diff --git a/main/data/daily_basic.ipynb b/main/data/daily_basic.ipynb index a9db707..6ee2828 100644 --- a/main/data/daily_basic.ipynb +++ b/main/data/daily_basic.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2", "metadata": { "ExecuteTime": { @@ -9,23 +10,25 @@ "start_time": "2025-03-02T09:47:07.512525Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 2 + ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "bc8f03e027027004", "metadata": { "ExecuteTime": { "end_time": "2025-03-02T09:47:10.242731Z", "start_time": "2025-03-02T09:47:08.470810Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "from datetime import datetime\n", "import pandas as pd\n", @@ -70,89 +73,50 @@ "name_change_dict = {}\n", "for ts_code, group in name_change_df.groupby('ts_code'):\n", " # 只保留 'ST' 和 '*ST' 的记录\n", - " st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", + " # st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", + " st_data = group[group['name'].str.contains('ST')]\n", " if not st_data.empty:\n", " name_change_dict[ts_code] = filter_rows(st_data)" - ], - "id": "bc8f03e027027004", - "outputs": [], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", "metadata": { - "scrolled": true, "ExecuteTime": { "end_time": "2025-03-02T08:33:15.997350Z", "start_time": "2025-03-02T08:17:08.727232Z" - } + }, + "scrolled": true }, - "source": [ - "import tushare as ts\n", - "import pandas as pd\n", - "import time\n", - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "\n", - "# 获取交易日历\n", - "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250301')\n", - "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", - "trade_dates = trade_cal['cal_date'].tolist() # 获取所有交易日期列表\n", - "\n", - "# 使用 HDFStore 存储数据\n", - "all_daily_data = []\n", - "\n", - "# API 调用计数和时间控制变量\n", - "api_call_count = 0\n", - "batch_start_time = time.time()\n", - "\n", - "\n", - "def get_data(trade_date):\n", - " daily_basic_data = pro.daily_basic(ts_code='', trade_date=trade_date)\n", - " if daily_basic_data is not None and not daily_basic_data.empty:\n", - " # 添加交易日期列标识\n", - " daily_basic_data['trade_date'] = trade_date\n", - " daily_basic_data['is_st'] = daily_basic_data.apply(\n", - " lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n", - " )\n", - " time.sleep(0.2)\n", - " # print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n", - " return daily_basic_data\n", - "\n", - "\n", - "# 遍历每个交易日期并获取数据\n", - "with ThreadPoolExecutor(max_workers=2) as executor:\n", - " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", - "\n", - " for future in as_completed(future_to_date):\n", - " trade_date = future_to_date[future] # 获取对应的交易日期\n", - " try:\n", - " result = future.result() # 获取任务执行的结果\n", - " all_daily_data.append(result)\n", - " print(f\"任务 {trade_date} 完成\")\n", - " except Exception as e:\n", - " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", - " # 计数一次 API 调用\n", - " api_call_count += 1\n", - "\n", - " # 每调用 300 次，检查时间是否少于 1 分钟，如果少于则等待剩余时间\n", - " if api_call_count % 150 == 0:\n", - " elapsed = time.time() - batch_start_time\n", - " if elapsed < 60:\n", - " sleep_time = 60 - elapsed\n", - " print(f\"已调用 150 次 API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", - " time.sleep(sleep_time)\n", - " # 重置批次起始时间\n", - " batch_start_time = time.time()\n", - "\n" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "任务 20250331 完成\n", + "任务 20250430 完成\n", + "任务 20250429 完成\n", + "任务 20250428 完成\n", + "任务 20250425 完成\n", + "任务 20250424 完成\n", + "任务 20250423 完成\n", + "任务 20250422 完成\n", + "任务 20250421 完成\n", + "任务 20250418 完成\n", + "任务 20250417 完成\n", + "任务 20250416 完成\n", + "任务 20250415 完成\n", + "任务 20250414 完成\n", + "任务 20250411 完成\n", + "任务 20250410 完成\n", + "任务 20250409 完成\n", + "任务 20250408 完成\n", + "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", @@ -165,14 +129,14 @@ "任务 20250317 完成\n", "任务 20250314 完成\n", "任务 20250313 完成\n", - "任务 20250311 完成\n", "任务 20250312 完成\n", + "任务 20250311 完成\n", "任务 20250310 完成\n", "任务 20250307 完成\n", "任务 20250306 完成\n", "任务 20250305 完成\n", - "任务 20250303 完成\n", "任务 20250304 完成\n", + "任务 20250303 完成\n", "任务 20250227 完成\n", "任务 20250228 完成\n", "任务 20250226 完成\n", @@ -194,14 +158,14 @@ "任务 20250127 完成\n", "任务 20250124 完成\n", "任务 20250123 完成\n", - "任务 20250122 完成\n", "任务 20250121 完成\n", + "任务 20250122 完成\n", "任务 20250120 完成\n", "任务 20250117 完成\n", "任务 20250116 完成\n", "任务 20250115 完成\n", - "任务 20250114 完成\n", "任务 20250113 完成\n", + "任务 20250114 完成\n", "任务 20250110 完成\n", "任务 20250109 完成\n", "任务 20250108 完成\n", @@ -211,27 +175,27 @@ "任务 20250102 完成\n", "任务 20241231 完成\n", "任务 20241230 完成\n", - "任务 20241227 完成\n", - "任务 20241225 完成\n", "任务 20241226 完成\n", + "任务 20241227 完成\n", "任务 20241224 完成\n", + "任务 20241225 完成\n", "任务 20241223 完成\n", "任务 20241220 完成\n", - "任务 20241219 完成\n", "任务 20241218 完成\n", - "任务 20241217 完成\n", + "任务 20241219 完成\n", "任务 20241216 完成\n", + "任务 20241217 完成\n", "任务 20241213 完成\n", "任务 20241212 完成\n", "任务 20241211 完成\n", "任务 20241210 完成\n", "任务 20241209 完成\n", - "任务 20241205 完成\n", "任务 20241206 完成\n", - "任务 20241204 完成\n", + "任务 20241205 完成\n", "任务 20241203 完成\n", - "任务 20241202 完成\n", + "任务 20241204 完成\n", "任务 20241129 完成\n", + "任务 20241202 完成\n", "任务 20241128 完成\n", "任务 20241127 完成\n", "任务 20241126 完成\n", @@ -241,8 +205,8 @@ "任务 20241120 完成\n", "任务 20241119 完成\n", "任务 20241118 完成\n", - "任务 20241115 完成\n", "任务 20241114 完成\n", + "任务 20241115 完成\n", "任务 20241113 完成\n", "任务 20241112 完成\n", "任务 20241111 完成\n", @@ -276,8 +240,8 @@ "任务 20240925 完成\n", "任务 20240924 完成\n", "任务 20240923 完成\n", - "任务 20240920 完成\n", "任务 20240919 完成\n", + "任务 20240920 完成\n", "任务 20240918 完成\n", "任务 20240913 完成\n", "任务 20240912 完成\n", @@ -285,16 +249,16 @@ "任务 20240910 完成\n", "任务 20240909 完成\n", "任务 20240906 完成\n", - "任务 20240905 完成\n", "任务 20240904 完成\n", + "任务 20240905 完成\n", "任务 20240903 完成\n", "任务 20240902 完成\n", "任务 20240830 完成\n", "任务 20240829 完成\n", "任务 20240828 完成\n", "任务 20240827 完成\n", - "任务 20240826 完成\n", "任务 20240823 完成\n", + "任务 20240826 完成\n", "任务 20240822 完成\n", "任务 20240821 完成\n", "任务 20240820 完成\n", @@ -303,36 +267,36 @@ "任务 20240815 完成\n", "任务 20240814 完成\n", "任务 20240813 完成\n", - "任务 20240812 完成\n", "任务 20240809 完成\n", + "任务 20240812 完成\n", "任务 20240808 完成\n", "任务 20240807 完成\n", "任务 20240806 完成\n", "任务 20240805 完成\n", "任务 20240802 完成\n", - "任务 20240801 完成\n", "任务 20240731 完成\n", - "任务 20240729 完成\n", + "任务 20240801 完成\n", "任务 20240730 完成\n", + "任务 20240729 完成\n", "任务 20240726 完成\n", "任务 20240725 完成\n", "任务 20240724 完成\n", - "任务 20240723 完成\n", "任务 20240722 完成\n", - "任务 20240719 完成\n", + "任务 20240723 完成\n", "任务 20240718 完成\n", + "任务 20240719 完成\n", "任务 20240717 完成\n", "任务 20240716 完成\n", - "任务 20240715 完成\n", "任务 20240712 完成\n", + "任务 20240715 完成\n", "任务 20240711 完成\n", "任务 20240710 完成\n", "任务 20240709 完成\n", "任务 20240708 完成\n", "任务 20240705 完成\n", "任务 20240704 完成\n", - "任务 20240703 完成\n", "任务 20240702 完成\n", + "任务 20240703 完成\n", "任务 20240701 完成\n", "任务 20240628 完成\n", "任务 20240627 完成\n", @@ -352,41 +316,41 @@ "任务 20240606 完成\n", "任务 20240605 完成\n", "任务 20240604 完成\n", - "任务 20240603 完成\n", "任务 20240531 完成\n", + "任务 20240603 完成\n", "任务 20240530 完成\n", "任务 20240529 完成\n", "任务 20240528 完成\n", "任务 20240527 完成\n", "任务 20240524 完成\n", - "任务 20240523 完成\n", "任务 20240522 完成\n", + "任务 20240523 完成\n", "任务 20240521 完成\n", "任务 20240520 完成\n", "任务 20240517 完成\n", - "任务 20240515 完成\n", "任务 20240516 完成\n", + "任务 20240515 完成\n", "任务 20240514 完成\n", "任务 20240513 完成\n", "任务 20240510 完成\n", "任务 20240509 完成\n", "任务 20240508 完成\n", - "任务 20240507 完成\n", "任务 20240506 完成\n", - "任务 20240430 完成\n", + "任务 20240507 完成\n", "任务 20240429 完成\n", + "任务 20240430 完成\n", "任务 20240426 完成\n", "任务 20240425 完成\n", - "任务 20240423 完成\n", "任务 20240424 完成\n", + "任务 20240423 完成\n", "任务 20240422 完成\n", "任务 20240419 完成\n", - "任务 20240417 完成\n", "任务 20240418 完成\n", + "任务 20240417 完成\n", "任务 20240416 完成\n", "任务 20240415 完成\n", - "任务 20240412 完成\n", "任务 20240411 完成\n", + "任务 20240412 完成\n", "任务 20240410 完成\n", "任务 20240409 完成\n", "任务 20240408 完成\n", @@ -395,8 +359,8 @@ "任务 20240401 完成\n", "任务 20240329 完成\n", "任务 20240328 完成\n", - "任务 20240327 完成\n", "任务 20240326 完成\n", + "任务 20240327 完成\n", "任务 20240325 完成\n", "任务 20240322 完成\n", "任务 20240321 完成\n", @@ -454,8 +418,8 @@ "任务 20231229 完成\n", "任务 20231228 完成\n", "任务 20231227 完成\n", - "任务 20231226 完成\n", "任务 20231225 完成\n", + "任务 20231226 完成\n", "任务 20231222 完成\n", "任务 20231221 完成\n", "任务 20231220 完成\n", @@ -470,14 +434,14 @@ "任务 20231207 完成\n", "任务 20231206 完成\n", "任务 20231205 完成\n", - "任务 20231204 完成\n", "任务 20231201 完成\n", + "任务 20231204 完成\n", "任务 20231130 完成\n", "任务 20231129 完成\n", "任务 20231128 完成\n", "任务 20231127 完成\n", - "任务 20231123 完成\n", "任务 20231124 完成\n", + "任务 20231123 完成\n", "任务 20231122 完成\n", "任务 20231121 完成\n", "任务 20231120 完成\n", @@ -492,43 +456,43 @@ "任务 20231107 完成\n", "任务 20231106 完成\n", "任务 20231103 完成\n", + "任务 20231101 完成\n", "任务 20231102 完成\n", "任务 20231031 完成\n", - "任务 20231101 完成\n", "任务 20231030 完成\n", "任务 20231027 完成\n", "任务 20231026 完成\n", "任务 20231025 完成\n", "任务 20231024 完成\n", "任务 20231023 完成\n", - "任务 20231019 完成\n", "任务 20231020 完成\n", - "任务 20231017 完成\n", + "任务 20231019 完成\n", "任务 20231018 完成\n", + "任务 20231017 完成\n", "任务 20231016 完成\n", - "任务 20231013 完成\n", "任务 20231012 完成\n", + "任务 20231013 完成\n", "任务 20231011 完成\n", "任务 20231010 完成\n", "任务 20231009 完成\n", "任务 20230928 完成\n", "任务 20230927 完成\n", - "任务 20230926 完成\n", "任务 20230925 完成\n", + "任务 20230926 完成\n", "任务 20230922 完成\n", "任务 20230921 完成\n", "任务 20230920 完成\n", "任务 20230919 完成\n", "任务 20230918 完成\n", "任务 20230915 完成\n", - "任务 20230914 完成\n", "任务 20230913 完成\n", + "任务 20230914 完成\n", "任务 20230912 完成\n", "任务 20230911 完成\n", "任务 20230908 完成\n", "任务 20230907 完成\n", - "任务 20230906 完成\n", "任务 20230905 完成\n", + "任务 20230906 完成\n", "任务 20230904 完成\n", "任务 20230901 完成\n", "任务 20230831 完成\n", @@ -545,11 +509,11 @@ "任务 20230816 完成\n", "任务 20230815 完成\n", "任务 20230814 完成\n", - "任务 20230810 完成\n", "任务 20230811 完成\n", + "任务 20230810 完成\n", "任务 20230809 完成\n", - "任务 20230808 完成\n", "任务 20230807 完成\n", + "任务 20230808 完成\n", "任务 20230804 完成\n", "任务 20230803 完成\n", "任务 20230802 完成\n", @@ -562,11 +526,11 @@ "任务 20230724 完成\n", "任务 20230721 完成\n", "任务 20230720 完成\n", - "任务 20230719 完成\n", "任务 20230718 完成\n", + "任务 20230719 完成\n", "任务 20230717 完成\n", - "任务 20230714 完成\n", "任务 20230713 完成\n", + "任务 20230714 完成\n", "任务 20230712 完成\n", "任务 20230711 完成\n", "任务 20230710 完成\n", @@ -592,15 +556,15 @@ "任务 20230608 完成\n", "任务 20230607 完成\n", "任务 20230606 完成\n", - "任务 20230602 完成\n", "任务 20230605 完成\n", + "任务 20230602 完成\n", "任务 20230601 完成\n", "任务 20230531 完成\n", "任务 20230530 完成\n", - "任务 20230529 完成\n", "任务 20230526 完成\n", - "任务 20230525 完成\n", + "任务 20230529 完成\n", "任务 20230524 完成\n", + "任务 20230525 完成\n", "任务 20230523 完成\n", "任务 20230522 完成\n", "任务 20230519 完成\n", @@ -609,16 +573,16 @@ "任务 20230516 完成\n", "任务 20230515 完成\n", "任务 20230512 完成\n", - "任务 20230511 完成\n", "任务 20230510 完成\n", + "任务 20230511 完成\n", "任务 20230509 完成\n", "任务 20230508 完成\n", "任务 20230505 完成\n", "任务 20230504 完成\n", "任务 20230428 完成\n", + "任务 20230426 完成\n", "任务 20230427 完成\n", "任务 20230425 完成\n", - "任务 20230426 完成\n", "任务 20230424 完成\n", "任务 20230421 完成\n", "任务 20230420 完成\n", @@ -631,14 +595,14 @@ "任务 20230411 完成\n", "任务 20230410 完成\n", "任务 20230407 完成\n", - "任务 20230404 完成\n", "任务 20230406 完成\n", + "任务 20230404 完成\n", "任务 20230403 完成\n", "任务 20230331 完成\n", - "任务 20230330 完成\n", "任务 20230329 完成\n", "任务 20230328 完成\n", "任务 20230327 完成\n", + "任务 20230330 完成\n", "任务 20230324 完成\n", "任务 20230323 完成\n", "任务 20230322 完成\n", @@ -648,10 +612,10 @@ "任务 20230316 完成\n", "任务 20230315 完成\n", "任务 20230314 完成\n", - "任务 20230313 完成\n", "任务 20230310 完成\n", - "任务 20230309 完成\n", + "任务 20230313 完成\n", "任务 20230308 完成\n", + "任务 20230309 完成\n", "任务 20230307 完成\n", "任务 20230306 完成\n", "任务 20230303 完成\n", @@ -661,24 +625,24 @@ "任务 20230227 完成\n", "任务 20230224 完成\n", "任务 20230223 完成\n", - "任务 20230222 完成\n", "任务 20230221 完成\n", + "任务 20230222 完成\n", "任务 20230220 完成\n", "任务 20230217 完成\n", - "任务 20230216 完成\n", "任务 20230215 完成\n", + "任务 20230216 完成\n", "任务 20230214 完成\n", "任务 20230213 完成\n", "任务 20230210 完成\n", "任务 20230209 完成\n", "任务 20230208 完成\n", "任务 20230207 完成\n", - "任务 20230203 完成\n", "任务 20230206 完成\n", + "任务 20230203 完成\n", "任务 20230202 完成\n", "任务 20230201 完成\n", - "任务 20230131 完成\n", "任务 20230130 完成\n", + "任务 20230131 完成\n", "任务 20230120 完成\n", "任务 20230119 完成\n", "任务 20230118 完成\n", @@ -717,8 +681,8 @@ "任务 20221201 完成\n", "任务 20221130 完成\n", "任务 20221129 完成\n", - "任务 20221128 完成\n", "任务 20221125 完成\n", + "任务 20221128 完成\n", "任务 20221124 完成\n", "任务 20221123 完成\n", "任务 20221122 完成\n", @@ -728,8 +692,8 @@ "任务 20221116 完成\n", "任务 20221115 完成\n", "任务 20221114 完成\n", - "任务 20221111 完成\n", "任务 20221110 完成\n", + "任务 20221111 完成\n", "任务 20221109 完成\n", "任务 20221108 完成\n", "任务 20221107 完成\n", @@ -742,10 +706,10 @@ "任务 20221027 完成\n", "任务 20221026 完成\n", "任务 20221025 完成\n", - "任务 20221024 完成\n", "任务 20221021 完成\n", - "任务 20221020 完成\n", + "任务 20221024 完成\n", "任务 20221019 完成\n", + "任务 20221020 完成\n", "任务 20221018 完成\n", "任务 20221017 完成\n", "任务 20221014 完成\n", @@ -757,8 +721,8 @@ "任务 20220929 完成\n", "任务 20220928 完成\n", "任务 20220927 完成\n", - "任务 20220926 完成\n", "任务 20220923 完成\n", + "任务 20220926 完成\n", "任务 20220922 完成\n", "任务 20220921 完成\n", "任务 20220920 完成\n", @@ -768,8 +732,8 @@ "任务 20220914 完成\n", "任务 20220913 完成\n", "任务 20220909 完成\n", - "任务 20220908 完成\n", "任务 20220907 完成\n", + "任务 20220908 完成\n", "任务 20220906 完成\n", "任务 20220905 完成\n", "任务 20220902 完成\n", @@ -790,24 +754,24 @@ "任务 20220812 完成\n", "任务 20220811 完成\n", "任务 20220810 完成\n", - "任务 20220809 完成\n", "任务 20220808 完成\n", + "任务 20220809 完成\n", "任务 20220805 完成\n", "任务 20220804 完成\n", "任务 20220803 完成\n", "任务 20220802 完成\n", - "任务 20220801 完成\n", "任务 20220729 完成\n", + "任务 20220801 完成\n", "任务 20220728 完成\n", "任务 20220727 完成\n", - "任务 20220726 完成\n", "任务 20220725 完成\n", + "任务 20220726 完成\n", "任务 20220722 完成\n", "任务 20220721 完成\n", "任务 20220720 完成\n", "任务 20220719 完成\n", - "任务 20220718 完成\n", "任务 20220715 完成\n", + "任务 20220718 完成\n", "任务 20220714 完成\n", "任务 20220713 完成\n", "任务 20220712 完成\n", @@ -820,8 +784,8 @@ "任务 20220701 完成\n", "任务 20220630 完成\n", "任务 20220629 完成\n", - "任务 20220628 完成\n", "任务 20220627 完成\n", + "任务 20220628 完成\n", "任务 20220624 完成\n", "任务 20220623 完成\n", "任务 20220622 完成\n", @@ -843,20 +807,20 @@ "任务 20220530 完成\n", "任务 20220527 完成\n", "任务 20220526 完成\n", - "任务 20220525 完成\n", "任务 20220524 完成\n", - "任务 20220523 完成\n", + "任务 20220525 完成\n", "任务 20220520 完成\n", + "任务 20220523 完成\n", "任务 20220519 完成\n", "任务 20220518 完成\n", "任务 20220517 完成\n", "任务 20220516 完成\n", - "任务 20220513 完成\n", "任务 20220512 完成\n", - "任务 20220511 完成\n", + "任务 20220513 完成\n", "任务 20220510 完成\n", - "任务 20220509 完成\n", + "任务 20220511 完成\n", "任务 20220506 完成\n", + "任务 20220509 完成\n", "任务 20220505 完成\n", "任务 20220429 完成\n", "任务 20220428 完成\n", @@ -898,8 +862,8 @@ "任务 20220307 完成\n", "任务 20220304 完成\n", "任务 20220303 完成\n", - "任务 20220302 完成\n", "任务 20220301 完成\n", + "任务 20220302 完成\n", "任务 20220228 完成\n", "任务 20220225 完成\n", "任务 20220224 完成\n", @@ -925,16 +889,16 @@ "任务 20220120 完成\n", "任务 20220119 完成\n", "任务 20220118 完成\n", - "任务 20220117 完成\n", "任务 20220114 完成\n", - "任务 20220113 完成\n", + "任务 20220117 完成\n", "任务 20220112 完成\n", + "任务 20220113 完成\n", "任务 20220111 完成\n", "任务 20220110 完成\n", "任务 20220107 完成\n", "任务 20220106 完成\n", - "任务 20220105 完成\n", "任务 20220104 完成\n", + "任务 20220105 完成\n", "任务 20211231 完成\n", "任务 20211230 完成\n", "任务 20211229 完成\n", @@ -957,8 +921,8 @@ "任务 20211206 完成\n", "任务 20211203 完成\n", "任务 20211202 完成\n", - "任务 20211201 完成\n", "任务 20211130 完成\n", + "任务 20211201 完成\n", "任务 20211129 完成\n", "任务 20211126 完成\n", "任务 20211125 完成\n", @@ -971,12 +935,12 @@ "任务 20211116 完成\n", "任务 20211115 完成\n", "任务 20211112 完成\n", - "任务 20211111 完成\n", "任务 20211110 完成\n", + "任务 20211111 完成\n", "任务 20211109 完成\n", "任务 20211108 完成\n", - "任务 20211105 完成\n", "任务 20211104 完成\n", + "任务 20211105 完成\n", "任务 20211103 完成\n", "任务 20211102 完成\n", "任务 20211101 完成\n", @@ -995,18 +959,18 @@ "任务 20211013 完成\n", "任务 20211012 完成\n", "任务 20211011 完成\n", - "任务 20211008 完成\n", "任务 20210930 完成\n", + "任务 20211008 完成\n", "任务 20210929 完成\n", "任务 20210928 完成\n", "任务 20210927 完成\n", "任务 20210924 完成\n", "任务 20210923 完成\n", "任务 20210922 完成\n", - "任务 20210917 完成\n", "任务 20210916 完成\n", - "任务 20210915 完成\n", + "任务 20210917 完成\n", "任务 20210914 完成\n", + "任务 20210915 完成\n", "任务 20210913 完成\n", "任务 20210910 完成\n", "任务 20210909 完成\n", @@ -1045,8 +1009,8 @@ "任务 20210726 完成\n", "任务 20210723 完成\n", "任务 20210722 完成\n", - "任务 20210721 完成\n", "任务 20210720 完成\n", + "任务 20210721 完成\n", "任务 20210719 完成\n", "任务 20210716 完成\n", "任务 20210715 完成\n", @@ -1077,12 +1041,12 @@ "任务 20210609 完成\n", "任务 20210608 完成\n", "任务 20210607 完成\n", - "任务 20210604 完成\n", "任务 20210603 完成\n", + "任务 20210604 完成\n", "任务 20210602 完成\n", "任务 20210601 完成\n", - "任务 20210531 完成\n", "任务 20210528 完成\n", + "任务 20210531 完成\n", "任务 20210526 完成\n", "任务 20210527 完成\n", "任务 20210525 完成\n", @@ -1095,8 +1059,8 @@ "任务 20210514 完成\n", "任务 20210513 完成\n", "任务 20210512 完成\n", - "任务 20210511 完成\n", "任务 20210510 完成\n", + "任务 20210511 完成\n", "任务 20210507 完成\n", "任务 20210506 完成\n", "任务 20210430 完成\n", @@ -1105,10 +1069,10 @@ "任务 20210427 完成\n", "任务 20210426 完成\n", "任务 20210423 完成\n", - "任务 20210422 完成\n", "任务 20210421 完成\n", - "任务 20210420 完成\n", + "任务 20210422 完成\n", "任务 20210419 完成\n", + "任务 20210420 完成\n", "任务 20210416 完成\n", "任务 20210415 完成\n", "任务 20210414 完成\n", @@ -1134,8 +1098,8 @@ "任务 20210316 完成\n", "任务 20210315 完成\n", "任务 20210312 完成\n", - "任务 20210311 完成\n", "任务 20210310 完成\n", + "任务 20210311 完成\n", "任务 20210309 完成\n", "任务 20210308 完成\n", "任务 20210305 完成\n", @@ -1159,8 +1123,8 @@ "任务 20210202 完成\n", "任务 20210201 完成\n", "任务 20210129 完成\n", - "任务 20210128 完成\n", "任务 20210127 完成\n", + "任务 20210128 完成\n", "任务 20210126 完成\n", "任务 20210125 完成\n", "任务 20210122 完成\n", @@ -1168,8 +1132,8 @@ "任务 20210120 完成\n", "任务 20210119 完成\n", "任务 20210118 完成\n", - "任务 20210115 完成\n", "任务 20210114 完成\n", + "任务 20210115 完成\n", "任务 20210113 完成\n", "任务 20210112 完成\n", "任务 20210111 完成\n", @@ -1202,14 +1166,14 @@ "任务 20201202 完成\n", "任务 20201201 完成\n", "任务 20201130 完成\n", - "任务 20201127 完成\n", "任务 20201126 完成\n", + "任务 20201127 完成\n", "任务 20201125 完成\n", "任务 20201124 完成\n", - "任务 20201123 完成\n", "任务 20201120 完成\n", - "任务 20201119 完成\n", + "任务 20201123 完成\n", "任务 20201118 完成\n", + "任务 20201119 完成\n", "任务 20201117 完成\n", "任务 20201116 完成\n", "任务 20201113 完成\n", @@ -1227,8 +1191,8 @@ "任务 20201028 完成\n", "任务 20201027 完成\n", "任务 20201026 完成\n", - "任务 20201023 完成\n", "任务 20201022 完成\n", + "任务 20201023 完成\n", "任务 20201021 完成\n", "任务 20201020 完成\n", "任务 20201019 完成\n", @@ -1244,16 +1208,16 @@ "任务 20200925 完成\n", "任务 20200924 完成\n", "任务 20200923 完成\n", - "任务 20200922 完成\n", "任务 20200921 完成\n", + "任务 20200922 完成\n", "任务 20200918 完成\n", "任务 20200917 完成\n", "任务 20200916 完成\n", "任务 20200915 完成\n", "任务 20200914 完成\n", "任务 20200911 完成\n", - "任务 20200910 完成\n", "任务 20200909 完成\n", + "任务 20200910 完成\n", "任务 20200908 完成\n", "任务 20200907 完成\n", "任务 20200904 完成\n", @@ -1264,22 +1228,22 @@ "任务 20200828 完成\n", "任务 20200827 完成\n", "任务 20200826 完成\n", - "任务 20200825 完成\n", "任务 20200824 完成\n", - "任务 20200821 完成\n", + "任务 20200825 完成\n", "任务 20200820 完成\n", + "任务 20200821 完成\n", "任务 20200819 完成\n", "任务 20200818 完成\n", "任务 20200817 完成\n", "任务 20200814 完成\n", - "任务 20200813 完成\n", "任务 20200812 完成\n", - "任务 20200811 完成\n", + "任务 20200813 完成\n", "任务 20200810 完成\n", + "任务 20200811 完成\n", "任务 20200807 完成\n", "任务 20200806 完成\n", - "任务 20200805 完成\n", "任务 20200804 完成\n", + "任务 20200805 完成\n", "任务 20200803 完成\n", "任务 20200731 完成\n", "任务 20200730 完成\n", @@ -1306,20 +1270,20 @@ "任务 20200701 完成\n", "任务 20200630 完成\n", "任务 20200629 完成\n", - "任务 20200624 完成\n", "任务 20200623 完成\n", + "任务 20200624 完成\n", "任务 20200622 完成\n", "任务 20200619 完成\n", "任务 20200618 完成\n", "任务 20200617 完成\n", "任务 20200616 完成\n", "任务 20200615 完成\n", - "任务 20200612 完成\n", "任务 20200611 完成\n", + "任务 20200612 完成\n", "任务 20200610 完成\n", "任务 20200609 完成\n", - "任务 20200608 完成\n", "任务 20200605 完成\n", + "任务 20200608 完成\n", "任务 20200604 完成\n", "任务 20200603 完成\n", "任务 20200602 完成\n", @@ -1359,8 +1323,8 @@ "任务 20200410 完成\n", "任务 20200409 完成\n", "任务 20200408 完成\n", - "任务 20200407 完成\n", "任务 20200403 完成\n", + "任务 20200407 完成\n", "任务 20200402 完成\n", "任务 20200401 完成\n", "任务 20200331 完成\n", @@ -1373,8 +1337,8 @@ "任务 20200320 完成\n", "任务 20200319 完成\n", "任务 20200318 完成\n", - "任务 20200317 完成\n", "任务 20200316 完成\n", + "任务 20200317 完成\n", "任务 20200313 完成\n", "任务 20200312 完成\n", "任务 20200311 完成\n", @@ -1387,10 +1351,10 @@ "任务 20200302 完成\n", "任务 20200228 完成\n", "任务 20200227 完成\n", - "任务 20200226 完成\n", "任务 20200225 完成\n", - "任务 20200224 完成\n", + "任务 20200226 完成\n", "任务 20200221 完成\n", + "任务 20200224 完成\n", "任务 20200220 完成\n", "任务 20200219 完成\n", "任务 20200218 完成\n", @@ -1399,24 +1363,24 @@ "任务 20200213 完成\n", "任务 20200212 完成\n", "任务 20200211 完成\n", - "任务 20200210 完成\n", "任务 20200207 完成\n", + "任务 20200210 完成\n", "任务 20200206 完成\n", "任务 20200205 完成\n", - "任务 20200204 完成\n", "任务 20200203 完成\n", + "任务 20200204 完成\n", "任务 20200123 完成\n", "任务 20200122 完成\n", "任务 20200121 完成\n", "任务 20200120 完成\n", - "任务 20200117 完成\n", "任务 20200116 完成\n", + "任务 20200117 完成\n", "任务 20200115 完成\n", "任务 20200114 完成\n", "任务 20200113 完成\n", "任务 20200110 完成\n", - "任务 20200109 完成\n", "任务 20200108 完成\n", + "任务 20200109 完成\n", "任务 20200107 完成\n", "任务 20200106 完成\n", "任务 20200103 完成\n", @@ -1430,8 +1394,8 @@ "任务 20191223 完成\n", "任务 20191220 完成\n", "任务 20191219 完成\n", - "任务 20191218 完成\n", "任务 20191217 完成\n", + "任务 20191218 完成\n", "任务 20191216 完成\n", "任务 20191213 完成\n", "任务 20191212 完成\n", @@ -1466,8 +1430,8 @@ "任务 20191101 完成\n", "任务 20191031 完成\n", "任务 20191030 完成\n", - "任务 20191029 完成\n", "任务 20191028 完成\n", + "任务 20191029 完成\n", "任务 20191025 完成\n", "任务 20191024 完成\n", "任务 20191023 完成\n", @@ -1476,18 +1440,18 @@ "任务 20191018 完成\n", "任务 20191017 完成\n", "任务 20191016 完成\n", - "任务 20191015 完成\n", "任务 20191014 完成\n", + "任务 20191015 完成\n", "任务 20191011 完成\n", "任务 20191010 完成\n", "任务 20191009 完成\n", "任务 20191008 完成\n", "任务 20190930 完成\n", "任务 20190927 完成\n", - "任务 20190926 完成\n", "任务 20190925 完成\n", - "任务 20190924 完成\n", + "任务 20190926 完成\n", "任务 20190923 完成\n", + "任务 20190924 完成\n", "任务 20190920 完成\n", "任务 20190919 完成\n", "任务 20190918 完成\n", @@ -1504,8 +1468,8 @@ "任务 20190902 完成\n", "任务 20190830 完成\n", "任务 20190829 完成\n", - "任务 20190828 完成\n", "任务 20190827 完成\n", + "任务 20190828 完成\n", "任务 20190826 完成\n", "任务 20190823 完成\n", "任务 20190822 完成\n", @@ -1530,14 +1494,14 @@ "任务 20190726 完成\n", "任务 20190725 完成\n", "任务 20190724 完成\n", - "任务 20190723 完成\n", "任务 20190722 完成\n", - "任务 20190719 完成\n", + "任务 20190723 完成\n", "任务 20190718 完成\n", + "任务 20190719 完成\n", "任务 20190717 完成\n", "任务 20190716 完成\n", - "任务 20190715 完成\n", "任务 20190712 完成\n", + "任务 20190715 完成\n", "任务 20190711 完成\n", "任务 20190710 完成\n", "任务 20190709 完成\n", @@ -1561,8 +1525,8 @@ "任务 20190613 完成\n", "任务 20190612 完成\n", "任务 20190611 完成\n", - "任务 20190610 完成\n", "任务 20190606 完成\n", + "任务 20190610 完成\n", "任务 20190605 完成\n", "任务 20190604 完成\n", "任务 20190603 完成\n", @@ -1589,8 +1553,8 @@ "任务 20190430 完成\n", "任务 20190429 完成\n", "任务 20190426 完成\n", - "任务 20190425 完成\n", "任务 20190424 完成\n", + "任务 20190425 完成\n", "任务 20190423 完成\n", "任务 20190422 完成\n", "任务 20190419 完成\n", @@ -1637,16 +1601,16 @@ "任务 20190220 完成\n", "任务 20190219 完成\n", "任务 20190218 完成\n", - "任务 20190215 完成\n", "任务 20190214 完成\n", + "任务 20190215 完成\n", "任务 20190213 完成\n", "任务 20190212 完成\n", "任务 20190211 完成\n", "任务 20190201 完成\n", "任务 20190131 完成\n", "任务 20190130 完成\n", - "任务 20190129 完成\n", "任务 20190128 完成\n", + "任务 20190129 完成\n", "任务 20190125 完成\n", "任务 20190124 完成\n", "任务 20190123 完成\n", @@ -1655,14 +1619,14 @@ "任务 20190118 完成\n", "任务 20190117 完成\n", "任务 20190116 完成\n", - "任务 20190115 完成\n", "任务 20190114 完成\n", + "任务 20190115 完成\n", "任务 20190111 完成\n", "任务 20190110 完成\n", "任务 20190109 完成\n", "任务 20190108 完成\n", - "任务 20190107 完成\n", "任务 20190104 完成\n", + "任务 20190107 完成\n", "任务 20190103 完成\n", "任务 20190102 完成\n", "任务 20181228 完成\n", @@ -1678,8 +1642,8 @@ "任务 20181214 完成\n", "任务 20181213 完成\n", "任务 20181212 完成\n", - "任务 20181211 完成\n", "任务 20181210 完成\n", + "任务 20181211 完成\n", "任务 20181207 完成\n", "任务 20181206 完成\n", "任务 20181205 完成\n", @@ -1696,14 +1660,14 @@ "任务 20181120 完成\n", "任务 20181119 完成\n", "任务 20181116 完成\n", - "任务 20181115 完成\n", "任务 20181114 完成\n", + "任务 20181115 完成\n", "任务 20181113 完成\n", "任务 20181112 完成\n", - "任务 20181109 完成\n", "任务 20181108 完成\n", - "任务 20181106 完成\n", + "任务 20181109 完成\n", "任务 20181107 完成\n", + "任务 20181106 完成\n", "任务 20181105 完成\n", "任务 20181102 完成\n", "任务 20181101 完成\n", @@ -1717,8 +1681,8 @@ "任务 20181022 完成\n", "任务 20181019 完成\n", "任务 20181018 完成\n", - "任务 20181017 完成\n", "任务 20181016 完成\n", + "任务 20181017 完成\n", "任务 20181015 完成\n", "任务 20181012 完成\n", "任务 20181011 完成\n", @@ -1757,16 +1721,16 @@ "任务 20180817 完成\n", "任务 20180816 完成\n", "任务 20180815 完成\n", - "任务 20180814 完成\n", "任务 20180813 完成\n", + "任务 20180814 完成\n", "任务 20180810 完成\n", "任务 20180809 完成\n", "任务 20180808 完成\n", "任务 20180807 完成\n", "任务 20180806 完成\n", "任务 20180803 完成\n", - "任务 20180802 完成\n", "任务 20180801 完成\n", + "任务 20180802 完成\n", "任务 20180731 完成\n", "任务 20180730 完成\n", "任务 20180727 完成\n", @@ -1807,8 +1771,8 @@ "任务 20180607 完成\n", "任务 20180606 完成\n", "任务 20180605 完成\n", - "任务 20180604 完成\n", "任务 20180601 完成\n", + "任务 20180604 完成\n", "任务 20180531 完成\n", "任务 20180530 完成\n", "任务 20180529 完成\n", @@ -1836,8 +1800,8 @@ "任务 20180425 完成\n", "任务 20180424 完成\n", "任务 20180423 完成\n", - "任务 20180420 完成\n", "任务 20180419 完成\n", + "任务 20180420 完成\n", "任务 20180418 完成\n", "任务 20180417 完成\n", "任务 20180416 完成\n", @@ -1847,12 +1811,12 @@ "任务 20180410 完成\n", "任务 20180409 完成\n", "任务 20180404 完成\n", - "任务 20180403 完成\n", "任务 20180402 完成\n", + "任务 20180403 完成\n", "任务 20180330 完成\n", "任务 20180329 完成\n", - "任务 20180328 完成\n", "任务 20180327 完成\n", + "任务 20180328 完成\n", "任务 20180326 完成\n", "任务 20180323 完成\n", "任务 20180322 完成\n", @@ -1873,17 +1837,17 @@ "任务 20180301 完成\n", "任务 20180228 完成\n", "任务 20180227 完成\n", - "任务 20180226 完成\n", "任务 20180223 完成\n", + "任务 20180226 完成\n", "任务 20180222 完成\n", "任务 20180214 完成\n", "任务 20180213 完成\n", "任务 20180212 完成\n", "任务 20180209 完成\n", - "任务 20180207 完成\n", "任务 20180208 完成\n", - "任务 20180205 完成\n", + "任务 20180207 完成\n", "任务 20180206 完成\n", + "任务 20180205 完成\n", "任务 20180202 完成\n", "任务 20180201 完成\n", "任务 20180131 完成\n", @@ -1902,8 +1866,8 @@ "任务 20180112 完成\n", "任务 20180111 完成\n", "任务 20180110 完成\n", - "任务 20180109 完成\n", "任务 20180108 完成\n", + "任务 20180109 完成\n", "任务 20180105 完成\n", "任务 20180104 完成\n", "任务 20180103 完成\n", @@ -1942,8 +1906,8 @@ "任务 20171116 完成\n", "任务 20171115 完成\n", "任务 20171114 完成\n", - "任务 20171110 完成\n", "任务 20171113 完成\n", + "任务 20171110 完成\n", "任务 20171109 完成\n", "任务 20171108 完成\n", "任务 20171107 完成\n", @@ -1972,8 +1936,8 @@ "任务 20170928 完成\n", "任务 20170927 完成\n", "任务 20170926 完成\n", - "任务 20170922 完成\n", "任务 20170925 完成\n", + "任务 20170922 完成\n", "任务 20170921 完成\n", "任务 20170920 完成\n", "任务 20170919 完成\n", @@ -1986,18 +1950,18 @@ "任务 20170908 完成\n", "任务 20170907 完成\n", "任务 20170906 完成\n", - "任务 20170905 完成\n", "任务 20170904 完成\n", - "任务 20170901 完成\n", + "任务 20170905 完成\n", "任务 20170831 完成\n", + "任务 20170901 完成\n", "任务 20170830 完成\n", "任务 20170829 完成\n", "任务 20170828 完成\n", "任务 20170825 完成\n", - "任务 20170823 完成\n", "任务 20170824 完成\n", - "任务 20170821 完成\n", + "任务 20170823 完成\n", "任务 20170822 完成\n", + "任务 20170821 完成\n", "任务 20170818 完成\n", "任务 20170817 完成\n", "任务 20170816 完成\n", @@ -2036,12 +2000,12 @@ "任务 20170630 完成\n", "任务 20170629 完成\n", "任务 20170628 完成\n", - "任务 20170626 完成\n", "任务 20170627 完成\n", + "任务 20170626 完成\n", "任务 20170623 完成\n", "任务 20170622 完成\n", - "任务 20170621 完成\n", "任务 20170620 完成\n", + "任务 20170621 完成\n", "任务 20170619 完成\n", "任务 20170616 完成\n", "任务 20170615 完成\n", @@ -2084,14 +2048,14 @@ "任务 20170420 完成\n", "任务 20170419 完成\n", "任务 20170418 完成\n", - "任务 20170417 完成\n", "任务 20170414 完成\n", - "任务 20170413 完成\n", + "任务 20170417 完成\n", "任务 20170412 完成\n", + "任务 20170413 完成\n", "任务 20170411 完成\n", "任务 20170410 完成\n", - "任务 20170407 完成\n", "任务 20170406 完成\n", + "任务 20170407 完成\n", "任务 20170405 完成\n", "任务 20170331 完成\n", "任务 20170330 完成\n", @@ -2106,10 +2070,10 @@ "任务 20170317 完成\n", "任务 20170316 完成\n", "任务 20170315 完成\n", - "任务 20170314 完成\n", "任务 20170313 完成\n", - "任务 20170309 完成\n", + "任务 20170314 完成\n", "任务 20170310 完成\n", + "任务 20170309 完成\n", "任务 20170308 完成\n", "任务 20170307 完成\n", "任务 20170306 完成\n", @@ -2130,12 +2094,12 @@ "任务 20170213 完成\n", "任务 20170210 完成\n", "任务 20170209 完成\n", - "任务 20170208 完成\n", "任务 20170207 完成\n", + "任务 20170208 完成\n", "任务 20170206 完成\n", "任务 20170203 完成\n", - "任务 20170126 完成\n", "任务 20170125 完成\n", + "任务 20170126 完成\n", "任务 20170124 完成\n", "任务 20170123 完成\n", "任务 20170120 完成\n", @@ -2155,10 +2119,68 @@ ] } ], - "execution_count": 3 + "source": [ + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "\n", + "# 获取交易日历\n", + "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250501')\n", + "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", + "trade_dates = trade_cal['cal_date'].tolist() # 获取所有交易日期列表\n", + "\n", + "# 使用 HDFStore 存储数据\n", + "all_daily_data = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "\n", + "def get_data(trade_date):\n", + " daily_basic_data = pro.daily_basic(ts_code='', trade_date=trade_date)\n", + " if daily_basic_data is not None and not daily_basic_data.empty:\n", + " # 添加交易日期列标识\n", + " daily_basic_data['trade_date'] = trade_date\n", + " daily_basic_data['is_st'] = daily_basic_data.apply(\n", + " lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n", + " )\n", + " time.sleep(0.2)\n", + " # print(f\"成功获取并保存 {trade_date} 的每日基础数据\")\n", + " return daily_basic_data\n", + "\n", + "\n", + "# 遍历每个交易日期并获取数据\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " trade_date = future_to_date[future] # 获取对应的交易日期\n", + " try:\n", + " result = future.result() # 获取任务执行的结果\n", + " all_daily_data.append(result)\n", + " print(f\"任务 {trade_date} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用 300 次，检查时间是否少于 1 分钟，如果少于则等待剩余时间\n", + " if api_call_count % 150 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用 150 次 API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n" + ] }, { "cell_type": "code", + "execution_count": 4, "id": "97fdf8be-a86c-4404-bf0c-701f002cd81c", "metadata": { "ExecuteTime": { @@ -2166,75 +2188,75 @@ "start_time": "2025-03-02T08:33:16.033912Z" } }, - "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 002977.SZ 20250227 27.20 2.2311 3.0411 \n", - "1 688065.SH 20250227 48.17 0.7081 1.4224 \n", - "2 002563.SZ 20250227 6.30 0.7054 2.2058 \n", - "3 300044.SZ 20250227 7.29 11.8529 13.2447 \n", - "4 603219.SH 20250227 17.96 5.9145 28.1559 \n", + "0 603848.SH 20250430 14.36 0.5401 4.6897 \n", + "1 300290.SZ 20250430 16.30 2.8540 3.5686 \n", + "2 603877.SH 20250430 15.90 0.3794 1.2707 \n", + "3 834639.BJ 20250430 8.37 6.1158 7.8866 \n", + "4 000909.SZ 20250430 5.72 0.6104 1.0424 \n", "... ... ... ... ... ... \n", - "8372851 600708.SH 20170103 9.03 0.7694 1.0169 \n", - "8372852 600712.SH 20170103 10.29 0.5859 0.8028 \n", - "8372853 001872.SZ 20170103 19.33 1.0970 5.4258 \n", - "8372854 001914.SZ 20170103 12.37 3.2627 6.6991 \n", - "8372855 302132.SZ 20170103 23.28 0.4912 1.5149 \n", + "8594006 600708.SH 20170103 9.03 0.7694 1.0169 \n", + "8594007 600712.SH 20170103 10.29 0.5859 0.8028 \n", + "8594008 001872.SZ 20170103 19.33 1.0970 5.4258 \n", + "8594009 001914.SZ 20170103 12.37 3.2627 6.6991 \n", + "8594010 302132.SZ 20170103 23.28 0.4912 1.5149 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 0.98 65.2824 275.8084 3.0077 19.5374 20.5874 0.6618 \n", - "1 0.79 76.6697 70.6389 2.4679 13.2919 10.1678 NaN \n", - "2 1.53 15.1340 16.2489 1.4830 1.2425 1.1986 4.7619 \n", - "3 0.79 NaN NaN 9.0179 12.4251 19.5540 0.0000 \n", - "4 1.76 38.6030 47.7249 6.4916 3.7441 3.8039 1.6629 \n", - "... ... ... ... ... ... ... ... \n", - "8372851 0.85 23.3367 22.2458 1.4847 0.9613 0.9248 1.1074 \n", - "8372852 0.67 202.4855 287.1454 5.1852 2.3682 2.5386 0.1555 \n", - "8372853 0.77 23.6158 23.1883 2.7052 6.6556 6.5584 2.1211 \n", - "8372854 1.02 20.5631 15.1595 2.1186 1.4950 1.2600 0.4042 \n", - "8372855 0.74 91.3908 84.6980 6.9391 8.9531 8.8570 0.2291 \n", + " volume_ratio pe pe_ttm pb ps ps_ttm \\\n", + "0 1.31 23.3421 25.6176 2.3433 3.7254 3.8065 \n", + "1 1.00 NaN NaN 13.1076 13.5867 13.5756 \n", + "2 0.98 29.1494 33.6975 1.6522 1.1075 1.1304 \n", + "3 0.87 70.0984 215.1863 2.0171 0.8405 0.8329 \n", + "4 0.55 NaN NaN 2.3539 7.7727 8.2925 \n", + "... ... ... ... ... ... ... \n", + "8594006 0.85 23.3367 22.2458 1.4847 0.9613 0.9248 \n", + "8594007 0.67 202.4855 287.1454 5.1852 2.3682 2.5386 \n", + "8594008 0.77 23.6158 23.1883 2.7052 6.6556 6.5584 \n", + "8594009 1.02 20.5631 15.1595 2.1186 1.4950 1.2600 \n", + "8594010 0.74 91.3908 84.6980 6.9391 8.9531 8.8570 \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 0.6618 12012.0000 6662.0170 4887.5170 3.267264e+05 \n", - "1 NaN 58337.8039 58337.8039 29040.9774 2.810132e+06 \n", - "2 4.7619 269409.0160 220844.2340 70621.6215 1.697277e+06 \n", - "3 NaN 76386.9228 76377.3438 68351.0115 5.568607e+05 \n", - "4 1.6629 56140.0000 56140.0000 11792.9916 1.008274e+06 \n", - "... ... ... ... ... ... \n", - "8372851 1.1074 131871.9966 75088.9215 56812.2811 1.190804e+06 \n", - "8372852 0.1555 54465.5360 53795.9475 39266.3119 5.604504e+05 \n", - "8372853 2.1211 64476.3730 46486.6050 9398.8050 1.246328e+06 \n", - "8372854 0.4042 66696.1416 66678.0666 32475.1786 8.250313e+05 \n", - "8372855 0.2291 39384.0333 30419.3588 9862.3809 9.168603e+05 \n", + " dv_ratio dv_ttm total_share float_share free_share total_mv \\\n", + "0 2.0904 2.0904 40391.1511 40240.6511 4634.6511 5.800169e+05 \n", + "1 0.0000 NaN 63973.2569 63922.1969 51122.1969 1.042764e+06 \n", + "2 3.7471 3.7471 47382.5333 46932.3226 14014.3219 7.533823e+05 \n", + "3 NaN NaN 20160.0000 11721.5883 9089.7537 1.687392e+05 \n", + "4 0.0000 NaN 43771.4245 43771.0570 25634.2299 2.503725e+05 \n", + "... ... ... ... ... ... ... \n", + "8594006 1.1074 1.1074 131871.9966 75088.9215 56812.2811 1.190804e+06 \n", + "8594007 0.1555 0.1555 54465.5360 53795.9475 39266.3119 5.604504e+05 \n", + "8594008 2.1211 2.1211 64476.3730 46486.6050 9398.8050 1.246328e+06 \n", + "8594009 0.4042 0.4042 66696.1416 66678.0666 32475.1786 8.250313e+05 \n", + "8594010 0.2291 0.2291 39384.0333 30419.3588 9862.3809 9.168603e+05 \n", "\n", " circ_mv is_st \n", - "0 1.812069e+05 False \n", - "1 2.810132e+06 False \n", - "2 1.391319e+06 False \n", - "3 5.567908e+05 False \n", - "4 1.008274e+06 False \n", + "0 5.778557e+05 False \n", + "1 1.041932e+06 False \n", + "2 7.462239e+05 False \n", + "3 9.810969e+04 False \n", + "4 2.503704e+05 True \n", "... ... ... \n", - "8372851 6.780530e+05 False \n", - "8372852 5.535603e+05 False \n", - "8372853 8.985861e+05 False \n", - "8372854 8.248077e+05 False \n", - "8372855 7.081627e+05 False \n", + "8594006 6.780530e+05 False \n", + "8594007 5.535603e+05 False \n", + "8594008 8.985861e+05 False \n", + "8594009 8.248077e+05 False \n", + "8594010 7.081627e+05 False \n", "\n", - "[8372856 rows x 19 columns]\n" + "[8594011 rows x 19 columns]\n" ] } ], - "execution_count": 4 + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "print(all_daily_data_df)" + ] }, { "cell_type": "code", + "execution_count": 5, "id": "2b58a8bf-ffc5-4482-8e4d-bf24da9277de", "metadata": { "ExecuteTime": { @@ -2242,12 +2264,6 @@ "start_time": "2025-03-02T08:33:16.498221Z" } }, - "source": [ - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf('../../data/daily_basic.h5', key='daily_basic', mode='w', format='table', data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -2257,10 +2273,16 @@ ] } ], - "execution_count": 5 + "source": [ + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf('../../data/daily_basic.h5', key='daily_basic', mode='w', format='table', data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] }, { "cell_type": "code", + "execution_count": null, "id": "57ac1d86-5ce8-4bc9-812f-b45dcc2a3b4c", "metadata": { "ExecuteTime": { @@ -2268,14 +2290,13 @@ "start_time": "2025-03-02T08:34:49.775512Z" } }, - "source": [], "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, @@ -2289,7 +2310,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.19" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/main/data/daily_data.py b/main/data/daily_data.py deleted file mode 100644 index 9545597..0000000 --- a/main/data/daily_data.py +++ /dev/null @@ -1,11 +0,0 @@ -import tushare as ts -ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f') -pro = ts.pro_api() - -import pandas as pd -import time - -# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv） -df = ts.pro_bar(ts_code='000001.SZ', adj='hfq', start_date='20180101') -print(df) - diff --git a/main/data/finance.ipynb b/main/data/finance.ipynb new file mode 100644 index 0000000..9f7c404 --- /dev/null +++ b/main/data/finance.ipynb @@ -0,0 +1,11717 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T15:46:10.320268Z", + "start_time": "2025-02-10T15:46:10.310270Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello world\n" + ] + } + ], + "source": [ + "print('hello world')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f294ba92-512a-48e6-bbaa-e19401c691ba", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T15:46:11.019636Z", + "start_time": "2025-02-10T15:46:10.330271Z" + } + }, + "outputs": [], + "source": [ + "import tushare as ts\n", + "import pandas as pd\n", + "import time\n", + "\n", + "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", + "pro = ts.pro_api()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f403cc963e1d39b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T15:47:42.715615Z", + "start_time": "2025-02-10T15:46:11.085042Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "成功获取 000001.SZ 的数据\n", + "成功获取 000002.SZ 的数据\n", + "成功获取 000004.SZ 的数据\n", + "成功获取 000006.SZ 的数据\n", + "成功获取 000007.SZ 的数据\n", + "成功获取 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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", + " all_fina_indicator = pd.concat(cashflow_list, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有日线数据已保存到 cashflow.h5\n" + ] + } + ], + "source": [ + "\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", + "cashflow_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", + "# 循环遍历每个股票代码并获取数据\n", + "for idx, row in stocks_df.iterrows():\n", + " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", + " try:\n", + " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", + " cashflow = pro.cashflow(ts_code=ts_code)\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if not cashflow.empty:\n", + " cashflow['ts_code'] = ts_code\n", + " cashflow_list.append(cashflow)\n", + " print(f\"成功获取 {ts_code} 的数据\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 300 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + "# 合并所有获取到的日线数据\n", + "if cashflow_list:\n", + " all_cashflow = pd.concat(cashflow_list, ignore_index=True)\n", + " all_cashflow.to_hdf('../../data/cashflow.h5', key='cashflow', mode='w', format='table')\n", + " print(\"所有日线数据已保存到 cashflow.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85bdf760cb83fbd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T15:47:42.761559200Z", + "start_time": "2025-02-07T16:24:09.366158Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_27304\\2498023504.py:3: 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", + " all_fina_indicator = pd.concat(fina_indicator_list, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有日线数据已保存到 fina_indicator.h5\n" + ] + } + ], + "source": [ + "\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", + "fina_indicator_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", + "# 循环遍历每个股票代码并获取数据\n", + "for idx, row in stocks_df.iterrows():\n", + " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", + " try:\n", + " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", + " fina_indicator = pro.fina_indicator(ts_code=ts_code)\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if not fina_indicator.empty:\n", + " fina_indicator['ts_code'] = ts_code\n", + " fina_indicator_list.append(fina_indicator)\n", + " print(f\"成功获取 {ts_code} 的数据\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 300 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + "# 合并所有获取到的日线数据\n", + "if fina_indicator_list:\n", + " all_fina_indicator = pd.concat(fina_indicator_list, ignore_index=True)\n", + " all_fina_indicator.to_hdf('../../data/fina_indicator.h5', key='fina_indicator', mode='w', format='table')\n", + " print(\"所有日线数据已保存到 fina_indicator.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "83403f50", + 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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", + " all_balancesheet = pd.concat(balancesheet_list, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有日线数据已保存到 balancesheet.h5\n" + ] + } + ], + "source": [ + "\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", + "balancesheet_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", + "# 循环遍历每个股票代码并获取数据\n", + "for idx, row in stocks_df.iterrows():\n", + " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", + " try:\n", + " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", + " balancesheet = pro.balancesheet(ts_code=ts_code)\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if not balancesheet.empty:\n", + " balancesheet['ts_code'] = ts_code\n", + " balancesheet_list.append(balancesheet)\n", + " print(f\"成功获取 {ts_code} 的数据\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 300 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + "# 合并所有获取到的日线数据\n", + "if balancesheet_list:\n", + " all_balancesheet = pd.concat(balancesheet_list, ignore_index=True)\n", + " all_balancesheet.to_hdf('../../data/balancesheet.h5', key='balancesheet', mode='w', format='table')\n", + " print(\"所有日线数据已保存到 balancesheet.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b27bb37b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 5.271746e+12\n", + "1 5.274428e+12\n", + "2 5.255519e+12\n", + "3 5.272164e+12\n", + "4 5.243822e+12\n", + " ... \n", + "372441 1.150050e+09\n", + "372442 9.132874e+08\n", + "372443 4.395018e+08\n", + "372444 4.792661e+08\n", + "372445 3.738566e+08\n", + "Name: total_liab, Length: 372446, dtype: float64\n" + ] + } + ], + "source": [ + "print(all_balancesheet['total_liab'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/factor/__pycache__/factor.cpython-311.pyc b/main/factor/__pycache__/factor.cpython-311.pyc index b4b8b8f..7aa2d6c 100644 Binary files a/main/factor/__pycache__/factor.cpython-311.pyc and b/main/factor/__pycache__/factor.cpython-311.pyc differ diff --git a/main/factor/factor.csv b/main/factor/factor.csv new file mode 100644 index 0000000..fdf1950 --- /dev/null +++ b/main/factor/factor.csv @@ -0,0 +1,63 @@ +序号 因子名称 (Factor Name / Column Name) 因子类别 (Factor Category) 简要说明 +1 pe_ttm 价值类因子 (Value) 市盈率 TTM +2 return_5, return_20 动量类因子 (Momentum) 过去5日/20日收益率 +3 act_factor1 to act_factor4 动量类 / 技术类因子 (Momentum / Technical) 基于不同周期EMA斜率计算的动量/趋势因子 +4 std_return_5, std_return_90, std_return_90_2 波动率类因子 (Volatility) 不同窗口期或延迟窗口期的滚动收益率标准差 +5 upside_vol, downside_vol 波动率类因子 (Volatility) N日滚动上/下行波动率 +6 vol_ratio 波动率类因子 (Volatility) 上行波动率 / 下行波动率 +7 std_return_5 / std_return_90 波动率类因子 (Volatility) 短期波动率 / 长期波动率 比率 +8 std_return_90 - std_return_90_2 波动率类因子 (Volatility) 长期波动率与其10日前值的差值（波动变化） +9 volatility (来自指数计算) 波动率类 / 市场因子 (Volatility / Market) 指数（或个股）的20日滚动收益率标准差 +10 log(circ_mv) (或 log_circ_mv) 市值类因子 (Size) 流通市值的对数值 +11 cs_rank_size 市值类因子 (Size) 对数流通市值的截面排序 +12 vol 流动性类因子 (Liquidity) 成交量 (通常需要与其他指标结合或处理) +13 turnover_rate 流动性类因子 (Liquidity) 换手率 +14 volume_ratio 流动性类因子 (Liquidity) 量比 +15 turnover_deviation 流动性类因子 (Liquidity) 换手率与其3日滚动均值的标准差倍数偏离 +16 cat_turnover_spike 流动性类 / 分类因子 (Liquidity / Categorical) 换手率是否显著高于近期均值 +17 volume_change_rate 流动性类因子 (Liquidity) 短期滚动成交量均值 / 长期滚动成交量均值 - 1 +18 cat_volume_breakout 流动性类 / 分类因子 (Liquidity / Categorical) 当日成交量是否大于过去5日最大成交量 +19 avg_volume_ratio 流动性类因子 (Liquidity) 3日滚动量比均值 +20 cat_volume_ratio_breakout 流动性类 / 分类因子 (Liquidity / Categorical) 当日量比是否大于过去5日最大量比 +21 vol_spike (Rolling Mean Vol) 流动性类因子 (Liquidity) 20日滚动成交量均值 +22 vol_std_5 流动性类 / 波动率因子 (Liquidity / Volatility) 成交量日变化率的5日滚动标准差 +23 volume_growth 流动性类因子 (Liquidity) 20日成交量变化率 +24 turnover_std 流动性类 / 波动率因子 (Liquidity / Volatility) 换手率的20日滚动标准差 +25 flow_lg_elg_intensity 资金流 / 流动性类因子 (Money Flow / Liquidity) （大单+超大单）净买入量 / 总成交量 +26 flow_divergence_diff, flow_divergence_ratio 资金流 / 情绪类因子 (Money Flow / Sentiment) 散户与主力资金流的差异或比率 +27 lg_elg_buy_prop 资金流 / 流动性类因子 (Money Flow / Liquidity) （大单+超大单）买入量 / 总买入量 +28 flow_struct_buy_change 资金流 / 流动性类因子 (Money Flow / Liquidity) 主力买入占比的日变化 +29 flow_lg_elg_accel 资金流 / 动量类因子 (Money Flow / Momentum) 主力资金流加速度 +30 active_buy_volume_large/big/small 资金流 / 流动性类因子 (Money Flow / Liquidity) 不同规模主动买入量 / 净流入量 +31 buy_lg/elg_vol_minus_sell_lg/elg_vol 资金流 / 流动性类因子 (Money Flow / Liquidity) 不同规模净买入量 / 总净流入量 +32 cs_rank_net_lg_flow_val, cs_rank_elg_buy_ratio, cs_rank_lg_sm_flow_diverge, cs_rank_elg_buy_sell_sm_ratio 资金流 / 复合因子 (截面排序) 各种资金流指标的截面排序 +33 cs_rank_ind_adj_lg_flow 资金流 / 复合因子 (行业调整+截面排序) 行业调整后的大单净流入截面排序 +34 chip_concentration_range, chip_skewness, cost_support_15pct_change, weight_roc5, cost_stability, ctrl_strength, low_cost_dev, asymmetry, cost_conc_std_N, profit_pressure, underwater_resistance, cs_rank_rel_profit_margin, cs_rank_cost_breadth, cs_rank_dist_to_upper_cost 定位类因子 (Positioning) / 技术类 基于持仓成本分布 (cost_*, weight_avg) 计算的各种指标及其截面排序 +35 winner_rate, cs_rank_winner_rate 定位类因子 (Positioning) / 技术类 获利盘比例及其截面排序 +36 floating_chip_proxy, price_cost_divergence, high_cost_break_days, liquidity_risk, lock_factor, cost_atr_adj, smallcap_concentration, cat_golden_resonance 定位类因子 (Positioning) / 复合因子 结合持仓成本与其他信息（价格、成交、波动率、市值）的复合指标 +37 cat_winner_price_zone 定位类 / 分类因子 (Positioning / Categorical) 基于成本和获利盘划分的区域类别 +38 flow_chip_consistency, profit_taking_vs_absorb, vol_amp_loss, vol_drop_profit_cnt, cost_break_confirm_cnt, vol_wgt_hist_pos, cs_rank_vol_x_profit_margin, cs_rank_cost_dist_vol_ratio 定位类因子 (Positioning) / 复合因子 进一步结合定位、资金流、量价的复杂交互因子 +39 return_skew, return_kurtosis 技术类 / 统计特征 (Technical / Stats) 滚动收益率的偏度与峰度 +40 rsi_3 技术类 / 动量类因子 (Technical / Momentum) 3日相对强弱指数 +41 obv, maobv_6, obv-maobv_6 技术类 / 量价因子 (Technical / Volume) 能量潮及其均线、差离 +42 atr_14, atr_6 技术类 / 波动率类因子 (Technical / Volatility) 平均真实波幅 +43 log_close 技术类 / 量价因子 (Technical / Price) 收盘价对数 +44 up, down 技术类 / 量价因子 (Technical / Price Action) 标准化上影线、下影线长度 +45 alpha_22_improved, alpha_003, alpha_007, alpha_013 技术类 / Alpha因子 (Technical / Alpha) WorldQuant Alpha 因子实现 +46 atr_norm_channel_pos 技术类 / 量价因子 (Technical / Price Action) ATR 标准化的价格通道位置 +47 turnover_diff_skew 技术类 / 流动性类 (Technical / Liquidity) 换手率变化率的偏度 +48 pullback_strong_N_M 技术类 / 动量类因子 (Technical / Momentum) 近期强势股的回调幅度 +49 vol_adj_roc 技术类 / 复合因子 (动量+波动率) 波动率调整后的 N 日变化率 +50 ar, br, arbr 情绪类 / 技术类因子 (Sentiment / Technical) ARBR 人气意愿指标 +51 up_ratio_20d (来自指数计算) 情绪类 / 市场因子 (Sentiment / Market) 指数（或个股）过去20天上涨天数比例 +52 cat_up_limit, cat_down_limit, up_limit_count_10d, down_limit_count_10d, consecutive_up_limit 事件驱动 / 市场状态因子 (Event / Market State) 涨跌停相关状态和计数 +53 momentum_factor, resonance_factor 复合因子 (量价) (Composite - P/V) 基于量、价、换手率等的简单复合 +54 cat_af2, cat_af3, cat_af4 复合因子 / 分类因子 (Composite / Cat.) act_factor 之间的比较 +55 act_factor5, act_factor6 复合因子 (技术类) (Composite - Technical) act_factor 1-4 的组合 +56 mv_volatility, mv_growth, mv_turnover_ratio, mv_adjusted_volume, mv_weighted_turnover, nonlinear_mv_volume, mv_volume_ratio, mv_momentum 复合因子 (市值+流动性/量价) 考虑了市值影响的量价、流动性或动量指标 +57 cap_neutral_cost_metric (占位符) 复合因子 / Alpha因子 (占位符) 市值行业中性化的成本指标（需实现） +58 hurst_exponent_flow (占位符) 资金流 / 统计因子 (占位符) 资金流的 Hurst 指数（需实现） +59 intraday_lg_flow_corr_N (占位符) 复合因子 (价格行为+资金流) (占位符) 日内趋势与大单流相关性（需实现） +60 industry_* (来自 industry_df) 行业因子 (Industry) 对应行业的各种指标（如行业收益率、行业动量等） +61 *_deviation (来自 create_deviation_within_dates) 复合因子 (相对行业) 个股因子相对于行业均值的偏离 +62 complex_factor_gplearn_1 复合因子 (GP生成) DEAP/GP 找到的因子表达式 1 \ No newline at end of file diff --git a/main/factor/factor.py b/main/factor/factor.py index c8eefb5..fc7bcf5 100644 --- a/main/factor/factor.py +++ b/main/factor/factor.py @@ -7,33 +7,38 @@ def get_rolling_factor(df): old_columns = df.columns.tolist()[:] # 按股票和日期排序（如果尚未排序） - df = df.sort_values(by=['ts_code', 'trade_date']) + df = df.sort_values(by=["ts_code", "trade_date"]) - grouped = df.groupby('ts_code', group_keys=False) + grouped = df.groupby("ts_code", group_keys=False) epsilon = 1e-8 - df['lg_elg_net_buy_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol'] + df["lg_elg_net_buy_vol"] = ( + df["buy_lg_vol"] + df["buy_elg_vol"] - df["sell_lg_vol"] - df["sell_elg_vol"] + ) # 检查 'volume' 列是否存在且有效 - df['flow_lg_elg_intensity'] = df['lg_elg_net_buy_vol'] / (df['vol'] + epsilon) + df["flow_lg_elg_intensity"] = df["lg_elg_net_buy_vol"] / (df["vol"] + epsilon) # 2. 散户与主力背离度 (Retail vs Institutional Divergence) # 衡量小单净流入与（大单+超大单）净流入的差异或比率 - df['sm_net_buy_vol'] = df['buy_sm_vol'] - df['sell_sm_vol'] - df['flow_divergence_diff'] = df['sm_net_buy_vol'] - df['lg_elg_net_buy_vol'] + df["sm_net_buy_vol"] = df["buy_sm_vol"] - df["sell_sm_vol"] + df["flow_divergence_diff"] = df["sm_net_buy_vol"] - df["lg_elg_net_buy_vol"] # 比率形式可能更稳定 - df['flow_divergence_ratio'] = df['sm_net_buy_vol'] / ( - df['lg_elg_net_buy_vol'] + np.sign(df['lg_elg_net_buy_vol']) * epsilon + epsilon) # 复杂处理避免0/0 + df["flow_divergence_ratio"] = df["sm_net_buy_vol"] / ( + df["lg_elg_net_buy_vol"] + np.sign(df["lg_elg_net_buy_vol"]) * epsilon + epsilon + ) # 复杂处理避免0/0 # 3. 资金流结构变动 (Flow Structure Change - Relative Strength of Large Flow) # 大单+超大单买入额占总买入额的比例的变化 - df['total_buy_vol'] = df['buy_sm_vol'] + df['buy_lg_vol'] + df['buy_elg_vol'] - df['lg_elg_buy_prop'] = (df['buy_lg_vol'] + df['buy_elg_vol']) / (df['total_buy_vol'] + epsilon) - df['flow_struct_buy_change'] = grouped['lg_elg_buy_prop'].diff(1) # 1日变化 + df["total_buy_vol"] = df["buy_sm_vol"] + df["buy_lg_vol"] + df["buy_elg_vol"] + df["lg_elg_buy_prop"] = (df["buy_lg_vol"] + df["buy_elg_vol"]) / ( + df["total_buy_vol"] + epsilon + ) + df["flow_struct_buy_change"] = grouped["lg_elg_buy_prop"].diff(1) # 1日变化 # 4. 资金流加速度 (Flow Acceleration) # 净主力资金流的变化率（二阶导） - df['lg_elg_net_buy_vol_change'] = grouped['lg_elg_net_buy_vol'].diff(1) - df['flow_lg_elg_accel'] = grouped['lg_elg_net_buy_vol_change'].diff(1) + df["lg_elg_net_buy_vol_change"] = grouped["lg_elg_net_buy_vol"].diff(1) + df["flow_lg_elg_accel"] = grouped["lg_elg_net_buy_vol_change"].diff(1) # # 5. 极端资金流事件 (Categorical: Extreme Flow Event) # # 定义主力资金流强度是否处于其历史极端水平（例如，过去N天的90分位数以上或10分位数以下） @@ -62,81 +67,123 @@ def get_rolling_factor(df): # 6. 筹码集中度 (Chip Concentration) # 衡量筹码分布的紧密程度，例如 95% 与 5% 成本价的差距，相对于当前价格进行标准化 # 检查 'close' 列是否存在且有效 - df['chip_concentration_range'] = (df['cost_95pct'] - df['cost_5pct']) / (df['close'] + epsilon) + df["chip_concentration_range"] = (df["cost_95pct"] - df["cost_5pct"]) / ( + df["close"] + epsilon + ) # 7. 筹码分布偏度 (Chip Distribution Skewness Proxy) # 比较中位数成本 (cost_50pct) 和加权平均成本 (weight_avg) # weight_avg > cost_50pct 暗示高成本区有较多筹码（右偏） - df['chip_skewness'] = (df['weight_avg'] - df['cost_50pct']) / (df['cost_50pct'] + epsilon) + df["chip_skewness"] = (df["weight_avg"] - df["cost_50pct"]) / ( + df["cost_50pct"] + epsilon + ) # 8. 浮筹比例 (Floating Chips Proxy) # 衡量短期内（例如15%成本线以下）的筹码比例与总获利盘比例的关系 # winner_rate 高但 cost_15pct 接近当前价，可能意味着大部分获利盘成本不高，易浮动 # 这里简化为：获利盘比例 与 （当前价-15%成本价）/当前价 的乘积 - price_dist_cost15 = (df['close'] - df['cost_15pct']) / (df['close'] + epsilon) - df['floating_chip_proxy'] = df['winner_rate'] * np.maximum(0, price_dist_cost15) # 只考虑价格高于15%成本线的情况 + price_dist_cost15 = (df["close"] - df["cost_15pct"]) / (df["close"] + epsilon) + df["floating_chip_proxy"] = df["winner_rate"] * np.maximum( + 0, price_dist_cost15 + ) # 只考虑价格高于15%成本线的情况 # 9. 成本支撑强度变化 (Cost Support Strength Change) # 观察低位筹码成本（如 5% 或 15% 分位点）的变化率，看支撑位是上移还是下移 - df['cost_support_15pct_change'] = grouped['cost_15pct'].pct_change(1) * 100 # 百分比变化 + df["cost_support_15pct_change"] = ( + grouped["cost_15pct"].pct_change(1) * 100 + ) # 百分比变化 # 10. 获利盘压力/支撑区 (Categorical: Winner Rate Zone & Price Position) # 结合获利盘比例和当前价格相对于筹码成本的位置 # 例如: 价格在 85% 成本线之上 & 获利盘 > 0.8 -> 高位派发风险区? # 价格在 15% 成本线之下 & 获利盘 < 0.2 -> 低位吸筹潜力区? conditions_winner = [ - (df['close'] > df['cost_85pct']) & (df['winner_rate'] > 0.8), # 高位 & 高获利盘 - (df['close'] < df['cost_15pct']) & (df['winner_rate'] < 0.2), # 低位 & 低获利盘 - (df['close'] > df['cost_50pct']) & (df['winner_rate'] > 0.5), # 中高位 & 多数获利 - (df['close'] < df['cost_50pct']) & (df['winner_rate'] < 0.5), # 中低位 & 多数亏损 + (df["close"] > df["cost_85pct"]) & (df["winner_rate"] > 0.8), # 高位 & 高获利盘 + (df["close"] < df["cost_15pct"]) & (df["winner_rate"] < 0.2), # 低位 & 低获利盘 + (df["close"] > df["cost_50pct"]) + & (df["winner_rate"] > 0.5), # 中高位 & 多数获利 + (df["close"] < df["cost_50pct"]) + & (df["winner_rate"] < 0.5), # 中低位 & 多数亏损 ] choices_winner = [1, 2, 3, 4] # 1:高风险区, 2:低潜力区, 3:中上获利区, 4:中下亏损区 - df['cat_winner_price_zone'] = np.select(conditions_winner, choices_winner, default=0) # 0: 其他 + df["cat_winner_price_zone"] = np.select( + conditions_winner, choices_winner, default=0 + ) # 0: 其他 # --- 结合因子 --- # 11. 主力行为与筹码结构一致性 (Flow-Chip Consistency) # 例如：主力净买入发生在价格接近下方筹码密集区（如 cost_15pct 到 cost_50pct）时 - price_near_low_support = (df['close'] > df['cost_15pct']) & (df['close'] < df['cost_50pct']) - df['flow_chip_consistency'] = df['lg_elg_net_buy_vol'] * price_near_low_support.astype(int) + price_near_low_support = (df["close"] > df["cost_15pct"]) & ( + df["close"] < df["cost_50pct"] + ) + df["flow_chip_consistency"] = df[ + "lg_elg_net_buy_vol" + ] * price_near_low_support.astype(int) # 可以进一步标准化或做成 categorical # 12. 获利了结压力/承接盘强度 (Profit-Taking Pressure vs Absorption) # 在高获利盘(winner_rate > 0.7)的情况下，观察主力资金是净流出（了结）还是净流入（高位换手/承接） - high_winner_rate_flag = (df['winner_rate'] > 0.7).astype(int) - df['profit_taking_vs_absorb'] = df['lg_elg_net_buy_vol'] * high_winner_rate_flag + high_winner_rate_flag = (df["winner_rate"] > 0.7).astype(int) + df["profit_taking_vs_absorb"] = df["lg_elg_net_buy_vol"] * high_winner_rate_flag # 正值表示高获利盘下主力仍在买入（承接），负值表示主力在卖出（了结） # 清理临时列和可能产生的 NaN (可选，根据需要处理) - cols_to_drop = ['lg_elg_net_buy_vol', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', - 'lg_elg_net_buy_vol_change', 'flow_lg_elg_intensity_rolling_high', - 'flow_lg_elg_intensity_rolling_low'] + cols_to_drop = [ + "lg_elg_net_buy_vol", + "sm_net_buy_vol", + "total_buy_vol", + "lg_elg_buy_prop", + "lg_elg_net_buy_vol_change", + "flow_lg_elg_intensity_rolling_high", + "flow_lg_elg_intensity_rolling_low", + ] # df = df.drop(columns=cols_to_drop) window = 20 - df['_is_positive'] = (df['pct_chg'] > 0).astype(int) - df['_is_negative'] = (df['pct_chg'] < 0).astype(int) - df['cat_is_positive'] = (df['pct_chg'] > 0).astype(int) + df["_is_positive"] = (df["pct_chg"] > 0).astype(int) + df["_is_negative"] = (df["pct_chg"] < 0).astype(int) + df["cat_is_positive"] = (df["pct_chg"] > 0).astype(int) # 分离正负收益率 (用于计算各自的均值和平方均值) # 注意：这里我们保留原始收益率用于计算，而不是 clip 到 0 - df['_pos_returns'] = df['pct_chg'].where(df['pct_chg'] > 0, 0) # 非正设为0，便于求和 - df['_neg_returns'] = df['pct_chg'].where(df['pct_chg'] < 0, 0) # 非负设为0，便于求和 + df["_pos_returns"] = df["pct_chg"].where( + df["pct_chg"] > 0, 0 + ) # 非正设为0，便于求和 + df["_neg_returns"] = df["pct_chg"].where( + df["pct_chg"] < 0, 0 + ) # 非负设为0，便于求和 # 计算收益率的平方 (用于计算 E[X^2]) - df['_pos_returns_sq'] = np.square(df['_pos_returns']) - df['_neg_returns_sq'] = np.square(df['_neg_returns']) # 平方后负数变正 + df["_pos_returns_sq"] = np.square(df["_pos_returns"]) + df["_neg_returns_sq"] = np.square(df["_neg_returns"]) # 平方后负数变正 # 4. 计算滚动统计量 (使用内置函数，速度较快) # 计算正收益日的统计量 - rolling_pos_count = grouped['_is_positive'].rolling(window, min_periods=max(1, window // 2)).sum() - rolling_pos_sum = grouped['_pos_returns'].rolling(window, min_periods=max(1, window // 2)).sum() - rolling_pos_sum_sq = grouped['_pos_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum() + rolling_pos_count = ( + grouped["_is_positive"].rolling(window, min_periods=max(1, window // 2)).sum() + ) + rolling_pos_sum = ( + grouped["_pos_returns"].rolling(window, min_periods=max(1, window // 2)).sum() + ) + rolling_pos_sum_sq = ( + grouped["_pos_returns_sq"] + .rolling(window, min_periods=max(1, window // 2)) + .sum() + ) # 计算负收益日的统计量 - rolling_neg_count = grouped['_is_negative'].rolling(window, min_periods=max(1, window // 2)).sum() - rolling_neg_sum = grouped['_neg_returns'].rolling(window, min_periods=max(1, window // 2)).sum() - rolling_neg_sum_sq = grouped['_neg_returns_sq'].rolling(window, min_periods=max(1, window // 2)).sum() + rolling_neg_count = ( + grouped["_is_negative"].rolling(window, min_periods=max(1, window // 2)).sum() + ) + rolling_neg_sum = ( + grouped["_neg_returns"].rolling(window, min_periods=max(1, window // 2)).sum() + ) + rolling_neg_sum_sq = ( + grouped["_neg_returns_sq"] + .rolling(window, min_periods=max(1, window // 2)) + .sum() + ) # 5. 计算方差和标准差 pos_mean_sq = rolling_pos_sum_sq / rolling_pos_count @@ -152,66 +199,98 @@ def get_rolling_factor(df): downside_vol = np.sqrt(neg_var) # rolling 操作后结果带有 MultiIndex，需要去除股票代码层级以便合并 - df['upside_vol'] = upside_vol.reset_index(level=0, drop=True) - df['downside_vol'] = downside_vol.reset_index(level=0, drop=True) + df["upside_vol"] = upside_vol.reset_index(level=0, drop=True) + df["downside_vol"] = downside_vol.reset_index(level=0, drop=True) - df['vol_ratio'] = df['upside_vol'] / df['downside_vol'] - df['vol_ratio'] = df['vol_ratio'].replace([np.inf, -np.inf], np.nan).fillna(0) # 或 fillna(np.nan) + df["vol_ratio"] = df["upside_vol"] / df["downside_vol"] + df["vol_ratio"] = ( + df["vol_ratio"].replace([np.inf, -np.inf], np.nan).fillna(0) + ) # 或 fillna(np.nan) - df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True) - df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True) + df["return_skew"] = ( + grouped["pct_chg"].rolling(window=5).skew().reset_index(0, drop=True) + ) + df["return_kurtosis"] = ( + grouped["pct_chg"].rolling(window=5).kurt().reset_index(0, drop=True) + ) # 因子 1：短期成交量变化率 - df['volume_change_rate'] = ( - grouped['vol'].rolling(window=2).mean() / - grouped['vol'].rolling(window=10).mean() - 1 - ).reset_index(level=0, drop=True) # 确保索引对齐 + df["volume_change_rate"] = ( + grouped["vol"].rolling(window=2).mean() + / grouped["vol"].rolling(window=10).mean() + - 1 + ).reset_index( + level=0, drop=True + ) # 确保索引对齐 # 因子 2：成交量突破信号 - max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐 - df['cat_volume_breakout'] = (df['vol'] > max_volume) + max_volume = ( + grouped["vol"].rolling(window=5).max().reset_index(level=0, drop=True) + ) # 确保索引对齐 + df["cat_volume_breakout"] = df["vol"] > max_volume # 因子 3：换手率均线偏离度 - mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True) - std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True) - df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover + mean_turnover = ( + grouped["turnover_rate"] + .rolling(window=3) + .mean() + .reset_index(level=0, drop=True) + ) + std_turnover = ( + grouped["turnover_rate"].rolling(window=3).std().reset_index(level=0, drop=True) + ) + df["turnover_deviation"] = (df["turnover_rate"] - mean_turnover) / std_turnover # 因子 4：换手率激增信号 - df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover) + df["cat_turnover_spike"] = df["turnover_rate"] > mean_turnover + 2 * std_turnover # 因子 5：量比均值 - df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True) + df["avg_volume_ratio"] = ( + grouped["volume_ratio"].rolling(window=3).mean().reset_index(level=0, drop=True) + ) # 因子 6：量比突破信号 - max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True) - df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio) - - df['vol_spike'] = grouped.apply( - lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index) + max_volume_ratio = ( + grouped["volume_ratio"].rolling(window=5).max().reset_index(level=0, drop=True) ) - df['vol_std_5'] = grouped['vol'].pct_change().rolling(window=5).std() + df["cat_volume_ratio_breakout"] = df["volume_ratio"] > max_volume_ratio + + df["vol_spike"] = grouped.apply( + lambda x: pd.Series(x["vol"].rolling(20).mean(), index=x.index) + ) + df["vol_std_5"] = grouped["vol"].pct_change().rolling(window=5).std() # 计算 ATR - df['atr_14'] = grouped.apply( - lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), - index=x.index) + df["atr_14"] = grouped.apply( + lambda x: pd.Series( + talib.ATR( + x["high"].values, x["low"].values, x["close"].values, timeperiod=14 + ), + index=x.index, + ) ) - df['atr_6'] = grouped.apply( - lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), - index=x.index) + df["atr_6"] = grouped.apply( + lambda x: pd.Series( + talib.ATR( + x["high"].values, x["low"].values, x["close"].values, timeperiod=6 + ), + index=x.index, + ) ) # 计算 OBV 及其均线 - df['obv'] = grouped.apply( - lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index) + df["obv"] = grouped.apply( + lambda x: pd.Series( + talib.OBV(x["close"].values, x["vol"].values), index=x.index + ) ) print(df.columns) - df['maobv_6'] = grouped.apply( - lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index) + df["maobv_6"] = grouped.apply( + lambda x: pd.Series(talib.SMA(x["obv"].values, timeperiod=6), index=x.index) ) - df['rsi_3'] = grouped.apply( - lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index) + df["rsi_3"] = grouped.apply( + lambda x: pd.Series(talib.RSI(x["close"].values, timeperiod=3), index=x.index) ) # df['rsi_6'] = grouped.apply( # lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index) @@ -221,53 +300,65 @@ def get_rolling_factor(df): # ) # 计算 return_10 和 return_20 - df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1) + df["return_5"] = grouped["close"].apply(lambda x: x / x.shift(5) - 1) # df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1) - df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1) + df["return_20"] = grouped["close"].apply(lambda x: x / x.shift(20) - 1) # df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x) # 计算标准差指标 - df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std()) + df["std_return_5"] = grouped["close"].apply( + lambda x: x.pct_change().rolling(window=5).std() + ) # df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std()) # df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std()) - df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std()) - df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std()) + df["std_return_90"] = grouped["close"].apply( + lambda x: x.pct_change().rolling(window=90).std() + ) + df["std_return_90_2"] = grouped["close"].apply( + lambda x: x.shift(10).pct_change().rolling(window=90).std() + ) # 计算 EMA 指标 - df['_ema_5'] = grouped['close'].apply( + df["_ema_5"] = grouped["close"].apply( lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index) ) - df['_ema_13'] = grouped['close'].apply( + df["_ema_13"] = grouped["close"].apply( lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index) ) - df['_ema_20'] = grouped['close'].apply( + df["_ema_20"] = grouped["close"].apply( lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index) ) - df['_ema_60'] = grouped['close'].apply( + df["_ema_60"] = grouped["close"].apply( lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index) ) # 计算 act_factor1, act_factor2, act_factor3, act_factor4 - df['act_factor1'] = grouped['_ema_5'].apply( + df["act_factor1"] = grouped["_ema_5"].apply( lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50 ) - df['act_factor2'] = grouped['_ema_13'].apply( + df["act_factor2"] = grouped["_ema_13"].apply( lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40 ) - df['act_factor3'] = grouped['_ema_20'].apply( + df["act_factor3"] = grouped["_ema_20"].apply( lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21 ) - df['act_factor4'] = grouped['_ema_60'].apply( + df["act_factor4"] = grouped["_ema_60"].apply( lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10 ) # 根据 trade_date 截面计算排名 - df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True) - df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True) - df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True) + df["rank_act_factor1"] = df.groupby("trade_date", group_keys=False)[ + "act_factor1" + ].rank(ascending=False, pct=True) + df["rank_act_factor2"] = df.groupby("trade_date", group_keys=False)[ + "act_factor2" + ].rank(ascending=False, pct=True) + df["rank_act_factor3"] = df.groupby("trade_date", group_keys=False)[ + "act_factor3" + ].rank(ascending=False, pct=True) - df['log_circ_mv'] = np.log(df['circ_mv']) + df["log_circ_mv"] = np.log(df["circ_mv"]) window_high_volume = 5 window_close_stddev = 20 @@ -275,105 +366,99 @@ def get_rolling_factor(df): # 计算每只股票的滚动协方差 def calculate_rolling_cov(group): - return group['high'].rolling(window_high_volume).cov(group['vol']) + return group["high"].rolling(window_high_volume).cov(group["vol"]) - df['cov'] = grouped.apply(calculate_rolling_cov) + df["cov"] = grouped.apply(calculate_rolling_cov) # 计算每只股票的协方差差分 def calculate_delta_cov(group): - return group['cov'].diff(period_delta) + return group["cov"].diff(period_delta) - df['delta_cov'] = grouped.apply(calculate_delta_cov) + df["delta_cov"] = grouped.apply(calculate_delta_cov) # 计算每只股票的滚动标准差 def calculate_stddev_close(group): - return group['close'].rolling(window_close_stddev).std() + return group["close"].rolling(window_close_stddev).std() - df['_stddev_close'] = grouped.apply(calculate_stddev_close) - df['_rank_stddev'] = df.groupby('trade_date')['_stddev_close'].rank(pct=True) - df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev'] + df["_stddev_close"] = grouped.apply(calculate_stddev_close) + df["_rank_stddev"] = df.groupby("trade_date")["_stddev_close"].rank(pct=True) + df["alpha_22_improved"] = -1 * df["delta_cov"] * df["_rank_stddev"] - df['alpha_003'] = np.where(df['high'] != df['low'], - (df['close'] - df['open']) / (df['high'] - df['low']), - 0) - - df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])) - df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True) - - df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum()) - df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True) - - df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停） - df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停） - df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, - drop=True) - df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0, - drop=True) - - # 3. 最近连续涨跌停天数 - def calculate_consecutive_limits(series): - """ - 计算连续涨停/跌停天数。 - """ - consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) - consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1) - return consecutive_up, consecutive_down - - # 连续涨停天数 - df['consecutive_up_limit'] = grouped['cat_up_limit'].apply( - lambda x: calculate_consecutive_limits(x)[0] + df["alpha_003"] = np.where( + df["high"] != df["low"], + (df["close"] - df["open"]) / (df["high"] - df["low"]), + 0, ) - df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0) + df["alpha_007"] = grouped.apply(lambda x: x["close"].rolling(5).corr(x["vol"])) + df["alpha_007"] = df.groupby("trade_date", group_keys=False)["alpha_007"].rank( + ascending=True, pct=True + ) - df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5)) + df["alpha_013"] = grouped["close"].transform( + lambda x: x.rolling(5).sum() - x.rolling(20).sum() + ) + df["alpha_013"] = df.groupby("trade_date", group_keys=False)["alpha_013"].rank( + ascending=True, pct=True + ) + + + df["vol_break"] = np.where( + (df["close"] > df["cost_85pct"]) & (df["volume_ratio"] > 2), 1, 0 + ) + + df["weight_roc5"] = grouped["weight_avg"].apply(lambda x: x.pct_change(5)) def rolling_corr(group): - roc_close = group['close'].pct_change() - roc_weight = group['weight_avg'].pct_change() + roc_close = group["close"].pct_change() + roc_weight = group["weight_avg"].pct_change() return roc_close.rolling(10).corr(roc_weight) - df['price_cost_divergence'] = grouped.apply(rolling_corr) + df["price_cost_divergence"] = grouped.apply(rolling_corr) - df['smallcap_concentration'] = (1 / df['log_circ_mv']) * (df['cost_85pct'] - df['cost_15pct']) + df["smallcap_concentration"] = (1 / df["log_circ_mv"]) * ( + df["cost_85pct"] - df["cost_15pct"] + ) # 16. 筹码稳定性指数 (20日波动率) - df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std()) - df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean()) + df["weight_std20"] = grouped["weight_avg"].apply(lambda x: x.rolling(20).std()) + df["cost_stability"] = df["weight_std20"] / grouped["weight_avg"].transform( + lambda x: x.rolling(20).mean() + ) # 17. 成本区间突破标记 - df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum()) + df["high_cost_break_days"] = grouped.apply( + lambda g: g["close"].gt(g["cost_95pct"]).rolling(5).sum() + ) # 20. 筹码-流动性风险 - df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * ( - 1 / grouped['vol'].transform(lambda x: x.rolling(10).mean())) + df["liquidity_risk"] = (df["cost_95pct"] - df["cost_5pct"]) * ( + 1 / grouped["vol"].transform(lambda x: x.rolling(10).mean()) + ) # 7. 市值波动率因子 (使用 grouped) - df['turnover_std'] = grouped['turnover_rate'].transform(lambda x: x.rolling(window=20).std()) - df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['log_circ_mv']) + df["turnover_std"] = grouped["turnover_rate"].transform( + lambda x: x.rolling(window=20).std() + ) + df["mv_volatility"] = grouped.apply(lambda x: x["turnover_std"] / x["log_circ_mv"]) # 8. 市值成长性因子 - df['volume_growth'] = grouped['vol'].pct_change(periods=20) - df['mv_growth'] = df['volume_growth'] / df['log_circ_mv'] + df["volume_growth"] = grouped["vol"].pct_change(periods=20) + df["mv_growth"] = df["volume_growth"] / df["log_circ_mv"] - # AR 指标 - df["ar"] = grouped.apply( - lambda x: (x["high"].div(x["open"]).rolling(3).sum()) / (x["open"].div(x["low"]).rolling(3).sum()) * 100) - - # BR 指标 - df["pre_close"] = grouped["close"].shift(1) - df["br_up"] = (df["high"] - df["pre_close"]).clip(lower=0) - df["br_down"] = (df["pre_close"] - df["low"]).clip(lower=0) - df["br"] = grouped.apply(lambda x: (x["br_up"].rolling(3).sum()) / (x["br_down"].rolling(3).sum()) * 100) - - # ARBR - df['arbr'] = df['ar'] - df['br'] - df.drop(columns=["pre_close", "br_up", "br_down", 'ar', 'br'], inplace=True) - - df.drop(columns=['weight_std20'], inplace=True, errors='ignore') + df.drop(columns=["weight_std20"], inplace=True, errors="ignore") df.drop( - columns=['_is_positive', '_is_negative', '_pos_returns', '_neg_returns', '_pos_returns_sq', '_neg_returns_sq'], - inplace=True, errors='ignore') + columns=[ + "_is_positive", + "_is_negative", + "_pos_returns", + "_neg_returns", + "_pos_returns_sq", + "_neg_returns_sq", + ], + inplace=True, + errors="ignore", + ) new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns] return df, new_columns @@ -381,80 +466,100 @@ def get_rolling_factor(df): def get_simple_factor(df): old_columns = df.columns.tolist()[:] - df = df.sort_values(by=['ts_code', 'trade_date']) + df = df.sort_values(by=["ts_code", "trade_date"]) alpha = 0.5 - df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation'] - df['resonance_factor'] = df['volume_ratio'] * df['pct_chg'] - df['log_close'] = np.log(df['close']) + df["momentum_factor"] = df["volume_change_rate"] + alpha * df["turnover_deviation"] + df["resonance_factor"] = df["volume_ratio"] * df["pct_chg"] + df["log_close"] = np.log(df["close"]) - df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike'] + df["cat_vol_spike"] = df["vol"] > 2 * df["vol_spike"] - df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close'] - df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close'] + df["up"] = (df["high"] - df[["close", "open"]].max(axis=1)) / df["close"] + df["down"] = (df[["close", "open"]].min(axis=1) - df["low"]) / df["close"] - df['obv_maobv_6'] = df['obv'] - df['maobv_6'] + df["obv_maobv_6"] = df["obv"] - df["maobv_6"] # 计算比值指标 - df['std_return_5_over_std_return_90'] = df['std_return_5'] / df['std_return_90'] + df["std_return_5_over_std_return_90"] = df["std_return_5"] / df["std_return_90"] # df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25'] # 计算标准差差值 - df['std_return_90_minus_std_return_90_2'] = df['std_return_90'] - df['std_return_90_2'] + df["std_return_90_minus_std_return_90_2"] = ( + df["std_return_90"] - df["std_return_90_2"] + ) # df['cat_af1'] = df['act_factor1'] > 0 - df['cat_af2'] = df['act_factor2'] > df['act_factor1'] - df['cat_af3'] = df['act_factor3'] > df['act_factor2'] - df['cat_af4'] = df['act_factor4'] > df['act_factor3'] + df["cat_af2"] = df["act_factor2"] > df["act_factor1"] + df["cat_af3"] = df["act_factor3"] > df["act_factor2"] + df["cat_af4"] = df["act_factor4"] > df["act_factor3"] # 计算 act_factor5 和 act_factor6 - df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4'] - df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt( - df['act_factor1'] ** 2 + df['act_factor2'] ** 2) + df["act_factor5"] = ( + df["act_factor1"] + df["act_factor2"] + df["act_factor3"] + df["act_factor4"] + ) + df["act_factor6"] = (df["act_factor1"] - df["act_factor2"]) / np.sqrt( + df["act_factor1"] ** 2 + df["act_factor2"] ** 2 + ) - df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol'] - df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol'] - df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol'] + df["active_buy_volume_large"] = df["buy_lg_vol"] / df["net_mf_vol"] + df["active_buy_volume_big"] = df["buy_elg_vol"] / df["net_mf_vol"] + df["active_buy_volume_small"] = df["buy_sm_vol"] / df["net_mf_vol"] - df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol'] - df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol'] + df["buy_lg_vol_minus_sell_lg_vol"] = (df["buy_lg_vol"] - df["sell_lg_vol"]) / df[ + "net_mf_vol" + ] + df["buy_elg_vol_minus_sell_elg_vol"] = ( + df["buy_elg_vol"] - df["sell_elg_vol"] + ) / df["net_mf_vol"] - df['log_circ_mv'] = np.log(df['circ_mv']) + df["log_circ_mv"] = np.log(df["circ_mv"]) - df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low']) + df["ctrl_strength"] = (df["cost_85pct"] - df["cost_15pct"]) / ( + df["his_high"] - df["his_low"] + ) - df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct']) + df["low_cost_dev"] = (df["close"] - df["cost_5pct"]) / ( + df["cost_50pct"] - df["cost_5pct"] + ) - df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct']) + df["asymmetry"] = (df["cost_95pct"] - df["cost_50pct"]) / ( + df["cost_50pct"] - df["cost_5pct"] + ) - df['lock_factor'] = df['turnover_rate'] * ( - 1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low'])) + df["lock_factor"] = df["turnover_rate"] * ( + 1 - (df["cost_95pct"] - df["cost_5pct"]) / (df["his_high"] - df["his_low"]) + ) - df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2) + df["cat_vol_break"] = (df["close"] > df["cost_85pct"]) & (df["volume_ratio"] > 2) - df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14'] + df["cost_atr_adj"] = (df["cost_95pct"] - df["cost_5pct"]) / df["atr_14"] # 12. 小盘股筹码集中度 - df['smallcap_concentration'] = (1 / df['log_circ_mv']) * (df['cost_85pct'] - df['cost_15pct']) + df["smallcap_concentration"] = (1 / df["log_circ_mv"]) * ( + df["cost_85pct"] - df["cost_15pct"] + ) - df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) & - (df['volume_ratio'] > 1.5) & - (df['winner_rate'] > 0.7)) + df["cat_golden_resonance"] = ( + (df["close"] > df["weight_avg"]) + & (df["volume_ratio"] > 1.5) + & (df["winner_rate"] > 0.7) + ) - df['mv_turnover_ratio'] = df['turnover_rate'] / df['log_circ_mv'] + df["mv_turnover_ratio"] = df["turnover_rate"] / df["log_circ_mv"] - df['mv_adjusted_volume'] = df['vol'] / df['log_circ_mv'] + df["mv_adjusted_volume"] = df["vol"] / df["log_circ_mv"] - df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['log_circ_mv']) + df["mv_weighted_turnover"] = df["turnover_rate"] * (1 / df["log_circ_mv"]) - df['nonlinear_mv_volume'] = df['vol'] / df['log_circ_mv'] + df["nonlinear_mv_volume"] = df["vol"] / df["log_circ_mv"] - df['mv_volume_ratio'] = df['volume_ratio'] / df['log_circ_mv'] + df["mv_volume_ratio"] = df["volume_ratio"] / df["log_circ_mv"] - df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['log_circ_mv'] + df["mv_momentum"] = df["turnover_rate"] * df["volume_ratio"] / df["log_circ_mv"] - drop_columns = [col for col in df.columns if col.startswith('_')] - df.drop(columns=drop_columns, inplace=True, errors='ignore') + drop_columns = [col for col in df.columns if col.startswith("_")] + df.drop(columns=drop_columns, inplace=True, errors="ignore") new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns] return df, new_columns @@ -462,44 +567,64 @@ def get_simple_factor(df): import pandas as pd import numpy as np -from scipy.stats import linregress # For factor 4 (if implementing slope directly) +from scipy.stats import linregress # For factor 4 (if implementing slope directly) + # from hurst import compute_Hc # For factor 18, needs pip install hurst # import statsmodels.api as sm # For factor 16, needs pip install statsmodels # --- Constants --- -epsilon = 1e-10 # Prevent division by zero +epsilon = 1e-10 # Prevent division by zero + # --- Helper Functions --- def _safe_divide(a, b, default_val=0): """Safe division, returns default_val for division by zero or NaN/inf results.""" - with np.errstate(divide='ignore', invalid='ignore'): + with np.errstate(divide="ignore", invalid="ignore"): result = a / b # Replace NaN, Inf, -Inf resulting from division or invalid ops result[~np.isfinite(result)] = default_val return result + # --- Factor Calculation Functions (In-Place Modification) --- + # Category 1: Large Player Intent & Behavior -def lg_flow_mom_corr(df: pd.DataFrame, N: int = 20, M: int = 60, factor_name: str = None): +def lg_flow_mom_corr( + df: pd.DataFrame, N: int = 20, M: int = 60, factor_name: str = None +): """ Calculates Factor 1: Large Flow & Price Momentum Concordance (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'lg_flow_mom_corr_{N}_{M}' + factor_name = f"lg_flow_mom_corr_{N}_{M}" print(f"Calculating {factor_name}...") - _temp_cols = ['_net_lg_flow_val', '_rolling_net_lg_flow', '_price_mom'] + _temp_cols = ["_net_lg_flow_val", "_rolling_net_lg_flow", "_price_mom"] try: - df['_net_lg_flow_val'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close'] - df['_rolling_net_lg_flow'] = df.groupby('ts_code')['_net_lg_flow_val'].rolling(N, min_periods=max(1, N // 2)).sum().reset_index(level=0, drop=True) - df['_price_mom'] = df.groupby('ts_code')['close'].pct_change(N) + df["_net_lg_flow_val"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) * df["close"] + df["_rolling_net_lg_flow"] = ( + df.groupby("ts_code")["_net_lg_flow_val"] + .rolling(N, min_periods=max(1, N // 2)) + .sum() + .reset_index(level=0, drop=True) + ) + df["_price_mom"] = df.groupby("ts_code")["close"].pct_change(N) # Calculate correlation on the temporary Series to handle alignment - factor_series = df['_rolling_net_lg_flow'].rolling(M, min_periods=max(1, M // 2)).corr(df['_price_mom']) + factor_series = ( + df["_rolling_net_lg_flow"] + .rolling(M, min_periods=max(1, M // 2)) + .corr(df["_price_mom"]) + ) df[factor_name] = factor_series except Exception as e: print(f"Error calculating {factor_name}: {e}") - df[factor_name] = np.nan # Assign NaN on error + df[factor_name] = np.nan # Assign NaN on error finally: # Cleanup intermediate columns cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -507,24 +632,51 @@ def lg_flow_mom_corr(df: pd.DataFrame, N: int = 20, M: int = 60, factor_name: st df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def lg_buy_consolidation(df: pd.DataFrame, N: int = 20, vol_quantile: float = 0.2, factor_name: str = None): + +def lg_buy_consolidation( + df: pd.DataFrame, N: int = 20, vol_quantile: float = 0.2, factor_name: str = None +): """ Calculates Factor 2: Large Buying during Consolidation (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'lg_buy_consolidation_{N}' + factor_name = f"lg_buy_consolidation_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_rolling_std', '_net_lg_flow_ratio', '_rolling_net_lg_flow_ratio_mean', '_std_threshold'] + _temp_cols = [ + "_rolling_std", + "_net_lg_flow_ratio", + "_rolling_net_lg_flow_ratio_mean", + "_std_threshold", + ] try: - df['_rolling_std'] = df.groupby('ts_code')['close'].rolling(N, min_periods=max(1, N // 2)).std().reset_index(level=0, drop=True) - df['_net_lg_flow_ratio'] = _safe_divide( - (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']), - df['vol'] + df["_rolling_std"] = ( + df.groupby("ts_code")["close"] + .rolling(N, min_periods=max(1, N // 2)) + .std() + .reset_index(level=0, drop=True) + ) + df["_net_lg_flow_ratio"] = _safe_divide( + ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ), + df["vol"], + ) + df["_rolling_net_lg_flow_ratio_mean"] = ( + df.groupby("ts_code")["_net_lg_flow_ratio"] + .rolling(N, min_periods=max(1, N // 2)) + .mean() + .reset_index(level=0, drop=True) + ) + df["_std_threshold"] = df.groupby("trade_date")["_rolling_std"].transform( + lambda x: x.quantile(vol_quantile) + ) + df[factor_name] = df["_rolling_net_lg_flow_ratio_mean"].where( + df["_rolling_std"] < df["_std_threshold"] ) - df['_rolling_net_lg_flow_ratio_mean'] = df.groupby('ts_code')['_net_lg_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True) - df['_std_threshold'] = df.groupby('trade_date')['_rolling_std'].transform(lambda x: x.quantile(vol_quantile)) - df[factor_name] = df['_rolling_net_lg_flow_ratio_mean'].where(df['_rolling_std'] < df['_std_threshold']) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -534,16 +686,22 @@ def lg_buy_consolidation(df: pd.DataFrame, N: int = 20, vol_quantile: float = 0. df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def lg_flow_accel(df: pd.DataFrame, factor_name: str = 'lg_flow_accel'): + +def lg_flow_accel(df: pd.DataFrame, factor_name: str = "lg_flow_accel"): """ Calculates Factor 3: Large Flow Acceleration (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_net_lg_flow_vol'] + _temp_cols = ["_net_lg_flow_vol"] try: - df['_net_lg_flow_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol'] - df[factor_name] = df.groupby('ts_code')['_net_lg_flow_vol'].diff(1).diff(1) + df["_net_lg_flow_vol"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) + df[factor_name] = df.groupby("ts_code")["_net_lg_flow_vol"].diff(1).diff(1) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -553,6 +711,7 @@ def lg_flow_accel(df: pd.DataFrame, factor_name: str = 'lg_flow_accel'): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def intraday_lg_flow_corr(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 4: (Approx) Intraday Trend & Large Flow Correlation (In-place). @@ -561,24 +720,30 @@ def intraday_lg_flow_corr(df: pd.DataFrame, N: int = 20, factor_name: str = None WARNING: Modifies df in-place. Placeholder implementation returns NaN. """ if factor_name is None: - factor_name = f'intraday_lg_flow_corr_{N}' + factor_name = f"intraday_lg_flow_corr_{N}" print(f"Calculating {factor_name} (Placeholder - complex implementation)...") - df[factor_name] = np.nan # Placeholder, see previous thought process for detailed logic needed + df[factor_name] = ( + np.nan + ) # Placeholder, see previous thought process for detailed logic needed print(f"Finished {factor_name} (Placeholder).") # Category 2: Cost Basis & PnL Status -def profit_pressure(df: pd.DataFrame, factor_name: str = 'profit_pressure'): +def profit_pressure(df: pd.DataFrame, factor_name: str = "profit_pressure"): """ Calculates Factor 5: Profit Pressure Index (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_profit_margin_85', '_profit_margin_95'] + _temp_cols = ["_profit_margin_85", "_profit_margin_95"] try: - df['_profit_margin_85'] = _safe_divide(df['close'], df['cost_85pct']) - 1 - df['_profit_margin_95'] = _safe_divide(df['close'], df['cost_95pct']) - 1 - df[factor_name] = df['winner_rate'] * 0.5 * (df['_profit_margin_85'] + df['_profit_margin_95']) + df["_profit_margin_85"] = _safe_divide(df["close"], df["cost_85pct"]) - 1 + df["_profit_margin_95"] = _safe_divide(df["close"], df["cost_95pct"]) - 1 + df[factor_name] = ( + df["winner_rate"] + * 0.5 + * (df["_profit_margin_85"] + df["_profit_margin_95"]) + ) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -588,17 +753,20 @@ def profit_pressure(df: pd.DataFrame, factor_name: str = 'profit_pressure'): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def underwater_resistance(df: pd.DataFrame, factor_name: str = 'underwater_resistance'): + +def underwater_resistance(df: pd.DataFrame, factor_name: str = "underwater_resistance"): """ Calculates Factor 6: Resistance from Underwater Positions (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_underwater_ratio', '_dist_to_cost_15'] + _temp_cols = ["_underwater_ratio", "_dist_to_cost_15"] try: - df['_underwater_ratio'] = 1.0 - df['winner_rate'] - df['_dist_to_cost_15'] = np.maximum(0, df['cost_15pct'] - df['close']) / (df['close'] + epsilon) - df[factor_name] = df['_underwater_ratio'] * df['_dist_to_cost_15'] + df["_underwater_ratio"] = 1.0 - df["winner_rate"] + df["_dist_to_cost_15"] = np.maximum(0, df["cost_15pct"] - df["close"]) / ( + df["close"] + epsilon + ) + df[factor_name] = df["_underwater_ratio"] * df["_dist_to_cost_15"] except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -608,22 +776,27 @@ def underwater_resistance(df: pd.DataFrame, factor_name: str = 'underwater_resis df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def cost_conc_std(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 7: Cost Concentration Change (Std Dev) (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'cost_conc_std_{N}' + factor_name = f"cost_conc_std_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_cost_range_norm'] + _temp_cols = ["_cost_range_norm"] try: - df['_cost_range_norm'] = _safe_divide( - (df['cost_85pct'] - df['cost_15pct']), - (df['weight_avg'] + epsilon) + df["_cost_range_norm"] = _safe_divide( + (df["cost_85pct"] - df["cost_15pct"]), (df["weight_avg"] + epsilon) ) # Need to calculate rolling std on the temp col before dropping it - factor_series = df.groupby('ts_code')['_cost_range_norm'].rolling(N, min_periods=max(1, N//2)).std().reset_index(level=0, drop=True) + factor_series = ( + df.groupby("ts_code")["_cost_range_norm"] + .rolling(N, min_periods=max(1, N // 2)) + .std() + .reset_index(level=0, drop=True) + ) df[factor_name] = factor_series except Exception as e: print(f"Error calculating {factor_name}: {e}") @@ -634,19 +807,22 @@ def cost_conc_std(df: pd.DataFrame, N: int = 20, factor_name: str = None): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def profit_decay(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 8: Profit Expectation Decay (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'profit_decay_{N}' + factor_name = f"profit_decay_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_ret_N', '_winner_rate_change_N'] + _temp_cols = ["_ret_N", "_winner_rate_change_N"] try: - df['_ret_N'] = _safe_divide(df['close'], df.groupby('ts_code')['close'].shift(N)) - 1 - df['_winner_rate_change_N'] = df.groupby('ts_code')['winner_rate'].diff(N) - df[factor_name] = _safe_divide(df['_ret_N'], df['_winner_rate_change_N']) + df["_ret_N"] = ( + _safe_divide(df["close"], df.groupby("ts_code")["close"].shift(N)) - 1 + ) + df["_winner_rate_change_N"] = df.groupby("ts_code")["winner_rate"].diff(N) + df[factor_name] = _safe_divide(df["_ret_N"], df["_winner_rate_change_N"]) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -664,13 +840,20 @@ def vol_amp_loss(df: pd.DataFrame, N: int = 20, factor_name: str = None): WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'vol_amp_loss_{N}' + factor_name = f"vol_amp_loss_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_vol_N', '_loss_degree'] + _temp_cols = ["_vol_N", "_loss_degree"] try: - df['_vol_N'] = df.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(1, N // 2)).std().reset_index(level=0, drop=True) - df['_loss_degree'] = np.maximum(0, df['weight_avg'] - df['close']) / (df['close'] + epsilon) - df[factor_name] = df['_vol_N'] * df['_loss_degree'] + df["_vol_N"] = ( + df.groupby("ts_code")["pct_chg"] + .rolling(N, min_periods=max(1, N // 2)) + .std() + .reset_index(level=0, drop=True) + ) + df["_loss_degree"] = np.maximum(0, df["weight_avg"] - df["close"]) / ( + df["close"] + epsilon + ) + df[factor_name] = df["_vol_N"] * df["_loss_degree"] except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -680,23 +863,59 @@ def vol_amp_loss(df: pd.DataFrame, N: int = 20, factor_name: str = None): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def vol_drop_profit_cnt(df: pd.DataFrame, N: int = 20, M: int = 5, profit_thresh: float = 0.1, drop_thresh: float = -0.03, vol_multiple: float = 2.0, factor_name: str = None): + +def vol_drop_profit_cnt( + df: pd.DataFrame, + N: int = 20, + M: int = 5, + profit_thresh: float = 0.1, + drop_thresh: float = -0.03, + vol_multiple: float = 2.0, + factor_name: str = None, +): """ Calculates Factor 10: High Volume Drop when Profitable (Count over M days) (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'vol_drop_profit_cnt_{M}' + factor_name = f"vol_drop_profit_cnt_{M}" print(f"Calculating {factor_name}...") - _temp_cols = ['_is_profitable', '_is_dropping', '_rolling_mean_vol', '_rolling_std_vol', '_is_high_vol', '_event'] + _temp_cols = [ + "_is_profitable", + "_is_dropping", + "_rolling_mean_vol", + "_rolling_std_vol", + "_is_high_vol", + "_event", + ] try: - df['_is_profitable'] = df['close'] > df['weight_avg'] * (1 + profit_thresh) - df['_is_dropping'] = df['pct_chg'] < drop_thresh - df['_rolling_mean_vol'] = df.groupby('ts_code')['vol'].rolling(N, min_periods=1).mean().reset_index(level=0, drop=True) - df['_rolling_std_vol'] = df.groupby('ts_code')['vol'].rolling(N, min_periods=2).std().reset_index(level=0, drop=True).fillna(0) - df['_is_high_vol'] = df['vol'] > (df['_rolling_mean_vol'] + vol_multiple * df['_rolling_std_vol']) - df['_event'] = (df['_is_profitable'] & df['_is_dropping'] & df['_is_high_vol']).astype(int) - factor_series = df.groupby('ts_code')['_event'].rolling(M, min_periods=1).sum().reset_index(level=0, drop=True) + df["_is_profitable"] = df["close"] > df["weight_avg"] * (1 + profit_thresh) + df["_is_dropping"] = df["pct_chg"] < drop_thresh + df["_rolling_mean_vol"] = ( + df.groupby("ts_code")["vol"] + .rolling(N, min_periods=1) + .mean() + .reset_index(level=0, drop=True) + ) + df["_rolling_std_vol"] = ( + df.groupby("ts_code")["vol"] + .rolling(N, min_periods=2) + .std() + .reset_index(level=0, drop=True) + .fillna(0) + ) + df["_is_high_vol"] = df["vol"] > ( + df["_rolling_mean_vol"] + vol_multiple * df["_rolling_std_vol"] + ) + df["_event"] = ( + df["_is_profitable"] & df["_is_dropping"] & df["_is_high_vol"] + ).astype(int) + factor_series = ( + df.groupby("ts_code")["_event"] + .rolling(M, min_periods=1) + .sum() + .reset_index(level=0, drop=True) + ) df[factor_name] = factor_series except Exception as e: print(f"Error calculating {factor_name}: {e}") @@ -707,22 +926,46 @@ def vol_drop_profit_cnt(df: pd.DataFrame, N: int = 20, M: int = 5, profit_thresh df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def lg_flow_vol_interact(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 11: Large Flow Driven Volatility (Interaction Term) (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'lg_flow_vol_interact_{N}' + factor_name = f"lg_flow_vol_interact_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_vol_N', '_net_lg_flow_val', '_total_val', '_abs_net_lg_flow_ratio', '_abs_net_lg_flow_ratio_N'] + _temp_cols = [ + "_vol_N", + "_net_lg_flow_val", + "_total_val", + "_abs_net_lg_flow_ratio", + "_abs_net_lg_flow_ratio_N", + ] try: - df['_vol_N'] = df.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(1, N // 2)).std().reset_index(level=0, drop=True) - df['_net_lg_flow_val'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close'] - df['_total_val'] = df['vol'] * df['close'] - df['_abs_net_lg_flow_ratio'] = abs(df['_net_lg_flow_val']) / (df['_total_val'] + epsilon) - df['_abs_net_lg_flow_ratio_N'] = df.groupby('ts_code')['_abs_net_lg_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True) - df[factor_name] = df['_vol_N'] * df['_abs_net_lg_flow_ratio_N'] + df["_vol_N"] = ( + df.groupby("ts_code")["pct_chg"] + .rolling(N, min_periods=max(1, N // 2)) + .std() + .reset_index(level=0, drop=True) + ) + df["_net_lg_flow_val"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) * df["close"] + df["_total_val"] = df["vol"] * df["close"] + df["_abs_net_lg_flow_ratio"] = abs(df["_net_lg_flow_val"]) / ( + df["_total_val"] + epsilon + ) + df["_abs_net_lg_flow_ratio_N"] = ( + df.groupby("ts_code")["_abs_net_lg_flow_ratio"] + .rolling(N, min_periods=max(1, N // 2)) + .mean() + .reset_index(level=0, drop=True) + ) + df[factor_name] = df["_vol_N"] * df["_abs_net_lg_flow_ratio_N"] except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -732,25 +975,47 @@ def lg_flow_vol_interact(df: pd.DataFrame, N: int = 20, factor_name: str = None) df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def cost_break_confirm_cnt(df: pd.DataFrame, M: int = 5, factor_name: str = None): """ Calculates Factor 12: Cost Breakout Confirmation Count (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'cost_break_confirm_cnt_{M}' + factor_name = f"cost_break_confirm_cnt_{M}" print(f"Calculating {factor_name}...") - _temp_cols = ['_prev_cost_85', '_prev_cost_15', '_break_up', '_break_down', '_net_lg_flow_vol', '_confirm_up', '_confirm_down', '_net_confirm'] + _temp_cols = [ + "_prev_cost_85", + "_prev_cost_15", + "_break_up", + "_break_down", + "_net_lg_flow_vol", + "_confirm_up", + "_confirm_down", + "_net_confirm", + ] try: - df['_prev_cost_85'] = df.groupby('ts_code')['cost_85pct'].shift(1) - df['_prev_cost_15'] = df.groupby('ts_code')['cost_15pct'].shift(1) - df['_break_up'] = df['close'] > df['_prev_cost_85'] - df['_break_down'] = df['close'] < df['_prev_cost_15'] - df['_net_lg_flow_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol'] - df['_confirm_up'] = (df['_break_up'] & (df['_net_lg_flow_vol'] > 0)).astype(int) - df['_confirm_down'] = (df['_break_down'] & (df['_net_lg_flow_vol'] < 0)).astype(int) - df['_net_confirm'] = df['_confirm_up'] - df['_confirm_down'] - factor_series = df.groupby('ts_code')['_net_confirm'].rolling(M, min_periods=1).sum().reset_index(level=0, drop=True) + df["_prev_cost_85"] = df.groupby("ts_code")["cost_85pct"].shift(1) + df["_prev_cost_15"] = df.groupby("ts_code")["cost_15pct"].shift(1) + df["_break_up"] = df["close"] > df["_prev_cost_85"] + df["_break_down"] = df["close"] < df["_prev_cost_15"] + df["_net_lg_flow_vol"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) + df["_confirm_up"] = (df["_break_up"] & (df["_net_lg_flow_vol"] > 0)).astype(int) + df["_confirm_down"] = (df["_break_down"] & (df["_net_lg_flow_vol"] < 0)).astype( + int + ) + df["_net_confirm"] = df["_confirm_up"] - df["_confirm_down"] + factor_series = ( + df.groupby("ts_code")["_net_confirm"] + .rolling(M, min_periods=1) + .sum() + .reset_index(level=0, drop=True) + ) df[factor_name] = factor_series except Exception as e: print(f"Error calculating {factor_name}: {e}") @@ -769,18 +1034,36 @@ def atr_norm_channel_pos(df: pd.DataFrame, N: int = 14, factor_name: str = None) WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'atr_norm_channel_pos_{N}' + factor_name = f"atr_norm_channel_pos_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_prev_close', '_h_l', '_h_pc', '_l_pc', '_tr', '_atr_N', '_roll_low_N'] + _temp_cols = [ + "_prev_close", + "_h_l", + "_h_pc", + "_l_pc", + "_tr", + "_atr_N", + "_roll_low_N", + ] try: - df['_prev_close'] = df.groupby('ts_code')['close'].shift(1) - df['_h_l'] = df['high'] - df['low'] - df['_h_pc'] = abs(df['high'] - df['_prev_close']) - df['_l_pc'] = abs(df['low'] - df['_prev_close']) - df['_tr'] = df[['_h_l', '_h_pc', '_l_pc']].max(axis=1) - df['_atr_N'] = df.groupby('ts_code')['_tr'].rolling(N, min_periods=max(1, N//2)).mean().reset_index(level=0, drop=True) - df['_roll_low_N'] = df.groupby('ts_code')['low'].rolling(N, min_periods=max(1, N//2)).min().reset_index(level=0, drop=True) - df[factor_name] = _safe_divide((df['close'] - df['_roll_low_N']), df['_atr_N']) + df["_prev_close"] = df.groupby("ts_code")["close"].shift(1) + df["_h_l"] = df["high"] - df["low"] + df["_h_pc"] = abs(df["high"] - df["_prev_close"]) + df["_l_pc"] = abs(df["low"] - df["_prev_close"]) + df["_tr"] = df[["_h_l", "_h_pc", "_l_pc"]].max(axis=1) + df["_atr_N"] = ( + df.groupby("ts_code")["_tr"] + .rolling(N, min_periods=max(1, N // 2)) + .mean() + .reset_index(level=0, drop=True) + ) + df["_roll_low_N"] = ( + df.groupby("ts_code")["low"] + .rolling(N, min_periods=max(1, N // 2)) + .min() + .reset_index(level=0, drop=True) + ) + df[factor_name] = _safe_divide((df["close"] - df["_roll_low_N"]), df["_atr_N"]) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -790,19 +1073,25 @@ def atr_norm_channel_pos(df: pd.DataFrame, N: int = 14, factor_name: str = None) df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def turnover_diff_skew(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 14: Skewness of Turnover Rate Change (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'turnover_diff_skew_{N}' + factor_name = f"turnover_diff_skew_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_turnover_diff'] + _temp_cols = ["_turnover_diff"] try: # Assuming turnover_rate is in percentage points, diff is fine - df['_turnover_diff'] = df.groupby('ts_code')['turnover_rate'].diff(1) - factor_series = df.groupby('ts_code')['_turnover_diff'].rolling(N, min_periods=max(3, N//2)).skew().reset_index(level=0, drop=True) + df["_turnover_diff"] = df.groupby("ts_code")["turnover_rate"].diff(1) + factor_series = ( + df.groupby("ts_code")["_turnover_diff"] + .rolling(N, min_periods=max(3, N // 2)) + .skew() + .reset_index(level=0, drop=True) + ) df[factor_name] = factor_series except Exception as e: print(f"Error calculating {factor_name}: {e}") @@ -813,27 +1102,47 @@ def turnover_diff_skew(df: pd.DataFrame, N: int = 20, factor_name: str = None): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def lg_sm_flow_diverge(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 15: Divergence between Large and Small Flow (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'lg_sm_flow_diverge_{N}' + factor_name = f"lg_sm_flow_diverge_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_lg_flow_ratio', '_sm_flow_ratio', '_lg_flow_ratio_N', '_sm_flow_ratio_N'] + _temp_cols = [ + "_lg_flow_ratio", + "_sm_flow_ratio", + "_lg_flow_ratio_N", + "_sm_flow_ratio_N", + ] try: - df['_lg_flow_ratio'] = _safe_divide( - (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']), - df['vol'] + df["_lg_flow_ratio"] = _safe_divide( + ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ), + df["vol"], ) - df['_sm_flow_ratio'] = _safe_divide( - (df['buy_sm_vol'] - df['sell_sm_vol']), - df['vol'] + df["_sm_flow_ratio"] = _safe_divide( + (df["buy_sm_vol"] - df["sell_sm_vol"]), df["vol"] ) - df['_lg_flow_ratio_N'] = df.groupby('ts_code')['_lg_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True) - df['_sm_flow_ratio_N'] = df.groupby('ts_code')['_sm_flow_ratio'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True) - df[factor_name] = df['_lg_flow_ratio_N'] - df['_sm_flow_ratio_N'] + df["_lg_flow_ratio_N"] = ( + df.groupby("ts_code")["_lg_flow_ratio"] + .rolling(N, min_periods=max(1, N // 2)) + .mean() + .reset_index(level=0, drop=True) + ) + df["_sm_flow_ratio_N"] = ( + df.groupby("ts_code")["_sm_flow_ratio"] + .rolling(N, min_periods=max(1, N // 2)) + .mean() + .reset_index(level=0, drop=True) + ) + df[factor_name] = df["_lg_flow_ratio_N"] - df["_sm_flow_ratio_N"] except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -844,7 +1153,9 @@ def lg_sm_flow_diverge(df: pd.DataFrame, N: int = 20, factor_name: str = None): print(f"Finished {factor_name}.") -def cap_neutral_cost_metric(df: pd.DataFrame, factor_name: str = 'cap_neutral_cost_metric'): +def cap_neutral_cost_metric( + df: pd.DataFrame, factor_name: str = "cap_neutral_cost_metric" +): """ Calculates Factor 16: Market Cap Neutralized Cost Metric (Placeholder). Requires statsmodels and complex implementation. @@ -855,20 +1166,35 @@ def cap_neutral_cost_metric(df: pd.DataFrame, factor_name: str = 'cap_neutral_co print(f"Finished {factor_name} (Placeholder).") -def pullback_strong(df: pd.DataFrame, N: int = 20, M: int = 20, gain_thresh: float = 0.2, factor_name: str = None): +def pullback_strong( + df: pd.DataFrame, + N: int = 20, + M: int = 20, + gain_thresh: float = 0.2, + factor_name: str = None, +): """ Calculates Factor 17: Pullback Depth from Recent High for Strong Stocks (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'pullback_strong_{N}_{M}' + factor_name = f"pullback_strong_{N}_{M}" print(f"Calculating {factor_name}...") - _temp_cols = ['_high_N', '_pullback_depth', '_recent_gain_M'] + _temp_cols = ["_high_N", "_pullback_depth", "_recent_gain_M"] try: - df['_high_N'] = df.groupby('ts_code')['high'].rolling(N, min_periods=max(1, N // 2)).max().reset_index(level=0, drop=True) - df['_pullback_depth'] = _safe_divide((df['_high_N'] - df['close']), df['_high_N']) - df['_recent_gain_M'] = _safe_divide(df['close'], df.groupby('ts_code')['close'].shift(M)) - 1 - df[factor_name] = _safe_divide(df['_pullback_depth'], df['_recent_gain_M']) + df["_high_N"] = ( + df.groupby("ts_code")["high"] + .rolling(N, min_periods=max(1, N // 2)) + .max() + .reset_index(level=0, drop=True) + ) + df["_pullback_depth"] = _safe_divide( + (df["_high_N"] - df["close"]), df["_high_N"] + ) + df["_recent_gain_M"] = ( + _safe_divide(df["close"], df.groupby("ts_code")["close"].shift(M)) - 1 + ) + df[factor_name] = _safe_divide(df["_pullback_depth"], df["_recent_gain_M"]) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -878,23 +1204,27 @@ def pullback_strong(df: pd.DataFrame, N: int = 20, M: int = 20, gain_thresh: flo df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def hurst_exponent_flow(df: pd.DataFrame, N: int = 60, flow_col: str = 'net_mf_vol', factor_name: str = None): + +def hurst_exponent_flow( + df: pd.DataFrame, N: int = 60, flow_col: str = "net_mf_vol", factor_name: str = None +): """ Calculates Factor 18: Hurst Exponent of Money Flow (Placeholder). Requires 'hurst' library and potentially slow rolling apply. WARNING: Modifies df in-place. Placeholder implementation returns NaN. """ if factor_name is None: - factor_name = f'hurst_{flow_col}_{N}' + factor_name = f"hurst_{flow_col}_{N}" print(f"Calculating {factor_name} (Placeholder - requires hurst library)...") try: from hurst import compute_Hc + # Logic would go here, likely using rolling().apply() which is slow # factor_series = df.groupby('ts_code')[flow_col]....apply(hurst_calc_func...) - df[factor_name] = np.nan # Placeholder + df[factor_name] = np.nan # Placeholder except ImportError: - print("Error: 'hurst' library not installed. Cannot calculate factor.") - df[factor_name] = np.nan + print("Error: 'hurst' library not installed. Cannot calculate factor.") + df[factor_name] = np.nan except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -907,14 +1237,21 @@ def vol_wgt_hist_pos(df: pd.DataFrame, N: int = 20, factor_name: str = None): WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'vol_wgt_hist_pos_{N}' + factor_name = f"vol_wgt_hist_pos_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_hist_pos', '_rolling_mean_vol', '_vol_rel_strength'] + _temp_cols = ["_hist_pos", "_rolling_mean_vol", "_vol_rel_strength"] try: - df['_hist_pos'] = _safe_divide((df['close'] - df['his_low']), (df['his_high'] - df['his_low'])).clip(0, 1) - df['_rolling_mean_vol'] = df.groupby('ts_code')['vol'].rolling(N, min_periods=max(1, N // 2)).mean().reset_index(level=0, drop=True) - df['_vol_rel_strength'] = _safe_divide(df['vol'], df['_rolling_mean_vol']) - df[factor_name] = df['_hist_pos'] * df['_vol_rel_strength'] + df["_hist_pos"] = _safe_divide( + (df["close"] - df["his_low"]), (df["his_high"] - df["his_low"]) + ).clip(0, 1) + df["_rolling_mean_vol"] = ( + df.groupby("ts_code")["vol"] + .rolling(N, min_periods=max(1, N // 2)) + .mean() + .reset_index(level=0, drop=True) + ) + df["_vol_rel_strength"] = _safe_divide(df["vol"], df["_rolling_mean_vol"]) + df[factor_name] = df["_hist_pos"] * df["_vol_rel_strength"] except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -924,19 +1261,28 @@ def vol_wgt_hist_pos(df: pd.DataFrame, N: int = 20, factor_name: str = None): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + def vol_adj_roc(df: pd.DataFrame, N: int = 20, factor_name: str = None): """ Calculates Factor 20: Volatility-Adjusted ROC (In-place). WARNING: Modifies df in-place. """ if factor_name is None: - factor_name = f'vol_adj_roc_{N}' + factor_name = f"vol_adj_roc_{N}" print(f"Calculating {factor_name}...") - _temp_cols = ['_roc_N', '_vol_N'] + _temp_cols = ["_roc_N", "_vol_N"] try: - df['_roc_N'] = _safe_divide(df['close'], df.groupby('ts_code')['close'].shift(N)) - 1 - df['_vol_N'] = df.groupby('ts_code')['pct_chg'].rolling(N, min_periods=max(2, N // 2)).std().reset_index(level=0, drop=True).fillna(0) - df[factor_name] = _safe_divide(df['_roc_N'], df['_vol_N']) + df["_roc_N"] = ( + _safe_divide(df["close"], df.groupby("ts_code")["close"].shift(N)) - 1 + ) + df["_vol_N"] = ( + df.groupby("ts_code")["pct_chg"] + .rolling(N, min_periods=max(2, N // 2)) + .std() + .reset_index(level=0, drop=True) + .fillna(0) + ) + df[factor_name] = _safe_divide(df["_roc_N"], df["_vol_N"]) except Exception as e: print(f"Error calculating {factor_name}: {e}") df[factor_name] = np.nan @@ -946,7 +1292,10 @@ def vol_adj_roc(df: pd.DataFrame, N: int = 20, factor_name: str = None): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def calculate_complex_factor(df: pd.DataFrame, factor_name: str = "complex_factor_deap_1"): + +def calculate_complex_factor( + df: pd.DataFrame, factor_name: str = "complex_factor_deap_1" +): """ 表达式: sub(protected_div_torch(A, B), C) 其中 A, B, C 及内部组件依赖于多个预计算因子列。 @@ -959,60 +1308,71 @@ def calculate_complex_factor(df: pd.DataFrame, factor_name: str = "complex_facto 如果在计算过程中缺少任何必需的列，将打印错误并填充 NaN。 """ print(f"开始计算因子: {factor_name} (原地修改)...") - _temp_cols_list = [] # 用于记录中间计算列的名称 + _temp_cols_list = [] # 用于记录中间计算列的名称 try: # --- 分解计算表达式的各个部分 --- # 计算组件 D # D = sub(mul(pullback_strong_20_20, div(log_close, industry_return_5)), div(add(vol_adj_roc_20, vol_drop_profit_cnt_5), sub(nonlinear_mv_volume, alpha_007))) - _temp_d_term1_div = _safe_divide(df['log_close'], df['industry_return_5']) - _temp_d_term1 = df['pullback_strong_20_20'] * _temp_d_term1_div - _temp_d_term2_sub = df['nonlinear_mv_volume'] - df['alpha_007'] - _temp_d_term2_add = df['vol_adj_roc_20'] + df['vol_drop_profit_cnt_5'] + _temp_d_term1_div = _safe_divide(df["log_close"], df["industry_return_5"]) + _temp_d_term1 = df["pullback_strong_20_20"] * _temp_d_term1_div + _temp_d_term2_sub = df["nonlinear_mv_volume"] - df["alpha_007"] + _temp_d_term2_add = df["vol_adj_roc_20"] + df["vol_drop_profit_cnt_5"] _temp_d_term2 = _safe_divide(_temp_d_term2_add, _temp_d_term2_sub) - df['_temp_D'] = _temp_d_term1 - _temp_d_term2 - _temp_cols_list.extend(['_temp_D', '_temp_d_term1_div', '_temp_d_term1', '_temp_d_term2_sub', '_temp_d_term2_add', '_temp_d_term2']) + df["_temp_D"] = _temp_d_term1 - _temp_d_term2 + _temp_cols_list.extend( + [ + "_temp_D", + "_temp_d_term1_div", + "_temp_d_term1", + "_temp_d_term2_sub", + "_temp_d_term2_add", + "_temp_d_term2", + ] + ) # 计算组件 A # A = add(add(mul(D, lg_buy_consolidation_20), lg_buy_consolidation_20), pullback_strong_20_20) - _temp_a_term1 = df['_temp_D'] * df['lg_buy_consolidation_20'] - _temp_a_term2 = _temp_a_term1 + df['lg_buy_consolidation_20'] - df['_temp_A'] = _temp_a_term2 + df['pullback_strong_20_20'] - _temp_cols_list.extend(['_temp_A', '_temp_a_term1', '_temp_a_term2']) + _temp_a_term1 = df["_temp_D"] * df["lg_buy_consolidation_20"] + _temp_a_term2 = _temp_a_term1 + df["lg_buy_consolidation_20"] + df["_temp_A"] = _temp_a_term2 + df["pullback_strong_20_20"] + _temp_cols_list.extend(["_temp_A", "_temp_a_term1", "_temp_a_term2"]) # 计算组件 F # F = mul(add(net_mf_vol, std_return_5), sub(arbr, industry_act_factor5)) - _temp_f_term1 = df['net_mf_vol'] + df['std_return_5'] - _temp_f_term2 = df['arbr'] - df['industry_act_factor5'] - df['_temp_F'] = _temp_f_term1 * _temp_f_term2 - _temp_cols_list.extend(['_temp_F', '_temp_f_term1', '_temp_f_term2']) + _temp_f_term1 = df["net_mf_vol"] + df["std_return_5"] + _temp_f_term2 = df["arbr"] - df["industry_act_factor5"] + df["_temp_F"] = _temp_f_term1 * _temp_f_term2 + _temp_cols_list.extend(["_temp_F", "_temp_f_term1", "_temp_f_term2"]) # 计算组件 H # H = add(add(industry_act_factor1, low_cost_dev), mul(mv_weighted_turnover, act_factor4)) - _temp_h_term1 = df['industry_act_factor1'] + df['low_cost_dev'] - _temp_h_term2 = df['mv_weighted_turnover'] * df['act_factor4'] - df['_temp_H'] = _temp_h_term1 + _temp_h_term2 - _temp_cols_list.extend(['_temp_H', '_temp_h_term1', '_temp_h_term2']) + _temp_h_term1 = df["industry_act_factor1"] + df["low_cost_dev"] + _temp_h_term2 = df["mv_weighted_turnover"] * df["act_factor4"] + df["_temp_H"] = _temp_h_term1 + _temp_h_term2 + _temp_cols_list.extend(["_temp_H", "_temp_h_term1", "_temp_h_term2"]) # 计算组件 B # B = div(add(add(F, vol), H), lg_elg_buy_prop) - _temp_b_term1 = df['_temp_F'] + df['vol'] - _temp_b_term2 = _temp_b_term1 + df['_temp_H'] - df['_temp_B'] = _safe_divide(_temp_b_term2, df['lg_elg_buy_prop']) - _temp_cols_list.extend(['_temp_B', '_temp_b_term1', '_temp_b_term2']) + _temp_b_term1 = df["_temp_F"] + df["vol"] + _temp_b_term2 = _temp_b_term1 + df["_temp_H"] + df["_temp_B"] = _safe_divide(_temp_b_term2, df["lg_elg_buy_prop"]) + _temp_cols_list.extend(["_temp_B", "_temp_b_term1", "_temp_b_term2"]) # 计算组件 C # C = div(div(intraday_lg_flow_corr_20, lg_elg_buy_prop), lg_elg_buy_prop) # 注意: intraday_lg_flow_corr_20 可能本身就是 NaN 或需要特殊处理 - _temp_c_term1 = _safe_divide(df.get('intraday_lg_flow_corr_20', np.nan), df['lg_elg_buy_prop']) # 使用 .get 处理可能不存在的列 - df['_temp_C'] = _safe_divide(_temp_c_term1, df['lg_elg_buy_prop']) - _temp_cols_list.extend(['_temp_C', '_temp_c_term1']) + _temp_c_term1 = _safe_divide( + df.get("intraday_lg_flow_corr_20", np.nan), df["lg_elg_buy_prop"] + ) # 使用 .get 处理可能不存在的列 + df["_temp_C"] = _safe_divide(_temp_c_term1, df["lg_elg_buy_prop"]) + _temp_cols_list.extend(["_temp_C", "_temp_c_term1"]) # --- 计算最终表达式 --- # final = sub(div(A, B), C) - _temp_final_term1 = _safe_divide(df['_temp_A'], df['_temp_B']) - final_factor_series = _temp_final_term1 - df['_temp_C'] + _temp_final_term1 = _safe_divide(df["_temp_A"], df["_temp_B"]) + final_factor_series = _temp_final_term1 - df["_temp_C"] # --- 将最终结果赋值给 df 的新列 (原地修改) --- df[factor_name] = final_factor_series @@ -1024,12 +1384,12 @@ def calculate_complex_factor(df: pd.DataFrame, factor_name: str = "complex_facto print(f"错误: 计算 {factor_name} 时缺少必需的列: {e}") print("请确保输入的 DataFrame 包含所有表达式中引用的因子列。") print("将为因子 {factor_name} 填充 NaN。") - df[factor_name] = np.nan # 出错时填充 NaN + df[factor_name] = np.nan # 出错时填充 NaN except Exception as e: # 捕获其他可能的计算错误 print(f"错误: 计算 {factor_name} 时发生意外错误: {e}") print(f"将为因子 {factor_name} 填充 NaN。") - df[factor_name] = np.nan # 出错时填充 NaN + df[factor_name] = np.nan # 出错时填充 NaN finally: # --- 清理所有中间计算列 --- cols_to_drop = [col for col in _temp_cols_list if col in df.columns] @@ -1039,13 +1399,16 @@ def calculate_complex_factor(df: pd.DataFrame, factor_name: str = "complex_facto print(f"因子 {factor_name} 计算流程结束。") # 函数不返回任何值，因为 df 是原地修改的 + import pandas as pd import numpy as np + # from scipy.stats import rankdata # rankdata is not needed if using pandas rank # import statsmodels.api as sm # Needed for factor 19 # --- Constants --- -epsilon = 1e-10 # Prevent division by zero +epsilon = 1e-10 # Prevent division by zero + # --- Helper Functions --- def _safe_divide(numerator, denominator, default_val=0): @@ -1060,40 +1423,53 @@ def _safe_divide(numerator, denominator, default_val=0): Returns: pd.Series: 除法结果. """ - with np.errstate(divide='ignore', invalid='ignore'): + with np.errstate(divide="ignore", invalid="ignore"): # Convert inputs to numeric, coercing errors to NaN before division - num = pd.to_numeric(numerator, errors='coerce') - den = pd.to_numeric(denominator, errors='coerce') + num = pd.to_numeric(numerator, errors="coerce") + den = pd.to_numeric(denominator, errors="coerce") # Perform division where denominator is not close to zero and inputs are valid numbers result = np.where(np.abs(den) > epsilon, num / den, default_val) # Ensure result is float, handle potential NaNs from coercion or division - result = pd.to_numeric(result, errors='coerce') + result = pd.to_numeric(result, errors="coerce") # Fill remaining NaNs if necessary - result = np.nan_to_num(result, nan=default_val, posinf=default_val, neginf=default_val) + result = np.nan_to_num( + result, nan=default_val, posinf=default_val, neginf=default_val + ) # Ensure result index matches numerator's index if numerator is a Series if isinstance(numerator, pd.Series): return pd.Series(result, index=numerator.index) else: - return pd.Series(result) # Fallback if numerator is not a Series (less likely) + return pd.Series(result) # Fallback if numerator is not a Series (less likely) + # --- Cross-Sectional Factor Calculation Functions (In-Place Modification) --- + # Category 1: Cross-Sectional Flow & Behavior Strength -def cs_rank_net_lg_flow_val(df: pd.DataFrame, factor_name: str = 'cs_rank_net_lg_flow_val'): +def cs_rank_net_lg_flow_val( + df: pd.DataFrame, factor_name: str = "cs_rank_net_lg_flow_val" +): """ Factor 1: 大单净额截面排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_net_lg_flow_val'] + _temp_cols = ["_net_lg_flow_val"] try: - df['_net_lg_flow_val'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close'] - df[factor_name] = df.groupby('trade_date')['_net_lg_flow_val'].rank(pct=True) + df["_net_lg_flow_val"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) * df["close"] + df[factor_name] = df.groupby("trade_date")["_net_lg_flow_val"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1101,29 +1477,38 @@ def cs_rank_net_lg_flow_val(df: pd.DataFrame, factor_name: str = 'cs_rank_net_lg df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_flow_divergence(df: pd.DataFrame, factor_name: str = 'cs_rank_flow_divergence'): + +def cs_rank_flow_divergence( + df: pd.DataFrame, factor_name: str = "cs_rank_flow_divergence" +): """ Factor 2: 大小单流向背离度截面排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_lg_ratio', '_sm_ratio', '_divergence'] + _temp_cols = ["_lg_ratio", "_sm_ratio", "_divergence"] try: - df['_lg_ratio'] = _safe_divide( - (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']), - df['vol'] + df["_lg_ratio"] = _safe_divide( + ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ), + df["vol"], ) - df['_sm_ratio'] = _safe_divide( - (df['buy_sm_vol'] - df['sell_sm_vol']), - df['vol'] + df["_sm_ratio"] = _safe_divide( + (df["buy_sm_vol"] - df["sell_sm_vol"]), df["vol"] ) - df['_divergence'] = df['_lg_ratio'] - df['_sm_ratio'] - df[factor_name] = df.groupby('trade_date')['_divergence'].rank(pct=True) + df["_divergence"] = df["_lg_ratio"] - df["_sm_ratio"] + df[factor_name] = df.groupby("trade_date")["_divergence"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1131,27 +1516,43 @@ def cs_rank_flow_divergence(df: pd.DataFrame, factor_name: str = 'cs_rank_flow_d df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_industry_adj_lg_flow(df: pd.DataFrame, factor_name: str = 'cs_rank_ind_adj_lg_flow'): + +def cs_rank_industry_adj_lg_flow( + df: pd.DataFrame, factor_name: str = "cs_rank_ind_adj_lg_flow" +): """ Factor 3: 行业内大单流强度排序 (In-place). Requires 'cat_l2_code'. WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_net_lg_flow_vol', '_industry_avg_flow', '_deviation'] - if 'cat_l2_code' not in df.columns: - print(f"Error calculating {factor_name}: Missing 'cat_l2_code' column. Assigning NaN.") + _temp_cols = ["_net_lg_flow_vol", "_industry_avg_flow", "_deviation"] + if "cat_l2_code" not in df.columns: + print( + f"Error calculating {factor_name}: Missing 'cat_l2_code' column. Assigning NaN." + ) df[factor_name] = np.nan return try: - df['_net_lg_flow_vol'] = (df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol']) * df['close'] # Or use vol - df['_industry_avg_flow'] = df.groupby(['trade_date', 'cat_l2_code'])['_net_lg_flow_vol'].transform('mean') - df['_deviation'] = df['_net_lg_flow_vol'] - df['_industry_avg_flow'] - df[factor_name] = df.groupby('trade_date')['_deviation'].rank(pct=True) + df["_net_lg_flow_vol"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) * df[ + "close" + ] # Or use vol + df["_industry_avg_flow"] = df.groupby(["trade_date", "cat_l2_code"])[ + "_net_lg_flow_vol" + ].transform("mean") + df["_deviation"] = df["_net_lg_flow_vol"] - df["_industry_avg_flow"] + df[factor_name] = df.groupby("trade_date")["_deviation"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1159,21 +1560,24 @@ def cs_rank_industry_adj_lg_flow(df: pd.DataFrame, factor_name: str = 'cs_rank_i df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_elg_buy_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_elg_buy_ratio'): + +def cs_rank_elg_buy_ratio(df: pd.DataFrame, factor_name: str = "cs_rank_elg_buy_ratio"): """ Factor 4: 超大单买入占比排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_elg_buy_ratio'] + _temp_cols = ["_elg_buy_ratio"] try: - df['_elg_buy_ratio'] = _safe_divide(df['buy_elg_vol'], df['vol']) - df[factor_name] = df.groupby('trade_date')['_elg_buy_ratio'].rank(pct=True) + df["_elg_buy_ratio"] = _safe_divide(df["buy_elg_vol"], df["vol"]) + df[factor_name] = df.groupby("trade_date")["_elg_buy_ratio"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1181,22 +1585,29 @@ def cs_rank_elg_buy_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_elg_buy_ df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + # Category 2: Cross-Sectional Cost Basis & PnL Status -def cs_rank_rel_profit_margin(df: pd.DataFrame, factor_name: str = 'cs_rank_rel_profit_margin'): +def cs_rank_rel_profit_margin( + df: pd.DataFrame, factor_name: str = "cs_rank_rel_profit_margin" +): """ Factor 5: 相对盈利幅度排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_profit_margin'] + _temp_cols = ["_profit_margin"] try: - df['_profit_margin'] = _safe_divide((df['close'] - df['weight_avg']), df['close']) - df[factor_name] = df.groupby('trade_date')['_profit_margin'].rank(pct=True) + df["_profit_margin"] = _safe_divide( + (df["close"] - df["weight_avg"]), df["close"] + ) + df[factor_name] = df.groupby("trade_date")["_profit_margin"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1204,21 +1615,26 @@ def cs_rank_rel_profit_margin(df: pd.DataFrame, factor_name: str = 'cs_rank_rel_ df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_cost_breadth(df: pd.DataFrame, factor_name: str = 'cs_rank_cost_breadth'): + +def cs_rank_cost_breadth(df: pd.DataFrame, factor_name: str = "cs_rank_cost_breadth"): """ Factor 6: 成本分布宽度截面排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_cost_breadth'] + _temp_cols = ["_cost_breadth"] try: - df['_cost_breadth'] = _safe_divide((df['cost_85pct'] - df['cost_15pct']), df['weight_avg']) - df[factor_name] = df.groupby('trade_date')['_cost_breadth'].rank(pct=True) + df["_cost_breadth"] = _safe_divide( + (df["cost_85pct"] - df["cost_15pct"]), df["weight_avg"] + ) + df[factor_name] = df.groupby("trade_date")["_cost_breadth"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1226,21 +1642,26 @@ def cs_rank_cost_breadth(df: pd.DataFrame, factor_name: str = 'cs_rank_cost_brea df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_dist_to_upper_cost(df: pd.DataFrame, factor_name: str = 'cs_rank_dist_to_upper_cost'): + +def cs_rank_dist_to_upper_cost( + df: pd.DataFrame, factor_name: str = "cs_rank_dist_to_upper_cost" +): """ Factor 7: 股价相对高成本位距离排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_dist_to_95'] + _temp_cols = ["_dist_to_95"] try: - df['_dist_to_95'] = _safe_divide(df['close'], df['cost_95pct']) - df[factor_name] = df.groupby('trade_date')['_dist_to_95'].rank(pct=True) + df["_dist_to_95"] = _safe_divide(df["close"], df["cost_95pct"]) + df[factor_name] = df.groupby("trade_date")["_dist_to_95"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1248,40 +1669,47 @@ def cs_rank_dist_to_upper_cost(df: pd.DataFrame, factor_name: str = 'cs_rank_dis df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_winner_rate(df: pd.DataFrame, factor_name: str = 'cs_rank_winner_rate'): + +def cs_rank_winner_rate(df: pd.DataFrame, factor_name: str = "cs_rank_winner_rate"): """ Factor 8: 获利盘比例截面排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") try: - df[factor_name] = df.groupby('trade_date')['winner_rate'].rank(pct=True) + df[factor_name] = df.groupby("trade_date")["winner_rate"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: print(f"Finished {factor_name}.") # Category 3: Cross-Sectional Price Action & Volatility -def cs_rank_intraday_range(df: pd.DataFrame, factor_name: str = 'cs_rank_intraday_range'): +def cs_rank_intraday_range( + df: pd.DataFrame, factor_name: str = "cs_rank_intraday_range" +): """ Factor 9: 日内相对振幅排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_norm_range'] + _temp_cols = ["_norm_range"] try: - df['_norm_range'] = _safe_divide((df['high'] - df['low']), df['close']) - df[factor_name] = df.groupby('trade_date')['_norm_range'].rank(pct=True) + df["_norm_range"] = _safe_divide((df["high"] - df["low"]), df["close"]) + df[factor_name] = df.groupby("trade_date")["_norm_range"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1289,21 +1717,28 @@ def cs_rank_intraday_range(df: pd.DataFrame, factor_name: str = 'cs_rank_intrada df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_close_pos_in_range(df: pd.DataFrame, factor_name: str = 'cs_rank_close_pos_in_range'): + +def cs_rank_close_pos_in_range( + df: pd.DataFrame, factor_name: str = "cs_rank_close_pos_in_range" +): """ Factor 10: 收盘价在日内位置排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_close_pos'] + _temp_cols = ["_close_pos"] try: - df['_close_pos'] = _safe_divide((df['close'] - df['low']), (df['high'] - df['low']), default_val=0.5) # Assign 0.5 if high==low - df[factor_name] = df.groupby('trade_date')['_close_pos'].rank(pct=True) + df["_close_pos"] = _safe_divide( + (df["close"] - df["low"]), (df["high"] - df["low"]), default_val=0.5 + ) # Assign 0.5 if high==low + df[factor_name] = df.groupby("trade_date")["_close_pos"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1311,25 +1746,32 @@ def cs_rank_close_pos_in_range(df: pd.DataFrame, factor_name: str = 'cs_rank_clo df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_opening_gap(df: pd.DataFrame, factor_name: str = 'cs_rank_opening_gap'): + +def cs_rank_opening_gap(df: pd.DataFrame, factor_name: str = "cs_rank_opening_gap"): """ Factor 11: 开盘相对跳空幅度排序 (In-place). Needs pre_close. WARNING: Modifies df in-place. Assumes 'pre_close' exists. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_gap'] - if 'pre_close' not in df.columns: - print(f"Error calculating {factor_name}: Missing 'pre_close' column. Assigning NaN.") + _temp_cols = ["_gap"] + if "pre_close" not in df.columns: + print( + f"Error calculating {factor_name}: Missing 'pre_close' column. Assigning NaN." + ) df[factor_name] = np.nan return try: - df['_gap'] = _safe_divide(df['open'], df['pre_close']) - 1 - df[factor_name] = df.groupby('trade_date')['_gap'].rank(pct=True) + df["_gap"] = _safe_divide(df["open"], df["pre_close"]) - 1 + df[factor_name] = df.groupby("trade_date")["_gap"].rank(pct=True) except KeyError as e: - print(f"Error calculating {factor_name}: Missing column {e} (likely 'open'). Assigning NaN.") + print( + f"Error calculating {factor_name}: Missing column {e} (likely 'open'). Assigning NaN." + ) df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1337,21 +1779,30 @@ def cs_rank_opening_gap(df: pd.DataFrame, factor_name: str = 'cs_rank_opening_ga df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_pos_in_hist_range(df: pd.DataFrame, factor_name: str = 'cs_rank_pos_in_hist_range'): + +def cs_rank_pos_in_hist_range( + df: pd.DataFrame, factor_name: str = "cs_rank_pos_in_hist_range" +): """ Factor 12: 相对历史波动位置排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_hist_pos'] + _temp_cols = ["_hist_pos"] try: - df['_hist_pos'] = _safe_divide((df['close'] - df['his_low']), (df['his_high'] - df['his_low'])).clip(0, 1) # Clip to 0-1 range - df[factor_name] = df.groupby('trade_date')['_hist_pos'].rank(pct=True) + df["_hist_pos"] = _safe_divide( + (df["close"] - df["his_low"]), (df["his_high"] - df["his_low"]) + ).clip( + 0, 1 + ) # Clip to 0-1 range + df[factor_name] = df.groupby("trade_date")["_hist_pos"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1361,23 +1812,29 @@ def cs_rank_pos_in_hist_range(df: pd.DataFrame, factor_name: str = 'cs_rank_pos_ # Category 4: Cross-Sectional Interaction & Composite Indicators -def cs_rank_vol_x_profit_margin(df: pd.DataFrame, factor_name: str = 'cs_rank_vol_x_profit_margin'): +def cs_rank_vol_x_profit_margin( + df: pd.DataFrame, factor_name: str = "cs_rank_vol_x_profit_margin" +): """ Factor 13: 波动率与盈亏状态交互排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_daily_vol', '_profit_margin', '_interaction'] + _temp_cols = ["_daily_vol", "_profit_margin", "_interaction"] try: - df['_daily_vol'] = abs(df['pct_chg']) - df['_profit_margin'] = _safe_divide((df['close'] - df['weight_avg']), df['close']) - df['_interaction'] = df['_daily_vol'] * df['_profit_margin'] - df[factor_name] = df.groupby('trade_date')['_interaction'].rank(pct=True) + df["_daily_vol"] = abs(df["pct_chg"]) + df["_profit_margin"] = _safe_divide( + (df["close"] - df["weight_avg"]), df["close"] + ) + df["_interaction"] = df["_daily_vol"] * df["_profit_margin"] + df[factor_name] = df.groupby("trade_date")["_interaction"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1385,22 +1842,32 @@ def cs_rank_vol_x_profit_margin(df: pd.DataFrame, factor_name: str = 'cs_rank_vo df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_lg_flow_price_concordance(df: pd.DataFrame, factor_name: str = 'cs_rank_lg_flow_price_concordance'): + +def cs_rank_lg_flow_price_concordance( + df: pd.DataFrame, factor_name: str = "cs_rank_lg_flow_price_concordance" +): """ Factor 14: 大单流向与价格变动一致性排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_net_lg_flow_vol', '_concordance'] + _temp_cols = ["_net_lg_flow_vol", "_concordance"] try: - df['_net_lg_flow_vol'] = df['buy_lg_vol'] + df['buy_elg_vol'] - df['sell_lg_vol'] - df['sell_elg_vol'] - df['_concordance'] = df['_net_lg_flow_vol'] * df['pct_chg'] - df[factor_name] = df.groupby('trade_date')['_concordance'].rank(pct=True) + df["_net_lg_flow_vol"] = ( + df["buy_lg_vol"] + + df["buy_elg_vol"] + - df["sell_lg_vol"] + - df["sell_elg_vol"] + ) + df["_concordance"] = df["_net_lg_flow_vol"] * df["pct_chg"] + df[factor_name] = df.groupby("trade_date")["_concordance"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1408,21 +1875,30 @@ def cs_rank_lg_flow_price_concordance(df: pd.DataFrame, factor_name: str = 'cs_r df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_turnover_per_winner(df: pd.DataFrame, factor_name: str = 'cs_rank_turnover_per_winner'): + +def cs_rank_turnover_per_winner( + df: pd.DataFrame, factor_name: str = "cs_rank_turnover_per_winner" +): """ Factor 15: 高换手获利盘占比排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_turnover_per_winner'] + _temp_cols = ["_turnover_per_winner"] try: - df['_turnover_per_winner'] = _safe_divide(df['turnover_rate'], df['winner_rate']) - df[factor_name] = df.groupby('trade_date')['_turnover_per_winner'].rank(pct=True) + df["_turnover_per_winner"] = _safe_divide( + df["turnover_rate"], df["winner_rate"] + ) + df[factor_name] = df.groupby("trade_date")["_turnover_per_winner"].rank( + pct=True + ) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1430,7 +1906,10 @@ def cs_rank_turnover_per_winner(df: pd.DataFrame, factor_name: str = 'cs_rank_tu df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_ind_cap_neutral_pe(df: pd.DataFrame, factor_name: str = 'cs_rank_ind_cap_neutral_pe'): + +def cs_rank_ind_cap_neutral_pe( + df: pd.DataFrame, factor_name: str = "cs_rank_ind_cap_neutral_pe" +): """ Factor 16: 行业市值中性化PE排序 (Placeholder). Requires statsmodels and complex cross-sectional regression implementation. @@ -1440,7 +1919,8 @@ def cs_rank_ind_cap_neutral_pe(df: pd.DataFrame, factor_name: str = 'cs_rank_ind df[factor_name] = np.nan print(f"Finished {factor_name} (Placeholder).") -def cs_rank_volume_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_volume_ratio'): + +def cs_rank_volume_ratio(df: pd.DataFrame, factor_name: str = "cs_rank_volume_ratio"): """ Factor 17: 成交量相对强度排序 (In-place). WARNING: Modifies df in-place. @@ -1448,31 +1928,38 @@ def cs_rank_volume_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_volume_ra print(f"Calculating {factor_name}...") try: # Assumes 'volume_ratio' (量比) column already exists - df[factor_name] = df.groupby('trade_date')['volume_ratio'].rank(pct=True) + df[factor_name] = df.groupby("trade_date")["volume_ratio"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: print(f"Finished {factor_name}.") -def cs_rank_elg_buy_sell_sm_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_elg_buy_sell_sm_ratio'): + +def cs_rank_elg_buy_sell_sm_ratio( + df: pd.DataFrame, factor_name: str = "cs_rank_elg_buy_sell_sm_ratio" +): """ Factor 18: 超大单买入与小单卖出比排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_ratio'] + _temp_cols = ["_ratio"] try: - df['_ratio'] = _safe_divide(df['buy_elg_vol'], df['sell_sm_vol']) - df[factor_name] = df.groupby('trade_date')['_ratio'].rank(pct=True) + df["_ratio"] = _safe_divide(df["buy_elg_vol"], df["sell_sm_vol"]) + df[factor_name] = df.groupby("trade_date")["_ratio"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1480,26 +1967,33 @@ def cs_rank_elg_buy_sell_sm_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_ df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_cost_dist_vol_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_cost_dist_vol_ratio'): + +def cs_rank_cost_dist_vol_ratio( + df: pd.DataFrame, factor_name: str = "cs_rank_cost_dist_vol_ratio" +): """ Factor 19: 价格偏离成本程度与成交量放大交互排序 (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_dist', '_interaction'] - if 'volume_ratio' not in df.columns: - print(f"Error calculating {factor_name}: Missing 'volume_ratio' column. Assigning NaN.") - df[factor_name] = np.nan - return + _temp_cols = ["_dist", "_interaction"] + if "volume_ratio" not in df.columns: + print( + f"Error calculating {factor_name}: Missing 'volume_ratio' column. Assigning NaN." + ) + df[factor_name] = np.nan + return try: - df['_dist'] = abs(df['close'] - df['weight_avg']) / (df['close'] + epsilon) - df['_interaction'] = df['_dist'] * df['volume_ratio'] - df[factor_name] = df.groupby('trade_date')['_interaction'].rank(pct=True) + df["_dist"] = abs(df["close"] - df["weight_avg"]) / (df["close"] + epsilon) + df["_interaction"] = df["_dist"] * df["volume_ratio"] + df[factor_name] = df.groupby("trade_date")["_interaction"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1507,22 +2001,25 @@ def cs_rank_cost_dist_vol_ratio(df: pd.DataFrame, factor_name: str = 'cs_rank_co df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") -def cs_rank_size(df: pd.DataFrame, factor_name: str = 'cs_rank_size'): + +def cs_rank_size(df: pd.DataFrame, factor_name: str = "cs_rank_size"): """ Factor 20: 市值因子暴露度排序 (Log of circ_mv) (In-place). WARNING: Modifies df in-place. """ print(f"Calculating {factor_name}...") - _temp_cols = ['_log_circ_mv'] + _temp_cols = ["_log_circ_mv"] try: # Use log1p for stability if circ_mv can be zero or very small - df['_log_circ_mv'] = np.log1p(df['circ_mv']) - df[factor_name] = df.groupby('trade_date')['_log_circ_mv'].rank(pct=True) + df["_log_circ_mv"] = np.log1p(df["circ_mv"]) + df[factor_name] = df.groupby("trade_date")["_log_circ_mv"].rank(pct=True) except KeyError as e: print(f"Error calculating {factor_name}: Missing column {e}. Assigning NaN.") df[factor_name] = np.nan except Exception as e: - print(f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN.") + print( + f"An unexpected error occurred calculating {factor_name}: {e}. Assigning NaN." + ) df[factor_name] = np.nan finally: cols_to_drop = [col for col in _temp_cols if col in df.columns] @@ -1530,3 +2027,523 @@ def cs_rank_size(df: pd.DataFrame, factor_name: str = 'cs_rank_size'): df.drop(columns=cols_to_drop, inplace=True) print(f"Finished {factor_name}.") + +def add_financial_factor( + main_df: pd.DataFrame, + financial_df: pd.DataFrame, + ts_code_col: str = "ts_code", + trade_date_col: str = "trade_date", + ann_date_col: str = "ann_date", # 公告日期 + f_ann_date_col: str = "f_ann_date", # 实际公告日期 (优先使用) + factor_value_col: str = "undist_profit_ps", # 财务指标值所在的列 + new_factor_col_name: str = "retained_profit_per_share", # 新因子列的名称 +) -> pd.DataFrame: + """ + 将财务指标数据（如每股未分配利润）作为因子添加到主时间序列 DataFrame 中。 + + 使用 merge_asof 根据股票代码和公告日期，将最新的财务指标值匹配到每个交易日。 + + Args: + main_df: 包含时间序列交易数据的主 DataFrame (至少包含 ts_code_col 和 trade_date_col)。 + financial_df: 包含财务指标数据的 DataFrame (至少包含 ts_code_col, + ann_date_col 或 f_ann_date_col, 以及 factor_value_col)。 + ts_code_col: 股票代码列在两个 DataFrame 中的名称。默认为 'ts_code'。 + trade_date_col: 交易日期列在 main_df 中的名称。默认为 'trade_date'。 + ann_date_col: 公告日期列在 financial_df 中的名称（作为 f_ann_date_col 的备选）。默认为 'ann_date'。 + f_ann_date_col: 实际公告日期列在 financial_df 中的名称（优先使用）。默认为 'f_ann_date'。 + factor_value_col: 财务指标值（即要添加的因子值）在 financial_df 中的列名。默认为 'undistr_pft_ps'。 + new_factor_col_name: 添加到 main_df 中的新因子列的名称。默认为 'retained_profit_per_share'。 + + Returns: + 包含新因子列的 main_df DataFrame。 + """ + # --- 数据校验 --- + required_main_cols = [ts_code_col, trade_date_col] + if not all(col in main_df.columns for col in required_main_cols): + raise ValueError(f"主 DataFrame 必须包含列: {required_main_cols}") + + required_financial_cols = [ts_code_col, factor_value_col] + if f_ann_date_col and f_ann_date_col in financial_df.columns: + effective_date_col = f_ann_date_col + elif ann_date_col and ann_date_col in financial_df.columns: + effective_date_col = ann_date_col + else: + raise ValueError( + f"财务指标 DataFrame 必须包含列 '{f_ann_date_col}' 或 '{ann_date_col}' 作为数据生效日期" + ) + required_financial_cols.append(effective_date_col) + + if not all(col in financial_df.columns for col in required_financial_cols): + raise ValueError(f"财务指标 DataFrame 必须包含列: {required_financial_cols}") + + # --- 数据预处理 --- + + # 复制 main_df 避免修改原始 DataFrame + main_df = main_df.copy() + + # 确保日期列是 datetime 类型 + main_df[trade_date_col] = pd.to_datetime(main_df[trade_date_col]) + financial_df[effective_date_col] = pd.to_datetime(financial_df[effective_date_col]) + + # 确保股票代码是字符串类型，以便合并时类型一致 + main_df[ts_code_col] = main_df[ts_code_col].astype(str) + financial_df[ts_code_col] = financial_df[ts_code_col].astype(str) + + # 选取 financial_df 中需要合并的列，并为 merge_asof 准备日期列 + financial_data_subset = financial_df[ + [ts_code_col, effective_date_col, factor_value_col] + ].copy() + # 重命名 effective_date_col 为一个统一的名称，方便 merge_asof + # merge_asof 需要 right_on 参数，使用原始列名即可，不需要重命名 + + # 为了使用 merge_asof，两个 DataFrame 都必须按合并键 (ts_code) 和日期列排序 + main_df = main_df.sort_values(by=[ts_code_col, trade_date_col]) + financial_data_subset = financial_data_subset.sort_values( + by=[ts_code_col, effective_date_col] + ) + + # --- 使用 merge_asof 计算因子 --- + + # 执行 as-of 合并 + df_with_factor = pd.merge_asof( + main_df, + financial_data_subset, + left_on=trade_date_col, # main_df 中用于匹配的日期列 + right_on=effective_date_col, # financial_data_subset 中用于匹配的日期列 + by=ts_code_col, # 按股票代码进行分组匹配 + direction="backward", # 匹配方向：向后查找（即找 <= trade_date 的最近数据） + # 如果您需要容忍日期上的微小差异，可以使用 tolerance 参数 + # tolerance=pd.Timedelta('1 days') + ) + + # 清理：移除用于匹配的 effective_date_col，以及原始 financial_df 中可能带来的其他重复列 + # merge_asof 默认不会带上 right DataFrame 中用于合并的 key 列，但如果名称不同可能会带上 + # 这里的清理主要针对 effective_date_col + if ( + effective_date_col in df_with_factor.columns + and effective_date_col != trade_date_col + ): + # 确保不是trade_date_col本身被意外重命名 + df_with_factor = df_with_factor.drop(columns=[effective_date_col]) + + # 重命名新加入的因子列 + # merge_asof 会将 factor_value_col 直接带入，名称不变 + # 我们将其重命名为 new_factor_col_name + if factor_value_col != new_factor_col_name: + if factor_value_col in df_with_factor.columns: + df_with_factor = df_with_factor.rename( + columns={factor_value_col: new_factor_col_name} + ) + else: + print( + f"警告: 合并后未找到列 '{factor_value_col}'，无法重命名为 '{new_factor_col_name}'。" + ) + + # --- 返回结果 --- + return df_with_factor + + +# --- ARBR 因子计算函数 --- + + +def calculate_arbr(df: pd.DataFrame, N: int = 26): + """ + 计算 AR 和 BR 指标，并将结果原地添加到 DataFrame 中。 + + Args: + df (pd.DataFrame): 输入的 DataFrame，必须包含 'ts_code', 'trade_date', + 'open', 'high', 'low', 'close' 列。 + 建议预先按 ts_code, trade_date 排序。 + N (int): 计算 AR, BR 的窗口期，默认为 26。 + + WARNING: 此函数会原地修改输入的 DataFrame 'df'。 + """ + ar_col_name = 'AR' + br_col_name = 'BR' + print(f"开始计算因子: {ar_col_name}, {br_col_name} (原地修改)...") + + _temp_cols = [] # 记录中间列 + + try: + # 0. 确保排序 (虽然 groupby 会处理，但有序更保险) + # df.sort_values(['ts_code', 'trade_date'], inplace=True) # 如果不确定df已排序 + + # 1. 计算所需中间值 + df["_h_minus_o"] = df["high"] - df["open"] + df["_o_minus_l"] = df["open"] - df["low"] + df["_prev_close"] = df.groupby("ts_code")["close"].shift(1) + # BR 计算需要 max(0, H-PC) 和 max(0, PC-L) + df["_h_minus_pc_pos"] = np.maximum(0, df["high"] - df["_prev_close"]) + df["_pc_minus_l_pos"] = np.maximum(0, df["_prev_close"] - df["low"]) + _temp_cols.extend( + [ + "_h_minus_o", + "_o_minus_l", + "_prev_close", + "_h_minus_pc_pos", + "_pc_minus_l_pos", + ] + ) + + # 2. 计算滚动和 + # 使用 min_periods=N 确保有完整的窗口数据才计算，也可以用 N//2 等 + min_p = N # 严格要求 N 天数据 + grouped = df.groupby("ts_code") + + sum_h_minus_o = ( + grouped["_h_minus_o"] + .rolling(N, min_periods=min_p) + .sum() + .reset_index(level=0, drop=True) + ) + sum_o_minus_l = ( + grouped["_o_minus_l"] + .rolling(N, min_periods=min_p) + .sum() + .reset_index(level=0, drop=True) + ) + sum_h_minus_pc_pos = ( + grouped["_h_minus_pc_pos"] + .rolling(N, min_periods=min_p) + .sum() + .reset_index(level=0, drop=True) + ) + sum_pc_minus_l_pos = ( + grouped["_pc_minus_l_pos"] + .rolling(N, min_periods=min_p) + .sum() + .reset_index(level=0, drop=True) + ) + + # 3. 计算 AR 和 BR + df[ar_col_name] = ( + _safe_divide(sum_h_minus_o, sum_o_minus_l, default_val=np.nan) * 100 + ) # AR 通常乘以 100 + df[br_col_name] = ( + _safe_divide(sum_h_minus_pc_pos, sum_pc_minus_l_pos, default_val=np.nan) + * 100 + ) # BR 通常乘以 100 + df[f'{ar_col_name}_{br_col_name}'] = df[ar_col_name] - df[br_col_name] + + print(f"因子 {ar_col_name}, {br_col_name} 计算成功。") + + except KeyError as e: + print(f"错误: 计算 ARBR 时缺少必需的列: {e}") + print(f"将为因子 {ar_col_name}, {br_col_name} 填充 NaN。") + if ar_col_name not in df.columns: + df[ar_col_name] = np.nan + if br_col_name not in df.columns: + df[br_col_name] = np.nan + except Exception as e: + print(f"错误: 计算 ARBR 时发生意外错误: {e}") + print(f"将为因子 {ar_col_name}, {br_col_name} 填充 NaN。") + if ar_col_name not in df.columns: + df[ar_col_name] = np.nan + if br_col_name not in df.columns: + df[br_col_name] = np.nan + finally: + # 4. 清理中间列 + cols_to_drop = [col for col in _temp_cols if col in df.columns] + if cols_to_drop: + df.drop(columns=cols_to_drop, inplace=True) + print(f"因子 {ar_col_name}, {br_col_name} 计算流程结束。") + +def add_financial_factor( + main_df: pd.DataFrame, + financial_df: pd.DataFrame, + factor_value_col: str, # 财务指标值所在的列 + ts_code_col: str = 'ts_code', + trade_date_col: str = 'trade_date', + ann_date_col: str = 'ann_date', # 公告日期 + f_ann_date_col: str = 'f_ann_date', # 实际公告日期 (优先使用) +) -> pd.DataFrame: + """ + 将财务指标数据（如每股未分配利润）作为因子添加到主时间序列 DataFrame 中。 + + 使用 merge_asof 根据股票代码和公告日期，将最新的财务指标值匹配到每个交易日。 + + Args: + main_df: 包含时间序列交易数据的主 DataFrame (至少包含 ts_code_col 和 trade_date_col)。 + financial_df: 包含财务指标数据的 DataFrame (至少包含 ts_code_col, + ann_date_col 或 f_ann_date_col, 以及 factor_value_col)。 + ts_code_col: 股票代码列在两个 DataFrame 中的名称。默认为 'ts_code'。 + trade_date_col: 交易日期列在 main_df 中的名称。默认为 'trade_date'。 + ann_date_col: 公告日期列在 financial_df 中的名称（作为 f_ann_date_col 的备选）。默认为 'ann_date'。 + f_ann_date_col: 实际公告日期列在 financial_df 中的名称（优先使用）。默认为 'f_ann_date'。 + factor_value_col: 财务指标值（即要添加的因子值）在 financial_df 中的列名。默认为 'undistr_pft_ps'。 + new_factor_col_name: 添加到 main_df 中的新因子列的名称。默认为 'undist_profit_ps'。 + + Returns: + 包含新因子列的 main_df DataFrame。 + """ + if factor_value_col in main_df.columns: + return main_df + new_factor_col_name = factor_value_col + # --- 数据校验 --- + required_main_cols = [ts_code_col, trade_date_col] + if not all(col in main_df.columns for col in required_main_cols): + raise ValueError(f"主 DataFrame 必须包含列: {required_main_cols}") + + required_financial_cols = [ts_code_col, factor_value_col] + if f_ann_date_col and f_ann_date_col in financial_df.columns: + effective_date_col = f_ann_date_col + print(f"使用 '{f_ann_date_col}' 作为财务数据生效日期。") + elif ann_date_col and ann_date_col in financial_df.columns: + effective_date_col = ann_date_col + print(f"使用 '{ann_date_col}' 作为财务数据生效日期。") + else: + raise ValueError(f"财务指标 DataFrame 必须包含列 '{f_ann_date_col}' 或 '{ann_date_col}' 作为数据生效日期") + required_financial_cols.append(effective_date_col) + + if not all(col in financial_df.columns for col in required_financial_cols): + raise ValueError(f"财务指标 DataFrame 必须包含列: {required_financial_cols}") + + # --- 数据准备和清理 --- + # 确保日期列是 datetime 类型 + # 使用 .copy() 避免 SettingWithCopyWarning + main_df = main_df.copy() + financial_df = financial_df.copy() + main_df[trade_date_col] = pd.to_datetime(main_df[trade_date_col], errors='coerce') + financial_df[effective_date_col] = pd.to_datetime(financial_df[effective_date_col], errors='coerce') + + # 确保股票代码是字符串类型 + main_df[ts_code_col] = main_df[ts_code_col].astype(str) + financial_df[ts_code_col] = financial_df[ts_code_col].astype(str) + + # 选取 financial_df 中需要合并的列 + financial_data_subset = financial_df[[ts_code_col, effective_date_col, factor_value_col]].copy() + + # *** 新增：处理右表合并键中的空值 *** + initial_rows_financial = len(financial_data_subset) + financial_data_subset = financial_data_subset.dropna(subset=[ts_code_col, effective_date_col]) + rows_dropped = initial_rows_financial - len(financial_data_subset) + if rows_dropped > 0: + print(f"警告: 从 financial_data_subset 中移除了 {rows_dropped} 行，因为其 '{ts_code_col}' 或 '{effective_date_col}' 列存在空值。") + + if financial_data_subset.empty: + print(f"警告: 清理空值后 financial_data_subset 为空，无法添加因子 '{new_factor_col_name}'。将填充 NaN。") + main_df[new_factor_col_name] = np.nan + return main_df + + + # *** 修改：修正排序顺序以满足 merge_asof 要求 *** + # 先按 ts_code 排序，再按日期排序 + # main_df = main_df.sort_values(by=[ts_code_col, trade_date_col]) + # financial_data_subset = financial_data_subset.sort_values(by=[ts_code_col, effective_date_col]) + main_df = main_df.sort_values(by=[trade_date_col, ts_code_col]) + financial_data_subset = financial_data_subset.sort_values(by=[effective_date_col, ts_code_col]) + + # --- 使用 merge_asof 计算因子 --- + try: + df_with_factor = pd.merge_asof( + main_df, + financial_data_subset, + left_on=trade_date_col, + right_on=effective_date_col, + by=ts_code_col, + direction='backward' + ) + except Exception as e: + print(f"merge_asof 执行失败: {e}") + # 根据需要决定如何处理错误，这里填充 NaN + main_df[new_factor_col_name] = np.nan + return main_df + + + # --- 清理与重命名 --- + # 移除右表的日期列（如果它与左表日期列名称不同） + if effective_date_col in df_with_factor.columns and effective_date_col != trade_date_col: + df_with_factor = df_with_factor.drop(columns=[effective_date_col]) + + # 重命名新加入的因子列 + if factor_value_col != new_factor_col_name: + if factor_value_col in df_with_factor.columns: + df_with_factor = df_with_factor.rename(columns={factor_value_col: new_factor_col_name}) + else: + # 这种情况理论上不应发生，因为 merge_asof 应该会把右表的非 key 列带过来 + print(f"警告: 合并后未找到原始因子列 '{factor_value_col}'，无法重命名。") + # 如果 factor_value_col 已是目标名称，则无需重命名 + if new_factor_col_name not in df_with_factor.columns: + # 如果目标名称也不存在，则可能合并失败或列名有问题 + df_with_factor[new_factor_col_name] = np.nan + + + # 如果 factor_value_col 就是目标名称，确保该列存在 + elif new_factor_col_name not in df_with_factor.columns: + print(f"警告: 合并后未找到目标因子列 '{new_factor_col_name}'。填充 NaN。") + df_with_factor[new_factor_col_name] = np.nan + + + return df_with_factor + + +def calculate_cashflow_to_ev_factor(df: pd.DataFrame, cashflow_df: pd.DataFrame, balancesheet_df: pd.DataFrame, market_cap_col: str = 'total_mv', date_col: str = 'trade_date', ts_code_col: str = 'ts_code') -> pd.DataFrame: + """ + 计算经营活动产生的现金流量净额TTM / 企业价值因子。 + 企业价值 = 司市值 + 负债合计 - 货币资金。 + + 重要提示：本代码假设 add_financial_factor 能够将财务数据正确地合并到主数据框。 + 如果您使用 add_financial_factor 只获取单季度数据，那么 + n_cashflow_act 将不是 TTM 值，这将导致最终因子计算不准确。 + + Args: + df (pd.DataFrame): 包含市场数据（需有市值列）和日期、股票代码的主数据框。 + cashflow_df (pd.DataFrame): Tushare 现金流量表数据。 + balancesheet_df (pd.DataFrame): Tushare 资产负债表数据。 + market_cap_col (str): DataFrame 中代表公司总市值的列名，默认为 'total_mv'。 + date_col (str): DataFrame 中的日期列名，默认为 'trade_date'。 + ts_code_col (str): DataFrame 中的股票代码列名，默认为 'ts_code'。 + + Returns: + pd.DataFrame: 添加了 'cashflow_to_ev_factor' 列的 DataFrame。 + """ + df_factor = df.copy() # 创建副本以避免修改原始 DataFrame + + # 0. 确保必要的市场市值列存在 + if market_cap_col not in df_factor.columns: + print(f"错误: DataFrame 中缺少市值列 '{market_cap_col}'。无法计算因子。") + # 添加一个空的因子列并返回 + df_factor['cashflow_to_ev_factor'] = np.nan + return df_factor + + # 1. 获取经营活动产生的现金流量净额 (TTM - **注意这里的潜在不准确性**) + # 如果 add_financial_factor 只获取单季度，这里的 n_cashflow_act 将不是 TTM + df_factor = add_financial_factor(df_factor, cashflow_df, 'n_cashflow_act') + # 如果 add_financial_factor 能够正确处理 TTM，那么上面的调用是正确的。 + # 否则，您需要在 add_financial_factor 内部实现 TTM 逻辑，或者在调用 add_financial_factor + # 获取多个季度数据后，在这里手动进行 TTM 求和。 + # 为了符合您的描述，我们暂时假设 add_financial_factor 已经处理了 TTM 或我们接受单季度的值 + # 并命名为 ttm_n_cashflow_act 以示期望 + + # 重新命名获取的现金流列以便后续计算 + cashflow_col_name = 'n_cashflow_act' # 获取的列名 + ttm_cashflow_col = 'ttm_n_cashflow_act' # 因子计算中使用的列名 + if cashflow_col_name in df_factor.columns: + df_factor = df_factor.rename(columns={cashflow_col_name: ttm_cashflow_col}) + else: + # 如果 add_financial_factor 没成功添加列 + print(f"错误: add_financial_factor 未能成功添加 '{cashflow_col_name}' 列。") + df_factor['cashflow_to_ev_factor'] = np.nan + return df_factor + + + # 2. 获取负债合计 + df_factor = add_financial_factor(df_factor, balancesheet_df, 'total_liab') + liab_col_name = 'total_liab' + if liab_col_name not in df_factor.columns: + print(f"错误: add_financial_factor 未能成功添加 '{liab_col_name}' 列。") + df_factor['cashflow_to_ev_factor'] = np.nan + return df_factor + + + # 3. 获取货币资金 + df_factor = add_financial_factor(df_factor, balancesheet_df, 'money_cap') + money_col_name = 'money_cap' + if money_col_name not in df_factor.columns: + print(f"错误: add_financial_factor 未能成功添加 '{money_col_name}' 列。") + df_factor['cashflow_to_ev_factor'] = np.nan + return df_factor + + + # 4. 计算企业价值 (Enterprise Value) + # 确保参与计算的列是数值类型，并处理 NaN (NaN + X = NaN, NaN - X = NaN) + enterprise_value = df_factor[market_cap_col].astype(float) * 10000 + df_factor[liab_col_name].astype(float) - df_factor[money_col_name].astype(float) + + # 5. 计算最终因子：经营活动产生的现金流量净额TTM / 企业价值 + # 使用之前定义的安全除法 + df_factor['cashflow_to_ev_factor'] = _safe_divide(df_factor[ttm_cashflow_col], enterprise_value) + + # 6. 删除临时添加的财务数据列 + cols_to_drop = [ttm_cashflow_col, liab_col_name, money_col_name] + df_factor = df_factor.drop(columns=[col for col in cols_to_drop if col in df_factor.columns]) + + + return df_factor + + + +def caculate_book_to_price_ratio(df: pd.DataFrame, fina_indicator_df: pd.DataFrame) -> pd.DataFrame: + if 'bps' not in df.columns: + df = add_financial_factor(df, fina_indicator_df, factor_value_col='bps') + df['book_to_price_ratio'] = df['bps'] / df['close'] + df = df.drop(columns=['bps']) + return df + + +def turnover_rate_n(df: pd.DataFrame, n: int) -> pd.DataFrame: + df[f'turnover_rate_mean_{n}'] = df.groupby('ts_code', group_keys=False)['turnover_rate'].rolling(n).mean().reset_index(level=0, drop=True) + return df + +def variance_n(df: pd.DataFrame, n: int) -> pd.DataFrame: + df[f'variance_{n}'] = df.groupby('ts_code', group_keys=False)['pct_chg'].rolling(n).var().reset_index(level=0, drop=True) + + return df + +def bbi_ratio_factor(df: pd.DataFrame) -> pd.DataFrame: + df_factor = df + + # 确保数据按股票代码和日期排序，这对滚动计算非常重要 + df_factor = df_factor.sort_values(by=['ts_code', 'trade_date']) + + # 获取收盘价列 + close_prices = df_factor['close'] + + # 1. 根据 ts_code 分组计算各周期的简单移动平均线 (SMA) + grouped = df_factor.groupby('ts_code', group_keys=False) + + # 计算不同周期的 SMA，并使用 reset_index 展平索引 + sma3 = grouped['close'].rolling(3).mean().reset_index(level=0, drop=True) + sma6 = grouped['close'].rolling(6).mean().reset_index(level=0, drop=True) + sma12 = grouped['close'].rolling(12).mean().reset_index(level=0, drop=True) + sma24 = grouped['close'].rolling(24).mean().reset_index(level=0, drop=True) + + # 2. 计算 BBI = (SMA3 + SMA6 + SMA12 + SMA24) / 4 + print("计算 BBI...") + # 注意：如果任何一个 SMA 在某个位置是 NaN (例如，数据点不足)，那么它们的和也将是 NaN + bbi = (sma3 + sma6 + sma12 + sma24) / 4 + + # 3. 计算最终因子 = BBI / 收盘价 (使用安全除法) + df_factor['bbi_ratio_factor'] = _safe_divide(bbi, close_prices) + + return df_factor + + +def limit_factor(df: pd.DataFrame) -> pd.DataFrame: + grouped = df.groupby('ts_code', group_keys=False) + df["cat_up_limit"] = ( + df["close"] == df["up_limit"] + ) # 是否涨停（1表示涨停，0表示未涨停） + df["cat_down_limit"] = ( + df["close"] == df["down_limit"] + ) # 是否跌停（1表示跌停，0表示未跌停） + df["up_limit_count_10d"] = ( + grouped["cat_up_limit"] + .rolling(window=10, min_periods=1) + .sum() + .reset_index(level=0, drop=True) + ) + df["down_limit_count_10d"] = ( + grouped["cat_down_limit"] + .rolling(window=10, min_periods=1) + .sum() + .reset_index(level=0, drop=True) + ) + + # 3. 最近连续涨跌停天数 + def calculate_consecutive_limits(series): + """ + 计算连续涨停/跌停天数。 + """ + consecutive_up = series * ( + series.groupby((series != series.shift()).cumsum()).cumcount() + 1 + ) + consecutive_down = series * ( + series.groupby((series != series.shift()).cumsum()).cumcount() + 1 + ) + return consecutive_up, consecutive_down + + # 连续涨停天数 + df["consecutive_up_limit"] = grouped["cat_up_limit"].apply( + lambda x: calculate_consecutive_limits(x)[0] + ) + return df diff --git a/main/factor/factor.txt b/main/factor/factor.txt new file mode 100644 index 0000000..fdf1950 --- /dev/null +++ b/main/factor/factor.txt @@ -0,0 +1,63 @@ +序号 因子名称 (Factor Name / Column Name) 因子类别 (Factor Category) 简要说明 +1 pe_ttm 价值类因子 (Value) 市盈率 TTM +2 return_5, return_20 动量类因子 (Momentum) 过去5日/20日收益率 +3 act_factor1 to act_factor4 动量类 / 技术类因子 (Momentum / Technical) 基于不同周期EMA斜率计算的动量/趋势因子 +4 std_return_5, std_return_90, std_return_90_2 波动率类因子 (Volatility) 不同窗口期或延迟窗口期的滚动收益率标准差 +5 upside_vol, downside_vol 波动率类因子 (Volatility) N日滚动上/下行波动率 +6 vol_ratio 波动率类因子 (Volatility) 上行波动率 / 下行波动率 +7 std_return_5 / std_return_90 波动率类因子 (Volatility) 短期波动率 / 长期波动率 比率 +8 std_return_90 - std_return_90_2 波动率类因子 (Volatility) 长期波动率与其10日前值的差值（波动变化） +9 volatility (来自指数计算) 波动率类 / 市场因子 (Volatility / Market) 指数（或个股）的20日滚动收益率标准差 +10 log(circ_mv) (或 log_circ_mv) 市值类因子 (Size) 流通市值的对数值 +11 cs_rank_size 市值类因子 (Size) 对数流通市值的截面排序 +12 vol 流动性类因子 (Liquidity) 成交量 (通常需要与其他指标结合或处理) +13 turnover_rate 流动性类因子 (Liquidity) 换手率 +14 volume_ratio 流动性类因子 (Liquidity) 量比 +15 turnover_deviation 流动性类因子 (Liquidity) 换手率与其3日滚动均值的标准差倍数偏离 +16 cat_turnover_spike 流动性类 / 分类因子 (Liquidity / Categorical) 换手率是否显著高于近期均值 +17 volume_change_rate 流动性类因子 (Liquidity) 短期滚动成交量均值 / 长期滚动成交量均值 - 1 +18 cat_volume_breakout 流动性类 / 分类因子 (Liquidity / Categorical) 当日成交量是否大于过去5日最大成交量 +19 avg_volume_ratio 流动性类因子 (Liquidity) 3日滚动量比均值 +20 cat_volume_ratio_breakout 流动性类 / 分类因子 (Liquidity / Categorical) 当日量比是否大于过去5日最大量比 +21 vol_spike (Rolling Mean Vol) 流动性类因子 (Liquidity) 20日滚动成交量均值 +22 vol_std_5 流动性类 / 波动率因子 (Liquidity / Volatility) 成交量日变化率的5日滚动标准差 +23 volume_growth 流动性类因子 (Liquidity) 20日成交量变化率 +24 turnover_std 流动性类 / 波动率因子 (Liquidity / Volatility) 换手率的20日滚动标准差 +25 flow_lg_elg_intensity 资金流 / 流动性类因子 (Money Flow / Liquidity) （大单+超大单）净买入量 / 总成交量 +26 flow_divergence_diff, flow_divergence_ratio 资金流 / 情绪类因子 (Money Flow / Sentiment) 散户与主力资金流的差异或比率 +27 lg_elg_buy_prop 资金流 / 流动性类因子 (Money Flow / Liquidity) （大单+超大单）买入量 / 总买入量 +28 flow_struct_buy_change 资金流 / 流动性类因子 (Money Flow / Liquidity) 主力买入占比的日变化 +29 flow_lg_elg_accel 资金流 / 动量类因子 (Money Flow / Momentum) 主力资金流加速度 +30 active_buy_volume_large/big/small 资金流 / 流动性类因子 (Money Flow / Liquidity) 不同规模主动买入量 / 净流入量 +31 buy_lg/elg_vol_minus_sell_lg/elg_vol 资金流 / 流动性类因子 (Money Flow / Liquidity) 不同规模净买入量 / 总净流入量 +32 cs_rank_net_lg_flow_val, cs_rank_elg_buy_ratio, cs_rank_lg_sm_flow_diverge, cs_rank_elg_buy_sell_sm_ratio 资金流 / 复合因子 (截面排序) 各种资金流指标的截面排序 +33 cs_rank_ind_adj_lg_flow 资金流 / 复合因子 (行业调整+截面排序) 行业调整后的大单净流入截面排序 +34 chip_concentration_range, chip_skewness, cost_support_15pct_change, weight_roc5, cost_stability, ctrl_strength, low_cost_dev, asymmetry, cost_conc_std_N, profit_pressure, underwater_resistance, cs_rank_rel_profit_margin, cs_rank_cost_breadth, cs_rank_dist_to_upper_cost 定位类因子 (Positioning) / 技术类 基于持仓成本分布 (cost_*, weight_avg) 计算的各种指标及其截面排序 +35 winner_rate, cs_rank_winner_rate 定位类因子 (Positioning) / 技术类 获利盘比例及其截面排序 +36 floating_chip_proxy, price_cost_divergence, high_cost_break_days, liquidity_risk, lock_factor, cost_atr_adj, smallcap_concentration, cat_golden_resonance 定位类因子 (Positioning) / 复合因子 结合持仓成本与其他信息（价格、成交、波动率、市值）的复合指标 +37 cat_winner_price_zone 定位类 / 分类因子 (Positioning / Categorical) 基于成本和获利盘划分的区域类别 +38 flow_chip_consistency, profit_taking_vs_absorb, vol_amp_loss, vol_drop_profit_cnt, cost_break_confirm_cnt, vol_wgt_hist_pos, cs_rank_vol_x_profit_margin, cs_rank_cost_dist_vol_ratio 定位类因子 (Positioning) / 复合因子 进一步结合定位、资金流、量价的复杂交互因子 +39 return_skew, return_kurtosis 技术类 / 统计特征 (Technical / Stats) 滚动收益率的偏度与峰度 +40 rsi_3 技术类 / 动量类因子 (Technical / Momentum) 3日相对强弱指数 +41 obv, maobv_6, obv-maobv_6 技术类 / 量价因子 (Technical / Volume) 能量潮及其均线、差离 +42 atr_14, atr_6 技术类 / 波动率类因子 (Technical / Volatility) 平均真实波幅 +43 log_close 技术类 / 量价因子 (Technical / Price) 收盘价对数 +44 up, down 技术类 / 量价因子 (Technical / Price Action) 标准化上影线、下影线长度 +45 alpha_22_improved, alpha_003, alpha_007, alpha_013 技术类 / Alpha因子 (Technical / Alpha) WorldQuant Alpha 因子实现 +46 atr_norm_channel_pos 技术类 / 量价因子 (Technical / Price Action) ATR 标准化的价格通道位置 +47 turnover_diff_skew 技术类 / 流动性类 (Technical / Liquidity) 换手率变化率的偏度 +48 pullback_strong_N_M 技术类 / 动量类因子 (Technical / Momentum) 近期强势股的回调幅度 +49 vol_adj_roc 技术类 / 复合因子 (动量+波动率) 波动率调整后的 N 日变化率 +50 ar, br, arbr 情绪类 / 技术类因子 (Sentiment / Technical) ARBR 人气意愿指标 +51 up_ratio_20d (来自指数计算) 情绪类 / 市场因子 (Sentiment / Market) 指数（或个股）过去20天上涨天数比例 +52 cat_up_limit, cat_down_limit, up_limit_count_10d, down_limit_count_10d, consecutive_up_limit 事件驱动 / 市场状态因子 (Event / Market State) 涨跌停相关状态和计数 +53 momentum_factor, resonance_factor 复合因子 (量价) (Composite - P/V) 基于量、价、换手率等的简单复合 +54 cat_af2, cat_af3, cat_af4 复合因子 / 分类因子 (Composite / Cat.) act_factor 之间的比较 +55 act_factor5, act_factor6 复合因子 (技术类) (Composite - Technical) act_factor 1-4 的组合 +56 mv_volatility, mv_growth, mv_turnover_ratio, mv_adjusted_volume, mv_weighted_turnover, nonlinear_mv_volume, mv_volume_ratio, mv_momentum 复合因子 (市值+流动性/量价) 考虑了市值影响的量价、流动性或动量指标 +57 cap_neutral_cost_metric (占位符) 复合因子 / Alpha因子 (占位符) 市值行业中性化的成本指标（需实现） +58 hurst_exponent_flow (占位符) 资金流 / 统计因子 (占位符) 资金流的 Hurst 指数（需实现） +59 intraday_lg_flow_corr_N (占位符) 复合因子 (价格行为+资金流) (占位符) 日内趋势与大单流相关性（需实现） +60 industry_* (来自 industry_df) 行业因子 (Industry) 对应行业的各种指标（如行业收益率、行业动量等） +61 *_deviation (来自 create_deviation_within_dates) 复合因子 (相对行业) 个股因子相对于行业均值的偏离 +62 complex_factor_gplearn_1 复合因子 (GP生成) DEAP/GP 找到的因子表达式 1 \ No newline at end of file diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb new file mode 100644 index 0000000..35b0692 --- /dev/null +++ b/main/train/Classify2.ipynb @@ -0,0 +1,1903 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:46:06.987506Z", + "start_time": "2025-04-03T12:46:06.259551Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e:\\PyProject\\NewStock\\main\\train\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import gc\n", + "import os\n", + "import sys\n", + "sys.path.append('../../')\n", + "print(os.getcwd())\n", + "import pandas as pd\n", + "from main.factor.factor import get_rolling_factor, get_simple_factor\n", + "from main.utils.factor import read_industry_data\n", + "from main.utils.factor_processor import calculate_score\n", + "from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:00.212859Z", + "start_time": "2025-04-03T12:46:06.998047Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8509852 entries, 0 to 8509851\n", + "Data columns (total 32 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 total_mv float64 \n", + " 12 volume_ratio float64 \n", + " 13 is_st bool \n", + " 14 up_limit float64 \n", + " 15 down_limit float64 \n", + " 16 buy_sm_vol float64 \n", + " 17 sell_sm_vol float64 \n", + " 18 buy_lg_vol float64 \n", + " 19 sell_lg_vol float64 \n", + " 20 buy_elg_vol float64 \n", + " 21 sell_elg_vol float64 \n", + " 22 net_mf_vol float64 \n", + " 23 his_low float64 \n", + " 24 his_high float64 \n", + " 25 cost_5pct float64 \n", + " 26 cost_15pct float64 \n", + " 27 cost_50pct float64 \n", + " 28 cost_85pct float64 \n", + " 29 cost_95pct float64 \n", + " 30 weight_avg float64 \n", + " 31 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n", + "memory usage: 2.0+ GB\n", + "None\n" + ] + } + ], + "source": [ + "from main.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', 'total_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": "2025-04-03T12:47:10.527104Z", + "start_time": "2025-04-03T12:47:00.488715Z" + } + }, + "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", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.719252Z", + "start_time": "2025-04-03T12:47:10.541247Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df = df.sort_values('trade_date')\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " return df\n", + "\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line',\n", + " 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.821169Z", + "start_time": "2025-04-03T12:47:10.751831Z" + } + }, + "outputs": [], + "source": [ + "import talib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.944254Z", + "start_time": "2025-04-03T12:47:10.826179Z" + }, + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "from main.utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.969344Z", + "start_time": "2025-04-03T12:47:15.963327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "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": "2025-04-03T12:47:16.089879Z", + "start_time": "2025-04-03T12:47:15.990101Z" + } + }, + "outputs": [], + "source": [ + "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n", + " df=None)\n", + "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", + " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", + " df=None)\n", + "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", + " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", + " df=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a1c5858e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['ts_code', 'ann_date', 'money_cap', 'total_liab'], dtype='object')\n" + ] + } + ], + "source": [ + "print(balancesheet_df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:01.612695Z", + "start_time": "2025-04-03T12:47:16.121802Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'future_return', 'cat_up_limit', 'label', 'lg_elg_net_buy_vol',\n", + " 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n", + " 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n", + " 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n", + " 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n", + " 'floating_chip_proxy', 'cost_support_15pct_change',\n", + " 'cat_winner_price_zone', 'flow_chip_consistency',\n", + " 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n", + " 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n", + " '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n", + " 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", + " dtype='object')\n", + "\n", + "Index: 4450396 entries, 0 to 4450395\n", + "Columns: 118 entries, ts_code to mv_growth\n", + "dtypes: bool(5), datetime64[ns](1), float64(106), int32(4), object(2)\n", + "memory usage: 3.7+ GB\n", + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'future_return', 'cat_up_limit', 'label', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', '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', '_ema_5', '_ema_13', '_ema_20', '_ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', '_stddev_close', '_rank_stddev', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth']\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2019-01-01']\n", + " if 'in_date' in df.columns:\n", + " df = df.drop(columns=['in_date'])\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "gc.collect()\n", + "\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "df, _ = get_rolling_factor(df)\n", + "\n", + "# lg_flow_mom_corr(df, N=20, M=60)\n", + "# lg_flow_accel(df)\n", + "# profit_pressure(df)\n", + "# underwater_resistance(df)\n", + "# cost_conc_std(df, N=20)\n", + "# profit_decay(df, N=20)\n", + "# vol_amp_loss(df, N=20)\n", + "# vol_drop_profit_cnt(df, N=20, M=5)\n", + "# lg_flow_vol_interact(df, N=20)\n", + "# cost_break_confirm_cnt(df, M=5)\n", + "# atr_norm_channel_pos(df, N=14)\n", + "# turnover_diff_skew(df, N=20)\n", + "# lg_sm_flow_diverge(df, N=20)\n", + "# pullback_strong(df, N=20, M=20)\n", + "# vol_wgt_hist_pos(df, N=20)\n", + "# vol_adj_roc(df, N=20)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "b87b938028afa206", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.658725Z", + "start_time": "2025-04-03T13:08:02.469611Z" + } + }, + "outputs": [], + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.670700Z", + "start_time": "2025-04-03T13:08:03.665739Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import statsmodels.api as sm # 用于中性化回归\n", + "from tqdm import tqdm # 可选，用于显示进度条\n", + "\n", + "# --- 常量 ---\n", + "epsilon = 1e-10 # 防止除零\n", + "\n", + "# --- 1. 中位数去极值 (MAD) ---\n", + "\n", + "def cs_mad_filter(df: pd.DataFrame,\n", + " features: list,\n", + " k: float = 3.0,\n", + " scale_factor: float = 1.4826):\n", + " \"\"\"\n", + " 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n", + "\n", + " 方法: 对每日截面数据，计算 median 和 MAD，\n", + " 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n", + " 替换为边界值 (Winsorization)。\n", + " scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n", + " scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"MAD Filtering\"):\n", + " try:\n", + " # 计算截面中位数\n", + " median = grouped[col].transform('median')\n", + " # 计算截面 MAD (Median Absolute Deviation from Median)\n", + " mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n", + "\n", + " # 计算上下边界\n", + " lower_bound = median - k * scale_factor * mad\n", + " upper_bound = median + k * scale_factor * mad\n", + "\n", + " # 原地应用 clip\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n", + "\n", + " print(\"截面 MAD 去极值处理完成。\")\n", + "\n", + "\n", + "# --- 2. 行业市值中性化 ---\n", + "\n", + "def cs_neutralize_industry_cap(df: pd.DataFrame,\n", + " features: list,\n", + " industry_col: str = 'cat_l2_code',\n", + " market_cap_col: str = 'circ_mv'):\n", + " \"\"\"\n", + " 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n", + " 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n", + " 将回归残差写回原特征列。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n", + " industry_col, market_cap_col。\n", + " features (list): 需要处理的特征列名列表。\n", + " industry_col (str): 行业分类列名。\n", + " market_cap_col (str): 流通市值列名。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n", + " 计算量较大，可能耗时较长。\n", + " 需要安装 statsmodels 库 (pip install statsmodels)。\n", + " \"\"\"\n", + " print(\"开始截面行业市值中性化...\")\n", + " required_cols = features + ['trade_date', industry_col, market_cap_col]\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n", + " return\n", + "\n", + " # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n", + " log_cap_col = '_log_market_cap'\n", + " df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n", + " # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n", + " # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n", + " # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n", + "\n", + " dates = df['trade_date'].unique()\n", + " all_residuals = [] # 用于收集所有日期的残差\n", + "\n", + " for date in tqdm(dates, desc=\"Neutralizing\"):\n", + " daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n", + "\n", + " # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n", + " X = daily_data[[log_cap_col]]\n", + " X = sm.add_constant(X, prepend=True) # 添加常数项\n", + " # 创建行业哑变量 (drop_first=True 避免共线性)\n", + " industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n", + " industry_dummies = industry_dummies.astype(int)\n", + " X = pd.concat([X, industry_dummies], axis=1)\n", + "\n", + " daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n", + "\n", + " for col in features:\n", + " Y = daily_data[col]\n", + "\n", + " # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n", + " valid_mask = Y.notna() & X.notna().all(axis=1)\n", + " if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n", + " print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " continue\n", + "\n", + " Y_valid = Y[valid_mask]\n", + " X_valid = X[valid_mask]\n", + "\n", + " # 执行 OLS 回归\n", + " try:\n", + " model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n", + " results = model.fit()\n", + " # 将残差填回对应位置\n", + " daily_residuals.loc[valid_mask, col] = results.resid\n", + " daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n", + " except Exception as e:\n", + " print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " break\n", + "\n", + " all_residuals.append(daily_residuals)\n", + "\n", + " # 合并所有日期的残差结果\n", + " if all_residuals:\n", + " residuals_df = pd.concat(all_residuals)\n", + " # 将残差结果更新回原始 df (原地修改)\n", + " # 使用 update 比 merge 更适合基于索引的原地更新\n", + " # 确保 residuals_df 的索引与 df 中对应部分一致\n", + " df.update(residuals_df)\n", + " else:\n", + " print(\"没有有效的残差结果可以合并。\")\n", + "\n", + "\n", + " # 清理临时列\n", + " df.drop(columns=[log_cap_col], inplace=True)\n", + " print(\"截面行业市值中性化完成。\")\n", + "\n", + "\n", + "# --- 3. Z-Score 标准化 ---\n", + "\n", + "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n", + " \"\"\"\n", + " 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n", + " 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " epsilon (float): 防止除以零的小常数。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"Standardizing\"):\n", + " try:\n", + " # 使用 transform 计算截面均值和标准差\n", + " mean = grouped[col].transform('mean')\n", + " std = grouped[col].transform('std')\n", + "\n", + " # 计算 Z-Score 并原地赋值\n", + " df[col] = (df[col] - mean) / (std + epsilon)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n", + "\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + "\n", + "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n", + "\n", + " 参数:\n", + " df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n", + " feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n", + "\n", + " 返回:\n", + " pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n", + " \"\"\"\n", + " processed_df = df.copy() # 在副本上操作，保留原始数据\n", + "\n", + " # 确保 trade_date 是 datetime 类型以便正确分组\n", + " processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n", + "\n", + " def _fill_daily_nan(group):\n", + " # group 是某一个交易日的 DataFrame\n", + "\n", + " # 遍历指定的特征列\n", + " for feature_col in feature_columns:\n", + " # 检查列是否存在于当前分组中\n", + " if feature_col in group.columns:\n", + " # 计算当日该特征的中位数\n", + " median_val = group[feature_col].median()\n", + "\n", + " # 使用当日中位数填充该特征列的 NaN 值\n", + " # inplace=True 会直接修改 group DataFrame\n", + " group[feature_col].fillna(median_val, inplace=True)\n", + " else:\n", + " print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", + "\n", + " return group\n", + "\n", + " # 按交易日期分组，并应用每日填充函数\n", + " # group_keys=False 避免将分组键添加到结果索引中\n", + " filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n", + "\n", + " return filled_df" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "40e6b68a91b30c79", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:04.694262Z", + "start_time": "2025-04-03T13:08:03.694904Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n", + " log=True):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " if log:\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " # 使用已有的 pct_chg 字段计算波动率\n", + " volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + " return score\n", + "\n", + " # # 确保 DataFrame 按照股票代码和交易日期排序\n", + " # df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 对每个股票分别计算 score\n", + " df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n", + "\n", + " return df['score']\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n", + " 对特征在行业内部进行市值中性化。\n", + "\n", + " Args:\n", + " df: 输入的 DataFrame，包含特征、行业分类和市值列。\n", + " features: 需要进行中性化的特征列名列表。\n", + " industry_col: 行业分类列的列名。\n", + " mkt_cap_col: 市值列的列名。\n", + "\n", + " Returns:\n", + " 中性化后的 DataFrame。\n", + " \"\"\"\n", + "\n", + " df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n", + " df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n", + " df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n", + "\n", + " if df_cleaned.empty:\n", + " print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n", + " return df # 返回原始或空df，取决于清理前的状态\n", + "\n", + " processed_df = df\n", + "\n", + " for col in features:\n", + " if col not in df_cleaned.columns:\n", + " print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n", + " # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " # 跳过对控制变量本身进行中性化\n", + " if col == mkt_cap_col or col == industry_col:\n", + " print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n", + " # 在结果 df 中也保持原样\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n", + "\n", + " # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n", + " df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n", + "\n", + " if not df_subset_factor.empty:\n", + " for industry, group in df_subset_factor.groupby(industry_col):\n", + " x = group[mkt_cap_col] # 市值对数\n", + " y = group[col] # 因子值\n", + "\n", + " # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n", + " # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n", + " if len(group) > 1 and np.var(x) > 1e-9:\n", + " try:\n", + " beta = np.cov(y, x)[0, 1] / np.var(x)\n", + " alpha = np.mean(y) - beta * np.mean(x)\n", + "\n", + " # 计算残差\n", + " resid = y - (alpha + beta * x)\n", + "\n", + " # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n", + " residual_series.loc[resid.index] = resid\n", + "\n", + " except Exception as e:\n", + " # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n", + " print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n", + " # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n", + " # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n", + "\n", + " else:\n", + " residual_series.loc[group.index] = group[col] # 保留原始因子值\n", + " processed_df.loc[residual_series.index, col] = residual_series\n", + "\n", + "\n", + " else:\n", + " processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n", + "\n", + " return processed_df\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "\n", + "def mad_filter(df, features, n=3):\n", + " for col in features:\n", + " median = df[col].median()\n", + " mad = np.median(np.abs(df[col] - median))\n", + " upper = median + n * mad\n", + " lower = median - n * mad\n", + " df[col] = np.clip(df[col], lower, upper) # 截断极值\n", + " return df\n", + "\n", + "\n", + "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n", + " for col in features:\n", + " # 按日期分组计算上下百分位数\n", + " lower_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(lower_percentile)\n", + " )\n", + " upper_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(upper_percentile)\n", + " )\n", + " # 截断超出范围的值\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + " return df\n", + "\n", + "\n", + "from scipy.stats import iqr\n", + "\n", + "\n", + "def iqr_filter(df, features):\n", + " for col in features:\n", + " df[col] = df.groupby('trade_date')[col].transform(\n", + " lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n", + " )\n", + " return df\n", + "\n", + "\n", + "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", + " df = df.copy()\n", + " for col in features:\n", + " # 计算 rolling 统计量，需要按日期进行 groupby\n", + " rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n", + " rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n", + "\n", + " # 对数据进行裁剪\n", + " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", + " \n", + " return df\n", + "\n", + "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n", + " \"\"\"\n", + " 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n", + "\n", + " Args:\n", + " df: 包含特征列和目标列的 Pandas DataFrame。\n", + " feature_columns: 包含所有待评估特征列名的列表。\n", + " n: 希望选取的 RankIC 绝对值最高的特征数量。\n", + " target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n", + "\n", + " Returns:\n", + " 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n", + " \"\"\"\n", + " numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " if target_column not in df.columns:\n", + " raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n", + "\n", + " rankic_scores = {}\n", + " for feature in numeric_columns:\n", + " if feature not in df.columns:\n", + " print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n", + " continue\n", + "\n", + " # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n", + " # dropna() 是为了处理缺失值，确保相关性计算不失败\n", + " valid_data = df[[feature, target_column]].dropna()\n", + " if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n", + " # 计算斯皮尔曼相关性\n", + " correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n", + " rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n", + " else:\n", + " rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n", + "\n", + " # 将 RankIC 分数转换为 Series 便于排序\n", + " rankic_series = pd.Series(rankic_scores)\n", + "\n", + " # 按 RankIC 绝对值降序排序，选取前 n 个特征\n", + " # handle case where n might be larger than available features\n", + " n_actual = min(n, len(rankic_series))\n", + " top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n", + " top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n", + " return top_features" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "47c12bb34062ae7a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:50.841165Z", + "start_time": "2025-04-03T14:49:25.889057Z" + } + }, + "outputs": [], + "source": [ + "days = 2\n", + "validation_days = 120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df.groupby('ts_code')['open'].shift(-1)\n", + "\n", + "df['cat_up_limit'] = df['pct_chg'] > 5\n", + "df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n", + "\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "\n", + "# for col in [col for col in df.columns]:\n", + "# train_data[col] = train_data[col].astype('str')\n", + "# test_data[col] = test_data[col].astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "b76ea08a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "2738 000001.SZ 2019-01-03 16.583965\n", + "5477 000001.SZ 2019-01-04 16.633371\n", + "['undist_profit_ps', 'AR_BR', 'pe_ttm', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'cashflow_to_ev_factor', 'ocfps', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor']\n", + "去除极值\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 24/24 [00:04<00:00, 5.00it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 24/24 [00:03<00:00, 6.27it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "feature_columns: ['undist_profit_ps', 'AR_BR', 'pe_ttm', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'cashflow_to_ev_factor', 'ocfps', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor']\n", + "df最小日期: 2019-01-02\n", + "df最大日期: 2025-04-09\n", + "2058298\n", + "train_data最小日期: 2020-01-02\n", + "train_data最大日期: 2022-12-30\n", + "1678529\n", + "test_data最小日期: 2023-01-03\n", + "test_data最大日期: 2025-04-09\n", + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "2738 000001.SZ 2019-01-03 16.583965\n", + "5477 000001.SZ 2019-01-04 16.633371\n" + ] + } + ], + "source": [ + "train_data = df[filter_index & (df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2020-01-01')]\n", + "test_data = df[(df['trade_date'] >= '2023-01-01')]\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, feature_columns)\n", + "# test_data, _ = create_deviation_within_dates(test_data, feature_columns)\n", + "\n", + "feature_columns = [\n", + " 'undist_profit_ps', \n", + " 'AR_BR', \n", + " 'pe_ttm',\n", + " 'alpha_22_improved', \n", + " 'alpha_003', \n", + " 'alpha_007', \n", + " 'alpha_013', \n", + " 'cat_up_limit', \n", + " 'cat_down_limit', \n", + " 'up_limit_count_10d', \n", + " 'down_limit_count_10d', \n", + " 'consecutive_up_limit', \n", + " 'vol_break', \n", + " 'weight_roc5', \n", + " 'price_cost_divergence', \n", + " 'smallcap_concentration', \n", + " 'cost_stability', \n", + " 'high_cost_break_days', \n", + " 'liquidity_risk', \n", + " 'turnover_std', \n", + " 'mv_volatility', \n", + " 'volume_growth', \n", + " 'mv_growth', \n", + " 'lg_flow_mom_corr_20_60', \n", + " 'lg_flow_accel', \n", + " 'profit_pressure', \n", + " 'underwater_resistance', \n", + " 'cost_conc_std_20', \n", + " 'profit_decay_20', \n", + " 'vol_amp_loss_20', \n", + " 'vol_drop_profit_cnt_5', \n", + " 'lg_flow_vol_interact_20', \n", + " 'cost_break_confirm_cnt_5', \n", + " 'atr_norm_channel_pos_14', \n", + " 'turnover_diff_skew_20', \n", + " 'lg_sm_flow_diverge_20', \n", + " 'pullback_strong_20_20', \n", + " 'vol_wgt_hist_pos_20', \n", + " 'vol_adj_roc_20',\n", + " 'cashflow_to_ev_factor',\n", + " 'ocfps',\n", + " 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5',\n", + " 'variance_20',\n", + " 'bbi_ratio_factor'\n", + "]\n", + "feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n", + "print(feature_columns)\n", + "\n", + "train_data = fill_nan_with_daily_median(train_data, feature_columns)\n", + "test_data = fill_nan_with_daily_median(test_data, feature_columns)\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=feature_columns)\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "transform_feature_columns = feature_columns\n", + "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat')]\n", + "# transform_feature_columns.remove('undist_profit_ps')\n", + "print('去除极值')\n", + "cs_mad_filter(train_data, transform_feature_columns)\n", + "# print('中性化')\n", + "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", + "# print('标准化')\n", + "# cs_zscore_standardize(train_data, transform_feature_columns)\n", + "\n", + "cs_mad_filter(test_data, transform_feature_columns)\n", + "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "\n", + "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat')]\n", + "cs_mad_filter(train_data, mad_filter_feature_columns)\n", + "cs_mad_filter(test_data, mad_filter_feature_columns)\n", + "\n", + "\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "\n", + "print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(train_data))\n", + "print(f\"train_data最小日期: {train_data['trade_date'].min().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\"test_data最小日期: {test_data['trade_date'].min().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 feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "e23d1759", + "metadata": {}, + "outputs": [], + "source": [ + "feature_columns = [\n", + " 'undist_profit_ps', \n", + " 'AR_BR', \n", + " 'pe_ttm',\n", + " 'alpha_22_improved', \n", + " 'alpha_003', \n", + " 'alpha_007', \n", + " 'alpha_013', \n", + " 'cat_up_limit', \n", + " 'cat_down_limit', \n", + " 'up_limit_count_10d', \n", + " 'down_limit_count_10d', \n", + " 'consecutive_up_limit', \n", + " 'vol_break', \n", + " 'weight_roc5', \n", + " 'price_cost_divergence', \n", + " 'smallcap_concentration', \n", + " 'cost_stability', \n", + " 'high_cost_break_days', \n", + " 'liquidity_risk', \n", + " 'turnover_std', \n", + " 'mv_volatility', \n", + " 'volume_growth', \n", + " 'mv_growth', \n", + " 'lg_flow_mom_corr_20_60', \n", + " 'lg_flow_accel', \n", + " 'profit_pressure', \n", + " 'underwater_resistance', \n", + " 'cost_conc_std_20', \n", + " 'profit_decay_20', \n", + " 'vol_amp_loss_20', \n", + " 'vol_drop_profit_cnt_5', \n", + " 'lg_flow_vol_interact_20', \n", + " 'cost_break_confirm_cnt_5', \n", + " 'atr_norm_channel_pos_14', \n", + " 'turnover_diff_skew_20', \n", + " 'lg_sm_flow_diverge_20', \n", + " 'pullback_strong_20_20', \n", + " 'vol_wgt_hist_pos_20', \n", + " 'vol_adj_roc_20',\n", + " 'cashflow_to_ev_factor',\n", + " 'ocfps',\n", + " 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5',\n", + " 'variance_20',\n", + " 'bbi_ratio_factor'\n", + "]\n", + "feature_columns = [col for col in feature_columns if col in train_data.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "8f134d435f71e9e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:51.050696Z", + "start_time": "2025-04-03T14:57:51.034030Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime # 用于日期计算\n", + "from catboost import CatBoostClassifier\n", + "from catboost import Pool\n", + "\n", + "\n", + "def train_model(train_data_df, feature_columns,\n", + " print_info=True, # 调整参数名，更通用\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " target_column='label'): # 增加目标列参数\n", + "\n", + " print('train data size: ', len(train_data_df))\n", + "\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " # 识别数值型特征列\n", + " numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n", + "\n", + " # 去除标签为空的样本\n", + " initial_len = len(train_data_df)\n", + " train_data_df = train_data_df.dropna(subset=[target_column])\n", + "\n", + " if print_info:\n", + " print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n", + "\n", + " train_data_split = train_data_df\n", + "\n", + " # 提取特征和标签，只取数值型特征用于线性回归\n", + " \n", + " if split_date is None:\n", + " all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n", + " split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n", + " train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n", + " val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + " \n", + " X_train = train_data_split[feature_columns]\n", + " y_train = train_data_split[target_column]\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 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n", + " scaler = StandardScaler()\n", + " # X_train = scaler.fit_transform(X_train)\n", + "\n", + " # 训练线性回归模型\n", + " # model = LogisticRegression(random_state=42)\n", + " \n", + " # # 使用处理后的特征和样本权重进行训练\n", + " # model.fit(X_train, y_train)\n", + " \n", + " \n", + " cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n", + " print(f'cat_features: {cat_features}')\n", + " # cat_features = []\n", + "\n", + " params = {\n", + " 'loss_function': 'CrossEntropy', # 适用于二分类\n", + " 'eval_metric': 'Precision', # 评估指标\n", + " 'iterations': 500,\n", + " 'learning_rate': 0.03,\n", + " 'depth': 8, # 控制模型复杂度\n", + " 'l2_leaf_reg': 1, # L2 正则化\n", + " 'verbose': 500,\n", + " 'early_stopping_rounds': 50,\n", + " # 'one_hot_max_size': 50,\n", + " # 'class_weights': [0.6, 1.2]\n", + " 'task_type': 'GPU'\n", + " }\n", + "\n", + " train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n", + " val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n", + "\n", + "\n", + " model = CatBoostClassifier(**params)\n", + " model.fit(train_pool,\n", + " eval_set=val_pool, plot=True, use_best_model=True)\n", + "\n", + "\n", + " return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:03:18.426481Z", + "start_time": "2025-04-03T15:02:19.926352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 363894\n", + "原始样本数: 363894, 去除标签为空后样本数: 363894\n", + "cat_features: [7]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2c891d9511d34cd69044024e529641fc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0:\tlearn: 0.6250000\ttest: 0.0000000\tbest: 0.0000000 (0)\ttotal: 11.5ms\tremaining: 5.74s\n", + "bestTest = 0.5761589404\n", + "bestIteration = 1\n", + "Shrink model to first 2 iterations.\n" + ] + } + ], + "source": [ + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", + "# light_params['feature_contri'] = feature_contri\n", + "# print(f'feature_contri: {feature_contri}')\n", + "model, scaler, pca = train_model(train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(500, 'total_mv')), feature_columns)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:04:39.656944Z", + "start_time": "2025-04-03T15:04:39.298483Z" + } + }, + "outputs": [], + "source": [ + "# train_data = train_data.sort_values(by='trade_date')\n", + "# all_dates = train_data['trade_date'].unique() # 获取所有唯一的 trade_date\n", + "# split_date = all_dates[-120] # 划分点为倒数第 validation_days 天\n", + "# print(split_date)\n", + "# print(all_dates)\n", + "# val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", + "\n", + "score_df = test_data\n", + "score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", + "score_df = score_df[score_df['pe_ttm'] > 0]\n", + "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in feature_columns if col in numeric_columns]\n", + "# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n", + "# score_df = cross_sectional_standardization(score_df, numeric_columns)\n", + "\n", + "score_df['score'] = model.predict_proba(score_df[feature_columns])[:, 1]\n", + "score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n", + "\n", + "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n", + ").reset_index(drop=True) # drop=True 避免添加旧索引列\n", + "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n", + "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1, 'total_mv')).reset_index()\n", + "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n", + "# " + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "340a82b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59971\n", + "0 10.03\n", + "1 8.26\n", + "2 10.03\n", + "3 6.06\n", + "4 6.06\n", + " ... \n", + "179989 -0.82\n", + "179990 5.03\n", + "179991 5.78\n", + "179992 5.44\n", + "179993 2.34\n", + "Name: pct_chg, Length: 179994, dtype: float64\n" + ] + } + ], + "source": [ + "print(len(score_df[score_df['label'] == 1]))\n", + "print(score_df['pct_chg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "7e9023cc", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_factors(\n", + " df: pd.DataFrame,\n", + " feature_columns: list[str],\n", + " target_column: str = 'target', # 假设目标列默认为 'target'\n", + " trade_date_col: str = 'trade_date', # 假设日期列默认为 'trade_date'\n", + " mcap_col: str = 'total_mv', # 新增: 市值列名称\n", + " mcap_bins: int = 5 # 新增: 市值分位数的数量 (例如 5 表示五分位数)\n", + ") -> pd.DataFrame:\n", + " \"\"\"\n", + " 分析DataFrame中指定特征列的各种指标，包括基本统计、相关性、日间IC、ICIR以及在不同市值分位数上的IC。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 包含日期、目标列、特征列和市值列的DataFrame。\n", + " 需要包含 trade_date_col, target_column, feature_columns 和 mcap_col 中的所有列。\n", + " feature_columns (list[str]): 需要分析的特征列名称列表。\n", + " target_column (str): 目标变量列的名称。\n", + " trade_date_col (str): 交易日期列的名称。\n", + " mcap_col (str): 市值列的名称。\n", + " mcap_bins (int): 市值分位数的数量 (例如 5 表示五分位数)。\n", + "\n", + " Returns:\n", + " pd.DataFrame: 包含各个因子分析指标的汇总DataFrame。\n", + " 同时打印因子在不同市值分位数上的平均IC表格。\n", + " 如果输入数据或列有问题，可能返回空或包含NaN的DataFrame。\n", + " \"\"\"\n", + "\n", + " # --- 数据校验 ---\n", + " required_cols = [trade_date_col, target_column, mcap_col] + feature_columns\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: 输入DataFrame缺少必需的列: {missing}\")\n", + " return pd.DataFrame() # 返回空DataFrame\n", + "\n", + " # 确保日期列是 datetime 类型\n", + " df = df.copy() # 在副本上操作\n", + " df[trade_date_col] = pd.to_datetime(df[trade_date_col], errors='coerce')\n", + " df.dropna(subset=[trade_date_col], inplace=True) # 移除日期转换失败的行\n", + "\n", + " # 过滤掉那些在 feature_columns, target_column, mcap_col 上有 NaN 的行，以确保后续计算是在完整数据上\n", + " # 直接在 df 副本上进行清洗\n", + " initial_rows_before_clean = len(df)\n", + " df.dropna(subset=feature_columns + [target_column, mcap_col], inplace=True)\n", + " rows_dropped_clean = initial_rows_before_clean - len(df)\n", + " if rows_dropped_clean > 0:\n", + " print(f\"警告: 移除了 {rows_dropped_clean} 行，因为其特征、目标或市值列存在空值。\")\n", + "\n", + " if df.empty:\n", + " print(\"错误: 清理缺失值后数据为空，无法进行因子分析。\")\n", + " return pd.DataFrame() # 返回空DataFrame\n", + "\n", + "\n", + " print(f\"开始分析 {len(feature_columns)} 个因子指标...\")\n", + "\n", + " # --- 1. 基本因子统计量 ---\n", + " basic_stats = df[feature_columns].describe().T\n", + "\n", + " print(\"\\n--- 基本因子统计量 ---\")\n", + " print(basic_stats)\n", + "\n", + " # --- 2. 因子与目标变量的整体相关性 ---\n", + " overall_correlation = {}\n", + " for feature in feature_columns:\n", + " # 在清理后的 df 上计算相关性\n", + " if df[[feature, target_column]].dropna().shape[0] > 1: # 确保至少有两个有效数据点\n", + " overall_correlation[feature] = {\n", + " 'Pearson_Correlation_with_Target': df[feature].corr(df[target_column], method='pearson'),\n", + " 'Spearman_Correlation_with_Target': df[feature].corr(df[target_column], method='spearman')\n", + " }\n", + " else:\n", + " overall_correlation[feature] = {\n", + " 'Pearson_Correlation_with_Target': np.nan,\n", + " 'Spearman_Correlation_with_Target': np.nan\n", + " }\n", + " overall_corr_df = pd.DataFrame.from_dict(overall_correlation, orient='index')\n", + "\n", + " print(\"\\n--- 因子与目标变量的整体相关性 ---\")\n", + " print(overall_corr_df)\n", + "\n", + " # --- 3. 因子之间的相关性矩阵 ---\n", + " # 在清理后的 df 上计算相关性\n", + " factor_correlation_matrix = df[feature_columns].corr(method='spearman') # 改回 Spearman\n", + "\n", + " print(\"\\n--- 因子之间的相关性矩阵 (Spearman) ---\") # 修正打印信息\n", + " print(factor_correlation_matrix)\n", + "\n", + " # --- 4. 日间 IC 和 ICIR ---\n", + " print(\"\\n--- 计算日间 IC (Spearman 相关性) 和 ICIR ---\")\n", + "\n", + " # 直接在清理后的 df 上计算每日 IC\n", + " if df.empty: # 理论上上面已经检查过，这里再检查一次更安全\n", + " daily_ic_series = pd.Series(dtype=float) # 空 Series\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n", + " }, index=feature_columns)\n", + " else:\n", + " daily_ic_series = df.groupby(trade_date_col).apply(\n", + " lambda day_group: {\n", + " feature: day_group[feature].corr(day_group[target_column], method='spearman')\n", + " for feature in feature_columns if day_group.shape[0] > 1 # 确保每日数据点多于1才能计算相关性\n", + " }\n", + " ).apply(pd.Series) # 将字典结果转换为 DataFrame\n", + "\n", + " # 计算 IC 的统计量\n", + " if not daily_ic_series.empty:\n", + " ic_mean = daily_ic_series.mean()\n", + " ic_std = daily_ic_series.std()\n", + " # 避免除以零\n", + " ic_ir = ic_mean / ic_std.replace(0, np.nan) # 使用 replace 0 为 NaN\n", + "\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': ic_mean,\n", + " 'Std_Dev_IC': ic_std,\n", + " 'ICIR': ic_ir\n", + " })\n", + " print(\"\\n--- 日间 IC 和 ICIR (Spearman) ---\")\n", + " print(ic_stats)\n", + " else:\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n", + " }, index=feature_columns)\n", + "\n", + "\n", + " # --- 5. 因子在不同市值分位数上的平均 IC ---\n", + " print(f\"\\n--- 计算因子在 {mcap_bins} 个市值分位数上的平均 IC (Spearman) ---\")\n", + "\n", + " # 在清理后的 df 上计算每日市值分位数，直接添加到 df 中\n", + " # 使用 transform() 和 qcut() 在每个日期分组内计算分位数\n", + " # labels=False 返回整数 0 to mcap_bins-1\n", + " # duplicates='drop' 处理在某些日期股票数量少于 bins 导致分位数边缘重复的情况，会返回 NaN\n", + " # 添加一个临时列来存储分位数\n", + " mcap_bin_col_name = f'_mcap_bin_{mcap_bins}'\n", + " df[mcap_bin_col_name] = df.groupby(trade_date_col)[mcap_col].transform(\n", + " lambda x: pd.qcut(x, q=mcap_bins, labels=False, duplicates='drop') if len(x) >= mcap_bins else np.nan # 确保股票数量足够进行分位数划分\n", + " )\n", + "\n", + " # 过滤掉无法划分分位数 (NaN) 的行，进行分位数 IC 计算\n", + " # 创建一个临时 DataFrame df_binned_analysis\n", + " df_binned_analysis = df.dropna(subset=[mcap_bin_col_name]).copy()\n", + "\n", + " if df_binned_analysis.empty:\n", + " print(\"错误: 划分市值分位数后数据为空，无法计算分位数上的 IC。\")\n", + " avg_ic_by_bin = pd.DataFrame(index=range(mcap_bins), columns=feature_columns) # Placeholder\n", + " else:\n", + " # 按日期和市值分位数分组，计算每个分组内的因子与目标变量的截面相关性 (分位数IC)\n", + " binned_ic_by_day = df_binned_analysis.groupby([trade_date_col, mcap_bin_col_name]).apply(\n", + " lambda group: {\n", + " feature: group[feature].corr(group[target_column], method='spearman')\n", + " for feature in feature_columns if group.shape[0] > 1 # 确保分位数组内数据点多于1\n", + " }\n", + " ).apply(pd.Series) # 将嵌套结果转为 DataFrame\n", + "\n", + " # 对每个分位数组的每日 IC 求平均\n", + " # unstack(level=mcap_bin_col_name) 将 mcap_bin 作为列\n", + " # mean(axis=0) 对日期索引求平均\n", + " avg_ic_by_bin = binned_ic_by_day.unstack(level=mcap_bin_col_name).mean(axis=0).unstack()\n", + "\n", + " # 重命名索引和列，使表格更清晰\n", + " if not avg_ic_by_bin.empty:\n", + " # Index name will be the original column name used for grouping ('_mcap_bin_X')\n", + " # Rename the index name explicitly\n", + " avg_ic_by_bin.index.name = 'MarketCap_Bin'\n", + " avg_ic_by_bin.columns.name = 'Feature'\n", + " # 可以根据需要对分位数 bin 索引进行排序 (虽然 pd.qcut labels=False usually sorts)\n", + " avg_ic_by_bin = avg_ic_by_bin.sort_index()\n", + "\n", + " print(avg_ic_by_bin)\n", + "\n", + "\n", + " # --- 6. 汇总所有指标 ---\n", + " # 将基本统计、整体相关性、IC/ICIR 合并到一个 DataFrame\n", + " # 注意：合并时需要根据索引进行对齐 (因子名称)\n", + " summary_df = basic_stats\n", + " summary_df = summary_df.merge(overall_corr_df, left_index=True, right_index=True, how='left')\n", + " summary_df = summary_df.merge(ic_stats, left_index=True, right_index=True, how='left')\n", + "\n", + " # print(\"\\n--- 因子分析汇总报告 ---\")\n", + " # print(summary_df)\n", + "\n", + " # --- 清理临时列 'mcap_bin' ---\n", + " # 修正：在函数结束时从我们一直在操作的 df 副本中删除临时列\n", + " if mcap_bin_col_name in df.columns:\n", + " df.drop(columns=[mcap_bin_col_name], inplace=True)\n", + "\n", + "\n", + " return summary_df # 主要返回汇总报告，分位数IC单独打印\n", + "\n", + "# # 运行分析函数\n", + "# factor_analysis_report = analyze_factors(test_data.copy(), feature_columns, 'future_return')\n", + "\n", + "# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n", + "# print(factor_analysis_report)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/train/LinearRegression.ipynb b/main/train/LinearRegression.ipynb new file mode 100644 index 0000000..fbf9a81 --- /dev/null +++ b/main/train/LinearRegression.ipynb @@ -0,0 +1,1929 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "79a7758178bafdd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:46:06.987506Z", + "start_time": "2025-04-03T12:46:06.259551Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "e:\\PyProject\\NewStock\\main\\train\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import gc\n", + "import os\n", + "import sys\n", + "sys.path.append('../../')\n", + "print(os.getcwd())\n", + "import pandas as pd\n", + "from main.factor.factor import get_rolling_factor, get_simple_factor\n", + "from main.utils.factor import read_industry_data\n", + "from main.utils.factor_processor import calculate_score\n", + "from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a79cafb06a7e0e43", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:00.212859Z", + "start_time": "2025-04-03T12:46:06.998047Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "daily data\n", + "daily basic\n", + "inner merge on ['ts_code', 'trade_date']\n", + "stk limit\n", + "left merge on ['ts_code', 'trade_date']\n", + "money flow\n", + "left merge on ['ts_code', 'trade_date']\n", + "cyq perf\n", + "left merge on ['ts_code', 'trade_date']\n", + "\n", + "RangeIndex: 8509852 entries, 0 to 8509851\n", + "Data columns (total 32 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object \n", + " 1 trade_date datetime64[ns]\n", + " 2 open float64 \n", + " 3 close float64 \n", + " 4 high float64 \n", + " 5 low float64 \n", + " 6 vol float64 \n", + " 7 pct_chg float64 \n", + " 8 turnover_rate float64 \n", + " 9 pe_ttm float64 \n", + " 10 circ_mv float64 \n", + " 11 total_mv float64 \n", + " 12 volume_ratio float64 \n", + " 13 is_st bool \n", + " 14 up_limit float64 \n", + " 15 down_limit float64 \n", + " 16 buy_sm_vol float64 \n", + " 17 sell_sm_vol float64 \n", + " 18 buy_lg_vol float64 \n", + " 19 sell_lg_vol float64 \n", + " 20 buy_elg_vol float64 \n", + " 21 sell_elg_vol float64 \n", + " 22 net_mf_vol float64 \n", + " 23 his_low float64 \n", + " 24 his_high float64 \n", + " 25 cost_5pct float64 \n", + " 26 cost_15pct float64 \n", + " 27 cost_50pct float64 \n", + " 28 cost_85pct float64 \n", + " 29 cost_95pct float64 \n", + " 30 weight_avg float64 \n", + " 31 winner_rate float64 \n", + "dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n", + "memory usage: 2.0+ GB\n", + "None\n" + ] + } + ], + "source": [ + "from main.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', 'total_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": "2025-04-03T12:47:10.527104Z", + "start_time": "2025-04-03T12:47:00.488715Z" + } + }, + "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", + " columns=['ts_code', 'l2_code', 'in_date'],\n", + " df=None, on=['ts_code'], join='left')\n", + "\n", + "\n", + "def merge_with_industry_data(df, industry_df):\n", + " # 确保日期字段是 datetime 类型\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'])\n", + " industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n", + "\n", + " # 对 industry_df 按 ts_code 和 in_date 排序\n", + " industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n", + "\n", + " # 对原始 df 按 ts_code 和 trade_date 排序\n", + " df_sorted = df.sort_values(['trade_date', 'ts_code'])\n", + "\n", + " # 使用 merge_asof 进行向后合并\n", + " merged = pd.merge_asof(\n", + " df_sorted,\n", + " industry_df_sorted,\n", + " by='ts_code', # 按 ts_code 分组\n", + " left_on='trade_date',\n", + " right_on='in_date',\n", + " direction='backward'\n", + " )\n", + "\n", + " # 获取每个 ts_code 的最早 in_date 记录\n", + " min_in_date_per_ts = (industry_df_sorted\n", + " .groupby('ts_code')\n", + " .first()\n", + " .reset_index()[['ts_code', 'l2_code']])\n", + "\n", + " # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n", + " merged['l2_code'] = merged['l2_code'].fillna(\n", + " merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n", + " )\n", + "\n", + " # 保留需要的列并重置索引\n", + " result = merged.reset_index(drop=True)\n", + " return result\n", + "\n", + "\n", + "# 使用示例\n", + "df = merge_with_industry_data(df, industry_df)\n", + "# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4e9e1d31da6dba6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.719252Z", + "start_time": "2025-04-03T12:47:10.541247Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "def calculate_indicators(df):\n", + " \"\"\"\n", + " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", + " \"\"\"\n", + " df = df.sort_values('trade_date')\n", + " df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n", + " # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n", + " delta = df['close'].diff()\n", + " gain = delta.where(delta > 0, 0)\n", + " loss = -delta.where(delta < 0, 0)\n", + " avg_gain = gain.rolling(window=14).mean()\n", + " avg_loss = loss.rolling(window=14).mean()\n", + " rs = avg_gain / avg_loss\n", + " df['RSI'] = 100 - (100 / (1 + rs))\n", + "\n", + " # 计算MACD\n", + " ema12 = df['close'].ewm(span=12, adjust=False).mean()\n", + " ema26 = df['close'].ewm(span=26, adjust=False).mean()\n", + " df['MACD'] = ema12 - ema26\n", + " df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n", + " df['MACD_hist'] = df['MACD'] - df['Signal_line']\n", + "\n", + " # 4. 情绪因子1：市场上涨比例（Up Ratio）\n", + " df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n", + " df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n", + "\n", + " # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n", + " df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n", + " df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n", + "\n", + " # 6. 情绪因子3：波动率（Volatility）\n", + " df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n", + "\n", + " # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n", + " df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n", + " df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n", + "\n", + " return df\n", + "\n", + "\n", + "def generate_index_indicators(h5_filename):\n", + " df = pd.read_hdf(h5_filename, key='index_data')\n", + " df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n", + " df = df.sort_values('trade_date')\n", + "\n", + " # 计算每个ts_code的相关指标\n", + " df_indicators = []\n", + " for ts_code in df['ts_code'].unique():\n", + " df_index = df[df['ts_code'] == ts_code].copy()\n", + " df_index = calculate_indicators(df_index)\n", + " df_indicators.append(df_index)\n", + "\n", + " # 合并所有指数的结果\n", + " df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n", + "\n", + " # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n", + " df_final = df_all_indicators.pivot_table(\n", + " index='trade_date',\n", + " columns='ts_code',\n", + " values=['daily_return', 'RSI', 'MACD', 'Signal_line',\n", + " 'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',\n", + " 'amount_change_rate', 'amount_mean'],\n", + " aggfunc='last'\n", + " )\n", + "\n", + " df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n", + " df_final = df_final.reset_index()\n", + "\n", + " return df_final\n", + "\n", + "\n", + "# 使用函数\n", + "h5_filename = '../../data/index_data.h5'\n", + "index_data = generate_index_indicators(h5_filename)\n", + "index_data = index_data.dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a735bc02ceb4d872", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:10.821169Z", + "start_time": "2025-04-03T12:47:10.751831Z" + } + }, + "outputs": [], + "source": [ + "import talib\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "53f86ddc0677a6d7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.944254Z", + "start_time": "2025-04-03T12:47:10.826179Z" + }, + "jupyter": { + "source_hidden": true + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "from main.utils.factor import get_act_factor\n", + "\n", + "\n", + "def read_industry_data(h5_filename):\n", + " # 读取 H5 文件中所有的行业数据\n", + " industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n", + " 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n", + " ]) # 假设 H5 文件的键是 'industry_data'\n", + " industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n", + " industry_data = industry_data.reindex()\n", + " industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n", + "\n", + " grouped = industry_data.groupby('ts_code', group_keys=False)\n", + " industry_data['obv'] = grouped.apply(\n", + " lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n", + " )\n", + " industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n", + " industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n", + "\n", + " industry_data = get_act_factor(industry_data, cat=False)\n", + " industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n", + "\n", + " # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n", + " # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n", + " # \n", + " # for factor in factor_columns:\n", + " # if factor in industry_data.columns:\n", + " # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n", + " # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n", + " # lambda x: x - x.mean())\n", + "\n", + " industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n", + " lambda x: x.rank(pct=True))\n", + " industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n", + "\n", + " industry_data = industry_data.rename(\n", + " columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n", + "\n", + " industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n", + " return industry_data\n", + "\n", + "\n", + "industry_df = read_industry_data('../../data/sw_daily.h5')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dbe2fd8021b9417f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T12:47:15.969344Z", + "start_time": "2025-04-03T12:47:15.963327Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n" + ] + } + ], + "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": "2025-04-03T12:47:16.089879Z", + "start_time": "2025-04-03T12:47:15.990101Z" + } + }, + "outputs": [], + "source": [ + "fina_indicator_df = read_and_merge_h5_data('../../data/fina_indicator.h5', key='fina_indicator',\n", + " columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n", + " df=None)\n", + "cashflow_df = read_and_merge_h5_data('../../data/cashflow.h5', key='cashflow',\n", + " columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n", + " df=None)\n", + "balancesheet_df = read_and_merge_h5_data('../../data/balancesheet.h5', key='balancesheet',\n", + " columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n", + " df=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a1c5858e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['ts_code', 'ann_date', 'money_cap', 'total_liab'], dtype='object')\n" + ] + } + ], + "source": [ + "print(balancesheet_df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92d84ce15a562ec6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:01.612695Z", + "start_time": "2025-04-03T12:47:16.121802Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "开始计算因子: AR, BR (原地修改)...\n", + "因子 AR, BR 计算成功。\n", + "因子 AR, BR 计算流程结束。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "计算 BBI...\n", + "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", + " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n", + " 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n", + " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", + " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", + " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", + " 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", + " 'future_return', 'future_volatility', 'label', 'lg_elg_net_buy_vol',\n", + " 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n", + " 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n", + " 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n", + " 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n", + " 'floating_chip_proxy', 'cost_support_15pct_change',\n", + " 'cat_winner_price_zone', 'flow_chip_consistency',\n", + " 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n", + " 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n", + " '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n", + " 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", + " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", + " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", + " dtype='object')\n", + "\n", + "Index: 4450396 entries, 0 to 4450395\n", + "Columns: 123 entries, ts_code to mv_growth\n", + "dtypes: bool(6), datetime64[ns](1), float64(110), int32(3), int64(1), object(2)\n", + "memory usage: 3.9+ GB\n", + "None\n", + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'future_return', 'future_volatility', 'label', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', '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', '_ema_5', '_ema_13', '_ema_20', '_ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', '_stddev_close', '_rank_stddev', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'cat_down_limit', 'up_limit_count_10d', 'down_limit_count_10d', 'consecutive_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth']\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "from main.factor.factor import *\n", + "\n", + "def filter_data(df):\n", + " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", + " df = df[~df['is_st']]\n", + " df = df[~df['ts_code'].str.endswith('BJ')]\n", + " df = df[~df['ts_code'].str.startswith('30')]\n", + " df = df[~df['ts_code'].str.startswith('68')]\n", + " df = df[~df['ts_code'].str.startswith('8')]\n", + " df = df[df['trade_date'] >= '2019-01-01']\n", + " if 'in_date' in df.columns:\n", + " df = df.drop(columns=['in_date'])\n", + " df = df.reset_index(drop=True)\n", + " return df\n", + "\n", + "gc.collect()\n", + "\n", + "df = filter_data(df)\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n", + "df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n", + "calculate_arbr(df, N=26)\n", + "df['log_circ_mv'] = np.log(df['circ_mv'])\n", + "df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n", + "df = caculate_book_to_price_ratio(df, fina_indicator_df)\n", + "df = turnover_rate_n(df, n=5)\n", + "df = variance_n(df, n=20)\n", + "df = bbi_ratio_factor(df)\n", + "# df, _ = get_rolling_factor(df)\n", + "\n", + "# lg_flow_mom_corr(df, N=20, M=60)\n", + "# lg_flow_accel(df)\n", + "# profit_pressure(df)\n", + "# underwater_resistance(df)\n", + "# cost_conc_std(df, N=20)\n", + "# profit_decay(df, N=20)\n", + "# vol_amp_loss(df, N=20)\n", + "# vol_drop_profit_cnt(df, N=20, M=5)\n", + "# lg_flow_vol_interact(df, N=20)\n", + "# cost_break_confirm_cnt(df, M=5)\n", + "# atr_norm_channel_pos(df, N=14)\n", + "# turnover_diff_skew(df, N=20)\n", + "# lg_sm_flow_diverge(df, N=20)\n", + "# pullback_strong(df, N=20, M=20)\n", + "# vol_wgt_hist_pos(df, N=20)\n", + "# vol_adj_roc(df, N=20)\n", + "\n", + "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", + "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", + "\n", + "# df = df.merge(index_data, on='trade_date', how='left')\n", + "\n", + "print(df.info())\n", + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "b87b938028afa206", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.658725Z", + "start_time": "2025-04-03T13:08:02.469611Z" + } + }, + "outputs": [], + "source": [ + "from scipy.stats import ks_2samp, wasserstein_distance\n", + "\n", + "\n", + "def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n", + " importance_threshold=0.05):\n", + " dropped_features = []\n", + "\n", + " # **统计数据漂移**\n", + " numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " for feature in numeric_columns:\n", + " ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n", + " wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n", + "\n", + " if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n", + " dropped_features.append(feature)\n", + "\n", + " print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n", + "\n", + " # **应用阈值进行最终筛选**\n", + " filtered_features = [f for f in feature_columns if f not in dropped_features]\n", + "\n", + " return filtered_features, dropped_features\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "f4f16d63ad18d1bc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:03.670700Z", + "start_time": "2025-04-03T13:08:03.665739Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import statsmodels.api as sm # 用于中性化回归\n", + "from tqdm import tqdm # 可选，用于显示进度条\n", + "\n", + "# --- 常量 ---\n", + "epsilon = 1e-10 # 防止除零\n", + "\n", + "# --- 1. 中位数去极值 (MAD) ---\n", + "\n", + "def cs_mad_filter(df: pd.DataFrame,\n", + " features: list,\n", + " k: float = 3.0,\n", + " scale_factor: float = 1.4826):\n", + " \"\"\"\n", + " 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n", + "\n", + " 方法: 对每日截面数据，计算 median 和 MAD，\n", + " 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n", + " 替换为边界值 (Winsorization)。\n", + " scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n", + " scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"MAD Filtering\"):\n", + " try:\n", + " # 计算截面中位数\n", + " median = grouped[col].transform('median')\n", + " # 计算截面 MAD (Median Absolute Deviation from Median)\n", + " mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n", + "\n", + " # 计算上下边界\n", + " lower_bound = median - k * scale_factor * mad\n", + " upper_bound = median + k * scale_factor * mad\n", + "\n", + " # 原地应用 clip\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n", + "\n", + " print(\"截面 MAD 去极值处理完成。\")\n", + "\n", + "\n", + "# --- 2. 行业市值中性化 ---\n", + "\n", + "def cs_neutralize_industry_cap(df: pd.DataFrame,\n", + " features: list,\n", + " industry_col: str = 'cat_l2_code',\n", + " market_cap_col: str = 'circ_mv'):\n", + " \"\"\"\n", + " 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n", + " 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n", + " 将回归残差写回原特征列。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n", + " industry_col, market_cap_col。\n", + " features (list): 需要处理的特征列名列表。\n", + " industry_col (str): 行业分类列名。\n", + " market_cap_col (str): 流通市值列名。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n", + " 计算量较大，可能耗时较长。\n", + " 需要安装 statsmodels 库 (pip install statsmodels)。\n", + " \"\"\"\n", + " print(\"开始截面行业市值中性化...\")\n", + " required_cols = features + ['trade_date', industry_col, market_cap_col]\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n", + " return\n", + "\n", + " # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n", + " log_cap_col = '_log_market_cap'\n", + " df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n", + " # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n", + " # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n", + " # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n", + "\n", + " dates = df['trade_date'].unique()\n", + " all_residuals = [] # 用于收集所有日期的残差\n", + "\n", + " for date in tqdm(dates, desc=\"Neutralizing\"):\n", + " daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n", + "\n", + " # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n", + " X = daily_data[[log_cap_col]]\n", + " X = sm.add_constant(X, prepend=True) # 添加常数项\n", + " # 创建行业哑变量 (drop_first=True 避免共线性)\n", + " industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n", + " industry_dummies = industry_dummies.astype(int)\n", + " X = pd.concat([X, industry_dummies], axis=1)\n", + "\n", + " daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n", + "\n", + " for col in features:\n", + " Y = daily_data[col]\n", + "\n", + " # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n", + " valid_mask = Y.notna() & X.notna().all(axis=1)\n", + " if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n", + " print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " continue\n", + "\n", + " Y_valid = Y[valid_mask]\n", + " X_valid = X[valid_mask]\n", + "\n", + " # 执行 OLS 回归\n", + " try:\n", + " model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n", + " results = model.fit()\n", + " # 将残差填回对应位置\n", + " daily_residuals.loc[valid_mask, col] = results.resid\n", + " daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n", + " except Exception as e:\n", + " print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n", + " daily_residuals[col] = np.nan\n", + " break\n", + "\n", + " all_residuals.append(daily_residuals)\n", + "\n", + " # 合并所有日期的残差结果\n", + " if all_residuals:\n", + " residuals_df = pd.concat(all_residuals)\n", + " # 将残差结果更新回原始 df (原地修改)\n", + " # 使用 update 比 merge 更适合基于索引的原地更新\n", + " # 确保 residuals_df 的索引与 df 中对应部分一致\n", + " df.update(residuals_df)\n", + " else:\n", + " print(\"没有有效的残差结果可以合并。\")\n", + "\n", + "\n", + " # 清理临时列\n", + " df.drop(columns=[log_cap_col], inplace=True)\n", + " print(\"截面行业市值中性化完成。\")\n", + "\n", + "\n", + "# --- 3. Z-Score 标准化 ---\n", + "\n", + "def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n", + " \"\"\"\n", + " 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n", + " 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n", + " features (list): 需要处理的特征列名列表。\n", + " epsilon (float): 防止除以零的小常数。\n", + "\n", + " WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n", + " \"\"\"\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + " if not all(col in df.columns for col in features):\n", + " missing = [col for col in features if col not in df.columns]\n", + " print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n", + " return\n", + "\n", + " grouped = df.groupby('trade_date')\n", + "\n", + " for col in tqdm(features, desc=\"Standardizing\"):\n", + " try:\n", + " # 使用 transform 计算截面均值和标准差\n", + " mean = grouped[col].transform('mean')\n", + " std = grouped[col].transform('std')\n", + "\n", + " # 计算 Z-Score 并原地赋值\n", + " df[col] = (df[col] - mean) / (std + epsilon)\n", + "\n", + " except KeyError:\n", + " print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n", + " except Exception as e:\n", + " print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n", + "\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + "\n", + "def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n", + "\n", + " 参数:\n", + " df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n", + " feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n", + "\n", + " 返回:\n", + " pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n", + " \"\"\"\n", + " processed_df = df.copy() # 在副本上操作，保留原始数据\n", + "\n", + " # 确保 trade_date 是 datetime 类型以便正确分组\n", + " processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n", + "\n", + " def _fill_daily_nan(group):\n", + " # group 是某一个交易日的 DataFrame\n", + "\n", + " # 遍历指定的特征列\n", + " for feature_col in feature_columns:\n", + " # 检查列是否存在于当前分组中\n", + " if feature_col in group.columns:\n", + " # 计算当日该特征的中位数\n", + " median_val = group[feature_col].median()\n", + "\n", + " # 使用当日中位数填充该特征列的 NaN 值\n", + " # inplace=True 会直接修改 group DataFrame\n", + " group[feature_col].fillna(median_val, inplace=True)\n", + " else:\n", + " print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", + "\n", + " return group\n", + "\n", + " # 按交易日期分组，并应用每日填充函数\n", + " # group_keys=False 避免将分组键添加到结果索引中\n", + " filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n", + "\n", + " return filled_df" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "40e6b68a91b30c79", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T13:08:04.694262Z", + "start_time": "2025-04-03T13:08:03.694904Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n", + " log=True):\n", + " if not (0 <= lower_percentile < upper_percentile <= 1):\n", + " raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n", + "\n", + " # Calculate lower and upper bounds based on percentiles\n", + " lower_bound = label.quantile(lower_percentile)\n", + " upper_bound = label.quantile(upper_percentile)\n", + "\n", + " # Filter out values outside the bounds\n", + " filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n", + "\n", + " # Print the number of removed outliers\n", + " if log:\n", + " print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n", + " return filtered_label\n", + "\n", + "\n", + "def calculate_risk_adjusted_target(df, days=5):\n", + " df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n", + " df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n", + " df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n", + "\n", + " df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n", + " level=0, drop=True)\n", + " sharpe_ratio = df['future_return'] * df['future_volatility']\n", + " sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n", + "\n", + " return sharpe_ratio\n", + "\n", + "\n", + "def calculate_score(df, days=5, lambda_param=1.0):\n", + " def calculate_max_drawdown(prices):\n", + " peak = prices.iloc[0] # 初始化峰值\n", + " max_drawdown = 0 # 初始化最大回撤\n", + "\n", + " for price in prices:\n", + " if price > peak:\n", + " peak = price # 更新峰值\n", + " else:\n", + " drawdown = (peak - price) / peak # 计算当前回撤\n", + " max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n", + "\n", + " return max_drawdown\n", + "\n", + " def compute_stock_score(stock_df):\n", + " stock_df = stock_df.sort_values(by=['trade_date'])\n", + " future_return = stock_df['future_return']\n", + " # 使用已有的 pct_chg 字段计算波动率\n", + " volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n", + " max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n", + " score = future_return - lambda_param * max_drawdown\n", + " return score\n", + "\n", + " # # 确保 DataFrame 按照股票代码和交易日期排序\n", + " # df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "\n", + " # 对每个股票分别计算 score\n", + " df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n", + "\n", + " return df['score']\n", + "\n", + "\n", + "def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n", + " numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n", + " if not numeric_features:\n", + " raise ValueError(\"No numeric features found in the provided data.\")\n", + "\n", + " corr_matrix = df[numeric_features].corr().abs()\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " remaining_features = [col for col in feature_columns if col not in to_drop\n", + " or 'act' in col or 'af' in col]\n", + " return remaining_features\n", + "\n", + "\n", + "def cross_sectional_standardization(df, features):\n", + " df_sorted = df.sort_values(by='trade_date') # 按时间排序\n", + " df_standardized = df_sorted.copy()\n", + "\n", + " for date in df_sorted['trade_date'].unique():\n", + " # 获取当前时间点的数据\n", + " current_data = df_standardized[df_standardized['trade_date'] == date]\n", + "\n", + " # 只对指定特征进行标准化\n", + " scaler = StandardScaler()\n", + " standardized_values = scaler.fit_transform(current_data[features])\n", + "\n", + " # 将标准化结果重新赋值回去\n", + " df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n", + "\n", + " return df_standardized\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n", + " 对特征在行业内部进行市值中性化。\n", + "\n", + " Args:\n", + " df: 输入的 DataFrame，包含特征、行业分类和市值列。\n", + " features: 需要进行中性化的特征列名列表。\n", + " industry_col: 行业分类列的列名。\n", + " mkt_cap_col: 市值列的列名。\n", + "\n", + " Returns:\n", + " 中性化后的 DataFrame。\n", + " \"\"\"\n", + "\n", + " df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n", + " df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n", + " df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n", + "\n", + " if df_cleaned.empty:\n", + " print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n", + " return df # 返回原始或空df，取决于清理前的状态\n", + "\n", + " processed_df = df\n", + "\n", + " for col in features:\n", + " if col not in df_cleaned.columns:\n", + " print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n", + " # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " # 跳过对控制变量本身进行中性化\n", + " if col == mkt_cap_col or col == industry_col:\n", + " print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n", + " # 在结果 df 中也保持原样\n", + " processed_df[col] = df[col] if col in df.columns else np.nan\n", + " continue\n", + "\n", + " residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n", + "\n", + " # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n", + " df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n", + "\n", + " if not df_subset_factor.empty:\n", + " for industry, group in df_subset_factor.groupby(industry_col):\n", + " x = group[mkt_cap_col] # 市值对数\n", + " y = group[col] # 因子值\n", + "\n", + " # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n", + " # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n", + " if len(group) > 1 and np.var(x) > 1e-9:\n", + " try:\n", + " beta = np.cov(y, x)[0, 1] / np.var(x)\n", + " alpha = np.mean(y) - beta * np.mean(x)\n", + "\n", + " # 计算残差\n", + " resid = y - (alpha + beta * x)\n", + "\n", + " # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n", + " residual_series.loc[resid.index] = resid\n", + "\n", + " except Exception as e:\n", + " # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n", + " print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n", + " # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n", + " # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n", + "\n", + " else:\n", + " residual_series.loc[group.index] = group[col] # 保留原始因子值\n", + " processed_df.loc[residual_series.index, col] = residual_series\n", + "\n", + "\n", + " else:\n", + " processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n", + "\n", + " return processed_df\n", + "\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "\n", + "def mad_filter(df, features, n=3):\n", + " for col in features:\n", + " median = df[col].median()\n", + " mad = np.median(np.abs(df[col] - median))\n", + " upper = median + n * mad\n", + " lower = median - n * mad\n", + " df[col] = np.clip(df[col], lower, upper) # 截断极值\n", + " return df\n", + "\n", + "\n", + "def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n", + " for col in features:\n", + " # 按日期分组计算上下百分位数\n", + " lower_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(lower_percentile)\n", + " )\n", + " upper_bound = df.groupby('trade_date')[col].transform(\n", + " lambda x: x.quantile(upper_percentile)\n", + " )\n", + " # 截断超出范围的值\n", + " df[col] = np.clip(df[col], lower_bound, upper_bound)\n", + " return df\n", + "\n", + "\n", + "from scipy.stats import iqr\n", + "\n", + "\n", + "def iqr_filter(df, features):\n", + " for col in features:\n", + " df[col] = df.groupby('trade_date')[col].transform(\n", + " lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n", + " )\n", + " return df\n", + "\n", + "\n", + "def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n", + " df = df.copy()\n", + " for col in features:\n", + " # 计算 rolling 统计量，需要按日期进行 groupby\n", + " rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n", + " rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n", + "\n", + " # 对数据进行裁剪\n", + " df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n", + " \n", + " return df\n", + "\n", + "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n", + " \"\"\"\n", + " 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n", + "\n", + " Args:\n", + " df: 包含特征列和目标列的 Pandas DataFrame。\n", + " feature_columns: 包含所有待评估特征列名的列表。\n", + " n: 希望选取的 RankIC 绝对值最高的特征数量。\n", + " target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n", + "\n", + " Returns:\n", + " 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n", + " \"\"\"\n", + " numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " if target_column not in df.columns:\n", + " raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n", + "\n", + " rankic_scores = {}\n", + " for feature in numeric_columns:\n", + " if feature not in df.columns:\n", + " print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n", + " continue\n", + "\n", + " # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n", + " # dropna() 是为了处理缺失值，确保相关性计算不失败\n", + " valid_data = df[[feature, target_column]].dropna()\n", + " if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n", + " # 计算斯皮尔曼相关性\n", + " correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n", + " rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n", + " else:\n", + " rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n", + "\n", + " # 将 RankIC 分数转换为 Series 便于排序\n", + " rankic_series = pd.Series(rankic_scores)\n", + "\n", + " # 按 RankIC 绝对值降序排序，选取前 n 个特征\n", + " # handle case where n might be larger than available features\n", + " n_actual = min(n, len(rankic_series))\n", + " top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n", + " top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n", + " return top_features" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "dd3018e3", + "metadata": {}, + "outputs": [], + "source": [ + "# transform_feature_columns = ['ARBR']\n", + "# df = df[df['trade_date'] >= '2020-01-01']\n", + "# print('去除极值')\n", + "# cs_mad_filter(df, transform_feature_columns)\n", + "# print('中性化')\n", + "# cs_neutralize_industry_cap(df, transform_feature_columns)\n", + "# print('标准化')\n", + "# cs_zscore_standardize(df, transform_feature_columns)\n", + "\n", + "# cs_mad_filter(test_data, transform_feature_columns)\n", + "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "\n", + "# print(f'feature_columns: {feature_columns}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "47c12bb34062ae7a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:50.841165Z", + "start_time": "2025-04-03T14:49:25.889057Z" + } + }, + "outputs": [], + "source": [ + "days = 5\n", + "validation_days = 120\n", + "\n", + "import gc\n", + "\n", + "gc.collect()\n", + "\n", + "df = df.sort_values(by=['ts_code', 'trade_date'])\n", + "df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n", + "# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n", + "# df.groupby('ts_code')['open'].shift(-1)\n", + "df['future_volatility'] = (\n", + " df.groupby('ts_code')['pct_chg']\n", + " .transform(lambda x: x.rolling(days).std().shift(-days))\n", + ")\n", + "\n", + "# df['future_score'] = calculate_score(df, days=2, lambda_param=0.3)\n", + "\n", + "filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n", + "\n", + "# df['label'] = df.groupby('trade_date', group_keys=False)['future_volatility'].transform(\n", + "# lambda x: pd.qcut(x, q=30, labels=False, duplicates='drop')\n", + "# )\n", + "\n", + "df['label'] = df['future_return']\n", + "\n", + "def symmetric_log_transform(values):\n", + " return np.sign(values) * np.log1p(np.abs(values))\n", + "\n", + "# for col in [col for col in df.columns]:\n", + "# train_data[col] = train_data[col].astype('str')\n", + "# test_data[col] = test_data[col].astype('str')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "b76ea08a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "2738 000001.SZ 2019-01-03 16.583965\n", + "5477 000001.SZ 2019-01-04 16.633371\n", + "['undist_profit_ps', 'AR_BR', 'pe_ttm', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'cat_down_limit', 'up_limit_count_10d', 'down_limit_count_10d', 'consecutive_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'cashflow_to_ev_factor', 'ocfps', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor']\n", + "去除极值\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 27/27 [00:05<00:00, 4.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 100%|██████████| 27/27 [00:04<00:00, 6.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "开始截面 MAD 去极值处理 (k=3.0)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MAD Filtering: 0it [00:00, ?it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "截面 MAD 去极值处理完成。\n", + "feature_columns: ['undist_profit_ps', 'AR_BR', 'pe_ttm', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'cat_down_limit', 'up_limit_count_10d', 'down_limit_count_10d', 'consecutive_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'cashflow_to_ev_factor', 'ocfps', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor']\n", + "df最小日期: 2019-01-02\n", + "df最大日期: 2025-04-09\n", + "2060133\n", + "train_data最小日期: 2020-01-02\n", + "train_data最大日期: 2022-12-30\n", + "1678529\n", + "test_data最小日期: 2023-01-03\n", + "test_data最大日期: 2025-04-09\n", + " ts_code trade_date log_circ_mv\n", + "0 000001.SZ 2019-01-02 16.574219\n", + "2738 000001.SZ 2019-01-03 16.583965\n", + "5477 000001.SZ 2019-01-04 16.633371\n" + ] + } + ], + "source": [ + "train_data = df[filter_index & (df['trade_date'] <= '2023-01-01') & (df['trade_date'] >= '2020-01-01')]\n", + "test_data = df[(df['trade_date'] >= '2023-01-01')]\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n", + "\n", + "industry_df = industry_df.sort_values(by=['trade_date'])\n", + "index_data = index_data.sort_values(by=['trade_date'])\n", + "\n", + "# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# train_data = train_data.merge(index_data, on='trade_date', how='left')\n", + "# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# test_data = test_data.merge(index_data, on='trade_date', how='left')\n", + "\n", + "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + "# feature_columns_new = feature_columns[:]\n", + "# train_data, _ = create_deviation_within_dates(train_data, feature_columns)\n", + "# test_data, _ = create_deviation_within_dates(test_data, feature_columns)\n", + "\n", + "feature_columns = [\n", + " 'undist_profit_ps', \n", + " 'AR_BR', \n", + " 'pe_ttm',\n", + " 'alpha_22_improved', \n", + " 'alpha_003', \n", + " 'alpha_007', \n", + " 'alpha_013', \n", + " 'cat_up_limit', \n", + " 'cat_down_limit', \n", + " 'up_limit_count_10d', \n", + " 'down_limit_count_10d', \n", + " 'consecutive_up_limit', \n", + " 'vol_break', \n", + " 'weight_roc5', \n", + " 'price_cost_divergence', \n", + " 'smallcap_concentration', \n", + " 'cost_stability', \n", + " 'high_cost_break_days', \n", + " 'liquidity_risk', \n", + " 'turnover_std', \n", + " 'mv_volatility', \n", + " 'volume_growth', \n", + " 'mv_growth', \n", + " 'lg_flow_mom_corr_20_60', \n", + " 'lg_flow_accel', \n", + " 'profit_pressure', \n", + " 'underwater_resistance', \n", + " 'cost_conc_std_20', \n", + " 'profit_decay_20', \n", + " 'vol_amp_loss_20', \n", + " 'vol_drop_profit_cnt_5', \n", + " 'lg_flow_vol_interact_20', \n", + " 'cost_break_confirm_cnt_5', \n", + " 'atr_norm_channel_pos_14', \n", + " 'turnover_diff_skew_20', \n", + " 'lg_sm_flow_diverge_20', \n", + " 'pullback_strong_20_20', \n", + " 'vol_wgt_hist_pos_20', \n", + " 'vol_adj_roc_20',\n", + " 'cashflow_to_ev_factor',\n", + " 'ocfps',\n", + " 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5',\n", + " 'variance_20',\n", + " 'bbi_ratio_factor'\n", + "]\n", + "feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n", + "print(feature_columns)\n", + "\n", + "train_data = fill_nan_with_daily_median(train_data, feature_columns)\n", + "test_data = fill_nan_with_daily_median(test_data, feature_columns)\n", + "\n", + "train_data = train_data.dropna(subset=feature_columns)\n", + "train_data = train_data.dropna(subset=['label'])\n", + "train_data = train_data.reset_index(drop=True)\n", + "# print(test_data.tail())\n", + "test_data = test_data.dropna(subset=feature_columns)\n", + "# test_data = test_data.dropna(subset=['label'])\n", + "test_data = test_data.reset_index(drop=True)\n", + "\n", + "transform_feature_columns = feature_columns\n", + "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat')]\n", + "# transform_feature_columns.remove('undist_profit_ps')\n", + "print('去除极值')\n", + "cs_mad_filter(train_data, transform_feature_columns)\n", + "# print('中性化')\n", + "# cs_neutralize_industry_cap(train_data, transform_feature_columns)\n", + "# print('标准化')\n", + "# cs_zscore_standardize(train_data, transform_feature_columns)\n", + "\n", + "cs_mad_filter(test_data, transform_feature_columns)\n", + "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", + "# cs_zscore_standardize(test_data, transform_feature_columns)\n", + "\n", + "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat')]\n", + "cs_mad_filter(train_data, mad_filter_feature_columns)\n", + "cs_mad_filter(test_data, mad_filter_feature_columns)\n", + "\n", + "\n", + "print(f'feature_columns: {feature_columns}')\n", + "\n", + "\n", + "print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n", + "print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n", + "print(len(train_data))\n", + "print(f\"train_data最小日期: {train_data['trade_date'].min().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\"test_data最小日期: {test_data['trade_date'].min().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 feature_columns if col.startswith('cat')]\n", + "for col in cat_columns:\n", + " train_data[col] = train_data[col].astype('category')\n", + " test_data[col] = test_data[col].astype('category')\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e23d1759", + "metadata": {}, + "outputs": [], + "source": [ + "feature_columns = [\n", + " 'undist_profit_ps', \n", + " 'AR_BR', \n", + " # 'pe_ttm',\n", + " # 'alpha_22_improved', \n", + " # 'alpha_003', \n", + " # 'alpha_007', \n", + " # 'alpha_013', \n", + " # 'cat_up_limit', \n", + " # 'cat_down_limit', \n", + " # 'up_limit_count_10d', \n", + " # 'down_limit_count_10d', \n", + " # 'consecutive_up_limit', \n", + " # 'vol_break', \n", + " # 'weight_roc5', \n", + " # 'price_cost_divergence', \n", + " # 'smallcap_concentration', \n", + " # 'cost_stability', \n", + " # 'high_cost_break_days', \n", + " # 'liquidity_risk', \n", + " # 'turnover_std', \n", + " # 'mv_volatility', \n", + " # 'volume_growth', \n", + " # 'mv_growth', \n", + " # 'lg_flow_mom_corr_20_60', \n", + " # 'lg_flow_accel', \n", + " # 'profit_pressure', \n", + " # 'underwater_resistance', \n", + " # 'cost_conc_std_20', \n", + " # 'profit_decay_20', \n", + " # 'vol_amp_loss_20', \n", + " # 'vol_drop_profit_cnt_5', \n", + " # 'lg_flow_vol_interact_20', \n", + " # 'cost_break_confirm_cnt_5', \n", + " # 'atr_norm_channel_pos_14', \n", + " # 'turnover_diff_skew_20', \n", + " # 'lg_sm_flow_diverge_20', \n", + " # 'pullback_strong_20_20', \n", + " # 'vol_wgt_hist_pos_20', \n", + " # 'vol_adj_roc_20',\n", + " 'cashflow_to_ev_factor',\n", + " 'ocfps',\n", + " 'book_to_price_ratio',\n", + " 'turnover_rate_mean_5',\n", + " 'variance_20',\n", + " 'bbi_ratio_factor'\n", + "]\n", + "feature_columns = [col for col in feature_columns if col in train_data.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "8f134d435f71e9e2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T14:57:51.050696Z", + "start_time": "2025-04-03T14:57:51.034030Z" + }, + "jupyter": { + "source_hidden": true + } + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression, BayesianRidge\n", + "import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datetime # 用于日期计算\n", + "\n", + "\n", + "def train_model(train_data_df, feature_columns,\n", + " print_info=True, # 调整参数名，更通用\n", + " validation_days=180, use_pca=False, split_date=None,\n", + " target_column='label'): # 增加目标列参数\n", + "\n", + " print('train data size: ', len(train_data_df))\n", + "\n", + " # 确保数据按时间排序\n", + " train_data_df = train_data_df.sort_values(by='trade_date')\n", + "\n", + " # 识别数值型特征列\n", + " numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n", + " \n", + " # 对数值特征进行横截面标准化（如果您的函数是这样实现的）\n", + " # 假设 cross_sectional_standardization 接受 DataFrame 和 列名列表\n", + " # if numeric_feature_columns: # 只对存在数值特征的列进行标准化\n", + " # train_data_df = cross_sectional_standardization(train_data_df, numeric_feature_columns)\n", + " # elif print_info:\n", + " # print(\"警告: 没有找到数值型特征列用于标准化。\")\n", + "\n", + "\n", + " # 去除标签为空的样本\n", + " initial_len = len(train_data_df)\n", + " train_data_df = train_data_df.dropna(subset=[target_column])\n", + "\n", + " if print_info:\n", + " print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n", + "\n", + " train_data_split = train_data_df\n", + "\n", + " # 提取特征和标签，只取数值型特征用于线性回归\n", + " X_train = train_data_split[numeric_feature_columns]\n", + " y_train = train_data_split[target_column]\n", + "\n", + " # # 标准化数值特征 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n", + " scaler = StandardScaler()\n", + " # X_train = scaler.fit_transform(X_train)\n", + "\n", + " # 训练线性回归模型\n", + " # model = LinearRegression()\n", + " model = BayesianRidge()\n", + " \n", + " # 使用处理后的特征和样本权重进行训练\n", + " model.fit(X_train, y_train)\n", + "\n", + "\n", + " return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:03:18.426481Z", + "start_time": "2025-04-03T15:02:19.926352Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data size: 36400\n", + "原始样本数: 36400, 去除标签为空后样本数: 36400\n" + ] + } + ], + "source": [ + "\n", + "gc.collect()\n", + "\n", + "use_pca = False\n", + "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", + "# light_params['feature_contri'] = feature_contri\n", + "# print(f'feature_contri: {feature_contri}')\n", + "model, scaler, pca = train_model(train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')), feature_columns)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "5d1522a7538db91b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-03T15:04:39.656944Z", + "start_time": "2025-04-03T15:04:39.298483Z" + } + }, + "outputs": [], + "source": [ + "# train_data = train_data.sort_values(by='trade_date')\n", + "# all_dates = train_data['trade_date'].unique() # 获取所有唯一的 trade_date\n", + "# split_date = all_dates[-120] # 划分点为倒数第 validation_days 天\n", + "# print(split_date)\n", + "# print(all_dates)\n", + "# val_data_split = train_data[train_data['trade_date'] >= split_date] # 验证集\n", + "\n", + "score_df = test_data\n", + "score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", + "score_df = score_df[score_df['pe_ttm'] > 0]\n", + "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n", + "numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n", + "numeric_columns = [col for col in feature_columns if col in numeric_columns]\n", + "# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n", + "# score_df = cross_sectional_standardization(score_df, numeric_columns)\n", + "\n", + "score_df['score'] = model.predict(score_df[numeric_columns])\n", + "score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n", + "\n", + "score_df = score_df.groupby('trade_date', group_keys=False).apply(\n", + " lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n", + ").reset_index(drop=True) # drop=True 避免添加旧索引列\n", + "# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n", + "save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1, 'total_mv')).reset_index()\n", + "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n", + "# " + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "340a82b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date AR_BR undist_profit_ps score \\\n", + "0 000100.SZ 2023-01-03 11.934320 1.4785 0.010065 \n", + "1 000166.SZ 2023-01-03 1.216701 1.3199 0.009441 \n", + "2 000518.SZ 2023-01-03 -15.537010 -0.5179 0.009575 \n", + "3 000533.SZ 2023-01-03 6.648137 -0.1828 0.009428 \n", + "4 000536.SZ 2023-01-03 -8.622002 -2.6896 0.016490 \n", + "... ... ... ... ... ... \n", + "168082 605208.SH 2025-04-09 89.845359 2.7720 0.026423 \n", + "168083 605228.SH 2025-04-09 9.234756 0.9922 0.027368 \n", + "168084 605488.SH 2025-04-09 38.082408 1.7606 0.028328 \n", + "168085 605555.SH 2025-04-09 47.834082 2.9659 0.030038 \n", + "168086 605577.SH 2025-04-09 30.978777 4.7919 0.029345 \n", + "\n", + " AR BR \n", + "0 67.759563 55.825243 \n", + "1 92.622951 91.406250 \n", + "2 90.694444 106.231454 \n", + "3 101.730104 95.081967 \n", + "4 70.720721 79.342723 \n", + "... ... ... \n", + "168082 177.835588 87.375312 \n", + "168083 94.088176 84.853420 \n", + "168084 107.369795 69.287387 \n", + "168085 98.648649 50.814566 \n", + "168086 114.000000 83.021223 \n", + "\n", + "[168087 rows x 7 columns]\n" + ] + } + ], + "source": [ + "print(score_df[['ts_code', 'trade_date', 'AR_BR', 'undist_profit_ps', 'score', 'AR', 'BR']])" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "fb2263ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "线性回归模型截距 (Intercept):\n", + "-0.0821873954475609\n", + "\n", + "线性回归模型系数 (Coefficients):\n", + "[-6.62598705e-05 -2.63806648e-05 -2.57090964e-05 1.14891132e-08\n", + " 1.14960472e-03 2.26826386e-03 7.33817048e-03 4.86240923e-15\n", + " 2.33841866e-15 -2.88389337e-15 -2.37944451e-01 -1.22219539e-04\n", + " -2.67783556e-02 9.18323390e-02 -4.07814304e-15 -7.28200346e-06\n", + " -2.50383369e-02 2.66757338e-01 -1.89189144e-02 2.41573603e-01\n", + " 4.69728616e-02 -1.97148056e-03 -1.14382817e-03 -8.75750113e-04\n", + " 4.21106078e-04 8.52579791e-02]\n" + ] + } + ], + "source": [ + "print(\"线性回归模型截距 (Intercept):\")\n", + "print(model.intercept_)\n", + "\n", + "print(\"\\n线性回归模型系数 (Coefficients):\")\n", + "# 打印系数数组\n", + "print(model.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "7e9023cc", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_factors(\n", + " df: pd.DataFrame,\n", + " feature_columns: list[str],\n", + " target_column: str = 'target', # 假设目标列默认为 'target'\n", + " trade_date_col: str = 'trade_date', # 假设日期列默认为 'trade_date'\n", + " mcap_col: str = 'total_mv', # 新增: 市值列名称\n", + " mcap_bins: int = 5 # 新增: 市值分位数的数量 (例如 5 表示五分位数)\n", + ") -> pd.DataFrame:\n", + " \"\"\"\n", + " 分析DataFrame中指定特征列的各种指标，包括基本统计、相关性、日间IC、ICIR以及在不同市值分位数上的IC。\n", + "\n", + " Args:\n", + " df (pd.DataFrame): 包含日期、目标列、特征列和市值列的DataFrame。\n", + " 需要包含 trade_date_col, target_column, feature_columns 和 mcap_col 中的所有列。\n", + " feature_columns (list[str]): 需要分析的特征列名称列表。\n", + " target_column (str): 目标变量列的名称。\n", + " trade_date_col (str): 交易日期列的名称。\n", + " mcap_col (str): 市值列的名称。\n", + " mcap_bins (int): 市值分位数的数量 (例如 5 表示五分位数)。\n", + "\n", + " Returns:\n", + " pd.DataFrame: 包含各个因子分析指标的汇总DataFrame。\n", + " 同时打印因子在不同市值分位数上的平均IC表格。\n", + " 如果输入数据或列有问题，可能返回空或包含NaN的DataFrame。\n", + " \"\"\"\n", + "\n", + " # --- 数据校验 ---\n", + " required_cols = [trade_date_col, target_column, mcap_col] + feature_columns\n", + " if not all(col in df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in df.columns]\n", + " print(f\"错误: 输入DataFrame缺少必需的列: {missing}\")\n", + " return pd.DataFrame() # 返回空DataFrame\n", + "\n", + " # 确保日期列是 datetime 类型\n", + " df = df.copy() # 在副本上操作\n", + " df[trade_date_col] = pd.to_datetime(df[trade_date_col], errors='coerce')\n", + " df.dropna(subset=[trade_date_col], inplace=True) # 移除日期转换失败的行\n", + "\n", + " # 过滤掉那些在 feature_columns, target_column, mcap_col 上有 NaN 的行，以确保后续计算是在完整数据上\n", + " # 直接在 df 副本上进行清洗\n", + " initial_rows_before_clean = len(df)\n", + " df.dropna(subset=feature_columns + [target_column, mcap_col], inplace=True)\n", + " rows_dropped_clean = initial_rows_before_clean - len(df)\n", + " if rows_dropped_clean > 0:\n", + " print(f\"警告: 移除了 {rows_dropped_clean} 行，因为其特征、目标或市值列存在空值。\")\n", + "\n", + " if df.empty:\n", + " print(\"错误: 清理缺失值后数据为空，无法进行因子分析。\")\n", + " return pd.DataFrame() # 返回空DataFrame\n", + "\n", + "\n", + " print(f\"开始分析 {len(feature_columns)} 个因子指标...\")\n", + "\n", + " # --- 1. 基本因子统计量 ---\n", + " basic_stats = df[feature_columns].describe().T\n", + "\n", + " print(\"\\n--- 基本因子统计量 ---\")\n", + " print(basic_stats)\n", + "\n", + " # --- 2. 因子与目标变量的整体相关性 ---\n", + " overall_correlation = {}\n", + " for feature in feature_columns:\n", + " # 在清理后的 df 上计算相关性\n", + " if df[[feature, target_column]].dropna().shape[0] > 1: # 确保至少有两个有效数据点\n", + " overall_correlation[feature] = {\n", + " 'Pearson_Correlation_with_Target': df[feature].corr(df[target_column], method='pearson'),\n", + " 'Spearman_Correlation_with_Target': df[feature].corr(df[target_column], method='spearman')\n", + " }\n", + " else:\n", + " overall_correlation[feature] = {\n", + " 'Pearson_Correlation_with_Target': np.nan,\n", + " 'Spearman_Correlation_with_Target': np.nan\n", + " }\n", + " overall_corr_df = pd.DataFrame.from_dict(overall_correlation, orient='index')\n", + "\n", + " print(\"\\n--- 因子与目标变量的整体相关性 ---\")\n", + " print(overall_corr_df)\n", + "\n", + " # --- 3. 因子之间的相关性矩阵 ---\n", + " # 在清理后的 df 上计算相关性\n", + " factor_correlation_matrix = df[feature_columns].corr(method='spearman') # 改回 Spearman\n", + "\n", + " print(\"\\n--- 因子之间的相关性矩阵 (Spearman) ---\") # 修正打印信息\n", + " print(factor_correlation_matrix)\n", + "\n", + " # --- 4. 日间 IC 和 ICIR ---\n", + " print(\"\\n--- 计算日间 IC (Spearman 相关性) 和 ICIR ---\")\n", + "\n", + " # 直接在清理后的 df 上计算每日 IC\n", + " if df.empty: # 理论上上面已经检查过，这里再检查一次更安全\n", + " daily_ic_series = pd.Series(dtype=float) # 空 Series\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n", + " }, index=feature_columns)\n", + " else:\n", + " daily_ic_series = df.groupby(trade_date_col).apply(\n", + " lambda day_group: {\n", + " feature: day_group[feature].corr(day_group[target_column], method='spearman')\n", + " for feature in feature_columns if day_group.shape[0] > 1 # 确保每日数据点多于1才能计算相关性\n", + " }\n", + " ).apply(pd.Series) # 将字典结果转换为 DataFrame\n", + "\n", + " # 计算 IC 的统计量\n", + " if not daily_ic_series.empty:\n", + " ic_mean = daily_ic_series.mean()\n", + " ic_std = daily_ic_series.std()\n", + " # 避免除以零\n", + " ic_ir = ic_mean / ic_std.replace(0, np.nan) # 使用 replace 0 为 NaN\n", + "\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': ic_mean,\n", + " 'Std_Dev_IC': ic_std,\n", + " 'ICIR': ic_ir\n", + " })\n", + " print(\"\\n--- 日间 IC 和 ICIR (Spearman) ---\")\n", + " print(ic_stats)\n", + " else:\n", + " ic_stats = pd.DataFrame({\n", + " 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n", + " }, index=feature_columns)\n", + "\n", + "\n", + " # --- 5. 因子在不同市值分位数上的平均 IC ---\n", + " print(f\"\\n--- 计算因子在 {mcap_bins} 个市值分位数上的平均 IC (Spearman) ---\")\n", + "\n", + " # 在清理后的 df 上计算每日市值分位数，直接添加到 df 中\n", + " # 使用 transform() 和 qcut() 在每个日期分组内计算分位数\n", + " # labels=False 返回整数 0 to mcap_bins-1\n", + " # duplicates='drop' 处理在某些日期股票数量少于 bins 导致分位数边缘重复的情况，会返回 NaN\n", + " # 添加一个临时列来存储分位数\n", + " mcap_bin_col_name = f'_mcap_bin_{mcap_bins}'\n", + " df[mcap_bin_col_name] = df.groupby(trade_date_col)[mcap_col].transform(\n", + " lambda x: pd.qcut(x, q=mcap_bins, labels=False, duplicates='drop') if len(x) >= mcap_bins else np.nan # 确保股票数量足够进行分位数划分\n", + " )\n", + "\n", + " # 过滤掉无法划分分位数 (NaN) 的行，进行分位数 IC 计算\n", + " # 创建一个临时 DataFrame df_binned_analysis\n", + " df_binned_analysis = df.dropna(subset=[mcap_bin_col_name]).copy()\n", + "\n", + " if df_binned_analysis.empty:\n", + " print(\"错误: 划分市值分位数后数据为空，无法计算分位数上的 IC。\")\n", + " avg_ic_by_bin = pd.DataFrame(index=range(mcap_bins), columns=feature_columns) # Placeholder\n", + " else:\n", + " # 按日期和市值分位数分组，计算每个分组内的因子与目标变量的截面相关性 (分位数IC)\n", + " binned_ic_by_day = df_binned_analysis.groupby([trade_date_col, mcap_bin_col_name]).apply(\n", + " lambda group: {\n", + " feature: group[feature].corr(group[target_column], method='spearman')\n", + " for feature in feature_columns if group.shape[0] > 1 # 确保分位数组内数据点多于1\n", + " }\n", + " ).apply(pd.Series) # 将嵌套结果转为 DataFrame\n", + "\n", + " # 对每个分位数组的每日 IC 求平均\n", + " # unstack(level=mcap_bin_col_name) 将 mcap_bin 作为列\n", + " # mean(axis=0) 对日期索引求平均\n", + " avg_ic_by_bin = binned_ic_by_day.unstack(level=mcap_bin_col_name).mean(axis=0).unstack()\n", + "\n", + " # 重命名索引和列，使表格更清晰\n", + " if not avg_ic_by_bin.empty:\n", + " # Index name will be the original column name used for grouping ('_mcap_bin_X')\n", + " # Rename the index name explicitly\n", + " avg_ic_by_bin.index.name = 'MarketCap_Bin'\n", + " avg_ic_by_bin.columns.name = 'Feature'\n", + " # 可以根据需要对分位数 bin 索引进行排序 (虽然 pd.qcut labels=False usually sorts)\n", + " avg_ic_by_bin = avg_ic_by_bin.sort_index()\n", + "\n", + " print(avg_ic_by_bin)\n", + "\n", + "\n", + " # --- 6. 汇总所有指标 ---\n", + " # 将基本统计、整体相关性、IC/ICIR 合并到一个 DataFrame\n", + " # 注意：合并时需要根据索引进行对齐 (因子名称)\n", + " summary_df = basic_stats\n", + " summary_df = summary_df.merge(overall_corr_df, left_index=True, right_index=True, how='left')\n", + " summary_df = summary_df.merge(ic_stats, left_index=True, right_index=True, how='left')\n", + "\n", + " # print(\"\\n--- 因子分析汇总报告 ---\")\n", + " # print(summary_df)\n", + "\n", + " # --- 清理临时列 'mcap_bin' ---\n", + " # 修正：在函数结束时从我们一直在操作的 df 副本中删除临时列\n", + " if mcap_bin_col_name in df.columns:\n", + " df.drop(columns=[mcap_bin_col_name], inplace=True)\n", + "\n", + "\n", + " return summary_df # 主要返回汇总报告，分位数IC单独打印\n", + "\n", + "# # 运行分析函数\n", + "# factor_analysis_report = analyze_factors(test_data.copy(), feature_columns, 'future_return')\n", + "\n", + "# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n", + "# print(factor_analysis_report)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/train/RollingRankCopy.ipynb b/main/train/RollingRankCopy.ipynb index 2d520fd..beb7f6b 100644 --- a/main/train/RollingRankCopy.ipynb +++ b/main/train/RollingRankCopy.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 113, + "execution_count": 1, "id": "79a7758178bafdd3", "metadata": { "ExecuteTime": { @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 2, "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 3, "id": "cac01788dac10678", "metadata": { "ExecuteTime": { @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 4, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { @@ -221,6 +221,8 @@ }, "outputs": [], "source": [ + "import talib\n", + "\n", "def calculate_indicators(df):\n", " \"\"\"\n", " 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n", @@ -300,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 5, "id": "53f86ddc0677a6d7", "metadata": { "ExecuteTime": { @@ -360,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 6, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { @@ -388,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 7, "id": "da6e43bd", "metadata": {}, "outputs": [], @@ -852,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 8, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { @@ -895,6 +897,8 @@ } ], "source": [ + "import numpy as np\n", + "\n", "def filter_data(df):\n", " # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n", " df = df[~df['is_st']]\n", @@ -928,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 9, "id": "f4f16d63ad18d1bc", "metadata": { "ExecuteTime": { @@ -980,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 35, "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { @@ -1239,12 +1243,56 @@ " 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" + " return df\n", + "\n", + "def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n", + " \"\"\"\n", + " 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n", + "\n", + " Args:\n", + " df: 包含特征列和目标列的 Pandas DataFrame。\n", + " feature_columns: 包含所有待评估特征列名的列表。\n", + " n: 希望选取的 RankIC 绝对值最高的特征数量。\n", + " target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n", + "\n", + " Returns:\n", + " 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n", + " \"\"\"\n", + " numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + " numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + " if target_column not in df.columns:\n", + " raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n", + "\n", + " rankic_scores = {}\n", + " for feature in numeric_columns:\n", + " if feature not in df.columns:\n", + " print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n", + " continue\n", + "\n", + " # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n", + " # dropna() 是为了处理缺失值，确保相关性计算不失败\n", + " valid_data = df[[feature, target_column]].dropna()\n", + " if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n", + " # 计算斯皮尔曼相关性\n", + " correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n", + " rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n", + " else:\n", + " rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n", + "\n", + " # 将 RankIC 分数转换为 Series 便于排序\n", + " rankic_series = pd.Series(rankic_scores)\n", + "\n", + " # 按 RankIC 绝对值降序排序，选取前 n 个特征\n", + " # handle case where n might be larger than available features\n", + " n_actual = min(n, len(rankic_series))\n", + " top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n", + " top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n", + " return top_features" ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 11, "id": "1c46817a-b5dd-4bec-8bb4-e6e80bfd9d66", "metadata": { "ExecuteTime": { @@ -1259,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 12, "id": "da2bb202843d9275", "metadata": { "ExecuteTime": { @@ -1369,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 13, "id": "ff19e3f1e051a489", "metadata": { "ExecuteTime": { @@ -1405,7 +1453,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 57, "id": "27dba27b2e108316", "metadata": { "ExecuteTime": { @@ -1425,7 +1473,7 @@ "source": [ "def select_pre_zt_stocks_dynamic(stock_df):\n", " def select_stocks(group):\n", - " max_stocks = 500\n", + " max_stocks = 1500\n", " return group.nsmallest(max_stocks, 'log_circ_mv')\n", "\n", " stock_df = stock_df.groupby('trade_date', group_keys=False).apply(select_stocks)\n", @@ -1442,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 58, "id": "ca96fb81e17c4a90", "metadata": { "ExecuteTime": { @@ -1489,7 +1537,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 59, "id": "81d4570663ae21d7", "metadata": { "ExecuteTime": { @@ -1511,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 60, "id": "92428d543f4727ad", "metadata": { "ExecuteTime": { @@ -1523,10 +1571,10 @@ { "data": { "text/plain": [ - "0" + "14" ] }, - "execution_count": 129, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1567,7 +1615,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 61, "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { @@ -1588,7 +1636,7 @@ " n = len(unique_dates)\n", "\n", " # 2. 计算需要跳过的天数，使后续窗口对齐\n", - " extra_days = (n - train_days) % test_days\n", + " extra_days = (n - train_days) % test_days + days\n", " start_index = extra_days # 从此索引开始滚动\n", "\n", " predictions_list = []\n", @@ -1609,6 +1657,8 @@ " # feature_columns, _ = remove_shifted_features(train_data, feature_columns_origin, size=0.8, log=False,\n", " # val_data=df[filter_index & (df['trade_date'] ==\n", " # unique_dates[start + train_days - days + 1])])\n", + " feature_columns = feature_columns_origin\n", + " # feature_columns = select_top_features_by_rankic(train_data, feature_columns, n=50)\n", "\n", " train_data = train_data.dropna(subset=feature_columns)\n", " train_data = train_data.dropna(subset=['label'])\n", @@ -1644,9 +1694,10 @@ " lgb.callback.record_evaluation(evals),\n", " # lgb.early_stopping(100, first_metric_only=True)\n", " ], evals,\n", - " num_boost_round=24, validation_days=validation_days,\n", + " num_boost_round=36, validation_days=validation_days,\n", " print_feature_importance=False, use_pca=False)\n", " except Exception as e:\n", + " print(e)\n", " print(train_data['label'].unique().tolist())\n", "\n", " score_df = test_data.copy()\n", @@ -1656,8 +1707,9 @@ " # predictions_list.append(score_df)\n", " # score_df['score'] = score_df['log_circ_mv']\n", " top_stock_ = score_df.nlargest(1, columns='score').reset_index(level=0)\n", - " final_selection = top_stock_[['trade_date', 'score', 'ts_code']]\n", - " predictions_list.append(final_selection)\n", + " final_selection = top_stock_\n", + " # final_selection = final_selection.sort_values(['log_circ_mv']).head(1)\n", + " predictions_list.append(final_selection[['trade_date', 'score', 'ts_code']])\n", "\n", " final_predictions = pd.concat(predictions_list, ignore_index=True)\n", " return final_predictions\n", @@ -1666,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 62, "id": "63235069-dc59-48fb-961a-e80373e41a61", "metadata": { "ExecuteTime": { @@ -1686,1700 +1738,2347 @@ "output_type": "stream", "text": [ "finish\n", - "train_data最大日期: 2022-12-01, 训练天数1\n", + "train_data最大日期: 2022-01-11, 训练天数5\n", + "test_data最大日期: 2022-01-18\n", + "划分后的训练集大小: 6946, 验证集大小: 1391\n", + "train_data最大日期: 2022-01-12, 训练天数5\n", + "test_data最大日期: 2022-01-19\n", + "划分后的训练集大小: 6946, 验证集大小: 1390\n", + "train_data最大日期: 2022-01-13, 训练天数5\n", + "test_data最大日期: 2022-01-20\n", + "划分后的训练集大小: 6943, 验证集大小: 1387\n", + "train_data最大日期: 2022-01-14, 训练天数5\n", + "test_data最大日期: 2022-01-21\n", + "划分后的训练集大小: 6932, 验证集大小: 1377\n", + "train_data最大日期: 2022-01-17, 训练天数5\n", + "test_data最大日期: 2022-01-24\n", + "划分后的训练集大小: 6931, 验证集大小: 1386\n", + "train_data最大日期: 2022-01-18, 训练天数5\n", + "test_data最大日期: 2022-01-25\n", + "划分后的训练集大小: 6928, 验证集大小: 1388\n", + "train_data最大日期: 2022-01-19, 训练天数5\n", + "test_data最大日期: 2022-01-26\n", + "划分后的训练集大小: 6915, 验证集大小: 1377\n", + "train_data最大日期: 2022-01-20, 训练天数5\n", + "test_data最大日期: 2022-01-27\n", + "划分后的训练集大小: 6894, 验证集大小: 1366\n", + "train_data最大日期: 2022-01-21, 训练天数5\n", + "test_data最大日期: 2022-01-28\n", + "划分后的训练集大小: 6904, 验证集大小: 1387\n", + "train_data最大日期: 2022-01-24, 训练天数5\n", + "test_data最大日期: 2022-02-07\n", + "划分后的训练集大小: 6910, 验证集大小: 1392\n", + "train_data最大日期: 2022-01-25, 训练天数5\n", + "test_data最大日期: 2022-02-08\n", + "划分后的训练集大小: 6918, 验证集大小: 1396\n", + "train_data最大日期: 2022-01-26, 训练天数5\n", + "test_data最大日期: 2022-02-09\n", + "划分后的训练集大小: 6939, 验证集大小: 1398\n", + "train_data最大日期: 2022-01-27, 训练天数5\n", + "test_data最大日期: 2022-02-10\n", + "划分后的训练集大小: 6949, 验证集大小: 1376\n", + "train_data最大日期: 2022-01-28, 训练天数5\n", + "test_data最大日期: 2022-02-11\n", + "划分后的训练集大小: 6950, 验证集大小: 1388\n", + "train_data最大日期: 2022-02-07, 训练天数5\n", + "test_data最大日期: 2022-02-14\n", + "划分后的训练集大小: 6947, 验证集大小: 1389\n", + "train_data最大日期: 2022-02-08, 训练天数5\n", + "test_data最大日期: 2022-02-15\n", + "划分后的训练集大小: 6940, 验证集大小: 1389\n", + "train_data最大日期: 2022-02-09, 训练天数5\n", + "test_data最大日期: 2022-02-16\n", + "划分后的训练集大小: 6933, 验证集大小: 1391\n", + "train_data最大日期: 2022-02-10, 训练天数5\n", + "test_data最大日期: 2022-02-17\n", + "划分后的训练集大小: 6960, 验证集大小: 1403\n", + "train_data最大日期: 2022-02-11, 训练天数5\n", + "test_data最大日期: 2022-02-18\n", + "划分后的训练集大小: 6978, 验证集大小: 1406\n", + "train_data最大日期: 2022-02-14, 训练天数5\n", + "test_data最大日期: 2022-02-21\n", + "划分后的训练集大小: 6991, 验证集大小: 1402\n", + "train_data最大日期: 2022-02-15, 训练天数5\n", + "test_data最大日期: 2022-02-22\n", + "划分后的训练集大小: 6997, 验证集大小: 1395\n", + "train_data最大日期: 2022-02-16, 训练天数5\n", + "test_data最大日期: 2022-02-23\n", + "划分后的训练集大小: 7003, 验证集大小: 1397\n", + "train_data最大日期: 2022-02-17, 训练天数5\n", + "test_data最大日期: 2022-02-24\n", + "划分后的训练集大小: 7001, 验证集大小: 1401\n", + "train_data最大日期: 2022-02-18, 训练天数5\n", + "test_data最大日期: 2022-02-25\n", + "划分后的训练集大小: 6996, 验证集大小: 1401\n", + "train_data最大日期: 2022-02-21, 训练天数5\n", + "test_data最大日期: 2022-02-28\n", + "划分后的训练集大小: 6990, 验证集大小: 1396\n", + "train_data最大日期: 2022-02-22, 训练天数5\n", + "test_data最大日期: 2022-03-01\n", + "划分后的训练集大小: 6989, 验证集大小: 1394\n", + "train_data最大日期: 2022-02-23, 训练天数5\n", + "test_data最大日期: 2022-03-02\n", + "划分后的训练集大小: 6996, 验证集大小: 1404\n", + "train_data最大日期: 2022-02-24, 训练天数5\n", + "test_data最大日期: 2022-03-03\n", + "划分后的训练集大小: 6987, 验证集大小: 1392\n", + "train_data最大日期: 2022-02-25, 训练天数5\n", + "test_data最大日期: 2022-03-04\n", + "划分后的训练集大小: 6989, 验证集大小: 1403\n", + "train_data最大日期: 2022-02-28, 训练天数5\n", + "test_data最大日期: 2022-03-07\n", + "划分后的训练集大小: 6988, 验证集大小: 1395\n", + "train_data最大日期: 2022-03-01, 训练天数5\n", + "test_data最大日期: 2022-03-08\n", + "划分后的训练集大小: 6993, 验证集大小: 1399\n", + "train_data最大日期: 2022-03-02, 训练天数5\n", + "test_data最大日期: 2022-03-09\n", + "划分后的训练集大小: 6985, 验证集大小: 1396\n", + "train_data最大日期: 2022-03-03, 训练天数5\n", + "test_data最大日期: 2022-03-10\n", + "划分后的训练集大小: 6979, 验证集大小: 1386\n", + "train_data最大日期: 2022-03-04, 训练天数5\n", + "test_data最大日期: 2022-03-11\n", + "划分后的训练集大小: 6976, 验证集大小: 1400\n", + "train_data最大日期: 2022-03-07, 训练天数5\n", + "test_data最大日期: 2022-03-14\n", + "划分后的训练集大小: 6976, 验证集大小: 1395\n", + "train_data最大日期: 2022-03-08, 训练天数5\n", + "test_data最大日期: 2022-03-15\n", + "划分后的训练集大小: 6964, 验证集大小: 1387\n", + "train_data最大日期: 2022-03-09, 训练天数5\n", + "test_data最大日期: 2022-03-16\n", + "划分后的训练集大小: 6963, 验证集大小: 1395\n", + "train_data最大日期: 2022-03-10, 训练天数5\n", + "test_data最大日期: 2022-03-17\n", + "划分后的训练集大小: 6978, 验证集大小: 1401\n", + "train_data最大日期: 2022-03-11, 训练天数5\n", + "test_data最大日期: 2022-03-18\n", + "划分后的训练集大小: 6987, 验证集大小: 1409\n", + "train_data最大日期: 2022-03-14, 训练天数5\n", + "test_data最大日期: 2022-03-21\n", + "划分后的训练集大小: 6996, 验证集大小: 1404\n", + "train_data最大日期: 2022-03-15, 训练天数5\n", + "test_data最大日期: 2022-03-22\n", + "划分后的训练集大小: 6994, 验证集大小: 1385\n", + "train_data最大日期: 2022-03-16, 训练天数5\n", + "test_data最大日期: 2022-03-23\n", + "划分后的训练集大小: 7002, 验证集大小: 1403\n", + "train_data最大日期: 2022-03-17, 训练天数5\n", + "test_data最大日期: 2022-03-24\n", + "划分后的训练集大小: 7005, 验证集大小: 1404\n", + "train_data最大日期: 2022-03-18, 训练天数5\n", + "test_data最大日期: 2022-03-25\n", + "划分后的训练集大小: 6999, 验证集大小: 1403\n", + "train_data最大日期: 2022-03-21, 训练天数5\n", + "test_data最大日期: 2022-03-28\n", + "划分后的训练集大小: 6992, 验证集大小: 1397\n", + "train_data最大日期: 2022-03-22, 训练天数5\n", + "test_data最大日期: 2022-03-29\n", + "划分后的训练集大小: 7018, 验证集大小: 1411\n", + "train_data最大日期: 2022-03-23, 训练天数5\n", + "test_data最大日期: 2022-03-30\n", + "划分后的训练集大小: 7022, 验证集大小: 1407\n", + "train_data最大日期: 2022-03-24, 训练天数5\n", + "test_data最大日期: 2022-03-31\n", + "划分后的训练集大小: 7024, 验证集大小: 1406\n", + "train_data最大日期: 2022-03-25, 训练天数5\n", + "test_data最大日期: 2022-04-01\n", + "划分后的训练集大小: 7015, 验证集大小: 1394\n", + "train_data最大日期: 2022-03-28, 训练天数5\n", + "test_data最大日期: 2022-04-06\n", + "划分后的训练集大小: 7018, 验证集大小: 1400\n", + "train_data最大日期: 2022-03-29, 训练天数5\n", + "test_data最大日期: 2022-04-07\n", + "划分后的训练集大小: 7016, 验证集大小: 1409\n", + "train_data最大日期: 2022-03-30, 训练天数5\n", + "test_data最大日期: 2022-04-08\n", + "划分后的训练集大小: 7011, 验证集大小: 1402\n", + "train_data最大日期: 2022-03-31, 训练天数5\n", + "test_data最大日期: 2022-04-11\n", + "划分后的训练集大小: 7009, 验证集大小: 1404\n", + "train_data最大日期: 2022-04-01, 训练天数5\n", + "test_data最大日期: 2022-04-12\n", + "划分后的训练集大小: 7017, 验证集大小: 1402\n", + "train_data最大日期: 2022-04-06, 训练天数5\n", + "test_data最大日期: 2022-04-13\n", + "划分后的训练集大小: 7001, 验证集大小: 1384\n", + "train_data最大日期: 2022-04-07, 训练天数5\n", + "test_data最大日期: 2022-04-14\n", + "划分后的训练集大小: 6992, 验证集大小: 1400\n", + "train_data最大日期: 2022-04-08, 训练天数5\n", + "test_data最大日期: 2022-04-15\n", + "划分后的训练集大小: 6989, 验证集大小: 1399\n", + "train_data最大日期: 2022-04-11, 训练天数5\n", + "test_data最大日期: 2022-04-18\n", + "划分后的训练集大小: 6985, 验证集大小: 1400\n", + "train_data最大日期: 2022-04-12, 训练天数5\n", + "test_data最大日期: 2022-04-19\n", + 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"test_data最大日期: 2025-03-05\n", - "划分后的训练集大小: 2407, 验证集大小: 481\n", + "划分后的训练集大小: 7304, 验证集大小: 1461\n", "train_data最大日期: 2025-02-27, 训练天数5\n", "test_data最大日期: 2025-03-06\n", - "划分后的训练集大小: 2412, 验证集大小: 481\n", + "划分后的训练集大小: 7314, 验证集大小: 1461\n", "train_data最大日期: 2025-02-28, 训练天数5\n", "test_data最大日期: 2025-03-07\n", - "划分后的训练集大小: 2410, 验证集大小: 482\n", + "划分后的训练集大小: 7312, 验证集大小: 1457\n", "train_data最大日期: 2025-03-03, 训练天数5\n", "test_data最大日期: 2025-03-10\n", - "划分后的训练集大小: 2408, 验证集大小: 482\n", + "划分后的训练集大小: 7300, 验证集大小: 1454\n", "train_data最大日期: 2025-03-04, 训练天数5\n", "test_data最大日期: 2025-03-11\n", - "划分后的训练集大小: 2405, 验证集大小: 479\n", + "划分后的训练集大小: 7294, 验证集大小: 1461\n", "train_data最大日期: 2025-03-05, 训练天数5\n", "test_data最大日期: 2025-03-12\n", - "划分后的训练集大小: 2403, 验证集大小: 479\n", + "划分后的训练集大小: 7290, 验证集大小: 1457\n", "train_data最大日期: 2025-03-06, 训练天数5\n", "test_data最大日期: 2025-03-13\n", - "划分后的训练集大小: 2404, 验证集大小: 482\n", + "划分后的训练集大小: 7289, 验证集大小: 1460\n", "train_data最大日期: 2025-03-07, 训练天数5\n", "test_data最大日期: 2025-03-14\n", - "划分后的训练集大小: 2405, 验证集大小: 483\n", + "划分后的训练集大小: 7298, 验证集大小: 1466\n", "train_data最大日期: 2025-03-10, 训练天数5\n", "test_data最大日期: 2025-03-17\n", - "划分后的训练集大小: 2404, 验证集大小: 481\n", + "划分后的训练集大小: 7304, 验证集大小: 1460\n", "train_data最大日期: 2025-03-11, 训练天数5\n", "test_data最大日期: 2025-03-18\n", - "划分后的训练集大小: 2406, 验证集大小: 481\n", + "划分后的训练集大小: 7305, 验证集大小: 1462\n", "train_data最大日期: 2025-03-12, 训练天数5\n", "test_data最大日期: 2025-03-19\n", - "划分后的训练集大小: 2404, 验证集大小: 477\n", + "划分后的训练集大小: 7306, 验证集大小: 1458\n", "train_data最大日期: 2025-03-13, 训练天数5\n", "test_data最大日期: 2025-03-20\n", - "划分后的训练集大小: 2401, 验证集大小: 479\n", + "划分后的训练集大小: 7294, 验证集大小: 1448\n", "train_data最大日期: 2025-03-14, 训练天数5\n", "test_data最大日期: 2025-03-21\n", - "划分后的训练集大小: 2396, 验证集大小: 478\n", + "划分后的训练集大小: 7277, 验证集大小: 1449\n", "train_data最大日期: 2025-03-17, 训练天数5\n", "test_data最大日期: 2025-03-24\n", - "划分后的训练集大小: 2397, 验证集大小: 482\n", + "划分后的训练集大小: 7272, 验证集大小: 1455\n", "train_data最大日期: 2025-03-18, 训练天数5\n", "test_data最大日期: 2025-03-25\n", - "划分后的训练集大小: 2397, 验证集大小: 481\n", + "划分后的训练集大小: 7253, 验证集大小: 1443\n", "train_data最大日期: 2025-03-19, 训练天数5\n", "test_data最大日期: 2025-03-26\n", - "划分后的训练集大小: 2402, 验证集大小: 482\n", + "划分后的训练集大小: 7248, 验证集大小: 1453\n", "train_data最大日期: 2025-03-20, 训练天数5\n", "test_data最大日期: 2025-03-27\n", - "划分后的训练集大小: 2403, 验证集大小: 480\n", + "划分后的训练集大小: 7241, 验证集大小: 1441\n", "train_data最大日期: 2025-03-21, 训练天数5\n", "test_data最大日期: 2025-03-28\n", - "划分后的训练集大小: 2403, 验证集大小: 478\n", + "划分后的训练集大小: 7238, 验证集大小: 1446\n", "train_data最大日期: 2025-03-24, 训练天数5\n", "test_data最大日期: 2025-03-31\n", - "划分后的训练集大小: 2403, 验证集大小: 482\n", + "划分后的训练集大小: 7237, 验证集大小: 1454\n", "train_data最大日期: 2025-03-25, 训练天数5\n", "test_data最大日期: 2025-04-01\n", - "划分后的训练集大小: 2404, 验证集大小: 482\n", + "划分后的训练集大小: 7252, 验证集大小: 1458\n", "train_data最大日期: 2025-03-26, 训练天数5\n", "test_data最大日期: 2025-04-02\n", - "划分后的训练集大小: 2407, 验证集大小: 485\n", + "划分后的训练集大小: 7261, 验证集大小: 1462\n", "train_data最大日期: 2025-03-27, 训练天数5\n", "test_data最大日期: 2025-04-03\n", - "划分后的训练集大小: 2413, 验证集大小: 486\n", + "划分后的训练集大小: 7281, 验证集大小: 1461\n", "train_data最大日期: 2025-03-28, 训练天数5\n", "test_data最大日期: 2025-04-07\n", - "划分后的训练集大小: 2417, 验证集大小: 482\n", + "划分后的训练集大小: 7274, 验证集大小: 1439\n", "train_data最大日期: 2025-03-31, 训练天数5\n", "test_data最大日期: 2025-04-08\n", - "划分后的训练集大小: 2373, 验证集大小: 438\n", + "划分后的训练集大小: 7083, 验证集大小: 1263\n", "train_data最大日期: 2025-04-01, 训练天数5\n", "test_data最大日期: 2025-04-09\n", - "划分后的训练集大小: 2349, 验证集大小: 458\n" + "划分后的训练集大小: 6969, 验证集大小: 1344\n" ] } ], @@ -3391,14 +4090,14 @@ "# qdf = qdf[qdf['trade_date'] >= '2022-01-01']\n", "\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", + " pdf[(pdf['trade_date'] >= '2022-01-01') & (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" ] }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 63, "id": "0dc75517-c857-4f1d-8815-e807400a6d33", "metadata": { "ExecuteTime": { @@ -3456,7 +4155,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 64, "id": "8299a6f461097f14", "metadata": { "ExecuteTime": { @@ -3478,7 +4177,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 65, "id": "3f5079aa2c937c22", "metadata": { "ExecuteTime": { @@ -3501,7 +4200,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 66, "id": "199b12e7e20e4e6a", "metadata": { "ExecuteTime": { diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json index b4e25f8..84c5869 100644 --- a/main/train/catboost_info/catboost_training.json +++ b/main/train/catboost_info/catboost_training.json @@ -1,5004 +1,56 @@ { -"meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Min","name":"Logloss"}],"learn_metrics":[{"best_value":"Min","name":"Logloss"}],"launch_mode":"Train","parameters":"","iteration_count":5000,"learn_sets":["learn"],"name":"experiment"}, +"meta":{"test_sets":["test"],"test_metrics":[{"best_value":"Max","name":"Precision"},{"best_value":"Min","name":"CrossEntropy"}],"learn_metrics":[{"best_value":"Max","name":"Precision"},{"best_value":"Min","name":"CrossEntropy"}],"launch_mode":"Train","parameters":"","iteration_count":500,"learn_sets":["learn"],"name":"experiment"}, "iterations":[ -{"learn":[0.6863319056],"iteration":0,"passed_time":0.009045906858,"remaining_time":45.22048838,"test":[0.6861052887]}, -{"learn":[0.6746464163],"iteration":1,"passed_time":0.01726818031,"remaining_time":43.1531826,"test":[0.6743600161]}, -{"learn":[0.6714135204],"iteration":2,"passed_time":0.03751901195,"remaining_time":62.49416756,"test":[0.6710211431]}, 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a/main/train/catboost_info/test/events.out.tfevents b/main/train/catboost_info/test/events.out.tfevents index c218e86..2e2ad54 100644 Binary files a/main/train/catboost_info/test/events.out.tfevents and b/main/train/catboost_info/test/events.out.tfevents differ diff --git a/main/train/catboost_info/test_error.tsv b/main/train/catboost_info/test_error.tsv index 2015169..5d39a10 100644 --- a/main/train/catboost_info/test_error.tsv +++ b/main/train/catboost_info/test_error.tsv @@ -1,5001 +1,53 @@ -iter Logloss -0 0.6861052887 -1 0.6743600161 -2 0.6710211431 -3 0.6648334068 -4 0.6541856344 -5 0.6488011302 -6 0.6434835621 -7 0.6384988858 -8 0.6359470921 -9 0.6275708523 -10 0.6193913675 -11 0.6171219209 -12 0.613084422 -13 0.6092213262 -14 0.6024870154 -15 0.5959876065 -16 0.592715908 -17 0.5909668695 -18 0.585207929 -19 0.5836811562 -20 0.5808259152 -21 0.5781049635 -22 0.5754473715 -23 0.5747250825 -24 0.5721040878 -25 0.569590143 -26 0.5682790198 -27 0.5659677904 -28 0.5636375471 -29 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ts.pro_api()\n", + "\n", + "from datetime import datetime\n", + "import pandas as pd\n", + "import warnings\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "dc90836a", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_rows(df):\n", + " # 按照 name 和 start_date 分组\n", + " def select_row(group):\n", + " # 如果有 end_date 不为 NaT 的行，优先保留这些行\n", + " valid_rows = group[group['end_date'].notna()]\n", + " if not valid_rows.empty:\n", + " return valid_rows.iloc[0] # 返回第一个有效行\n", + " else:\n", + " return group.iloc[0] # 如果没有有效行，返回第一行\n", + "\n", + " filtered_df = df.groupby(['name', 'start_date'], group_keys=False).apply(select_row)\n", + " filtered_df = filtered_df.reset_index(drop=True)\n", + " return filtered_df\n", + "\n", + "name_change_df = pd.read_hdf('E:/PyProject/NewStock/data/name_change.h5', key='name_change')\n", + "name_change_df = name_change_df.drop_duplicates(keep='first')\n", + "\n", + "# 确保 name_change_df 的日期格式正确\n", + "name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n", + "name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n", + "name_change_df = name_change_df[name_change_df.name.str.contains('ST')]\n", + "name_change_dict = {}\n", + "for ts_code, group in name_change_df.groupby('ts_code'):\n", + " # 只保留 'ST' 和 '*ST' 的记录\n", + " st_data = group[group['name'].str.contains('ST')]\n", + " if not st_data.empty and ts_code == '002569.SZ':\n", + " name_change_dict[ts_code] = filter_rows(st_data)\n", + " # name_change_dict[ts_code] = st_data" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f0ed36d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code name start_date end_date change_reason\n", + "7990 002569.SZ ST步森 2020-06-09 NaT 摘星\n", + "7992 002569.SZ *ST步森 2019-04-30 NaT *ST\n", + "7993 002569.SZ *ST步森 2019-04-30 2020-06-08 *ST\n", + " ts_code name start_date end_date change_reason\n", + "0 002569.SZ *ST步森 2019-04-30 2020-06-08 *ST\n", + "1 002569.SZ ST步森 2020-06-09 NaT 摘星\n" + ] + } + ], + "source": [ + "print(name_change_df[name_change_df['ts_code'] == '002569.SZ'])\n", + "print(filter_rows(name_change_df[name_change_df['ts_code'] == '002569.SZ']))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "3c526622", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code name start_date end_date change_reason\n", + "0 002569.SZ *ST步森 2019-04-30 2020-06-08 *ST\n", + "1 002569.SZ ST步森 2020-06-09 NaT 摘星\n", + " ts_code trade_date is_st\n", + "3753 002569.SZ 20230120 True\n" + ] + } + ], + "source": [ + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "\n", + "def is_st(name_change_dict, stock_code, target_date):\n", + " target_date = datetime.strptime(target_date, '%Y%m%d')\n", + " if stock_code not in name_change_dict.keys():\n", + " return False\n", + " df = name_change_dict[stock_code]\n", + " print(df)\n", + " for i in range(len(df)):\n", + " sds = df.iloc[i, 2]\n", + " eds = df.iloc[i, 3]\n", + " if eds is None or eds is pd.NaT:\n", + " eds = datetime.now()\n", + " if (target_date - sds).days >= 0 and (target_date - eds).days <= 0:\n", + " return True\n", + " return False\n", + "\n", + " \n", + "trade_date = '20230120'\n", + "daily_basic_data = pro.daily_basic(ts_code='', trade_date=trade_date)\n", + "daily_basic_data = daily_basic_data[daily_basic_data['ts_code'] == '002569.SZ']\n", + "if daily_basic_data is not None and not daily_basic_data.empty:\n", + " # 添加交易日期列标识\n", + " daily_basic_data['trade_date'] = trade_date\n", + " daily_basic_data['is_st'] = daily_basic_data.apply(\n", + " lambda row: is_st(name_change_dict, row['ts_code'], row['trade_date']), axis=1\n", + " )\n", + " \n", + "print(daily_basic_data[daily_basic_data['ts_code'] == '002569.SZ'][['ts_code', 'trade_date', 'is_st']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf4c9fd5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "4a3638e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code name start_date end_date change_reason\n", + "0 002569.SZ *ST步森 20250428 None *ST\n", + "1 002569.SZ ST步森 20200609 None 摘星\n", + "2 002569.SZ ST步森 20200609 None 摘星\n", + "3 002569.SZ ST步森 20200609 20250427 摘星\n", + "4 002569.SZ *ST步森 20190430 None *ST\n", + "5 002569.SZ *ST步森 20190430 20200608 *ST\n", + "6 002569.SZ *ST步森 20190430 20200608 *ST\n", + "7 002569.SZ 步森股份 20110412 20190429 其他\n", + "8 002569.SZ 步森股份 20110412 20190429 其他\n" + ] + } + ], + "source": [ + "df = pro.namechange(ts_code='002569.SZ', fields='ts_code,name,start_date,end_date,change_reason')\n", + "print(df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new_trader", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main/train/test.py b/main/train/test.py index 509dae1..fa9da18 100644 --- a/main/train/test.py +++ b/main/train/test.py @@ -3,12 +3,13 @@ from operator import index import tushare as ts import pandas as pd import time +import akshare as ak + +from main.factor.factor import calculate_arbr ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f') pro = ts.pro_api() -df = pro.index_member_all(ts_code='603579.SH') -print(df) +df = pro.balancesheet(ts_code='600000.SH', start_date='20180101', end_date='20180730') -df = pro.sw_daily(trade_date='20250305', fields='ts_code,name,open,close,vol,pe,pb') -print(df[df['ts_code'] == '851171.SI']) \ No newline at end of file +print(df['total_liab']) \ No newline at end of file