集大小: 208\n", - "最大日期: 2021-11-03\n", - "划分后的训练集大小: 657, 验证集大小: 126\n", - "最大日期: 2021-11-04\n", - "划分后的训练集大小: 644, 验证集大小: 88\n", - "最大日期: 2021-11-05\n", - "划分后的训练集大小: 608, 验证集大小: 92\n", - "最大日期: 2021-11-08\n", - "划分后的训练集大小: 616, 验证集大小: 102\n", - "最大日期: 2021-11-09\n", - "划分后的训练集大小: 627, 验证集大小: 219\n", - "最大日期: 2021-11-10\n", - "划分后的训练集大小: 659, 验证集大小: 158\n", - "最大日期: 2021-11-11\n", - "划分后的训练集大小: 739, 验证集大小: 168\n", - "最大日期: 2021-11-12\n", - "划分后的训练集大小: 936, 验证集大小: 289\n", - "最大日期: 2021-11-15\n", - "划分后的训练集大小: 965, 验证集大小: 131\n", - "最大日期: 2021-11-16\n", - "划分后的训练集大小: 874, 验证集大小: 128\n", - "最大日期: 2021-11-17\n", - "划分后的训练集大小: 816, 验证集大小: 100\n", - "最大日期: 2021-11-18\n", - "划分后的训练集大小: 785, 验证集大小: 137\n", - "最大日期: 2021-11-19\n", - "划分后的训练集大小: 682, 验证集大小: 186\n", - "最大日期: 2021-11-22\n", - "划分后的训练集大小: 698, 验证集大小: 147\n", - "最大日期: 2021-11-23\n", - "划分后的训练集大小: 665, 验证集大小: 95\n", - "最大日期: 2021-11-24\n", - "划分后的训练集大小: 657, 验证集大小: 92\n", - "最大日期: 2021-11-25\n", - "划分后的训练集大小: 611, 验证集大小: 91\n", - "最大日期: 2021-11-26\n", - "划分后的训练集大小: 536, 验证集大小: 111\n", - "最大日期: 2021-11-29\n", - "划分后的训练集大小: 643, 验证集大小: 254\n", - "最大日期: 2021-11-30\n", - "划分后的训练集大小: 801, 验证集大小: 253\n", - "最大日期: 2021-12-01\n", - "划分后的训练集大小: 804, 验证集大小: 95\n", - "最大日期: 2021-12-02\n", - "划分后的训练集大小: 806, 验证集大小: 93\n", - "最大日期: 2021-12-03\n", - "划分后的训练集大小: 831, 验证集大小: 136\n", - "最大日期: 2021-12-06\n", - "划分后的训练集大小: 698, 验证集大小: 121\n", - "最大日期: 2021-12-07\n", - "划分后的训练集大小: 556, 验证集大小: 111\n", - "最大日期: 2021-12-08\n", - "划分后的训练集大小: 644, 验证集大小: 183\n", - "最大日期: 2021-12-09\n", - "划分后的训练集大小: 688, 验证集大小: 137\n", - "最大日期: 2021-12-10\n", - "划分后的训练集大小: 649, 验证集大小: 97\n", - "最大日期: 2021-12-13\n", - "划分后的训练集大小: 709, 验证集大小: 181\n", - "最大日期: 2021-12-14\n", - "划分后的训练集大小: 691, 验证集大小: 93\n", - "最大日期: 2021-12-15\n", - "划分后的训练集大小: 637, 验证集大小: 129\n", - "最大日期: 2021-12-16\n", - "划分后的训练集大小: 585, 验证集大小: 85\n", - "最大日期: 2021-12-17\n", - "划分后的训练集大小: 591, 验证集大小: 103\n", - "最大日期: 2021-12-20\n", - "划分后的训练集大小: 515, 验证集大小: 105\n", - "最大日期: 2021-12-21\n", - "划分后的训练集大小: 552, 验证集大小: 130\n", - "最大日期: 2021-12-22\n", - "划分后的训练集大小: 536, 验证集大小: 113\n", - "最大日期: 2021-12-23\n", - "划分后的训练集大小: 601, 验证集大小: 150\n", - "最大日期: 2021-12-24\n", - "划分后的训练集大小: 584, 验证集大小: 86\n", - "最大日期: 2021-12-27\n", - "划分后的训练集大小: 612, 验证集大小: 133\n", - "最大日期: 2021-12-28\n", - "划分后的训练集大小: 645, 验证集大小: 163\n", - "最大日期: 2021-12-29\n", - "划分后的训练集大小: 651, 验证集大小: 119\n", - "最大日期: 2021-12-30\n", - "划分后的训练集大小: 747, 验证集大小: 246\n", - "最大日期: 2021-12-31\n", - "划分后的训练集大小: 827, 验证集大小: 166\n", - "最大日期: 2022-01-04\n", - "划分后的训练集大小: 805, 验证集大小: 111\n", - "最大日期: 2022-01-05\n", - "划分后的训练集大小: 731, 验证集大小: 89\n", - "最大日期: 2022-01-06\n", - "划分后的训练集大小: 777, 验证集大小: 165\n", - "最大日期: 2022-01-07\n", - "划分后的训练集大小: 667, 验证集大小: 136\n", - "最大日期: 2022-01-10\n", - "划分后的训练集大小: 660, 验证集大小: 159\n", - "最大日期: 2022-01-11\n", - "划分后的训练集大小: 652, 验证集大小: 103\n", - "最大日期: 2022-01-12\n", - "划分后的训练集大小: 671, 验证集大小: 108\n", - "最大日期: 2022-01-13\n", - "划分后的训练集大小: 603, 验证集大小: 97\n", - "最大日期: 2022-01-14\n", - "划分后的训练集大小: 556, 验证集大小: 89\n", - "最大日期: 2022-01-17\n", - "划分后的训练集大小: 620, 验证集大小: 223\n", - "最大日期: 2022-01-18\n", - "划分后的训练集大小: 699, 验证集大小: 182\n", - "最大日期: 2022-01-19\n", - "划分后的训练集大小: 779, 验证集大小: 188\n", - "最大日期: 2022-01-20\n", - "划分后的训练集大小: 876, 验证集大小: 194\n", - "最大日期: 2022-01-21\n", - "划分后的训练集大小: 914, 验证集大小: 127\n", - "最大日期: 2022-01-24\n", - "划分后的训练集大小: 778, 验证集大小: 87\n", - "最大日期: 2022-01-25\n", - "划分后的训练集大小: 715, 验证集大小: 119\n", - "最大日期: 2022-01-26\n", - "划分后的训练集大小: 656, 验证集大小: 129\n", - "最大日期: 2022-01-27\n", - "划分后的训练集大小: 555, 验证集大小: 93\n", - "最大日期: 2022-01-28\n", - "划分后的训练集大小: 515, 验证集大小: 87\n", - "最大日期: 2022-02-07\n", - "划分后的训练集大小: 536, 验证集大小: 108\n", - "最大日期: 2022-02-08\n", - "划分后的训练集大小: 527, 验证集大小: 110\n", - "最大日期: 2022-02-09\n", - "划分后的训练集大小: 527, 验证集大小: 129\n", - "最大日期: 2022-02-10\n", - "划分后的训练集大小: 527, 验证集大小: 93\n", - "最大日期: 2022-02-11\n", - "划分后的训练集大小: 581, 验证集大小: 141\n", - "最大日期: 2022-02-14\n", - "划分后的训练集大小: 730, 验证集大小: 257\n", - "最大日期: 2022-02-15\n", - "划分后的训练集大小: 747, 验证集大小: 127\n", - "最大日期: 2022-02-16\n", - "划分后的训练集大小: 775, 验证集大小: 157\n", - "最大日期: 2022-02-17\n", - "划分后的训练集大小: 803, 验证集大小: 121\n", - "最大日期: 2022-02-18\n", - "划分后的训练集大小: 760, 验证集大小: 98\n", - "最大日期: 2022-02-21\n", - "划分后的训练集大小: 637, 验证集大小: 134\n", - "最大日期: 2022-02-22\n", - "划分后的训练集大小: 790, 验证集大小: 280\n", - "最大日期: 2022-02-23\n", - "划分后的训练集大小: 799, 验证集大小: 166\n", - "最大日期: 2022-02-24\n", - "划分后的训练集大小: 820, 验证集大小: 142\n", - "最大日期: 2022-02-25\n", - "划分后的训练集大小: 831, 验证集大小: 109\n", - "最大日期: 2022-02-28\n", - "划分后的训练集大小: 818, 验证集大小: 121\n", - "最大日期: 2022-03-01\n", - "划分后的训练集大小: 784, 验证集大小: 246\n", - "最大日期: 2022-03-02\n", - "划分后的训练集大小: 705, 验证集大小: 87\n", - "最大日期: 2022-03-03\n", - "划分后的训练集大小: 659, 验证集大小: 96\n", - "最大日期: 2022-03-04\n", - "划分后的训练集大小: 666, 验证集大小: 116\n", - "最大日期: 2022-03-07\n", - "划分后的训练集大小: 653, 验证集大小: 108\n", - "最大日期: 2022-03-08\n", - "划分后的训练集大小: 502, 验证集大小: 95\n", - "最大日期: 2022-03-09\n", - "划分后的训练集大小: 548, 验证集大小: 133\n", - "最大日期: 2022-03-10\n", - "划分后的训练集大小: 584, 验证集大小: 132\n", - "最大日期: 2022-03-11\n", - "划分后的训练集大小: 573, 验证集大小: 105\n", - "最大日期: 2022-03-14\n", - "划分后的训练集大小: 563, 验证集大小: 98\n", - "最大日期: 2022-03-15\n", - "划分后的训练集大小: 553, 验证集大小: 85\n", - "最大日期: 2022-03-16\n", - "划分后的训练集大小: 537, 验证集大小: 117\n", - "最大日期: 2022-03-17\n", - "划分后的训练集大小: 619, 验证集大小: 214\n", - "最大日期: 2022-03-18\n", - "划分后的训练集大小: 632, 验证集大小: 118\n", - "最大日期: 2022-03-21\n", - "划分后的训练集大小: 638, 验证集大小: 104\n", - "最大日期: 2022-03-22\n", - "划分后的训练集大小: 713, 验证集大小: 160\n", - "最大日期: 2022-03-23\n", - "划分后的训练集大小: 790, 验证集大小: 194\n", - "最大日期: 2022-03-24\n", - "划分后的训练集大小: 729, 验证集大小: 153\n", - "最大日期: 2022-03-25\n", - "划分后的训练集大小: 694, 验证集大小: 83\n", - "最大日期: 2022-03-28\n", - "划分后的训练集大小: 712, 验证集大小: 122\n", - "最大日期: 2022-03-29\n", - "划分后的训练集大小: 739, 验证集大小: 187\n", - "最大日期: 2022-03-30\n", - "划分后的训练集大小: 666, 验证集大小: 121\n", - "最大日期: 2022-03-31\n", - "划分后的训练集大小: 716, 验证集大小: 203\n", - "最大日期: 2022-04-01\n", - "划分后的训练集大小: 730, 验证集大小: 97\n", - "最大日期: 2022-04-06\n", - "划分后的训练集大小: 744, 验证集大小: 136\n", - "最大日期: 2022-04-07\n", - "划分后的训练集大小: 665, 验证集大小: 108\n", - "最大日期: 2022-04-08\n", - "划分后的训练集大小: 629, 验证集大小: 85\n", - "最大日期: 2022-04-11\n", - "划分后的训练集大小: 527, 验证集大小: 101\n", - "最大日期: 2022-04-12\n", - "划分后的训练集大小: 575, 验证集大小: 145\n", - "最大日期: 2022-04-13\n", - "划分后的训练集大小: 522, 验证集大小: 83\n", - "最大日期: 2022-04-14\n", - "划分后的训练集大小: 495, 验证集大小: 81\n", - "最大日期: 2022-04-15\n", - "划分后的训练集大小: 599, 验证集大小: 189\n", - "最大日期: 2022-04-18\n", - "划分后的训练集大小: 629, 验证集大小: 131\n", - "最大日期: 2022-04-19\n", - "划分后的训练集大小: 598, 验证集大小: 114\n", - "最大日期: 2022-04-20\n", - "划分后的训练集大小: 609, 验证集大小: 94\n", - "最大日期: 2022-04-21\n", - "划分后的训练集大小: 727, 验证集大小: 199\n", - "最大日期: 2022-04-22\n", - "划分后的训练集大小: 635, 验证集大小: 97\n", - "最大日期: 2022-04-25\n", - "划分后的训练集大小: 581, 验证集大小: 77\n", - "最大日期: 2022-04-26\n", - "划分后的训练集大小: 560, 验证集大小: 93\n", - "最大日期: 2022-04-27\n", - "划分后的训练集大小: 554, 验证集大小: 88\n", - "最大日期: 2022-04-28\n", - "划分后的训练集大小: 464, 验证集大小: 109\n", - "最大日期: 2022-04-29\n", - "划分后的训练集大小: 518, 验证集大小: 151\n", - "最大日期: 2022-05-05\n", - "划分后的训练集大小: 596, 验证集大小: 155\n", - "最大日期: 2022-05-06\n", - "划分后的训练集大小: 592, 验证集大小: 89\n", - "最大日期: 2022-05-09\n", - "划分后的训练集大小: 591, 验证集大小: 87\n", - "最大日期: 2022-05-10\n", - "划分后的训练集大小: 565, 验证集大小: 83\n", - "最大日期: 2022-05-11\n", - "划分后的训练集大小: 562, 验证集大小: 148\n", - "最大日期: 2022-05-12\n", - "划分后的训练集大小: 539, 验证集大小: 132\n", - "最大日期: 2022-05-13\n", - "划分后的训练集大小: 549, 验证集大小: 99\n", - "最大日期: 2022-05-16\n", - "划分后的训练集大小: 589, 验证集大小: 127\n", - "最大日期: 2022-05-17\n", - "划分后的训练集大小: 671, 验证集大小: 165\n", - "最大日期: 2022-05-18\n", - "划分后的训练集大小: 642, 验证集大小: 119\n", - "最大日期: 2022-05-19\n", - "划分后的训练集大小: 603, 验证集大小: 93\n", - "最大日期: 2022-05-20\n", - "划分后的训练集大小: 595, 验证集大小: 91\n", - "最大日期: 2022-05-23\n", - "划分后的训练集大小: 596, 验证集大小: 128\n", - "最大日期: 2022-05-24\n", - "划分后的训练集大小: 521, 验证集大小: 90\n", - "最大日期: 2022-05-25\n", - "划分后的训练集大小: 511, 验证集大小: 109\n", - "最大日期: 2022-05-26\n", - "划分后的训练集大小: 524, 验证集大小: 106\n", - "最大日期: 2022-05-27\n", - "划分后的训练集大小: 543, 验证集大小: 110\n", - "最大日期: 2022-05-30\n", - "划分后的训练集大小: 537, 验证集大小: 122\n", - "最大日期: 2022-05-31\n", - "划分后的训练集大小: 565, 验证集大小: 118\n", - "最大日期: 2022-06-01\n", - "划分后的训练集大小: 544, 验证集大小: 88\n", - "最大日期: 2022-06-02\n", - "划分后的训练集大小: 563, 验证集大小: 125\n", - "最大日期: 2022-06-06\n", - "划分后的训练集大小: 550, 验证集大小: 97\n", - "最大日期: 2022-06-07\n", - "划分后的训练集大小: 548, 验证集大小: 120\n", - "最大日期: 2022-06-08\n", - "划分后的训练集大小: 560, 验证集大小: 130\n", - "最大日期: 2022-06-09\n", - "划分后的训练集大小: 587, 验证集大小: 115\n", - "最大日期: 2022-06-10\n", - "划分后的训练集大小: 606, 验证集大小: 144\n", - "最大日期: 2022-06-13\n", - "划分后的训练集大小: 613, 验证集大小: 104\n", - "最大日期: 2022-06-14\n", - "划分后的训练集大小: 602, 验证集大小: 109\n", - "最大日期: 2022-06-15\n", - "划分后的训练集大小: 648, 验证集大小: 176\n", - "最大日期: 2022-06-16\n", - "划分后的训练集大小: 652, 验证集大小: 119\n", - "最大日期: 2022-06-17\n", - "划分后的训练集大小: 705, 验证集大小: 197\n", - "最大日期: 2022-06-20\n", - "划分后的训练集大小: 762, 验证集大小: 161\n", - "最大日期: 2022-06-21\n", - "划分后的训练集大小: 745, 验证集大小: 92\n", - "最大日期: 2022-06-22\n", - "划分后的训练集大小: 769, 验证集大小: 200\n", - "最大日期: 2022-06-23\n", - "划分后的训练集大小: 739, 验证集大小: 89\n", - "最大日期: 2022-06-24\n", - "划分后的训练集大小: 640, 验证集大小: 98\n", - "最大日期: 2022-06-27\n", - "划分后的训练集大小: 570, 验证集大小: 91\n", - "最大日期: 2022-06-28\n", - "划分后的训练集大小: 766, 验证集大小: 288\n", - "最大日期: 2022-06-29\n", - "划分后的训练集大小: 727, 验证集大小: 161\n", - "最大日期: 2022-06-30\n", - "划分后的训练集大小: 720, 验证集大小: 82\n", - "最大日期: 2022-07-01\n", - "划分后的训练集大小: 724, 验证集大小: 102\n", - "最大日期: 2022-07-04\n", - "划分后的训练集大小: 758, 验证集大小: 125\n", - "最大日期: 2022-07-05\n", - "划分后的训练集大小: 564, 验证集大小: 94\n", - "最大日期: 2022-07-06\n", - "划分后的训练集大小: 510, 验证集大小: 107\n", - "最大日期: 2022-07-07\n", - "划分后的训练集大小: 573, 验证集大小: 145\n", - "最大日期: 2022-07-08\n", - "划分后的训练集大小: 584, 验证集大小: 113\n", - "最大日期: 2022-07-11\n", - "划分后的训练集大小: 594, 验证集大小: 135\n", - "最大日期: 2022-07-12\n", - "划分后的训练集大小: 616, 验证集大小: 116\n", - "最大日期: 2022-07-13\n", - "划分后的训练集大小: 622, 验证集大小: 113\n", - "最大日期: 2022-07-14\n", - "划分后的训练集大小: 644, 验证集大小: 167\n", - "最大日期: 2022-07-15\n", - "划分后的训练集大小: 625, 验证集大小: 94\n", - "最大日期: 2022-07-18\n", - "划分后的训练集大小: 638, 验证集大小: 148\n", - "最大日期: 2022-07-19\n", - "划分后的训练集大小: 665, 验证集大小: 143\n", - "最大日期: 2022-07-20\n", - "划分后的训练集大小: 675, 验证集大小: 123\n", - "最大日期: 2022-07-21\n", - "划分后的训练集大小: 596, 验证集大小: 88\n", - "最大日期: 2022-07-22\n", - "划分后的训练集大小: 622, 验证集大小: 120\n", - "最大日期: 2022-07-25\n", - "划分后的训练集大小: 613, 验证集大小: 139\n", - "最大日期: 2022-07-26\n", - "划分后的训练集大小: 561, 验证集大小: 91\n", - "最大日期: 2022-07-27\n", - "划分后的训练集大小: 591, 验证集大小: 153\n", - "最大日期: 2022-07-28\n", - "划分后的训练集大小: 648, 验证集大小: 145\n", - "最大日期: 2022-07-29\n", - "划分后的训练集大小: 713, 验证集大小: 185\n", - "最大日期: 2022-08-01\n", - "划分后的训练集大小: 733, 验证集大小: 159\n", - "最大日期: 2022-08-02\n", - "划分后的训练集大小: 806, 验证集大小: 164\n", - "最大日期: 2022-08-03\n", - "划分后的训练集大小: 871, 验证集大小: 218\n", - "最大日期: 2022-08-04\n", - "划分后的训练集大小: 890, 验证集大小: 164\n", - "最大日期: 2022-08-05\n", - "划分后的训练集大小: 833, 验证集大小: 128\n", - "最大日期: 2022-08-08\n", - "划分后的训练集大小: 813, 验证集大小: 139\n", - "最大日期: 2022-08-09\n", - "划分后的训练集大小: 869, 验证集大小: 220\n", - "最大日期: 2022-08-10\n", - "划分后的训练集大小: 969, 验证集大小: 318\n", - "最大日期: 2022-08-11\n", - "划分后的训练集大小: 985, 验证集大小: 180\n", - "最大日期: 2022-08-12\n", - "划分后的训练集大小: 948, 验证集大小: 91\n", - "最大日期: 2022-08-15\n", - "划分后的训练集大小: 985, 验证集大小: 176\n", - "最大日期: 2022-08-16\n", - "划分后的训练集大小: 894, 验证集大小: 129\n", - "最大日期: 2022-08-17\n", - "划分后的训练集大小: 735, 验证集大小: 159\n", - "最大日期: 2022-08-18\n", - "划分后的训练集大小: 740, 验证集大小: 185\n", - "最大日期: 2022-08-19\n", - "划分后的训练集大小: 906, 验证集大小: 257\n", - "最大日期: 2022-08-22\n", - "划分后的训练集大小: 934, 验证集大小: 204\n", - "最大日期: 2022-08-23\n", - "划分后的训练集大小: 1019, 验证集大小: 214\n", - "最大日期: 2022-08-24\n", - "划分后的训练集大小: 950, 验证集大小: 90\n", - "最大日期: 2022-08-25\n", - "划分后的训练集大小: 912, 验证集大小: 147\n", - "最大日期: 2022-08-26\n", - "划分后的训练集大小: 800, 验证集大小: 145\n", - "最大日期: 2022-08-29\n", - "划分后的训练集大小: 770, 验证集大小: 174\n", - "最大日期: 2022-08-30\n", - "划分后的训练集大小: 660, 验证集大小: 104\n", - "最大日期: 2022-08-31\n", - "划分后的训练集大小: 694, 验证集大小: 124\n", - "最大日期: 2022-09-01\n", - "划分后的训练集大小: 735, 验证集大小: 188\n", - "最大日期: 2022-09-02\n", - "划分后的训练集大小: 739, 验证集大小: 149\n", - "最大日期: 2022-09-05\n", - "划分后的训练集大小: 724, 验证集大小: 159\n", - "最大日期: 2022-09-06\n", - "划分后的训练集大小: 738, 验证集大小: 118\n", - "最大日期: 2022-09-07\n", - "划分后的训练集大小: 726, 验证集大小: 112\n", - "最大日期: 2022-09-08\n", - "划分后的训练集大小: 638, 验证集大小: 100\n", - "最大日期: 2022-09-09\n", - "划分后的训练集大小: 703, 验证集大小: 214\n", - "最大日期: 2022-09-13\n", - "划分后的训练集大小: 667, 验证集大小: 123\n", - "最大日期: 2022-09-14\n", - "划分后的训练集大小: 641, 验证集大小: 92\n", - "最大日期: 2022-09-15\n", - "划分后的训练集大小: 706, 验证集大小: 177\n", - "最大日期: 2022-09-16\n", - "划分后的训练集大小: 707, 验证集大小: 101\n", - "最大日期: 2022-09-19\n", - "划分后的训练集大小: 638, 验证集大小: 145\n", - "最大日期: 2022-09-20\n", - "划分后的训练集大小: 606, 验证集大小: 91\n", - "最大日期: 2022-09-21\n", - "划分后的训练集大小: 647, 验证集大小: 133\n", - "最大日期: 2022-09-22\n", - "划分后的训练集大小: 561, 验证集大小: 91\n", - "最大日期: 2022-09-23\n", - "划分后的训练集大小: 547, 验证集大小: 87\n", - "最大日期: 2022-09-26\n", - "划分后的训练集大小: 522, 验证集大小: 120\n", - "最大日期: 2022-09-27\n", - "划分后的训练集大小: 522, 验证集大小: 91\n", - "最大日期: 2022-09-28\n", - "划分后的训练集大小: 499, 验证集大小: 110\n", - "最大日期: 2022-09-29\n", - "划分后的训练集大小: 562, 验证集大小: 154\n", - "最大日期: 2022-09-30\n", - "划分后的训练集大小: 556, 验证集大小: 81\n", - "最大日期: 2022-10-10\n", - "划分后的训练集大小: 547, 验证集大小: 111\n", - "最大日期: 2022-10-11\n", - "划分后的训练集大小: 581, 验证集大小: 125\n", - "最大日期: 2022-10-12\n", - "划分后的训练集大小: 558, 验证集大小: 87\n", - "最大日期: 2022-10-13\n", - "划分后的训练集大小: 495, 验证集大小: 91\n", - "最大日期: 2022-10-14\n", - "划分后的训练集大小: 531, 验证集大小: 117\n", - "最大日期: 2022-10-17\n", - "划分后的训练集大小: 567, 验证集大小: 147\n", - "最大日期: 2022-10-18\n", - "划分后的训练集大小: 589, 验证集大小: 147\n", - "最大日期: 2022-10-19\n", - "划分后的训练集大小: 618, 验证集大小: 116\n", - "最大日期: 2022-10-20\n", - "划分后的训练集大小: 621, 验证集大小: 94\n", - "最大日期: 2022-10-21\n", - "划分后的训练集大小: 593, 验证集大小: 89\n", - "最大日期: 2022-10-24\n", - "划分后的训练集大小: 534, 验证集大小: 88\n", - "最大日期: 2022-10-25\n", - "划分后的训练集大小: 512, 验证集大小: 125\n", - "最大日期: 2022-10-26\n", - "划分后的训练集大小: 486, 验证集大小: 90\n", - "最大日期: 2022-10-27\n", - "划分后的训练集大小: 565, 验证集大小: 173\n", - "最大日期: 2022-10-28\n", - "划分后的训练集大小: 568, 验证集大小: 92\n", - "最大日期: 2022-10-31\n", - "划分后的训练集大小: 575, 验证集大小: 95\n", - "最大日期: 2022-11-01\n", - "划分后的训练集大小: 562, 验证集大小: 112\n", - "最大日期: 2022-11-02\n", - "划分后的训练集大小: 586, 验证集大小: 114\n", - "最大日期: 2022-11-03\n", - "划分后的训练集大小: 522, 验证集大小: 109\n", - "最大日期: 2022-11-04\n", - "划分后的训练集大小: 518, 验证集大小: 88\n", - "最大日期: 2022-11-07\n", - "划分后的训练集大小: 590, 验证集大小: 167\n", - "最大日期: 2022-11-08\n", - "划分后的训练集大小: 594, 验证集大小: 116\n", - "最大日期: 2022-11-09\n", - "划分后的训练集大小: 564, 验证集大小: 84\n", - "最大日期: 2022-11-10\n", - "划分后的训练集大小: 659, 验证集大小: 204\n", - "最大日期: 2022-11-11\n", - "划分后的训练集大小: 725, 验证集大小: 154\n", - "最大日期: 2022-11-14\n", - "划分后的训练集大小: 703, 验证集大小: 145\n", - "最大日期: 2022-11-15\n", - "划分后的训练集大小: 846, 验证集大小: 259\n", - "最大日期: 2022-11-16\n", - "划分后的训练集大小: 888, 验证集大小: 126\n", - "最大日期: 2022-11-17\n", - "划分后的训练集大小: 807, 验证集大小: 123\n", - "最大日期: 2022-11-18\n", - "划分后的训练集大小: 818, 验证集大小: 165\n", - "最大日期: 2022-11-21\n", - "划分后的训练集大小: 841, 验证集大小: 168\n", - "最大日期: 2022-11-22\n", - "划分后的训练集大小: 769, 验证集大小: 187\n", - "最大日期: 2022-11-23\n", - "划分后的训练集大小: 793, 验证集大小: 150\n", - "最大日期: 2022-11-24\n", - "划分后的训练集大小: 890, 验证集大小: 220\n", - "最大日期: 2022-11-25\n", - "划分后的训练集大小: 829, 验证集大小: 104\n", - "最大日期: 2022-11-28\n", - "划分后的训练集大小: 827, 验证集大小: 166\n", - "最大日期: 2022-11-29\n", - "划分后的训练集大小: 797, 验证集大小: 157\n", - "最大日期: 2022-11-30\n", - "划分后的训练集大小: 800, 验证集大小: 153\n", - "最大日期: 2022-12-01\n", - "划分后的训练集大小: 691, 验证集大小: 111\n", - "最大日期: 2022-12-02\n", - "划分后的训练集大小: 679, 验证集大小: 92\n", - "最大日期: 2022-12-05\n", - "划分后的训练集大小: 621, 验证集大小: 108\n", - "最大日期: 2022-12-06\n", - "划分后的训练集大小: 555, 验证集大小: 91\n", - "最大日期: 2022-12-07\n", - "划分后的训练集大小: 502, 验证集大小: 100\n", - "最大日期: 2022-12-08\n", - "划分后的训练集大小: 482, 验证集大小: 91\n", - "最大日期: 2022-12-09\n", - "划分后的训练集大小: 492, 验证集大小: 102\n", - "最大日期: 2022-12-12\n", - "划分后的训练集大小: 476, 验证集大小: 92\n", - "最大日期: 2022-12-13\n", - "划分后的训练集大小: 488, 验证集大小: 103\n", - "最大日期: 2022-12-14\n", - "划分后的训练集大小: 566, 验证集大小: 178\n", - "最大日期: 2022-12-15\n", - "划分后的训练集大小: 572, 验证集大小: 97\n", - "最大日期: 2022-12-16\n", - "划分后的训练集大小: 639, 验证集大小: 169\n", - "最大日期: 2022-12-19\n", - "划分后的训练集大小: 638, 验证集大小: 91\n", - "最大日期: 2022-12-20\n", - "划分后的训练集大小: 669, 验证集大小: 134\n", - "最大日期: 2022-12-21\n", - "划分后的训练集大小: 627, 验证集大小: 136\n", - "最大日期: 2022-12-22\n", - "划分后的训练集大小: 623, 验证集大小: 93\n", - "最大日期: 2022-12-23\n", - "划分后的训练集大小: 572, 验证集大小: 118\n", - "最大日期: 2022-12-26\n", - "划分后的训练集大小: 571, 验证集大小: 90\n", - "最大日期: 2022-12-27\n", - "划分后的训练集大小: 529, 验证集大小: 92\n", - "最大日期: 2022-12-28\n", - "划分后的训练集大小: 480, 验证集大小: 87\n", - "最大日期: 2022-12-29\n", - "划分后的训练集大小: 476, 验证集大小: 89\n", - "最大日期: 2022-12-30\n", - "划分后的训练集大小: 512, 验证集大小: 154\n", - "最大日期: 2023-01-03\n", - "划分后的训练集大小: 549, 验证集大小: 127\n", - "最大日期: 2023-01-04\n", - "划分后的训练集大小: 612, 验证集大小: 155\n", - "最大日期: 2023-01-05\n", - "划分后的训练集大小: 725, 验证集大小: 200\n", - "最大日期: 2023-01-06\n", - "划分后的训练集大小: 918, 验证集大小: 282\n", - "最大日期: 2023-01-09\n", - "划分后的训练集大小: 860, 验证集大小: 96\n", - "最大日期: 2023-01-10\n", - "划分后的训练集大小: 826, 验证集大小: 93\n", - "最大日期: 2023-01-11\n", - "划分后的训练集大小: 771, 验证集大小: 100\n", - "最大日期: 2023-01-12\n", - "划分后的训练集大小: 658, 验证集大小: 87\n", - "最大日期: 2023-01-13\n", - "划分后的训练集大小: 524, 验证集大小: 148\n", - "最大日期: 2023-01-16\n", - "划分后的训练集大小: 526, 验证集大小: 98\n", - "最大日期: 2023-01-17\n", - "划分后的训练集大小: 541, 验证集大小: 108\n", - "最大日期: 2023-01-18\n", - "划分后的训练集大小: 564, 验证集大小: 123\n", - "最大日期: 2023-01-19\n", - "划分后的训练集大小: 590, 验证集大小: 113\n", - "最大日期: 2023-01-20\n", - "划分后的训练集大小: 572, 验证集大小: 130\n", - "最大日期: 2023-01-30\n", - "划分后的训练集大小: 569, 验证集大小: 95\n", - "最大日期: 2023-01-31\n", - "划分后的训练集大小: 583, 验证集大小: 122\n", - "最大日期: 2023-02-01\n", - "划分后的训练集大小: 550, 验证集大小: 90\n", - "最大日期: 2023-02-02\n", - "划分后的训练集大小: 539, 验证集大小: 102\n", - "最大日期: 2023-02-03\n", - "划分后的训练集大小: 505, 验证集大小: 96\n", - "最大日期: 2023-02-06\n", - "划分后的训练集大小: 527, 验证集大小: 117\n", - "最大日期: 2023-02-07\n", - "划分后的训练集大小: 519, 验证集大小: 114\n", - "最大日期: 2023-02-08\n", - "划分后的训练集大小: 580, 验证集大小: 151\n", - "最大日期: 2023-02-09\n", - "划分后的训练集大小: 622, 验证集大小: 144\n", - "最大日期: 2023-02-10\n", - "划分后的训练集大小: 737, 验证集大小: 211\n", - "最大日期: 2023-02-13\n", - "划分后的训练集大小: 744, 验证集大小: 124\n", - "最大日期: 2023-02-14\n", - "划分后的训练集大小: 762, 验证集大小: 132\n", - "最大日期: 2023-02-15\n", - "划分后的训练集大小: 774, 验证集大小: 163\n", - "最大日期: 2023-02-16\n", - "划分后的训练集大小: 817, 验证集大小: 187\n", - "最大日期: 2023-02-17\n", - "划分后的训练集大小: 763, 验证集大小: 157\n", - "最大日期: 2023-02-20\n", - "划分后的训练集大小: 732, 验证集大小: 93\n", - "最大日期: 2023-02-21\n", - "划分后的训练集大小: 728, 验证集大小: 128\n", - "最大日期: 2023-02-22\n", - "划分后的训练集大小: 659, 验证集大小: 94\n", - "最大日期: 2023-02-23\n", - "划分后的训练集大小: 599, 验证集大小: 127\n", - "最大日期: 2023-02-24\n", - "划分后的训练集大小: 539, 验证集大小: 97\n", - "最大日期: 2023-02-27\n", - "划分后的训练集大小: 701, 验证集大小: 255\n", - "最大日期: 2023-02-28\n", - "划分后的训练集大小: 758, 验证集大小: 185\n", - "最大日期: 2023-03-01\n", - "划分后的训练集大小: 754, 验证集大小: 90\n", - "最大日期: 2023-03-02\n", - "划分后的训练集大小: 717, 验证集大小: 90\n", - "最大日期: 2023-03-03\n", - "划分后的训练集大小: 713, 验证集大小: 93\n", - "最大日期: 2023-03-06\n", - "划分后的训练集大小: 598, 验证集大小: 140\n", - "最大日期: 2023-03-07\n", - "划分后的训练集大小: 509, 验证集大小: 96\n", - "最大日期: 2023-03-08\n", - "划分后的训练集大小: 531, 验证集大小: 112\n", - "最大日期: 2023-03-09\n", - "划分后的训练集大小: 552, 验证集大小: 111\n", - "最大日期: 2023-03-10\n", - "划分后的训练集大小: 601, 验证集大小: 142\n", - "最大日期: 2023-03-13\n", - "划分后的训练集大小: 622, 验证集大小: 161\n", - "最大日期: 2023-03-14\n", - "划分后的训练集大小: 629, 验证集大小: 103\n", - "最大日期: 2023-03-15\n", - "划分后的训练集大小: 697, 验证集大小: 180\n", - "最大日期: 2023-03-16\n", - "划分后的训练集大小: 709, 验证集大小: 123\n", - "最大日期: 2023-03-17\n", - "划分后的训练集大小: 797, 验证集大小: 230\n", - "最大日期: 2023-03-20\n", - "划分后的训练集大小: 741, 验证集大小: 105\n", - "最大日期: 2023-03-21\n", - "划分后的训练集大小: 764, 验证集大小: 126\n", - "最大日期: 2023-03-22\n", - "划分后的训练集大小: 679, 验证集大小: 95\n", - "最大日期: 2023-03-23\n", - "划分后的训练集大小: 705, 验证集大小: 149\n", - "最大日期: 2023-03-24\n", - "划分后的训练集大小: 615, 验证集大小: 140\n", - "最大日期: 2023-03-27\n", - "划分后的训练集大小: 636, 验证集大小: 126\n", - "最大日期: 2023-03-28\n", - "划分后的训练集大小: 601, 验证集大小: 91\n", - "最大日期: 2023-03-29\n", - "划分后的训练集大小: 598, 验证集大小: 92\n", - "最大日期: 2023-03-30\n", - "划分后的训练集大小: 574, 验证集大小: 125\n", - "最大日期: 2023-03-31\n", - "划分后的训练集大小: 528, 验证集大小: 94\n", - "最大日期: 2023-04-03\n", - "划分后的训练集大小: 563, 验证集大小: 161\n", - "最大日期: 2023-04-04\n", - "划分后的训练集大小: 700, 验证集大小: 228\n", - "最大日期: 2023-04-06\n", - "划分后的训练集大小: 719, 验证集大小: 111\n", - "最大日期: 2023-04-07\n", - "划分后的训练集大小: 761, 验证集大小: 167\n", - "最大日期: 2023-04-10\n", - "划分后的训练集大小: 812, 验证集大小: 145\n", - "最大日期: 2023-04-11\n", - "划分后的训练集大小: 787, 验证集大小: 136\n", - "最大日期: 2023-04-12\n", - "划分后的训练集大小: 668, 验证集大小: 109\n", - "最大日期: 2023-04-13\n", - "划分后的训练集大小: 685, 验证集大小: 128\n", - "最大日期: 2023-04-14\n", - "划分后的训练集大小: 731, 验证集大小: 213\n", - "最大日期: 2023-04-17\n", - "划分后的训练集大小: 716, 验证集大小: 130\n", - "最大日期: 2023-04-18\n", - "划分后的训练集大小: 676, 验证集大小: 96\n", - "最大日期: 2023-04-19\n", - "划分后的训练集大小: 702, 验证集大小: 135\n", - "最大日期: 2023-04-20\n", - "划分后的训练集大小: 668, 验证集大小: 94\n", - "最大日期: 2023-04-21\n", - "划分后的训练集大小: 598, 验证集大小: 143\n", - "最大日期: 2023-04-24\n", - "划分后的训练集大小: 597, 验证集大小: 129\n", - "最大日期: 2023-04-25\n", - "划分后的训练集大小: 652, 验证集大小: 151\n", - "最大日期: 2023-04-26\n", - "划分后的训练集大小: 652, 验证集大小: 135\n", - "最大日期: 2023-04-27\n", - "划分后的训练集大小: 651, 验证集大小: 93\n", - "最大日期: 2023-04-28\n", - "划分后的训练集大小: 624, 验证集大小: 116\n", - "最大日期: 2023-05-04\n", - "划分后的训练集大小: 633, 验证集大小: 138\n", - "最大日期: 2023-05-05\n", - "划分后的训练集大小: 596, 验证集大小: 114\n", - "最大日期: 2023-05-08\n", - "划分后的训练集大小: 610, 验证集大小: 149\n", - "最大日期: 2023-05-09\n", - "划分后的训练集大小: 746, 验证集大小: 229\n", - "最大日期: 2023-05-10\n", - "划分后的训练集大小: 722, 验证集大小: 92\n", - "最大日期: 2023-05-11\n", - "划分后的训练集大小: 704, 验证集大小: 120\n", - "最大日期: 2023-05-12\n", - "划分后的训练集大小: 777, 验证集大小: 187\n", - "最大日期: 2023-05-15\n", - "划分后的训练集大小: 788, 验证集大小: 160\n", - "最大日期: 2023-05-16\n", - "划分后的训练集大小: 648, 验证集大小: 89\n", - "最大日期: 2023-05-17\n", - "划分后的训练集大小: 652, 验证集大小: 96\n", - "最大日期: 2023-05-18\n", - "划分后的训练集大小: 632, 验证集大小: 100\n", - "最大日期: 2023-05-19\n", - "划分后的训练集大小: 558, 验证集大小: 113\n", - "最大日期: 2023-05-22\n", - "划分后的训练集大小: 502, 验证集大小: 104\n", - "最大日期: 2023-05-23\n", - "划分后的训练集大小: 505, 验证集大小: 92\n", - "最大日期: 2023-05-24\n", - "划分后的训练集大小: 527, 验证集大小: 118\n", - "最大日期: 2023-05-25\n", - "划分后的训练集大小: 522, 验证集大小: 95\n", - "最大日期: 2023-05-26\n", - "划分后的训练集大小: 496, 验证集大小: 87\n", - "最大日期: 2023-05-29\n", - "划分后的训练集大小: 482, 验证集大小: 90\n", - "最大日期: 2023-05-30\n", - "划分后的训练集大小: 498, 验证集大小: 108\n", - "最大日期: 2023-05-31\n", - "划分后的训练集大小: 515, 验证集大小: 135\n", - "最大日期: 2023-06-01\n", - "划分后的训练集大小: 554, 验证集大小: 134\n", - "最大日期: 2023-06-02\n", - "划分后的训练集大小: 577, 验证集大小: 110\n", - "最大日期: 2023-06-05\n", - "划分后的训练集大小: 627, 验证集大小: 140\n", - "最大日期: 2023-06-06\n", - "划分后的训练集大小: 628, 验证集大小: 109\n", - "最大日期: 2023-06-07\n", - "划分后的训练集大小: 583, 验证集大小: 90\n", - "最大日期: 2023-06-08\n", - "划分后的训练集大小: 587, 验证集大小: 138\n", - "最大日期: 2023-06-09\n", - "划分后的训练集大小: 570, 验证集大小: 93\n", - "最大日期: 2023-06-12\n", - "划分后的训练集大小: 525, 验证集大小: 95\n", - "最大日期: 2023-06-13\n", - "划分后的训练集大小: 548, 验证集大小: 132\n", - "最大日期: 2023-06-14\n", - "划分后的训练集大小: 632, 验证集大小: 174\n", - "最大日期: 2023-06-15\n", - "划分后的训练集大小: 653, 验证集大小: 159\n", - "最大日期: 2023-06-16\n", - "划分后的训练集大小: 728, 验证集大小: 168\n", - "最大日期: 2023-06-19\n", - "划分后的训练集大小: 763, 验证集大小: 130\n", - "最大日期: 2023-06-20\n", - "划分后的训练集大小: 800, 验证集大小: 169\n", - "最大日期: 2023-06-21\n", - "划分后的训练集大小: 867, 验证集大小: 241\n", - "最大日期: 2023-06-26\n", - "划分后的训练集大小: 860, 验证集大小: 152\n", - "最大日期: 2023-06-27\n", - "划分后的训练集大小: 935, 验证集大小: 243\n", - "最大日期: 2023-06-28\n", - "划分后的训练集大小: 907, 验证集大小: 102\n", - "最大日期: 2023-06-29\n", - "划分后的训练集大小: 830, 验证集大小: 92\n", - "最大日期: 2023-06-30\n", - "划分后的训练集大小: 824, 验证集大小: 235\n", - "最大日期: 2023-07-03\n", - "划分后的训练集大小: 793, 验证集大小: 121\n", - "最大日期: 2023-07-04\n", - "划分后的训练集大小: 760, 验证集大小: 210\n", - "最大日期: 2023-07-05\n", - "划分后的训练集大小: 794, 验证集大小: 136\n", - "最大日期: 2023-07-06\n", - "划分后的训练集大小: 841, 验证集大小: 139\n", - "最大日期: 2023-07-07\n", - "划分后的训练集大小: 767, 验证集大小: 161\n", - "最大日期: 2023-07-10\n", - "划分后的训练集大小: 803, 验证集大小: 157\n", - "最大日期: 2023-07-11\n", - "划分后的训练集大小: 699, 验证集大小: 106\n", - "最大日期: 2023-07-12\n", - "划分后的训练集大小: 730, 验证集大小: 167\n", - "最大日期: 2023-07-13\n", - "划分后的训练集大小: 684, 验证集大小: 93\n", - "最大日期: 2023-07-14\n", - "划分后的训练集大小: 644, 验证集大小: 121\n", - "最大日期: 2023-07-17\n", - "划分后的训练集大小: 599, 验证集大小: 112\n", - "最大日期: 2023-07-18\n", - "划分后的训练集大小: 644, 验证集大小: 151\n", - "最大日期: 2023-07-19\n", - "划分后的训练集大小: 616, 验证集大小: 139\n", - "最大日期: 2023-07-20\n", - "划分后的训练集大小: 641, 验证集大小: 118\n", - "最大日期: 2023-07-21\n", - "划分后的训练集大小: 620, 验证集大小: 100\n", - "最大日期: 2023-07-24\n", - "划分后的训练集大小: 597, 验证集大小: 89\n", - "最大日期: 2023-07-25\n", - "划分后的训练集大小: 546, 验证集大小: 100\n", - "最大日期: 2023-07-26\n", - "划分后的训练集大小: 535, 验证集大小: 128\n", - "最大日期: 2023-07-27\n", - "划分后的训练集大小: 508, 验证集大小: 91\n", - "最大日期: 2023-07-28\n", - "划分后的训练集大小: 517, 验证集大小: 109\n", - "最大日期: 2023-07-31\n", - "划分后的训练集大小: 524, 验证集大小: 96\n", - "最大日期: 2023-08-01\n", - "划分后的训练集大小: 558, 验证集大小: 134\n", - "最大日期: 2023-08-02\n", - "划分后的训练集大小: 630, 验证集大小: 200\n", - "最大日期: 2023-08-03\n", - "划分后的训练集大小: 634, 验证集大小: 95\n", - "最大日期: 2023-08-04\n", - "划分后的训练集大小: 646, 验证集大小: 121\n", - "最大日期: 2023-08-07\n", - "划分后的训练集大小: 666, 验证集大小: 116\n", - "最大日期: 2023-08-08\n", - "划分后的训练集大小: 634, 验证集大小: 102\n", - "最大日期: 2023-08-09\n", - "划分后的训练集大小: 555, 验证集大小: 121\n", - "最大日期: 2023-08-10\n", - "划分后的训练集大小: 576, 验证集大小: 116\n", - "最大日期: 2023-08-11\n", - "划分后的训练集大小: 642, 验证集大小: 187\n", - "最大日期: 2023-08-14\n", - "划分后的训练集大小: 613, 验证集大小: 87\n", - "最大日期: 2023-08-15\n", - "划分后的训练集大小: 611, 验证集大小: 100\n", - "最大日期: 2023-08-16\n", - "划分后的训练集大小: 623, 验证集大小: 133\n", - "最大日期: 2023-08-17\n", - "划分后的训练集大小: 653, 验证集大小: 146\n", - "最大日期: 2023-08-18\n", - "划分后的训练集大小: 585, 验证集大小: 119\n", - "最大日期: 2023-08-21\n", - "划分后的训练集大小: 590, 验证集大小: 92\n", - "最大日期: 2023-08-22\n", - "划分后的训练集大小: 619, 验证集大小: 129\n", - "最大日期: 2023-08-23\n", - "划分后的训练集大小: 574, 验证集大小: 88\n", - "最大日期: 2023-08-24\n", - "划分后的训练集大小: 594, 验证集大小: 166\n", - "最大日期: 2023-08-25\n", - "划分后的训练集大小: 575, 验证集大小: 100\n", - "最大日期: 2023-08-28\n", - "划分后的训练集大小: 588, 验证集大小: 105\n", - "最大日期: 2023-08-29\n", - "划分后的训练集大小: 600, 验证集大小: 141\n", - "最大日期: 2023-08-30\n", - "划分后的训练集大小: 600, 验证集大小: 88\n", - "最大日期: 2023-08-31\n", - "划分后的训练集大小: 543, 验证集大小: 109\n", - "最大日期: 2023-09-01\n", - "划分后的训练集大小: 536, 验证集大小: 93\n", - "最大日期: 2023-09-04\n", - "划分后的训练集大小: 577, 验证集大小: 146\n", - "最大日期: 2023-09-05\n", - "划分后的训练集大小: 538, 验证集大小: 102\n", - "最大日期: 2023-09-06\n", - "划分后的训练集大小: 571, 验证集大小: 121\n", - "最大日期: 2023-09-07\n", - "划分后的训练集大小: 614, 验证集大小: 152\n", - "最大日期: 2023-09-08\n", - "划分后的训练集大小: 625, 验证集大小: 104\n", - "最大日期: 2023-09-11\n", - "划分后的训练集大小: 599, 验证集大小: 120\n", - "最大日期: 2023-09-12\n", - "划分后的训练集大小: 608, 验证集大小: 111\n", - "最大日期: 2023-09-13\n", - "划分后的训练集大小: 581, 验证集大小: 94\n", - "最大日期: 2023-09-14\n", - "划分后的训练集大小: 546, 验证集大小: 117\n", - "最大日期: 2023-09-15\n", - "划分后的训练集大小: 537, 验证集大小: 95\n", - "最大日期: 2023-09-18\n", - "划分后的训练集大小: 552, 验证集大小: 135\n", - "最大日期: 2023-09-19\n", - "划分后的训练集大小: 551, 验证集大小: 110\n", - "最大日期: 2023-09-20\n", - "划分后的训练集大小: 553, 验证集大小: 96\n", - "最大日期: 2023-09-21\n", - "划分后的训练集大小: 562, 验证集大小: 126\n", - "最大日期: 2023-09-22\n", - "划分后的训练集大小: 590, 验证集大小: 123\n", - "最大日期: 2023-09-25\n", - "划分后的训练集大小: 558, 验证集大小: 103\n", - "最大日期: 2023-09-26\n", - "划分后的训练集大小: 623, 验证集大小: 175\n", - "最大日期: 2023-09-27\n", - "划分后的训练集大小: 714, 验证集大小: 187\n", - "最大日期: 2023-09-28\n", - "划分后的训练集大小: 703, 验证集大小: 115\n", - "最大日期: 2023-10-09\n", - "划分后的训练集大小: 674, 验证集大小: 94\n", - "最大日期: 2023-10-10\n", - "划分后的训练集大小: 668, 验证集大小: 97\n", - "最大日期: 2023-10-11\n", - "划分后的训练集大小: 586, 验证集大小: 93\n", - "最大日期: 2023-10-12\n", - "划分后的训练集大小: 518, 验证集大小: 119\n", - "最大日期: 2023-10-13\n", - "划分后的训练集大小: 497, 验证集大小: 94\n", - "最大日期: 2023-10-16\n", - "划分后的训练集大小: 532, 验证集大小: 129\n", - "最大日期: 2023-10-17\n", - "划分后的训练集大小: 584, 验证集大小: 149\n", - "最大日期: 2023-10-18\n", - "划分后的训练集大小: 772, 验证集大小: 281\n", - "最大日期: 2023-10-19\n", - "划分后的训练集大小: 844, 验证集大小: 191\n", - "最大日期: 2023-10-20\n", - "划分后的训练集大小: 845, 验证集大小: 95\n", - "最大日期: 2023-10-23\n", - "划分后的训练集大小: 819, 验证集大小: 103\n", - "最大日期: 2023-10-24\n", - "划分后的训练集大小: 810, 验证集大小: 140\n", - "最大日期: 2023-10-25\n", - "划分后的训练集大小: 665, 验证集大小: 136\n", - "最大日期: 2023-10-26\n", - "划分后的训练集大小: 579, 验证集大小: 105\n", - "最大日期: 2023-10-27\n", - "划分后的训练集大小: 578, 验证集大小: 94\n", - "最大日期: 2023-10-30\n", - "划分后的训练集大小: 623, 验证集大小: 148\n", - "最大日期: 2023-10-31\n", - "划分后的训练集大小: 647, 验证集大小: 164\n", - "最大日期: 2023-11-01\n", - "划分后的训练集大小: 700, 验证集大小: 189\n", - "最大日期: 2023-11-02\n", - "划分后的训练集大小: 717, 验证集大小: 122\n", - "最大日期: 2023-11-03\n", - "划分后的训练集大小: 732, 验证集大小: 109\n", - "最大日期: 2023-11-06\n", - "划分后的训练集大小: 741, 验证集大小: 157\n", - "最大日期: 2023-11-07\n", - "划分后的训练集大小: 695, 验证集大小: 118\n", - "最大日期: 2023-11-08\n", - "划分后的训练集大小: 727, 验证集大小: 221\n", - "最大日期: 2023-11-09\n", - "划分后的训练集大小: 857, 验证集大小: 252\n", - "最大日期: 2023-11-10\n", - "划分后的训练集大小: 869, 验证集大小: 121\n", - "最大日期: 2023-11-13\n", - "划分后的训练集大小: 839, 验证集大小: 127\n", - "最大日期: 2023-11-14\n", - "划分后的训练集大小: 872, 验证集大小: 151\n", - "最大日期: 2023-11-15\n", - "划分后的训练集大小: 742, 验证集大小: 91\n", - "最大日期: 2023-11-16\n", - "划分后的训练集大小: 607, 验证集大小: 117\n", - "最大日期: 2023-11-17\n", - "划分后的训练集大小: 583, 验证集大小: 97\n", - "最大日期: 2023-11-20\n", - "划分后的训练集大小: 615, 验证集大小: 159\n", - "最大日期: 2023-11-21\n", - "划分后的训练集大小: 638, 验证集大小: 174\n", - "最大日期: 2023-11-22\n", - "划分后的训练集大小: 694, 验证集大小: 147\n", - "最大日期: 2023-11-23\n", - "划分后的训练集大小: 684, 验证集大小: 107\n", - "最大日期: 2023-11-24\n", - "划分后的训练集大小: 794, 验证集大小: 207\n", - "最大日期: 2023-11-27\n", - "划分后的训练集大小: 824, 验证集大小: 189\n", - "最大日期: 2023-11-28\n", - "划分后的训练集大小: 816, 验证集大小: 166\n", - "最大日期: 2023-11-29\n", - "划分后的训练集大小: 794, 验证集大小: 125\n", - "最大日期: 2023-11-30\n", - "划分后的训练集大小: 873, 验证集大小: 186\n", - "最大日期: 2023-12-01\n", - "划分后的训练集大小: 806, 验证集大小: 140\n", - "最大日期: 2023-12-04\n", - "划分后的训练集大小: 754, 验证集大小: 137\n", - "最大日期: 2023-12-05\n", - "划分后的训练集大小: 710, 验证集大小: 122\n", - "最大日期: 2023-12-06\n", - "划分后的训练集大小: 753, 验证集大小: 168\n", - "最大日期: 2023-12-07\n", - "划分后的训练集大小: 701, 验证集大小: 134\n", - "最大日期: 2023-12-08\n", - "划分后的训练集大小: 655, 验证集大小: 94\n", - "最大日期: 2023-12-11\n", - "划分后的训练集大小: 688, 验证集大小: 170\n", - "最大日期: 2023-12-12\n", - "划分后的训练集大小: 678, 验证集大小: 112\n", - "最大日期: 2023-12-13\n", - "划分后的训练集大小: 609, 验证集大小: 99\n", - "最大日期: 2023-12-14\n", - "划分后的训练集大小: 674, 验证集大小: 199\n", - "最大日期: 2023-12-15\n", - "划分后的训练集大小: 705, 验证集大小: 125\n", - "最大日期: 2023-12-18\n", - "划分后的训练集大小: 723, 验证集大小: 188\n", - "最大日期: 2023-12-19\n", - "划分后的训练集大小: 713, 验证集大小: 102\n", - "最大日期: 2023-12-20\n", - "划分后的训练集大小: 765, 验证集大小: 151\n", - "最大日期: 2023-12-21\n", - "划分后的训练集大小: 748, 验证集大小: 182\n", - "最大日期: 2023-12-22\n", - "划分后的训练集大小: 760, 验证集大小: 137\n", - "最大日期: 2023-12-25\n", - "划分后的训练集大小: 704, 验证集大小: 132\n", - "最大日期: 2023-12-26\n", - "划分后的训练集大小: 875, 验证集大小: 273\n", - "最大日期: 2023-12-27\n", - "划分后的训练集大小: 862, 验证集大小: 138\n", - "最大日期: 2023-12-28\n", - "划分后的训练集大小: 772, 验证集大小: 92\n", - "最大日期: 2023-12-29\n", - "划分后的训练集大小: 768, 验证集大小: 133\n", - "最大日期: 2024-01-02\n", - "划分后的训练集大小: 800, 验证集大小: 164\n", - "最大日期: 2024-01-03\n", - "划分后的训练集大小: 683, 验证集大小: 156\n", - "最大日期: 2024-01-04\n", - "划分后的训练集大小: 728, 验证集大小: 183\n", - "最大日期: 2024-01-05\n", - "划分后的训练集大小: 812, 验证集大小: 176\n", - "最大日期: 2024-01-08\n", - "划分后的训练集大小: 829, 验证集大小: 150\n", - "最大日期: 2024-01-09\n", - "划分后的训练集大小: 763, 验证集大小: 98\n", - "最大日期: 2024-01-10\n", - "划分后的训练集大小: 769, 验证集大小: 162\n", - "最大日期: 2024-01-11\n", - "划分后的训练集大小: 801, 验证集大小: 215\n", - "最大日期: 2024-01-12\n", - "划分后的训练集大小: 716, 验证集大小: 91\n", - "最大日期: 2024-01-15\n", - "划分后的训练集大小: 726, 验证集大小: 160\n", - "最大日期: 2024-01-16\n", - "划分后的训练集大小: 726, 验证集大小: 98\n", - "最大日期: 2024-01-17\n", - "划分后的训练集大小: 683, 验证集大小: 119\n", - "最大日期: 2024-01-18\n", - "划分后的训练集大小: 647, 验证集大小: 179\n", - "最大日期: 2024-01-19\n", - "划分后的训练集大小: 689, 验证集大小: 133\n", - "最大日期: 2024-01-22\n", - "划分后的训练集大小: 619, 验证集大小: 90\n", - "最大日期: 2024-01-23\n", - "划分后的训练集大小: 642, 验证集大小: 121\n", - "最大日期: 2024-01-24\n", - "划分后的训练集大小: 659, 验证集大小: 136\n", - "最大日期: 2024-01-25\n", - "划分后的训练集大小: 636, 验证集大小: 156\n", - "最大日期: 2024-01-26\n", - "划分后的训练集大小: 705, 验证集大小: 202\n", - "最大日期: 2024-01-29\n", - "划分后的训练集大小: 744, 验证集大小: 129\n", - "最大日期: 2024-01-30\n", - "划分后的训练集大小: 821, 验证集大小: 198\n", - "最大日期: 2024-01-31\n", - "划分后的训练集大小: 772, 验证集大小: 87\n", - "最大日期: 2024-02-01\n", - "划分后的训练集大小: 795, 验证集大小: 179\n", - "最大日期: 2024-02-02\n", - "划分后的训练集大小: 730, 验证集大小: 137\n", - "最大日期: 2024-02-05\n", - "划分后的训练集大小: 856, 验证集大小: 255\n", - "最大日期: 2024-02-06\n", - "划分后的训练集大小: 813, 验证集大小: 155\n", - "最大日期: 2024-02-07\n", - "划分后的训练集大小: 898, 验证集大小: 172\n", - "最大日期: 2024-02-08\n", - "划分后的训练集大小: 1535, 验证集大小: 816\n", - "最大日期: 2024-02-19\n", - "划分后的训练集大小: 2275, 验证集大小: 877\n", - "最大日期: 2024-02-20\n", - "划分后的训练集大小: 2114, 验证集大小: 94\n", - "最大日期: 2024-02-21\n", - "划分后的训练集大小: 2056, 验证集大小: 97\n", - "最大日期: 2024-02-22\n", - "划分后的训练集大小: 2119, 验证集大小: 235\n", - "最大日期: 2024-02-23\n", - "划分后的训练集大小: 1564, 验证集大小: 261\n", - "最大日期: 2024-02-26\n", - "划分后的训练集大小: 886, 验证集大小: 199\n", - "最大日期: 2024-02-27\n", - "划分后的训练集大小: 884, 验证集大小: 92\n", - "最大日期: 2024-02-28\n", - "划分后的训练集大小: 973, 验证集大小: 186\n", - "最大日期: 2024-02-29\n", - "划分后的训练集大小: 829, 验证集大小: 91\n", - "最大日期: 2024-03-01\n", - "划分后的训练集大小: 672, 验证集大小: 104\n", - "最大日期: 2024-03-04\n", - "划分后的训练集大小: 564, 验证集大小: 91\n", - "最大日期: 2024-03-05\n", - "划分后的训练集大小: 565, 验证集大小: 93\n", - "最大日期: 2024-03-06\n", - "划分后的训练集大小: 470, 验证集大小: 91\n", - "最大日期: 2024-03-07\n", - "划分后的训练集大小: 472, 验证集大小: 93\n", - "最大日期: 2024-03-08\n", - "划分后的训练集大小: 525, 验证集大小: 157\n", - "最大日期: 2024-03-11\n", - "划分后的训练集大小: 528, 验证集大小: 94\n", - "最大日期: 2024-03-12\n", - "划分后的训练集大小: 530, 验证集大小: 95\n", - "最大日期: 2024-03-13\n", - "划分后的训练集大小: 585, 验证集大小: 146\n", - "最大日期: 2024-03-14\n", - "划分后的训练集大小: 612, 验证集大小: 120\n", - "最大日期: 2024-03-15\n", - "划分后的训练集大小: 546, 验证集大小: 91\n", - "最大日期: 2024-03-18\n", - "划分后的训练集大小: 543, 验证集大小: 91\n", - "最大日期: 2024-03-19\n", - "划分后的训练集大小: 629, 验证集大小: 181\n", - "最大日期: 2024-03-20\n", - "划分后的训练集大小: 568, 验证集大小: 85\n", - "最大日期: 2024-03-21\n", - "划分后的训练集大小: 654, 验证集大小: 206\n", - "最大日期: 2024-03-22\n", - "划分后的训练集大小: 761, 验证集大小: 198\n", - "最大日期: 2024-03-25\n", - "划分后的训练集大小: 853, 验证集大小: 183\n", - "最大日期: 2024-03-26\n", - "划分后的训练集大小: 801, 验证集大小: 129\n", - "最大日期: 2024-03-27\n", - "划分后的训练集大小: 836, 验证集大小: 120\n", - "最大日期: 2024-03-28\n", - "划分后的训练集大小: 739, 验证集大小: 109\n", - "最大日期: 2024-03-29\n", - "划分后的训练集大小: 709, 验证集大小: 168\n", - "最大日期: 2024-04-01\n", - "划分后的训练集大小: 629, 验证集大小: 103\n", - "最大日期: 2024-04-02\n", - "划分后的训练集大小: 646, 验证集大小: 146\n", - "最大日期: 2024-04-03\n", - "划分后的训练集大小: 636, 验证集大小: 110\n", - "最大日期: 2024-04-08\n", - "划分后的训练集大小: 686, 验证集大小: 159\n", - "最大日期: 2024-04-09\n", - "划分后的训练集大小: 621, 验证集大小: 103\n", - "最大日期: 2024-04-10\n", - "划分后的训练集大小: 612, 验证集大小: 94\n", - "最大日期: 2024-04-11\n", - "划分后的训练集大小: 605, 验证集大小: 139\n", - "最大日期: 2024-04-12\n", - "划分后的训练集大小: 586, 验证集大小: 91\n", - "最大日期: 2024-04-15\n", - "划分后的训练集大小: 509, 验证集大小: 82\n", - "最大日期: 2024-04-16\n", - "划分后的训练集大小: 530, 验证集大小: 124\n", - "最大日期: 2024-04-17\n", - "划分后的训练集大小: 525, 验证集大小: 89\n", - "最大日期: 2024-04-18\n", - "划分后的训练集大小: 539, 验证集大小: 153\n", - "最大日期: 2024-04-19\n", - "划分后的训练集大小: 539, 验证集大小: 91\n", - "最大日期: 2024-04-22\n", - "划分后的训练集大小: 607, 验证集大小: 150\n", - "最大日期: 2024-04-23\n", - "划分后的训练集大小: 646, 验证集大小: 163\n", - "最大日期: 2024-04-24\n", - "划分后的训练集大小: 648, 验证集大小: 91\n", - "最大日期: 2024-04-25\n", - "划分后的训练集大小: 589, 验证集大小: 94\n", - "最大日期: 2024-04-26\n", - "划分后的训练集大小: 602, 验证集大小: 104\n", - "最大日期: 2024-04-29\n", - "划分后的训练集大小: 608, 验证集大小: 156\n", - "最大日期: 2024-04-30\n", - "划分后的训练集大小: 576, 验证集大小: 131\n", - "最大日期: 2024-05-06\n", - "划分后的训练集大小: 577, 验证集大小: 92\n", - "最大日期: 2024-05-07\n", - "划分后的训练集大小: 595, 验证集大小: 112\n", - "最大日期: 2024-05-08\n", - "划分后的训练集大小: 583, 验证集大小: 92\n", - "最大日期: 2024-05-09\n", - "划分后的训练集大小: 519, 验证集大小: 92\n", - "最大日期: 2024-05-10\n", - "划分后的训练集大小: 477, 验证集大小: 89\n", - "最大日期: 2024-05-13\n", - "划分后的训练集大小: 542, 验证集大小: 157\n", - "最大日期: 2024-05-14\n", - "划分后的训练集大小: 613, 验证集大小: 183\n", - "最大日期: 2024-05-15\n", - "划分后的训练集大小: 720, 验证集大小: 199\n", - "最大日期: 2024-05-16\n", - "划分后的训练集大小: 769, 验证集大小: 141\n", - "最大日期: 2024-05-17\n", - "划分后的训练集大小: 776, 验证集大小: 96\n", - "最大日期: 2024-05-20\n", - "划分后的训练集大小: 713, 验证集大小: 94\n", - "最大日期: 2024-05-21\n", - "划分后的训练集大小: 671, 验证集大小: 141\n", - "最大日期: 2024-05-22\n", - "划分后的训练集大小: 580, 验证集大小: 108\n", - "最大日期: 2024-05-23\n", - "划分后的训练集大小: 534, 验证集大小: 95\n", - "最大日期: 2024-05-24\n", - "划分后的训练集大小: 532, 验证集大小: 94\n", - "最大日期: 2024-05-27\n", - "划分后的训练集大小: 576, 验证集大小: 138\n", - "最大日期: 2024-05-28\n", - "划分后的训练集大小: 534, 验证集大小: 99\n", - "最大日期: 2024-05-29\n", - "划分后的训练集大小: 619, 验证集大小: 193\n", - "最大日期: 2024-05-30\n", - "划分后的训练集大小: 646, 验证集大小: 122\n", - "最大日期: 2024-05-31\n", - "划分后的训练集大小: 650, 验证集大小: 98\n", - "最大日期: 2024-06-03\n", - "划分后的训练集大小: 607, 验证集大小: 95\n", - "最大日期: 2024-06-04\n", - "划分后的训练集大小: 620, 验证集大小: 112\n", - "最大日期: 2024-06-05\n", - "划分后的训练集大小: 524, 验证集大小: 97\n", - "最大日期: 2024-06-06\n", - "划分后的训练集大小: 517, 验证集大小: 115\n", - "最大日期: 2024-06-07\n", - "划分后的训练集大小: 558, 验证集大小: 139\n", - "最大日期: 2024-06-11\n", - "划分后的训练集大小: 557, 验证集大小: 94\n", - "最大日期: 2024-06-12\n", - "划分后的训练集大小: 542, 验证集大小: 97\n", - "最大日期: 2024-06-13\n", - "划分后的训练集大小: 588, 验证集大小: 143\n", - "最大日期: 2024-06-14\n", - "划分后的训练集大小: 650, 验证集大小: 177\n", - "最大日期: 2024-06-17\n", - "划分后的训练集大小: 636, 验证集大小: 125\n", - "最大日期: 2024-06-18\n", - "划分后的训练集大小: 636, 验证集大小: 94\n", - "最大日期: 2024-06-19\n", - "划分后的训练集大小: 676, 验证集大小: 137\n", - "最大日期: 2024-06-20\n", - "划分后的训练集大小: 630, 验证集大小: 97\n", - "最大日期: 2024-06-21\n", - "划分后的训练集大小: 551, 验证集大小: 98\n", - "最大日期: 2024-06-24\n", - "划分后的训练集大小: 550, 验证集大小: 124\n", - "最大日期: 2024-06-25\n", - "划分后的训练集大小: 618, 验证集大小: 162\n", - "最大日期: 2024-06-26\n", - "划分后的训练集大小: 572, 验证集大小: 91\n", - "最大日期: 2024-06-27\n", - "划分后的训练集大小: 571, 验证集大小: 96\n", - "最大日期: 2024-06-28\n", - "划分后的训练集大小: 587, 验证集大小: 114\n", - "最大日期: 2024-07-01\n", - "划分后的训练集大小: 560, 验证集大小: 97\n", - "最大日期: 2024-07-02\n", - "划分后的训练集大小: 494, 验证集大小: 96\n", - "最大日期: 2024-07-03\n", - "划分后的训练集大小: 573, 验证集大小: 170\n", - "最大日期: 2024-07-04\n", - "划分后的训练集大小: 583, 验证集大小: 106\n", - "最大日期: 2024-07-05\n", - "划分后的训练集大小: 749, 验证集大小: 280\n", - "最大日期: 2024-07-08\n", - "划分后的训练集大小: 904, 验证集大小: 252\n", - "最大日期: 2024-07-09\n", - "划分后的训练集大小: 906, 验证集大小: 98\n", - "最大日期: 2024-07-10\n", - "划分后的训练集大小: 829, 验证集大小: 93\n", - "最大日期: 2024-07-11\n", - "划分后的训练集大小: 895, 验证集大小: 172\n", - "最大日期: 2024-07-12\n", - "划分后的训练集大小: 708, 验证集大小: 93\n", - "最大日期: 2024-07-15\n", - "划分后的训练集大小: 574, 验证集大小: 118\n", - "最大日期: 2024-07-16\n", - "划分后的训练集大小: 564, 验证集大小: 88\n", - "最大日期: 2024-07-17\n", - "划分后的训练集大小: 605, 验证集大小: 134\n", - "最大日期: 2024-07-18\n", - "划分后的训练集大小: 525, 验证集大小: 92\n", - "最大日期: 2024-07-19\n", - "划分后的训练集大小: 529, 验证集大小: 97\n", - "最大日期: 2024-07-22\n", - "划分后的训练集大小: 544, 验证集大小: 133\n", - "最大日期: 2024-07-23\n", - "划分后的训练集大小: 553, 验证集大小: 97\n", - "最大日期: 2024-07-24\n", - "划分后的训练集大小: 529, 验证集大小: 110\n", - "最大日期: 2024-07-25\n", - "划分后的训练集大小: 624, 验证集大小: 187\n", - "最大日期: 2024-07-26\n", - "划分后的训练集大小: 622, 验证集大小: 95\n", - "最大日期: 2024-07-29\n", - "划分后的训练集大小: 610, 验证集大小: 121\n", - "最大日期: 2024-07-30\n", - "划分后的训练集大小: 654, 验证集大小: 141\n", - "最大日期: 2024-07-31\n", - "划分后的训练集大小: 675, 验证集大小: 131\n", - "最大日期: 2024-08-01\n", - "划分后的训练集大小: 643, 验证集大小: 155\n", - "最大日期: 2024-08-02\n", - "划分后的训练集大小: 703, 验证集大小: 155\n", - "最大日期: 2024-08-05\n", - "划分后的训练集大小: 684, 验证集大小: 102\n", - "最大日期: 2024-08-06\n", - "划分后的训练集大小: 664, 验证集大小: 121\n", - "最大日期: 2024-08-07\n", - "划分后的训练集大小: 716, 验证集大小: 183\n", - "最大日期: 2024-08-08\n", - "划分后的训练集大小: 782, 验证集大小: 221\n", - "最大日期: 2024-08-09\n", - "划分后的训练集大小: 826, 验证集大小: 199\n", - "最大日期: 2024-08-12\n", - "划分后的训练集大小: 840, 验证集大小: 116\n", - "最大日期: 2024-08-13\n", - "划分后的训练集大小: 811, 验证集大小: 92\n", - "最大日期: 2024-08-14\n", - "划分后的训练集大小: 742, 验证集大小: 114\n", - "最大日期: 2024-08-15\n", - "划分后的训练集大小: 621, 验证集大小: 100\n", - "最大日期: 2024-08-16\n", - "划分后的训练集大小: 558, 验证集大小: 136\n", - "最大日期: 2024-08-19\n", - "划分后的训练集大小: 585, 验证集大小: 143\n", - "最大日期: 2024-08-20\n", - "划分后的训练集大小: 666, 验证集大小: 173\n", - "最大日期: 2024-08-21\n", - "划分后的训练集大小: 719, 验证集大小: 167\n", - "最大日期: 2024-08-22\n", - "划分后的训练集大小: 732, 验证集大小: 113\n", - "最大日期: 2024-08-23\n", - "划分后的训练集大小: 737, 验证集大小: 141\n", - "最大日期: 2024-08-26\n", - "划分后的训练集大小: 748, 验证集大小: 154\n", - "最大日期: 2024-08-27\n", - "划分后的训练集大小: 733, 验证集大小: 158\n", - "最大日期: 2024-08-28\n", - "划分后的训练集大小: 661, 验证集大小: 95\n", - "最大日期: 2024-08-29\n", - "划分后的训练集大小: 650, 验证集大小: 102\n", - "最大日期: 2024-08-30\n", - "划分后的训练集大小: 601, 验证集大小: 92\n", - "最大日期: 2024-09-02\n", - "划分后的训练集大小: 551, 验证集大小: 104\n", - "最大日期: 2024-09-03\n", - "划分后的训练集大小: 496, 验证集大小: 103\n", - "最大日期: 2024-09-04\n", - "划分后的训练集大小: 499, 验证集大小: 98\n", - "最大日期: 2024-09-05\n", - "划分后的训练集大小: 489, 验证集大小: 92\n", - "最大日期: 2024-09-06\n", - "划分后的训练集大小: 491, 验证集大小: 94\n", - "最大日期: 2024-09-09\n", - "划分后的训练集大小: 536, 验证集大小: 149\n", - "最大日期: 2024-09-10\n", - "划分后的训练集大小: 550, 验证集大小: 117\n", - "最大日期: 2024-09-11\n", - "划分后的训练集大小: 546, 验证集大小: 94\n", - "最大日期: 2024-09-12\n", - "划分后的训练集大小: 576, 验证集大小: 122\n", - "最大日期: 2024-09-13\n", - "划分后的训练集大小: 579, 验证集大小: 97\n", - "最大日期: 2024-09-18\n", - "划分后的训练集大小: 527, 验证集大小: 97\n", - "最大日期: 2024-09-19\n", - "划分后的训练集大小: 512, 验证集大小: 102\n", - "最大日期: 2024-09-20\n", - "划分后的训练集大小: 547, 验证集大小: 129\n", - "最大日期: 2024-09-23\n", - "划分后的训练集大小: 572, 验证集大小: 147\n", - "最大日期: 2024-09-24\n", - "划分后的训练集大小: 558, 验证集大小: 83\n", - "最大日期: 2024-09-25\n", - "划分后的训练集大小: 590, 验证集大小: 129\n", - "最大日期: 2024-09-26\n", - "划分后的训练集大小: 577, 验证集大小: 89\n", - "最大日期: 2024-09-27\n", - "划分后的训练集大小: 553, 验证集大小: 105\n", - "最大日期: 2024-09-30\n", - "划分后的训练集大小: 489, 验证集大小: 83\n", - "最大日期: 2024-10-08\n", - "划分后的训练集大小: 510, 验证集大小: 104\n", - "最大日期: 2024-10-09\n", - "划分后的训练集大小: 471, 验证集大小: 90\n", - "最大日期: 2024-10-10\n", - "划分后的训练集大小: 583, 验证集大小: 201\n", - "最大日期: 2024-10-11\n", - "划分后的训练集大小: 627, 验证集大小: 149\n", - "最大日期: 2024-10-14\n", - "划分后的训练集大小: 752, 验证集大小: 208\n", - "最大日期: 2024-10-15\n", - "划分后的训练集大小: 751, 验证集大小: 103\n", - "最大日期: 2024-10-16\n", - "划分后的训练集大小: 833, 验证集大小: 172\n", - "最大日期: 2024-10-17\n", - "划分后的训练集大小: 829, 验证集大小: 197\n", - "最大日期: 2024-10-18\n", - "划分后的训练集大小: 794, 验证集大小: 114\n", - "最大日期: 2024-10-21\n", - "划分后的训练集大小: 744, 验证集大小: 158\n", - "最大日期: 2024-10-22\n", - "划分后的训练集大小: 862, 验证集大小: 221\n", - "最大日期: 2024-10-23\n", - "划分后的训练集大小: 799, 验证集大小: 109\n", - "最大日期: 2024-10-24\n", - "划分后的训练集大小: 685, 验证集大小: 83\n", - "最大日期: 2024-10-25\n", - "划分后的训练集大小: 691, 验证集大小: 120\n", - "最大日期: 2024-10-28\n", - "划分后的训练集大小: 643, 验证集大小: 110\n", - "最大日期: 2024-10-29\n", - "划分后的训练集大小: 527, 验证集大小: 105\n", - "最大日期: 2024-10-30\n", - "划分后的训练集大小: 523, 验证集大小: 105\n", - "最大日期: 2024-10-31\n", - "划分后的训练集大小: 643, 验证集大小: 203\n", - "最大日期: 2024-11-01\n", - "划分后的训练集大小: 728, 验证集大小: 205\n", - "最大日期: 2024-11-04\n", - "划分后的训练集大小: 815, 验证集大小: 197\n", - "最大日期: 2024-11-05\n", - "划分后的训练集大小: 876, 验证集大小: 166\n", - "最大日期: 2024-11-06\n", - "划分后的训练集大小: 959, 验证集大小: 188\n", - "最大日期: 2024-11-07\n", - "划分后的训练集大小: 855, 验证集大小: 99\n", - "最大日期: 2024-11-08\n", - "划分后的训练集大小: 733, 验证集大小: 83\n", - "最大日期: 2024-11-11\n", - "划分后的训练集大小: 717, 验证集大小: 181\n", - "最大日期: 2024-11-12\n", - "划分后的训练集大小: 682, 验证集大小: 131\n", - "最大日期: 2024-11-13\n", - "划分后的训练集大小: 692, 验证集大小: 198\n", - "最大日期: 2024-11-14\n", - "划分后的训练集大小: 780, 验证集大小: 187\n", - "最大日期: 2024-11-15\n", - "划分后的训练集大小: 841, 验证集大小: 144\n", - "最大日期: 2024-11-18\n", - "划分后的训练集大小: 800, 验证集大小: 140\n", - "最大日期: 2024-11-19\n", - "划分后的训练集大小: 767, 验证集大小: 98\n", - "最大日期: 2024-11-20\n", - "划分后的训练集大小: 687, 验证集大小: 118\n", - "最大日期: 2024-11-21\n", - "划分后的训练集大小: 643, 验证集大小: 143\n", - "最大日期: 2024-11-22\n", - "划分后的训练集大小: 645, 验证集大小: 146\n", - "最大日期: 2024-11-25\n", - "划分后的训练集大小: 621, 验证集大小: 116\n", - "最大日期: 2024-11-26\n", - "划分后的训练集大小: 761, 验证集大小: 238\n", - "最大日期: 2024-11-27\n", - "划分后的训练集大小: 781, 验证集大小: 138\n", - "最大日期: 2024-11-28\n", - "划分后的训练集大小: 798, 验证集大小: 160\n", - "最大日期: 2024-11-29\n", - "划分后的训练集大小: 797, 验证集大小: 145\n", - "最大日期: 2024-12-02\n", - "划分后的训练集大小: 826, 验证集大小: 145\n", - "最大日期: 2024-12-03\n", - "划分后的训练集大小: 778, 验证集大小: 190\n", - "最大日期: 2024-12-04\n", - "划分后的训练集大小: 761, 验证集大小: 121\n", - "最大日期: 2024-12-05\n", - "划分后的训练集大小: 812, 验证集大小: 211\n", - "最大日期: 2024-12-06\n", - "划分后的训练集大小: 770, 验证集大小: 103\n", - "最大日期: 2024-12-09\n", - "划分后的训练集大小: 738, 验证集大小: 113\n", - "最大日期: 2024-12-10\n", - "划分后的训练集大小: 735, 验证集大小: 187\n", - "最大日期: 2024-12-11\n", - "划分后的训练集大小: 726, 验证集大小: 112\n", - "最大日期: 2024-12-12\n", - "划分后的训练集大小: 689, 验证集大小: 174\n", - "最大日期: 2024-12-13\n", - "划分后的训练集大小: 718, 验证集大小: 132\n", - "最大日期: 2024-12-16\n", - "划分后的训练集大小: 701, 验证集大小: 96\n", - "最大日期: 2024-12-17\n", - "划分后的训练集大小: 796, 验证集大小: 282\n", - "最大日期: 2024-12-18\n", - "划分后的训练集大小: 791, 验证集大小: 107\n", - "最大日期: 2024-12-19\n", - "划分后的训练集大小: 941, 验证集大小: 324\n", - "最大日期: 2024-12-20\n", - "划分后的训练集大小: 1044, 验证集大小: 235\n", - "最大日期: 2024-12-23\n", - "划分后的训练集大小: 1047, 验证集大小: 99\n", - "最大日期: 2024-12-24\n", - "划分后的训练集大小: 876, 验证集大小: 111\n", - "最大日期: 2024-12-25\n", - "划分后的训练集大小: 984, 验证集大小: 215\n", - "最大日期: 2024-12-26\n", - "划分后的训练集大小: 800, 验证集大小: 140\n", - "最大日期: 2024-12-27\n", - "划分后的训练集大小: 697, 验证集大小: 132\n", - "最大日期: 2024-12-30\n", - "划分后的训练集大小: 720, 验证集大小: 122\n", - "最大日期: 2024-12-31\n", - "划分后的训练集大小: 710, 验证集大小: 101\n", - "最大日期: 2025-01-02\n", - "划分后的训练集大小: 586, 验证集大小: 91\n", - "最大日期: 2025-01-03\n", - "划分后的训练集大小: 538, 验证集大小: 92\n", - "最大日期: 2025-01-06\n", - "划分后的训练集大小: 525, 验证集大小: 119\n", - "最大日期: 2025-01-07\n", - "划分后的训练集大小: 495, 验证集大小: 92\n", - "最大日期: 2025-01-08\n", - "划分后的训练集大小: 540, 验证集大小: 146\n", - "最大日期: 2025-01-09\n", - "划分后的训练集大小: 557, 验证集大小: 108\n", - "最大日期: 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"划分后的训练集大小: 594, 验证集大小: 124\n", - "最大日期: 2025-02-13\n", - "划分后的训练集大小: 687, 验证集大小: 224\n", - "最大日期: 2025-02-14\n", - "划分后的训练集大小: 699, 验证集大小: 104\n", - "最大日期: 2025-02-17\n", - "划分后的训练集大小: 780, 验证集大小: 196\n", - "最大日期: 2025-02-18\n", - "划分后的训练集大小: 798, 验证集大小: 150\n", - "最大日期: 2025-02-19\n", - "划分后的训练集大小: 787, 验证集大小: 113\n", - "最大日期: 2025-02-20\n", - "划分后的训练集大小: 655, 验证集大小: 92\n", - "最大日期: 2025-02-21\n", - "划分后的训练集大小: 724, 验证集大小: 173\n", - "最大日期: 2025-02-24\n", - "划分后的训练集大小: 718, 验证集大小: 190\n", - "最大日期: 2025-02-25\n", - "划分后的训练集大小: 701, 验证集大小: 133\n", - "最大日期: 2025-02-26\n", - "划分后的训练集大小: 828, 验证集大小: 240\n", - "最大日期: 2025-02-27\n", - "划分后的训练集大小: 862, 验证集大小: 126\n", - "最大日期: 2025-02-28\n", - "划分后的训练集大小: 913, 验证集大小: 224\n", - "最大日期: 2025-03-03\n", - "划分后的训练集大小: 841, 验证集大小: 118\n", - "最大日期: 2025-03-04\n", - "划分后的训练集大小: 841, 验证集大小: 133\n", - "最大日期: 2025-03-05\n", - "划分后的训练集大小: 750, 验证集大小: 149\n", - "最大日期: 2025-03-06\n", - "划分后的训练集大小: 727, 验证集大小: 103\n", - "最大日期: 2025-03-07\n", - "划分后的训练集大小: 682, 验证集大小: 179\n", - "最大日期: 2025-03-10\n", - "划分后的训练集大小: 723, 验证集大小: 159\n", - "最大日期: 2025-03-11\n", - "划分后的训练集大小: 682, 验证集大小: 92\n", - "最大日期: 2025-03-12\n", - "划分后的训练集大小: 684, 验证集大小: 151\n", - "最大日期: 2025-03-13\n", - "划分后的训练集大小: 796, 验证集大小: 215\n", - "最大日期: 2025-03-14\n", - "划分后的训练集大小: 820, 验证集大小: 203\n", - "最大日期: 2025-03-17\n", - "划分后的训练集大小: 767, 验证集大小: 106\n", - "最大日期: 2025-03-18\n", - "划分后的训练集大小: 822, 验证集大小: 147\n", - "最大日期: 2025-03-19\n", - "划分后的训练集大小: 758, 验证集大小: 87\n", - "最大日期: 2025-03-20\n", - "划分后的训练集大小: 656, 验证集大小: 113\n", - "最大日期: 2025-03-21\n", - "划分后的训练集大小: 595, 验证集大小: 142\n", - "最大日期: 2025-03-24\n", - "划分后的训练集大小: 645, 验证集大小: 156\n", - "最大日期: 2025-03-25\n", - "划分后的训练集大小: 603, 验证集大小: 105\n", - "最大日期: 2025-03-26\n", - "划分后的训练集大小: 665, 验证集大小: 149\n" - ] - } - ], - "execution_count": 64 + ] }, { "cell_type": "code", - "id": "10f15e935aa02a34", + "execution_count": 20, + "id": "e01fe33b-e30d-4bc6-bf40-de91e61862b4", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T18:01:46.449474Z", - "start_time": "2025-04-05T18:01:46.441493Z" + "end_time": "2025-04-07T16:30:26.382297Z", + "start_time": "2025-04-07T16:30:26.377596Z" } }, - "source": "print(pdf[pdf['trade_date'] == '2024-10-30'][['ts_code', 'label']])", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ts_code label\n", - "236729 603268.SH 19.0\n", - "236728 002685.SZ 1.0\n", - "236727 002693.SZ 1.0\n", - "236726 600839.SH 0.0\n", - "236725 002272.SZ 1.0\n", - "... ... ...\n", - "236954 002843.SZ 2.0\n", - "236953 600570.SH 10.0\n", - "236952 002988.SZ 1.0\n", - "236951 002708.SZ 0.0\n", - "236955 002760.SZ 9.0\n", - "\n", - "[240 rows x 2 columns]\n" + " 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" ] } ], - "execution_count": 61 + "source": [ + "print(final_predictions[['ts_code', 'trade_date', 'score']].tail())" + ] }, { "cell_type": "code", + "execution_count": 21, "id": "0dc75517-c857-4f1d-8815-e807400a6d33", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T18:01:46.512618Z", - "start_time": "2025-04-05T18:01:46.449474Z" + "end_time": "2025-04-07T16:30:26.392890Z", + "start_time": "2025-04-07T16:30:26.388311Z" } }, + "outputs": [], "source": [ - "train_data = pdf[filter_index & (pdf['trade_date'] == '2023-01-03')]\n", - "# train_data = train_data.reset_index(drop=True)\n", - "print(len(train_data))" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "167\n" - ] - } - ], - "execution_count": 62 + "# 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", + "#\n", + "#\n", + "# pdf1 = time_series_quantile_filter(pdf1, numeric_columns)\n", + "#\n", + "# # pdf1 = cross_sectional_standardization(pdf1, numeric_columns)\n", + "# # pdf1 = pdf1[pdf1['trade_date'] <= '2025-03-26']\n", + "# # pdf1 = pdf1.sort_values(by=['ts_code', 'trade_date'])\n", + "# filter_index1 = pdf1['future_return'].between(pdf1['future_return'].quantile(0.01), pdf1['future_return'].quantile(0.99))\n", + "#\n", + "# print('-----------------------------------------')\n", + "# pdf2 = select_pre_zt_stocks_dynamic(df[(df['trade_date'] >= '2022-03-26') & (df['trade_date'] <= '2025-03-26')])\n", + "# pdf2 = pdf2.replace([np.inf, -np.inf], np.nan)\n", + "#\n", + "#\n", + "# pdf2 = time_series_quantile_filter(pdf2, numeric_columns)\n", + "#\n", + "# # pdf2 = cross_sectional_standardization(pdf2, numeric_columns)\n", + "#\n", + "# # 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" + ] }, { + "cell_type": "code", + "execution_count": 22, + "id": "8299a6f461097f14", "metadata": { "ExecuteTime": { - "end_time": "2025-04-05T18:01:46.579788Z", - "start_time": "2025-04-05T18:01:46.566533Z" + "end_time": "2025-04-07T16:30:26.519826Z", + "start_time": "2025-04-07T16:30:26.513721Z" } }, - "cell_type": "code", + "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "\n", - "def analyze_nan_factors(df, factor_columns):\n", - " \"\"\"\n", - " 分析 DataFrame 中指定因子列的 NaN 值情况。\n", - "\n", - " Args:\n", - " df (pd.DataFrame): 包含因子数据的 DataFrame。\n", - " factor_columns (list): 包含因子列名的列表。\n", - " \"\"\"\n", - "\n", - " print(\"### 各因子 NaN 值占比 ###\")\n", - " nan_percentage = df[factor_columns].isnull().sum() / len(df) * 100\n", - " print(nan_percentage.sort_values(ascending=False))\n", - " print(\"\\n\")\n", - "\n", - " print(\"### 包含 NaN 值最多的前 5 个因子 ###\")\n", - " top_nan_factors = nan_percentage[nan_percentage > 0].sort_values(ascending=False).head(5)\n", - " print(top_nan_factors)\n", - " print(\"\\n\")\n", - "\n", - " if not top_nan_factors.empty:\n", - " for factor in top_nan_factors.index:\n", - " print(f\"### 因子 '{factor}' 的 NaN 值分析 ###\")\n", - "\n", - " # 按交易日期分组，计算每日 NaN 值数量\n", - " nan_by_date = df.groupby('trade_date')[factor].apply(lambda x: x.isnull().sum())\n", - " print(\"\\n每日 NaN 值数量:\")\n", - " print(nan_by_date.sort_values(ascending=False).head())\n", - "\n", - " # # 按股票代码分组，计算每只股票 NaN 值数量\n", - " # nan_by_stock = df.groupby('ts_code')[factor].apply(lambda x: x.isnull().sum())\n", - " # print(\"\\n每只股票 NaN 值数量:\")\n", - " # print(nan_by_stock.sort_values(ascending=False).head())\n", - "\n", - " print(\"-\" * 30)\n", - " print(\"\\n\")\n", - "\n", - "\n", - "# 假设您的 DataFrame 名称是 pdf，并且您已经定义了 feature_columns\n", - "if 'pdf' in locals() and 'feature_columns' in locals():\n", - " analyze_nan_factors(train_data.copy(), feature_columns)\n", - "else:\n", - " print(\"请确保您的 DataFrame 名称为 'pdf' 且因子列名列表为 'feature_columns' 后再运行代码。\")" - ], - "id": "53ce0624ed8e3123", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### 各因子 NaN 值占比 ###\n", - "mv_growth 2.395210\n", - "arbr 1.796407\n", - "industry_rank_act_factor3 1.197605\n", - "industry_act_factor1 1.197605\n", - "std_return_90 - std_return_90_2 1.197605\n", - " ... \n", - "obv 0.000000\n", - "momentum_factor 0.000000\n", - "resonance_factor 0.000000\n", - "cat_vol_spike 0.000000\n", - "vol 0.000000\n", - "Length: 94, dtype: float64\n", - "\n", - "\n", - "### 包含 NaN 值最多的前 5 个因子 ###\n", - "mv_growth 2.395210\n", - "arbr 1.796407\n", - "industry_rank_act_factor3 1.197605\n", - "industry_act_factor3 1.197605\n", - "industry_return_5 1.197605\n", - "dtype: float64\n", - "\n", - "\n", - "### 因子 'mv_growth' 的 NaN 值分析 ###\n", - "\n", - "每日 NaN 值数量:\n", - "trade_date\n", - "2023-01-03 4\n", - "Name: mv_growth, dtype: int64\n", - "------------------------------\n", - "\n", - "\n", - "### 因子 'arbr' 的 NaN 值分析 ###\n", - "\n", - "每日 NaN 值数量:\n", - "trade_date\n", - "2023-01-03 3\n", - "Name: arbr, dtype: int64\n", - "------------------------------\n", - "\n", - "\n", - "### 因子 'industry_rank_act_factor3' 的 NaN 值分析 ###\n", - "\n", - "每日 NaN 值数量:\n", - "trade_date\n", - "2023-01-03 2\n", - "Name: industry_rank_act_factor3, dtype: int64\n", - "------------------------------\n", - "\n", - "\n", - "### 因子 'industry_act_factor3' 的 NaN 值分析 ###\n", - "\n", - "每日 NaN 值数量:\n", - "trade_date\n", - "2023-01-03 2\n", - "Name: industry_act_factor3, dtype: int64\n", - "------------------------------\n", - "\n", - "\n", - "### 因子 'industry_return_5' 的 NaN 值分析 ###\n", - "\n", - "每日 NaN 值数量:\n", - "trade_date\n", - "2023-01-03 2\n", - "Name: industry_return_5, dtype: int64\n", - "------------------------------\n", - "\n", - "\n" - ] + "# are_equal = pdf1[filter_index1].equals(pdf2[filter_index2])\n", + "# print(are_equal) # 输出 True 或 False\n", + "#\n", + "# are_equal = pdf1.equals(pdf2)\n", + "# print(are_equal) # 输出 True 或 False\n", + "#\n", + "# are_equal = filter_index1.equals(filter_index2)\n", + "# print(are_equal) # 输出 True 或 False" + ] + }, + { + "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" } - ], - "execution_count": 63 + }, + "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", + "# days=days, validation_days=0, filter_index=filter_index1, params=light_params)\n", + "# final_predictions.to_csv('test1.tsv', index=False)\n", + "#\n", + "# final_predictions2 = rolling_train_predict(\n", + "# 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)" + ] + }, + { + "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" + } + }, + "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" + ] } ], "metadata": { diff --git a/code/train/predictions_test.tsv b/code/train/predictions_test.tsv index e22a583..89fbcd2 100644 --- a/code/train/predictions_test.tsv +++ b/code/train/predictions_test.tsv @@ -1,536 +1,563 @@ trade_date,score,ts_code -2023-02-08,1.3180969743375994,002587.SZ -2023-02-10,1.013107669851656,603583.SH -2023-05-22,1.0937730896898876,601858.SH -2023-01-17,1.7843173719929157,000670.SZ -2023-02-14,1.3804780553479405,002436.SZ -2023-01-18,1.254004141887051,001228.SZ -2023-01-12,1.3076223272584526,002528.SZ -2023-02-09,1.6596568030162229,002717.SZ -2023-02-13,1.6002900208167317,002882.SZ -2023-01-10,1.5897516469016344,000610.SZ -2023-03-14,2.526834545440147,601728.SH -2023-01-19,1.3357789615861235,002560.SZ -2023-01-16,1.5082755147432445,000972.SZ -2023-02-16,2.041720067798187,000635.SZ -2023-02-17,1.8967198721361143,002722.SZ -2023-02-22,1.8480781272414475,002328.SZ -2023-04-19,1.561790749473725,002222.SZ -2023-02-20,1.7810334528233012,002877.SZ -2023-03-13,2.3161577038437726,601698.SH -2023-01-20,1.5621704039151958,600501.SH -2023-02-21,1.4012683347413366,002878.SZ -2023-04-11,1.8651855141527631,002777.SZ -2023-02-23,0.9043477512925548,603533.SH -2023-03-21,1.8328132781261042,002236.SZ -2023-04-14,1.848552321057411,002222.SZ -2023-02-15,2.030475372006617,000584.SZ -2023-03-08,2.2248362875774665,601698.SH -2023-01-30,2.3148620229751127,002848.SZ -2023-03-15,1.4938461756767076,600118.SH -2023-05-11,1.564399150491813,600167.SH -2023-05-10,1.5048732579556283,000950.SZ -2023-02-06,1.3553301503472965,002241.SZ -2023-03-09,2.6111854013937523,601698.SH -2023-04-17,1.6142325871994039,002712.SZ -2023-03-10,2.2213653031759133,601698.SH -2023-05-24,2.2852035719847956,603070.SH -2023-04-24,2.593868902977932,601595.SH -2023-04-13,1.4258495555376962,603083.SH -2023-02-07,1.6201218516918532,002401.SZ -2023-05-12,1.4346501519601005,600686.SH -2023-03-01,1.8790887873627313,002339.SZ -2023-03-02,1.0379715224711328,601360.SH -2023-02-24,1.18832163454459,002229.SZ -2023-04-06,2.158317227632834,002555.SZ -2023-01-13,1.0169061706625795,000716.SZ -2023-07-10,1.126229991825094,001270.SZ -2023-04-20,1.3886095686453805,002076.SZ -2023-06-08,1.839377198768079,601900.SH -2023-03-16,2.177862911117618,600941.SH -2023-05-15,1.4164971807609326,002780.SZ -2023-01-11,1.8097354510708217,002528.SZ -2023-05-23,1.2048051478772688,003021.SZ -2023-04-10,1.7099150559652085,002777.SZ -2023-03-17,1.8065340077991792,601390.SH -2023-05-18,2.0827300306071983,600072.SH -2023-04-07,1.8053875501886132,603888.SH -2023-02-02,1.2225651552417267,000032.SZ -2023-03-27,2.408356412486003,603083.SH -2023-06-19,2.108179825328633,002281.SZ -2023-04-18,1.3993486563454507,002028.SZ -2023-02-27,1.2183427305864558,601512.SH -2023-03-23,1.9371188428061856,603083.SH -2023-06-09,1.9199941448682745,603163.SH -2023-03-22,1.5105335984906483,002528.SZ -2023-02-03,1.7964872624157562,002195.SZ -2023-05-17,2.2275069656979722,600072.SH -2023-06-02,1.9095259376259548,002843.SZ -2023-07-11,1.6549150121522307,603596.SH -2023-03-20,1.837351696902142,002115.SZ -2023-10-20,1.8421617559618955,600733.SH -2023-05-19,1.581004028647265,603029.SH -2023-06-12,1.8567878655608843,603918.SH -2023-06-13,1.656338977679562,000070.SZ -2023-06-07,1.505866998936347,002229.SZ -2023-09-07,0.38789223417144,600540.SH -2023-04-12,1.4065925807107165,002913.SZ -2023-08-07,1.0951602576370092,605188.SH -2023-06-20,2.25763731341619,600602.SH -2023-03-24,2.2939114106837963,002236.SZ -2023-04-25,2.405692758188327,601949.SH -2023-07-12,1.6641242141794526,002813.SZ -2023-09-05,1.875368595286382,000536.SZ -2023-06-21,1.6502571334828084,605186.SH -2023-08-08,1.8598483646943593,600621.SH -2023-08-17,1.882902941615791,601136.SH -2023-04-21,2.243809612312944,003031.SZ -2023-03-07,1.419135259160917,003035.SZ -2023-05-25,2.12098372868592,603662.SH -2023-02-01,1.802777270786742,002403.SZ -2023-08-09,1.499070785457802,000719.SZ -2023-07-31,1.4462474503753078,000090.SZ -2023-07-18,1.5277484230072078,601567.SH -2023-03-03,1.7616431580164755,002882.SZ -2023-03-31,1.7089998125419048,600373.SH -2023-08-11,1.1704055787326768,603383.SH -2023-07-07,1.4912681850209493,603786.SH -2023-06-14,1.8825368472151955,603918.SH -2023-07-19,1.6084678183670955,605196.SH -2023-05-09,1.6920914818017874,000928.SZ -2023-07-14,1.932205175689351,002513.SZ -2023-07-25,-0.39151231457161895,600239.SH -2023-04-04,1.785168102882366,603083.SH -2023-02-28,1.4559874950811376,603186.SH -2023-01-31,1.2424302242698562,001339.SZ -2023-05-29,2.4925578751023645,002366.SZ -2023-08-21,2.0137649216919487,002221.SZ -2023-07-24,1.8088141777352165,600708.SH -2023-09-13,2.225031152169712,600293.SH -2023-07-21,1.1927586921644102,002640.SZ -2023-08-04,1.9369486439425216,002377.SZ -2023-05-08,1.8094875600633689,603322.SH -2023-06-16,2.150902241456287,002281.SZ -2023-05-26,1.393234234354781,600032.SH -2023-08-18,1.1822228814858742,002146.SZ -2023-08-15,1.7632167993961063,002177.SZ -2023-07-13,0.895990987425987,002997.SZ -2023-06-26,1.6559001308830739,000063.SZ -2023-07-17,1.6194861099100217,002513.SZ -2023-03-06,1.9160313027912317,601698.SH -2023-09-08,1.6666566138049919,600189.SH -2023-10-11,1.633685099024151,600114.SH -2023-09-15,1.6842712278942933,000010.SZ -2023-06-27,1.417667592097079,002535.SZ -2023-03-29,1.4712301811895172,002261.SZ -2023-04-26,2.40086377297804,601811.SH -2023-08-10,1.3218615770271813,603730.SH -2023-06-15,1.5442859092486119,002902.SZ -2023-08-16,1.543201948071643,601086.SH -2023-10-10,1.814316702973364,000676.SZ -2023-05-16,1.1603782216389666,603629.SH -2023-10-19,1.3423545517382605,600599.SH -2023-07-20,1.4360111125074404,000014.SZ -2023-09-04,0.540069281531294,002310.SZ -2023-06-06,1.244415424595771,001259.SZ -2023-09-06,1.5818478453172151,002207.SZ -2023-07-06,1.59218179443019,600310.SH -2023-11-17,2.232110862015821,000056.SZ -2023-05-04,2.344500402717637,601999.SH -2023-08-22,2.000816714258229,000995.SZ -2024-01-12,1.9034935217126376,603711.SH -2023-10-23,2.0519628963324688,600733.SH -2023-08-14,1.8094325357708543,001299.SZ -2023-03-30,1.1119109413818073,600839.SH -2023-09-19,1.37311422550467,600792.SH 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