diff --git a/main/data/daily_basic.ipynb b/main/data/daily_basic.ipynb index 6fb2619..68d63ca 100644 --- a/main/data/daily_basic.ipynb +++ b/main/data/daily_basic.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2", "metadata": { "ExecuteTime": { @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "id": "bc8f03e027027004", "metadata": { "ExecuteTime": { @@ -69,19 +69,19 @@ "# 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_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['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", - " st_data = group[group['name'].str.contains('ST')]\n", + " st_data = group[(group['name'].str.contains('ST')) | (group['name'].str.contains('退'))]\n", " if not st_data.empty:\n", " name_change_dict[ts_code] = filter_rows(st_data)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "id": "b151990c", "metadata": {}, "outputs": [ @@ -89,18 +89,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "8929 ts_code name start_date end_date change_reason\n", - "0 002848.SZ *ST高斯 2025-04-28 NaT *ST\n" + " ts_code name start_date end_date change_reason\n", + "0 600242.SH *ST中昌 2015-05-04 2016-04-06 *ST\n", + "1 600242.SH *ST中昌 2020-07-01 2021-05-19 *ST\n", + "2 600242.SH *ST中昌 2022-05-06 2023-05-30 *ST\n", + "3 600242.SH *ST华龙 2006-05-09 2006-10-08 *ST\n", + "4 600242.SH NST华龙 2009-01-05 2009-01-05 其他\n", + "5 600242.SH S*ST华龙 2006-10-09 2009-01-04 未股改加S\n", + "6 600242.SH ST中昌 2021-05-20 2022-05-05 摘星\n", + "7 600242.SH ST华龙 2009-01-06 2011-04-06 恢复上市加N\n", + "8 600242.SH 退市中昌 2023-05-31 NaT 终止上市\n" ] } ], "source": [ - "print(name_change_dict['002848.SZ'])" + "print(name_change_dict['600242.SH'])\n", + "# print(name_change_df[name_change_df['ts_code'] == '600242.SH'])" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", "metadata": { "ExecuteTime": { @@ -120,13 +129,13 @@ "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 20250422 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 20250415 完成\n", "任务 20250411 完成\n", "任务 20250410 完成\n", "任务 20250409 完成\n", @@ -142,16 +151,16 @@ "任务 20250325 完成\n", "任务 20250324 完成\n", "任务 20250321 完成\n", - "任务 20250319 完成\n", "任务 20250320 完成\n", + "任务 20250319 完成\n", "任务 20250318 完成\n", "任务 20250317 完成\n", "任务 20250314 完成\n", - "任务 20250313 完成\n", "任务 20250312 完成\n", + "任务 20250313 完成\n", "任务 20250311 完成\n", - "任务 20250310 完成\n", "任务 20250307 完成\n", + "任务 20250310 完成\n", "任务 20250306 完成\n", "任务 20250305 完成\n", "任务 20250304 完成\n", @@ -168,8 +177,8 @@ "任务 20250217 完成\n", "任务 20250214 完成\n", "任务 20250213 完成\n", - "任务 20250211 完成\n", "任务 20250212 完成\n", + "任务 20250211 完成\n", "任务 20250210 完成\n", "任务 20250207 完成\n", "任务 20250206 完成\n", @@ -190,8 +199,8 @@ "任务 20250108 完成\n", "任务 20250107 完成\n", "任务 20250106 完成\n", - "任务 20250102 完成\n", "任务 20250103 完成\n", + "任务 20250102 完成\n", "任务 20241231 完成\n", "任务 20241230 完成\n", "任务 20241227 完成\n", @@ -216,8 +225,8 @@ "任务 20241202 完成\n", "任务 20241129 完成\n", "任务 20241128 完成\n", - "任务 20241127 完成\n", "任务 20241126 完成\n", + "任务 20241127 完成\n", "任务 20241125 完成\n", "任务 20241122 完成\n", "任务 20241121 完成\n", @@ -244,26 +253,26 @@ "任务 20241023 完成\n", "任务 20241022 完成\n", "任务 20241021 完成\n", - "任务 20241018 完成\n", "任务 20241017 完成\n", + "任务 20241018 完成\n", "任务 20241016 完成\n", "任务 20241015 完成\n", "任务 20241014 完成\n", "任务 20241011 完成\n", "任务 20241010 完成\n", "任务 20241009 完成\n", - "任务 20240930 完成\n", "任务 20241008 完成\n", + "任务 20240930 完成\n", "任务 20240927 完成\n", "任务 20240926 完成\n", - "任务 20240925 完成\n", "任务 20240924 完成\n", - "任务 20240920 完成\n", + "任务 20240925 完成\n", "任务 20240923 完成\n", - "任务 20240918 完成\n", + "任务 20240920 完成\n", "任务 20240919 完成\n", - "任务 20240912 完成\n", + "任务 20240918 完成\n", "任务 20240913 完成\n", + "任务 20240912 完成\n", "任务 20240911 完成\n", "任务 20240910 完成\n", "任务 20240909 完成\n", @@ -286,8 +295,8 @@ "任务 20240815 完成\n", "任务 20240814 完成\n", "任务 20240813 完成\n", - "任务 20240809 完成\n", "任务 20240812 完成\n", + "任务 20240809 完成\n", "任务 20240808 完成\n", "任务 20240807 完成\n", "任务 20240806 完成\n", @@ -300,14 +309,14 @@ "任务 20240726 完成\n", "任务 20240725 完成\n", "任务 20240724 完成\n", - "任务 20240722 完成\n", "任务 20240723 完成\n", - "任务 20240718 完成\n", + "任务 20240722 完成\n", "任务 20240719 完成\n", + "任务 20240718 完成\n", "任务 20240717 完成\n", "任务 20240716 完成\n", - "任务 20240712 完成\n", "任务 20240715 完成\n", + "任务 20240712 完成\n", "任务 20240711 完成\n", "任务 20240710 完成\n", "任务 20240709 完成\n", @@ -320,13 +329,13 @@ "任务 20240628 完成\n", "任务 20240627 完成\n", "任务 20240626 完成\n", - "任务 20240624 完成\n", "任务 20240625 完成\n", - "任务 20240620 完成\n", + "任务 20240624 完成\n", "任务 20240621 完成\n", + "任务 20240620 完成\n", "任务 20240619 完成\n", - "任务 20240618 完成\n", "任务 20240617 完成\n", + "任务 20240618 完成\n", "任务 20240614 完成\n", "任务 20240613 完成\n", "任务 20240612 完成\n", @@ -371,17 +380,17 @@ "任务 20240412 完成\n", "任务 20240411 完成\n", "任务 20240410 完成\n", - "任务 20240409 完成\n", "任务 20240408 完成\n", + "任务 20240409 完成\n", "任务 20240403 完成\n", "任务 20240402 完成\n", "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 20240328 完成\n", "任务 20240326 完成\n", - "任务 20240322 完成\n", "任务 20240325 完成\n", + "任务 20240322 完成\n", "任务 20240321 完成\n", "任务 20240320 完成\n", "任务 20240319 完成\n", @@ -394,8 +403,8 @@ "任务 20240308 完成\n", "任务 20240307 完成\n", "任务 20240306 完成\n", - "任务 20240304 完成\n", "任务 20240305 完成\n", + "任务 20240304 完成\n", "任务 20240301 完成\n", "任务 20240229 完成\n", "任务 20240228 完成\n", @@ -407,10 +416,10 @@ "任务 20240220 完成\n", "任务 20240219 完成\n", "任务 20240208 完成\n", - "任务 20240207 完成\n", "任务 20240206 完成\n", - "任务 20240205 完成\n", + "任务 20240207 完成\n", "任务 20240202 完成\n", + "任务 20240205 完成\n", "任务 20240201 完成\n", "任务 20240131 完成\n", "任务 20240130 完成\n", @@ -425,25 +434,25 @@ "任务 20240117 完成\n", "任务 20240116 完成\n", "任务 20240115 完成\n", - "任务 20240112 完成\n", "任务 20240111 完成\n", + "任务 20240112 完成\n", "任务 20240110 完成\n", "任务 20240109 完成\n", "任务 20240108 完成\n", "任务 20240105 完成\n", "任务 20240104 完成\n", - "任务 20240102 完成\n", "任务 20240103 完成\n", "任务 20231229 完成\n", + "任务 20240102 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 20231222 完成\n", - "任务 20231220 完成\n", "任务 20231221 完成\n", - "任务 20231218 完成\n", "任务 20231219 完成\n", + "任务 20231220 完成\n", + "任务 20231218 完成\n", "任务 20231215 完成\n", "任务 20231214 完成\n", "任务 20231213 完成\n", @@ -493,10 +502,10 @@ "任务 20231012 完成\n", "任务 20231011 完成\n", "任务 20231010 完成\n", - "任务 20230928 完成\n", "任务 20231009 完成\n", - "任务 20230926 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 20230922 完成\n", "任务 20230921 完成\n", @@ -521,10 +530,10 @@ "任务 20230825 完成\n", "任务 20230824 完成\n", "任务 20230823 完成\n", - "任务 20230821 完成\n", "任务 20230822 完成\n", - "任务 20230818 完成\n", + "任务 20230821 完成\n", "任务 20230817 完成\n", + "任务 20230818 完成\n", "任务 20230816 完成\n", "任务 20230815 完成\n", "任务 20230814 完成\n", @@ -547,8 +556,8 @@ "任务 20230720 完成\n", "任务 20230719 完成\n", "任务 20230718 完成\n", - "任务 20230714 完成\n", "任务 20230717 完成\n", + "任务 20230714 完成\n", "任务 20230713 完成\n", "任务 20230712 完成\n", "任务 20230711 完成\n", @@ -585,8 +594,8 @@ "任务 20230525 完成\n", "任务 20230524 完成\n", "任务 20230523 完成\n", - "任务 20230519 完成\n", "任务 20230522 完成\n", + "任务 20230519 完成\n", "任务 20230518 完成\n", "任务 20230517 完成\n", "任务 20230516 完成\n", @@ -599,22 +608,22 @@ "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 20230426 完成\n", "任务 20230425 完成\n", "任务 20230424 完成\n", "任务 20230421 完成\n", "任务 20230420 完成\n", - "任务 20230418 完成\n", "任务 20230419 完成\n", + "任务 20230418 完成\n", "任务 20230417 完成\n", "任务 20230414 完成\n", "任务 20230413 完成\n", "任务 20230412 完成\n", "任务 20230411 完成\n", "任务 20230410 完成\n", - "任务 20230407 完成\n", "任务 20230406 完成\n", + "任务 20230407 完成\n", "任务 20230404 完成\n", "任务 20230403 完成\n", "任务 20230331 完成\n", @@ -623,20 +632,20 @@ "任务 20230328 完成\n", "任务 20230327 完成\n", "任务 20230324 完成\n", - "任务 20230322 完成\n", "任务 20230323 完成\n", + "任务 20230322 完成\n", "任务 20230321 完成\n", "任务 20230320 完成\n", "任务 20230317 完成\n", "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 20230307 完成\n", + "任务 20230309 完成\n", "任务 20230306 完成\n", + "任务 20230307 完成\n", "任务 20230303 完成\n", "任务 20230302 完成\n", "任务 20230301 完成\n", @@ -721,8 +730,8 @@ "任务 20221102 完成\n", "任务 20221101 完成\n", "任务 20221031 完成\n", - "任务 20221028 完成\n", "任务 20221027 完成\n", + "任务 20221028 完成\n", "任务 20221026 完成\n", "任务 20221025 完成\n", "任务 20221024 完成\n", @@ -739,10 +748,10 @@ "任务 20220930 完成\n", "任务 20220929 完成\n", "任务 20220928 完成\n", - "任务 20220926 完成\n", "任务 20220927 完成\n", - "任务 20220922 完成\n", + "任务 20220926 完成\n", "任务 20220923 完成\n", + "任务 20220922 完成\n", "任务 20220921 完成\n", "任务 20220920 完成\n", "任务 20220919 完成\n", @@ -757,8 +766,8 @@ "任务 20220905 完成\n", "任务 20220902 完成\n", "任务 20220901 完成\n", - "任务 20220830 完成\n", "任务 20220831 完成\n", + "任务 20220830 完成\n", "任务 20220829 完成\n", "任务 20220826 完成\n", "任务 20220825 完成\n", @@ -771,12 +780,12 @@ "任务 20220816 完成\n", "任务 20220815 完成\n", "任务 20220812 完成\n", - "任务 20220810 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 20220804 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", @@ -787,8 +796,8 @@ "任务 20220725 完成\n", "任务 20220722 完成\n", "任务 20220721 完成\n", - "任务 20220719 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 20220714 完成\n", @@ -799,12 +808,12 @@ "任务 20220707 完成\n", "任务 20220706 完成\n", "任务 20220705 完成\n", - "任务 20220704 完成\n", "任务 20220701 完成\n", + "任务 20220704 完成\n", "任务 20220630 完成\n", "任务 20220629 完成\n", - "任务 20220627 完成\n", "任务 20220628 完成\n", + "任务 20220627 完成\n", "任务 20220624 完成\n", "任务 20220623 完成\n", "任务 20220622 完成\n", @@ -813,8 +822,8 @@ "任务 20220617 完成\n", "任务 20220616 完成\n", "任务 20220615 完成\n", - "任务 20220613 完成\n", "任务 20220614 完成\n", + "任务 20220613 完成\n", "任务 20220610 完成\n", "任务 20220609 完成\n", "任务 20220608 完成\n", @@ -887,11 +896,11 @@ "任务 20220225 完成\n", "任务 20220224 完成\n", "任务 20220223 完成\n", - "任务 20220222 完成\n", "任务 20220221 完成\n", + "任务 20220222 完成\n", "任务 20220218 完成\n", - "任务 20220217 完成\n", "任务 20220216 完成\n", + "任务 20220217 完成\n", "任务 20220215 完成\n", "任务 20220214 完成\n", "任务 20220211 完成\n", @@ -908,8 +917,8 @@ "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 20220117 完成\n", "任务 20220113 完成\n", "任务 20220112 完成\n", "任务 20220111 完成\n", @@ -951,10 +960,10 @@ "任务 20211119 完成\n", "任务 20211118 完成\n", "任务 20211117 完成\n", - "任务 20211115 完成\n", "任务 20211116 完成\n", - "任务 20211111 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 20211109 完成\n", "任务 20211108 完成\n", @@ -1034,8 +1043,8 @@ "任务 20210716 完成\n", "任务 20210715 完成\n", "任务 20210714 完成\n", - "任务 20210713 完成\n", "任务 20210712 完成\n", + "任务 20210713 完成\n", "任务 20210709 完成\n", "任务 20210708 完成\n", "任务 20210707 完成\n", @@ -1054,8 +1063,8 @@ "任务 20210618 完成\n", "任务 20210617 完成\n", "任务 20210616 完成\n", - "任务 20210615 完成\n", "任务 20210611 完成\n", + "任务 20210615 完成\n", "任务 20210610 完成\n", "任务 20210609 完成\n", "任务 20210608 完成\n", @@ -1066,8 +1075,8 @@ "任务 20210601 完成\n", "任务 20210531 完成\n", "任务 20210528 完成\n", - "任务 20210526 完成\n", "任务 20210527 完成\n", + "任务 20210526 完成\n", "任务 20210525 完成\n", "任务 20210524 完成\n", "任务 20210521 完成\n", @@ -1086,8 +1095,8 @@ "任务 20210429 完成\n", "任务 20210428 完成\n", "任务 20210427 完成\n", - "任务 20210423 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", @@ -1102,8 +1111,8 @@ "任务 20210407 完成\n", "任务 20210406 完成\n", "任务 20210402 完成\n", - "任务 20210331 完成\n", "任务 20210401 完成\n", + "任务 20210331 完成\n", "任务 20210330 完成\n", "任务 20210329 完成\n", "任务 20210326 完成\n", @@ -1130,8 +1139,8 @@ "任务 20210225 完成\n", "任务 20210224 完成\n", "任务 20210223 完成\n", - "任务 20210219 完成\n", "任务 20210222 完成\n", + "任务 20210219 完成\n", "任务 20210218 完成\n", "任务 20210210 完成\n", "任务 20210209 完成\n", @@ -1140,8 +1149,8 @@ "任务 20210204 完成\n", "任务 20210203 完成\n", "任务 20210202 完成\n", - "任务 20210129 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 20210126 完成\n", @@ -1153,8 +1162,8 @@ "任务 20210118 完成\n", "任务 20210115 完成\n", "任务 20210114 完成\n", - "任务 20210113 完成\n", "任务 20210112 完成\n", + "任务 20210113 完成\n", "任务 20210111 完成\n", "任务 20210108 完成\n", "任务 20210107 完成\n", @@ -1174,8 +1183,8 @@ "任务 20201217 完成\n", "任务 20201216 完成\n", "任务 20201215 完成\n", - "任务 20201211 完成\n", "任务 20201214 完成\n", + "任务 20201211 完成\n", "任务 20201210 完成\n", "任务 20201209 完成\n", "任务 20201208 完成\n", @@ -1263,8 +1272,8 @@ "任务 20200806 完成\n", "任务 20200805 完成\n", "任务 20200804 完成\n", - "任务 20200803 完成\n", "任务 20200731 完成\n", + "任务 20200803 完成\n", "任务 20200730 完成\n", "任务 20200729 完成\n", "任务 20200728 完成\n", @@ -1273,14 +1282,14 @@ "任务 20200723 完成\n", "任务 20200722 完成\n", "任务 20200721 完成\n", - "任务 20200720 完成\n", "任务 20200717 完成\n", + "任务 20200720 完成\n", "任务 20200716 完成\n", "任务 20200715 完成\n", "任务 20200714 完成\n", "任务 20200713 完成\n", - "任务 20200710 完成\n", "任务 20200709 完成\n", + "任务 20200710 完成\n", "任务 20200708 完成\n", "任务 20200707 完成\n", "任务 20200706 完成\n", @@ -1311,10 +1320,10 @@ "任务 20200528 完成\n", "任务 20200527 完成\n", "任务 20200526 完成\n", - "任务 20200525 完成\n", "任务 20200522 完成\n", - "任务 20200521 完成\n", + "任务 20200525 完成\n", "任务 20200520 完成\n", + "任务 20200521 完成\n", "任务 20200519 完成\n", "任务 20200518 完成\n", "任务 20200515 完成\n", @@ -1338,8 +1347,8 @@ "任务 20200416 完成\n", "任务 20200415 完成\n", "任务 20200414 完成\n", - "任务 20200410 完成\n", "任务 20200413 完成\n", + "任务 20200410 完成\n", "任务 20200409 完成\n", "任务 20200408 完成\n", "任务 20200407 完成\n", @@ -1374,16 +1383,16 @@ "任务 20200225 完成\n", "任务 20200224 完成\n", "任务 20200221 完成\n", - "任务 20200219 完成\n", "任务 20200220 完成\n", + "任务 20200219 完成\n", "任务 20200218 完成\n", "任务 20200217 完成\n", - "任务 20200213 完成\n", "任务 20200214 完成\n", + "任务 20200213 完成\n", "任务 20200212 完成\n", "任务 20200211 完成\n", - "任务 20200207 完成\n", "任务 20200210 完成\n", + "任务 20200207 完成\n", "任务 20200206 完成\n", "任务 20200205 完成\n", "任务 20200204 完成\n", @@ -1466,8 +1475,8 @@ "任务 20191009 完成\n", "任务 20191008 完成\n", "任务 20190930 完成\n", - "任务 20190926 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 20190923 完成\n", @@ -1505,8 +1514,8 @@ "任务 20190807 完成\n", "任务 20190806 完成\n", "任务 20190805 完成\n", - "任务 20190802 完成\n", "任务 20190801 完成\n", + "任务 20190802 完成\n", "任务 20190731 完成\n", "任务 20190730 完成\n", "任务 20190729 完成\n", @@ -1517,11 +1526,11 @@ "任务 20190722 完成\n", "任务 20190719 完成\n", "任务 20190718 完成\n", - "任务 20190717 完成\n", "任务 20190716 完成\n", + "任务 20190717 完成\n", "任务 20190715 完成\n", - "任务 20190711 完成\n", "任务 20190712 完成\n", + "任务 20190711 完成\n", "任务 20190710 完成\n", "任务 20190709 完成\n", "任务 20190708 完成\n", @@ -1651,8 +1660,8 @@ "任务 20181228 完成\n", "任务 20181227 完成\n", "任务 20181226 完成\n", - "任务 20181225 完成\n", "任务 20181224 完成\n", + "任务 20181225 完成\n", "任务 20181221 完成\n", "任务 20181220 完成\n", "任务 20181219 完成\n", @@ -1665,8 +1674,8 @@ "任务 20181210 完成\n", "任务 20181207 完成\n", "任务 20181206 完成\n", - "任务 20181204 完成\n", "任务 20181205 完成\n", + "任务 20181204 完成\n", "任务 20181203 完成\n", "任务 20181130 完成\n", "任务 20181129 完成\n", @@ -1775,8 +1784,8 @@ "任务 20180629 完成\n", "任务 20180628 完成\n", "任务 20180627 完成\n", - "任务 20180625 完成\n", "任务 20180626 完成\n", + "任务 20180625 完成\n", "任务 20180622 完成\n", "任务 20180621 完成\n", "任务 20180620 完成\n", @@ -1791,14 +1800,14 @@ "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 20180531 完成\n", + "任务 20180601 完成\n", "任务 20180530 完成\n", "任务 20180529 完成\n", "任务 20180528 完成\n", "任务 20180525 完成\n", - "任务 20180524 完成\n", "任务 20180523 完成\n", + "任务 20180524 完成\n", "任务 20180522 完成\n", "任务 20180521 完成\n", "任务 20180518 完成\n", @@ -1845,8 +1854,8 @@ "任务 20180316 完成\n", "任务 20180315 完成\n", "任务 20180314 完成\n", - "任务 20180313 完成\n", "任务 20180312 完成\n", + "任务 20180313 完成\n", "任务 20180309 完成\n", "任务 20180308 完成\n", "任务 20180307 完成\n", @@ -1878,8 +1887,8 @@ "任务 20180123 完成\n", "任务 20180122 完成\n", "任务 20180119 完成\n", - "任务 20180117 完成\n", "任务 20180118 完成\n", + "任务 20180117 完成\n", "任务 20180116 完成\n", "任务 20180115 完成\n", "任务 20180112 完成\n", @@ -1958,8 +1967,8 @@ "任务 20170925 完成\n", "任务 20170922 完成\n", "任务 20170921 完成\n", - "任务 20170919 完成\n", "任务 20170920 完成\n", + "任务 20170919 完成\n", "任务 20170918 完成\n", "任务 20170915 完成\n", "任务 20170914 完成\n", @@ -2007,8 +2016,8 @@ "任务 20170718 完成\n", "任务 20170717 完成\n", "任务 20170714 完成\n", - "任务 20170712 完成\n", "任务 20170713 完成\n", + "任务 20170712 完成\n", "任务 20170711 完成\n", "任务 20170710 完成\n", "任务 20170707 完成\n", @@ -2042,8 +2051,8 @@ "任务 20170526 完成\n", "任务 20170525 完成\n", "任务 20170524 完成\n", - "任务 20170522 完成\n", "任务 20170523 完成\n", + "任务 20170522 完成\n", "任务 20170519 完成\n", "任务 20170518 完成\n", "任务 20170517 完成\n", @@ -2064,8 +2073,8 @@ "任务 20170425 完成\n", "任务 20170424 完成\n", "任务 20170421 完成\n", - "任务 20170420 完成\n", "任务 20170419 完成\n", + "任务 20170420 完成\n", "任务 20170418 完成\n", "任务 20170417 完成\n", "任务 20170414 完成\n", @@ -2093,9 +2102,9 @@ "任务 20170313 完成\n", "任务 20170310 完成\n", "任务 20170309 完成\n", - "任务 20170307 完成\n", "任务 20170308 完成\n", "任务 20170306 完成\n", + "任务 20170307 完成\n", "任务 20170303 完成\n", "任务 20170302 完成\n", "任务 20170301 完成\n", @@ -2133,8 +2142,8 @@ "任务 20170109 完成\n", "任务 20170106 完成\n", "任务 20170105 完成\n", - "任务 20170103 完成\n", - "任务 20170104 完成\n" + "任务 20170104 完成\n", + "任务 20170103 完成\n" ] } ], @@ -2145,7 +2154,7 @@ "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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250601')\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", @@ -2165,7 +2174,7 @@ " 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", + " time.sleep(0.1)\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", @@ -2199,7 +2208,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "97fdf8be-a86c-4404-bf0c-701f002cd81c", "metadata": { "ExecuteTime": { @@ -2219,11 +2228,11 @@ "3 834639.BJ 20250430 8.37 6.1158 7.8866 \n", "4 000909.SZ 20250430 5.72 0.6104 1.0424 \n", "... ... ... ... ... ... \n", - "8599133 300498.SZ 20170104 35.92 0.5417 0.5459 \n", - "8599134 002826.SZ 20170104 27.33 23.8426 23.8426 \n", - "8599135 001872.SZ 20170104 19.46 1.2359 6.1126 \n", - "8599136 001914.SZ 20170104 12.24 2.9843 6.1273 \n", - "8599137 302132.SZ 20170104 23.69 0.8350 2.5754 \n", + "8599133 600708.SH 20170103 9.03 0.7694 1.0169 \n", + "8599134 600712.SH 20170103 10.29 0.5859 0.8028 \n", + "8599135 001872.SZ 20170103 19.33 1.0970 5.4258 \n", + "8599136 001914.SZ 20170103 12.37 3.2627 6.6991 \n", + "8599137 302132.SZ 20170103 23.28 0.4912 1.5149 \n", "\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", @@ -2232,37 +2241,37 @@ "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", - "8599133 1.45 25.1819 12.7518 5.7450 3.2395 2.6791 \n", - "8599134 0.94 103.6390 103.6390 9.6797 14.9427 14.9427 \n", - "8599135 1.07 23.7746 23.3442 2.7234 6.7003 6.6025 \n", - "8599136 0.82 20.3470 15.0001 2.0964 1.4792 1.2468 \n", - "8599137 1.49 93.0003 86.1897 7.0613 9.1108 9.0130 \n", + "8599133 0.85 23.3367 22.2458 1.4847 0.9613 0.9248 \n", + "8599134 0.67 202.4855 287.1454 5.1852 2.3682 2.5386 \n", + "8599135 0.77 23.6158 23.1883 2.7052 6.6556 6.5584 \n", + "8599136 1.02 20.5631 15.1595 2.1186 1.4950 1.2600 \n", + "8599137 0.74 91.3908 84.6980 6.9391 8.9531 8.8570 \n", "\n", - " dv_ratio dv_ttm total_share float_share free_share \\\n", - "0 2.0904 2.0904 40391.1511 40240.6511 4634.6511 \n", - "1 0.0000 NaN 63973.2569 63922.1969 51122.1969 \n", - "2 3.7471 3.7471 47382.5333 46932.3226 14014.3219 \n", - "3 NaN NaN 20160.0000 11721.5883 9089.7537 \n", - "4 0.0000 NaN 43771.4245 43771.0570 25634.2299 \n", - "... ... ... ... ... ... \n", - "8599133 2.5520 2.5520 435029.6856 336846.2386 334233.6683 \n", - "8599134 NaN NaN 18972.0000 4743.0000 4743.0000 \n", - "8599135 2.1069 2.1069 64476.3730 46486.6050 9398.8050 \n", - "8599136 0.4085 0.4085 66696.1416 66678.0666 32475.1786 \n", - "8599137 0.2251 0.2251 39384.0333 30419.3588 9862.3809 \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", + "8599133 1.1074 1.1074 131871.9966 75088.9215 56812.2811 1.190804e+06 \n", + "8599134 0.1555 0.1555 54465.5360 53795.9475 39266.3119 5.604504e+05 \n", + "8599135 2.1211 2.1211 64476.3730 46486.6050 9398.8050 1.246328e+06 \n", + "8599136 0.4042 0.4042 66696.1416 66678.0666 32475.1786 8.250313e+05 \n", + "8599137 0.2291 0.2291 39384.0333 30419.3588 9862.3809 9.168603e+05 \n", "\n", - " total_mv circ_mv is_st \n", - "0 5.800169e+05 5.778557e+05 False \n", - "1 1.042764e+06 1.041932e+06 False \n", - "2 7.533823e+05 7.462239e+05 False \n", - "3 1.687392e+05 9.810969e+04 False \n", - "4 2.503725e+05 2.503704e+05 True \n", - "... ... ... ... \n", - "8599133 1.562627e+07 1.209952e+07 False \n", - "8599134 5.185048e+05 1.296262e+05 False \n", - "8599135 1.254710e+06 9.046293e+05 False \n", - "8599136 8.163608e+05 8.161395e+05 False \n", - "8599137 9.330077e+05 7.206346e+05 False \n", + " circ_mv is_st \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", + "8599133 6.780530e+05 False \n", + "8599134 5.535603e+05 False \n", + "8599135 8.985861e+05 False \n", + "8599136 8.248077e+05 False \n", + "8599137 7.081627e+05 False \n", "\n", "[8599138 rows x 19 columns]\n" ] @@ -2275,7 +2284,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "2b58a8bf-ffc5-4482-8e4d-bf24da9277de", "metadata": { "ExecuteTime": { @@ -2301,7 +2310,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "57ac1d86-5ce8-4bc9-812f-b45dcc2a3b4c", "metadata": { "ExecuteTime": { @@ -2309,8 +2318,70 @@ "start_time": "2025-03-02T08:34:49.775512Z" } }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "2091 603021.SH 20250430 3.01 12.8015 21.6289 \n", + "9902 603021.SH 20250429 2.94 0.1629 0.2752 \n", + "12107 603021.SH 20250428 3.09 0.4293 0.7253 \n", + "21450 603021.SH 20250425 3.25 0.0824 0.1392 \n", + "26081 603021.SH 20250424 3.42 0.0789 0.1333 \n", + "... ... ... ... ... ... \n", + "8586274 603021.SH 20170109 46.84 5.9412 6.5949 \n", + "8589108 603021.SH 20170106 47.55 6.4862 7.1998 \n", + "8591964 603021.SH 20170105 50.08 5.2368 5.8130 \n", + "8594806 603021.SH 20170104 51.73 5.3821 5.9743 \n", + "8598822 603021.SH 20170103 50.79 2.3801 2.6419 \n", + "\n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "2091 76.02 NaN NaN 34.7434 2.3383 2.3991 0.0000 \n", + "9902 0.27 NaN NaN 33.9354 2.2840 2.3433 0.0000 \n", + "12107 0.42 NaN NaN 17.9426 2.4005 2.4005 0.0000 \n", + "21450 0.05 NaN NaN 18.8716 2.5248 2.5248 0.0000 \n", + "26081 0.04 NaN NaN 19.8587 2.6569 2.6569 0.0000 \n", + "... ... ... ... ... ... ... ... \n", + "8586274 1.31 115.0810 102.7773 4.2761 7.9402 8.4422 0.3657 \n", + "8589108 1.40 116.8254 104.3352 4.3409 8.0606 8.5702 0.3603 \n", + "8591964 1.05 123.0413 109.8866 4.5719 8.4894 9.0262 0.3421 \n", + "8594806 0.97 127.0952 113.5070 4.7225 8.7691 9.3236 0.3311 \n", + "8598822 0.37 124.7857 111.4445 4.6367 8.6098 9.1541 0.3373 \n", + "\n", + " dv_ttm total_share float_share free_share total_mv \\\n", + "2091 NaN 31994.807 31994.807 18936.7934 96304.3691 \n", + "9902 NaN 31994.807 31994.807 18936.7934 94064.7326 \n", + "12107 NaN 31994.807 31994.807 18936.7934 98863.9536 \n", + "21450 NaN 31994.807 31994.807 18936.7934 103983.1228 \n", + "26081 NaN 31994.807 31994.807 18936.7934 109422.2399 \n", + "... ... ... ... ... ... \n", + "8586274 0.3657 12305.695 6396.000 5762.0400 576398.7538 \n", + "8589108 0.3603 12305.695 6396.000 5762.0400 585135.7973 \n", + "8591964 0.3421 12305.695 6396.000 5762.0400 616269.2056 \n", + "8594806 0.3311 12305.695 6396.000 5762.0400 636573.6024 \n", + "8598822 0.3373 12305.695 6396.000 5762.0400 625006.2491 \n", + "\n", + " circ_mv is_st \n", + "2091 96304.3691 True \n", + "9902 94064.7326 True \n", + "12107 98863.9536 True \n", + "21450 103983.1228 True \n", + "26081 109422.2399 True \n", + "... ... ... \n", + "8586274 299588.6400 False \n", + "8589108 304129.8000 False \n", + "8591964 320311.6800 False \n", + "8594806 330865.0800 False \n", + "8598822 324852.8400 False \n", + "\n", + "[1932 rows x 19 columns]\n" + ] + } + ], + "source": [ + "print(all_daily_data_df[all_daily_data_df['ts_code'] == '603021.SH'])" + ] } ], "metadata": { diff --git a/main/data/name_change.ipynb b/main/data/name_change.ipynb index 4d94328..fd3eea6 100644 --- a/main/data/name_change.ipynb +++ b/main/data/name_change.ipynb @@ -183,7 +183,7 @@ "成功获取 000572.SZ 的数据\n", "成功获取 000573.SZ 的数据\n", "成功获取 000576.SZ 的数据\n", - "已调用300次API，等待 38.79 秒以满足速率限制...\n", + "已调用300次API，等待 40.75 秒以满足速率限制...\n", "成功获取 000581.SZ 的数据\n", "成功获取 000582.SZ 的数据\n", "成功获取 000584.SZ 的数据\n", @@ -334,7 +334,7 @@ "成功获取 000811.SZ 的数据\n", "成功获取 000812.SZ 的数据\n", "成功获取 000813.SZ 的数据\n", - "已调用300次API，等待 38.14 秒以满足速率限制...\n", + "已调用300次API，等待 8.60 秒以满足速率限制...\n", "成功获取 000815.SZ 的数据\n", "成功获取 000816.SZ 的数据\n", "成功获取 000818.SZ 的数据\n", @@ -485,7 +485,7 @@ "成功获取 001238.SZ 的数据\n", "成功获取 001239.SZ 的数据\n", "成功获取 001255.SZ 的数据\n", - "已调用300次API，等待 38.70 秒以满足速率限制...\n", + "已调用300次API，等待 16.22 秒以满足速率限制...\n", "成功获取 001256.SZ 的数据\n", "成功获取 001258.SZ 的数据\n", "成功获取 001259.SZ 的数据\n", @@ -636,7 +636,7 @@ "成功获取 002085.SZ 的数据\n", "成功获取 002086.SZ 的数据\n", "成功获取 002088.SZ 的数据\n", - "已调用300次API，等待 38.23 秒以满足速率限制...\n", + "已调用300次API，等待 16.73 秒以满足速率限制...\n", "成功获取 002090.SZ 的数据\n", "成功获取 002091.SZ 的数据\n", "成功获取 002092.SZ 的数据\n", @@ -787,7 +787,7 @@ "成功获取 002242.SZ 的数据\n", "成功获取 002243.SZ 的数据\n", "成功获取 002244.SZ 的数据\n", - "已调用300次API，等待 38.48 秒以满足速率限制...\n", + "已调用300次API，等待 21.90 秒以满足速率限制...\n", "成功获取 002245.SZ 的数据\n", "成功获取 002246.SZ 的数据\n", "成功获取 002247.SZ 的数据\n", @@ -938,7 +938,7 @@ "成功获取 002400.SZ 的数据\n", "成功获取 002401.SZ 的数据\n", "成功获取 002402.SZ 的数据\n", - "已调用300次API，等待 38.28 秒以满足速率限制...\n", + "已调用300次API，等待 38.45 秒以满足速率限制...\n", "成功获取 002403.SZ 的数据\n", "成功获取 002404.SZ 的数据\n", "成功获取 002405.SZ 的数据\n", @@ -1089,7 +1089,7 @@ "成功获取 002566.SZ 的数据\n", "成功获取 002567.SZ 的数据\n", "成功获取 002568.SZ 的数据\n", - "已调用300次API，等待 38.10 秒以满足速率限制...\n", + "已调用300次API，等待 39.17 秒以满足速率限制...\n", "成功获取 002569.SZ 的数据\n", "成功获取 002570.SZ 的数据\n", "成功获取 002571.SZ 的数据\n", @@ -1240,7 +1240,7 @@ "成功获取 002729.SZ 的数据\n", "成功获取 002730.SZ 的数据\n", "成功获取 002731.SZ 的数据\n", - "已调用300次API，等待 39.07 秒以满足速率限制...\n", + "已调用300次API，等待 38.52 秒以满足速率限制...\n", "成功获取 002732.SZ 的数据\n", "成功获取 002733.SZ 的数据\n", "成功获取 002734.SZ 的数据\n", @@ -1391,7 +1391,7 @@ "成功获取 002896.SZ 的数据\n", "成功获取 002897.SZ 的数据\n", "成功获取 002898.SZ 的数据\n", - "已调用300次API，等待 38.58 秒以满足速率限制...\n", + "已调用300次API，等待 38.60 秒以满足速率限制...\n", "成功获取 002899.SZ 的数据\n", "成功获取 002900.SZ 的数据\n", "成功获取 002901.SZ 的数据\n", @@ -1542,7 +1542,7 @@ "成功获取 300014.SZ 的数据\n", "成功获取 300015.SZ 的数据\n", "成功获取 300016.SZ 的数据\n", - "已调用300次API，等待 39.18 秒以满足速率限制...\n", + "已调用300次API，等待 37.75 秒以满足速率限制...\n", "成功获取 300017.SZ 的数据\n", "成功获取 300018.SZ 的数据\n", "成功获取 300019.SZ 的数据\n", @@ -1693,7 +1693,7 @@ "成功获取 300174.SZ 的数据\n", "成功获取 300175.SZ 的数据\n", "成功获取 300176.SZ 的数据\n", - "已调用300次API，等待 38.05 秒以满足速率限制...\n", + "已调用300次API，等待 40.54 秒以满足速率限制...\n", "成功获取 300177.SZ 的数据\n", "成功获取 300179.SZ 的数据\n", "成功获取 300180.SZ 的数据\n", @@ -1844,7 +1844,7 @@ "成功获取 300337.SZ 的数据\n", "成功获取 300338.SZ 的数据\n", "成功获取 300339.SZ 的数据\n", - "已调用300次API，等待 38.83 秒以满足速率限制...\n", + "已调用300次API，等待 40.50 秒以满足速率限制...\n", "成功获取 300340.SZ 的数据\n", "成功获取 300341.SZ 的数据\n", "成功获取 300342.SZ 的数据\n", @@ -1995,7 +1995,7 @@ "成功获取 300494.SZ 的数据\n", "成功获取 300496.SZ 的数据\n", "成功获取 300497.SZ 的数据\n", - "已调用300次API，等待 38.36 秒以满足速率限制...\n", + "已调用300次API，等待 38.45 秒以满足速率限制...\n", "成功获取 300498.SZ 的数据\n", "成功获取 300499.SZ 的数据\n", "成功获取 300500.SZ 的数据\n", @@ -2146,7 +2146,7 @@ "成功获取 300650.SZ 的数据\n", "成功获取 300651.SZ 的数据\n", "成功获取 300652.SZ 的数据\n", - "已调用300次API，等待 39.00 秒以满足速率限制...\n", + "已调用300次API，等待 40.83 秒以满足速率限制...\n", "成功获取 300653.SZ 的数据\n", "成功获取 300654.SZ 的数据\n", "成功获取 300655.SZ 的数据\n", @@ -2297,7 +2297,7 @@ "成功获取 300810.SZ 的数据\n", "成功获取 300811.SZ 的数据\n", "成功获取 300812.SZ 的数据\n", - "已调用300次API，等待 39.10 秒以满足速率限制...\n", + "已调用300次API，等待 40.11 秒以满足速率限制...\n", "成功获取 300813.SZ 的数据\n", "成功获取 300814.SZ 的数据\n", "成功获取 300815.SZ 的数据\n", @@ -2448,7 +2448,7 @@ "成功获取 300966.SZ 的数据\n", "成功获取 300967.SZ 的数据\n", "成功获取 300968.SZ 的数据\n", - "已调用300次API，等待 38.14 秒以满足速率限制...\n", + "已调用300次API，等待 40.87 秒以满足速率限制...\n", "成功获取 300969.SZ 的数据\n", "成功获取 300970.SZ 的数据\n", "成功获取 300971.SZ 的数据\n", @@ -2599,7 +2599,7 @@ "成功获取 301128.SZ 的数据\n", "成功获取 301129.SZ 的数据\n", "成功获取 301130.SZ 的数据\n", - "已调用300次API，等待 38.08 秒以满足速率限制...\n", + "已调用300次API，等待 40.16 秒以满足速率限制...\n", "成功获取 301131.SZ 的数据\n", "成功获取 301132.SZ 的数据\n", "成功获取 301133.SZ 的数据\n", @@ -2750,7 +2750,7 @@ "成功获取 301313.SZ 的数据\n", "成功获取 301314.SZ 的数据\n", "成功获取 301315.SZ 的数据\n", - "已调用300次API，等待 38.67 秒以满足速率限制...\n", + "已调用300次API，等待 40.63 秒以满足速率限制...\n", "成功获取 301316.SZ 的数据\n", "成功获取 301317.SZ 的数据\n", "成功获取 301318.SZ 的数据\n", @@ -2901,7 +2901,7 @@ "成功获取 301618.SZ 的数据\n", "成功获取 301622.SZ 的数据\n", "成功获取 301626.SZ 的数据\n", - "已调用300次API，等待 39.59 秒以满足速率限制...\n", + "已调用300次API，等待 39.95 秒以满足速率限制...\n", "成功获取 301628.SZ 的数据\n", "成功获取 301631.SZ 的数据\n", "成功获取 301633.SZ 的数据\n", @@ -3052,7 +3052,7 @@ "成功获取 600170.SH 的数据\n", "成功获取 600171.SH 的数据\n", "成功获取 600172.SH 的数据\n", - "已调用300次API，等待 38.63 秒以满足速率限制...\n", + "已调用300次API，等待 39.18 秒以满足速率限制...\n", "成功获取 600173.SH 的数据\n", "成功获取 600176.SH 的数据\n", "成功获取 600177.SH 的数据\n", @@ -3203,7 +3203,7 @@ "成功获取 600366.SH 的数据\n", "成功获取 600367.SH 的数据\n", "成功获取 600368.SH 的数据\n", - "已调用300次API，等待 38.00 秒以满足速率限制...\n", + "已调用300次API，等待 40.48 秒以满足速率限制...\n", "成功获取 600369.SH 的数据\n", "成功获取 600370.SH 的数据\n", "成功获取 600371.SH 的数据\n", @@ -3354,7 +3354,7 @@ "成功获取 600572.SH 的数据\n", "成功获取 600573.SH 的数据\n", "成功获取 600575.SH 的数据\n", - "已调用300次API，等待 36.61 秒以满足速率限制...\n", + "已调用300次API，等待 39.24 秒以满足速率限制...\n", "成功获取 600576.SH 的数据\n", "成功获取 600577.SH 的数据\n", "成功获取 600578.SH 的数据\n", @@ -3505,7 +3505,7 @@ "成功获取 600748.SH 的数据\n", "成功获取 600749.SH 的数据\n", "成功获取 600750.SH 的数据\n", - "已调用300次API，等待 38.88 秒以满足速率限制...\n", + "已调用300次API，等待 39.49 秒以满足速率限制...\n", "成功获取 600751.SH 的数据\n", "成功获取 600753.SH 的数据\n", "成功获取 600754.SH 的数据\n", @@ -3656,7 +3656,7 @@ "成功获取 600956.SH 的数据\n", "成功获取 600958.SH 的数据\n", "成功获取 600959.SH 的数据\n", - "已调用300次API，等待 38.49 秒以满足速率限制...\n", + "已调用300次API，等待 40.83 秒以满足速率限制...\n", "成功获取 600960.SH 的数据\n", "成功获取 600961.SH 的数据\n", "成功获取 600962.SH 的数据\n", @@ -3807,7 +3807,7 @@ "成功获取 601519.SH 的数据\n", "成功获取 601528.SH 的数据\n", "成功获取 601555.SH 的数据\n", - "已调用300次API，等待 38.62 秒以满足速率限制...\n", + "已调用300次API，等待 40.73 秒以满足速率限制...\n", "成功获取 601566.SH 的数据\n", "成功获取 601567.SH 的数据\n", "成功获取 601568.SH 的数据\n", @@ -3958,7 +3958,7 @@ "成功获取 603041.SH 的数据\n", "成功获取 603042.SH 的数据\n", "成功获取 603043.SH 的数据\n", - "已调用300次API，等待 38.79 秒以满足速率限制...\n", + "已调用300次API，等待 40.08 秒以满足速率限制...\n", "成功获取 603045.SH 的数据\n", "成功获取 603048.SH 的数据\n", "成功获取 603050.SH 的数据\n", @@ -4109,7 +4109,7 @@ "成功获取 603228.SH 的数据\n", "成功获取 603229.SH 的数据\n", "成功获取 603230.SH 的数据\n", - "已调用300次API，等待 39.75 秒以满足速率限制...\n", + "已调用300次API，等待 40.23 秒以满足速率限制...\n", "成功获取 603231.SH 的数据\n", "成功获取 603232.SH 的数据\n", "成功获取 603233.SH 的数据\n", @@ -4260,7 +4260,7 @@ "成功获取 603530.SH 的数据\n", "成功获取 603533.SH 的数据\n", "成功获取 603535.SH 的数据\n", - "已调用300次API，等待 38.97 秒以满足速率限制...\n", + "已调用300次API，等待 40.89 秒以满足速率限制...\n", "成功获取 603536.SH 的数据\n", "成功获取 603538.SH 的数据\n", "成功获取 603551.SH 的数据\n", @@ -4411,7 +4411,7 @@ "成功获取 603819.SH 的数据\n", "成功获取 603822.SH 的数据\n", "成功获取 603823.SH 的数据\n", - "已调用300次API，等待 39.13 秒以满足速率限制...\n", + "已调用300次API，等待 38.75 秒以满足速率限制...\n", "成功获取 603825.SH 的数据\n", "成功获取 603826.SH 的数据\n", "成功获取 603828.SH 的数据\n", @@ -4562,7 +4562,7 @@ "成功获取 605167.SH 的数据\n", "成功获取 605168.SH 的数据\n", "成功获取 605169.SH 的数据\n", - "已调用300次API，等待 39.25 秒以满足速率限制...\n", + "已调用300次API，等待 40.37 秒以满足速率限制...\n", "成功获取 605177.SH 的数据\n", "成功获取 605178.SH 的数据\n", "成功获取 605179.SH 的数据\n", @@ -4713,7 +4713,7 @@ "成功获取 688097.SH 的数据\n", "成功获取 688098.SH 的数据\n", "成功获取 688099.SH 的数据\n", - "已调用300次API，等待 38.88 秒以满足速率限制...\n", + "已调用300次API，等待 39.89 秒以满足速率限制...\n", "成功获取 688100.SH 的数据\n", "成功获取 688101.SH 的数据\n", "成功获取 688102.SH 的数据\n", @@ -4864,7 +4864,7 @@ "成功获取 688271.SH 的数据\n", "成功获取 688272.SH 的数据\n", "成功获取 688273.SH 的数据\n", - "已调用300次API，等待 35.24 秒以满足速率限制...\n", + "已调用300次API，等待 39.50 秒以满足速率限制...\n", "成功获取 688275.SH 的数据\n", "成功获取 688276.SH 的数据\n", "成功获取 688277.SH 的数据\n", @@ -5015,7 +5015,7 @@ "成功获取 688486.SH 的数据\n", "成功获取 688488.SH 的数据\n", "成功获取 688489.SH 的数据\n", - "已调用300次API，等待 37.62 秒以满足速率限制...\n", + "已调用300次API，等待 39.30 秒以满足速率限制...\n", "成功获取 688496.SH 的数据\n", "成功获取 688498.SH 的数据\n", "成功获取 688499.SH 的数据\n", @@ -5166,7 +5166,7 @@ "成功获取 688689.SH 的数据\n", "成功获取 688690.SH 的数据\n", "成功获取 688691.SH 的数据\n", - "已调用300次API，等待 39.35 秒以满足速率限制...\n", + "已调用300次API，等待 40.48 秒以满足速率限制...\n", "成功获取 688692.SH 的数据\n", "成功获取 688693.SH 的数据\n", "成功获取 688695.SH 的数据\n", @@ -5317,7 +5317,7 @@ "成功获取 835184.BJ 的数据\n", "成功获取 835185.BJ 的数据\n", "成功获取 835207.BJ 的数据\n", - "已调用300次API，等待 39.39 秒以满足速率限制...\n", + "已调用300次API，等待 40.17 秒以满足速率限制...\n", "成功获取 835237.BJ 的数据\n", "成功获取 835305.BJ 的数据\n", "成功获取 835368.BJ 的数据\n", @@ -5468,7 +5468,7 @@ "成功获取 000005.SZ 的数据\n", "成功获取 000013.SZ 的数据\n", "成功获取 000015.SZ 的数据\n", - "已调用300次API，等待 38.64 秒以满足速率限制...\n", + "已调用300次API，等待 40.97 秒以满足速率限制...\n", "成功获取 000018.SZ 的数据\n", "成功获取 000023.SZ 的数据\n", "成功获取 000024.SZ 的数据\n", @@ -5619,7 +5619,7 @@ "成功获取 300309.SZ 的数据\n", "成功获取 300312.SZ 的数据\n", "成功获取 300325.SZ 的数据\n", - "已调用300次API，等待 39.83 秒以满足速率限制...\n", + "已调用300次API，等待 39.81 秒以满足速率限制...\n", "成功获取 300330.SZ 的数据\n", "成功获取 300336.SZ 的数据\n", "成功获取 300356.SZ 的数据\n", @@ -5761,7 +5761,7 @@ "2 000001.SZ 深发展A 20070620 20120801 完成股改\n", "3 000001.SZ 深发展A 20070620 20120801 完成股改\n", "4 000001.SZ S深发展A 20061009 20070619 未股改加S\n", - "名称变化记录总数: 32258\n" + "名称变化记录总数: 32259\n" ] } ], diff --git a/main/data/update/cyq-perf.ipynb b/main/data/update/cyq-perf.ipynb index 7bb4859..01c10aa 100644 --- a/main/data/update/cyq-perf.ipynb +++ b/main/data/update/cyq-perf.ipynb @@ -32,22 +32,22 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date\n", - "0 000001.SZ 20250312\n", - "1 000002.SZ 20250312\n", - "2 000004.SZ 20250312\n", - "3 000006.SZ 20250312\n", - "4 000007.SZ 20250312\n", - "... ... ...\n", - "43070 920108.BJ 20250421\n", - "43071 920111.BJ 20250421\n", - "43072 920116.BJ 20250421\n", - "43073 920118.BJ 20250421\n", - "43074 920128.BJ 20250421\n", + " ts_code trade_date\n", + "0 000001.SZ 20250312\n", + "1 000002.SZ 20250312\n", + "2 000004.SZ 20250312\n", + "3 000006.SZ 20250312\n", + "4 000007.SZ 20250312\n", + "... ... ...\n", + "5381 920445.BJ 20250506\n", + "5382 920489.BJ 20250506\n", + "5383 920682.BJ 20250506\n", + "5384 920799.BJ 20250506\n", + "5385 920819.BJ 20250506\n", "\n", - "[7648931 rows x 2 columns]\n", - "20250430\n", - "start_date: 20250506\n" + "[7654317 rows x 2 columns]\n", + "20250506\n", + "start_date: 20250507\n" ] } ], @@ -88,28 +88,28 @@ "text": [ "任务 20250619 完成\n", "任务 20250620 完成\n", - "任务 20250618 完成\n", "任务 20250617 完成\n", - "任务 20250613 完成\n", + "任务 20250618 完成\n", "任务 20250616 完成\n", - "任务 20250611 完成\n", + "任务 20250613 完成\n", "任务 20250612 完成\n", + "任务 20250611 完成\n", "任务 20250610 完成\n", "任务 20250609 完成\n", "任务 20250606 完成\n", "任务 20250605 完成\n", "任务 20250604 完成\n", "任务 20250603 完成\n", - "任务 20250529 完成\n", "任务 20250530 完成\n", + "任务 20250529 完成\n", "任务 20250528 完成\n", "任务 20250527 完成\n", "任务 20250526 完成\n", "任务 20250523 完成\n", "任务 20250522 完成\n", "任务 20250521 完成\n", - "任务 20250519 完成\n", "任务 20250520 完成\n", + "任务 20250519 完成\n", "任务 20250516 完成\n", "任务 20250515 完成\n", "任务 20250514 完成\n", @@ -117,8 +117,7 @@ "任务 20250512 完成\n", "任务 20250509 完成\n", "任务 20250508 完成\n", - "任务 20250507 完成\n", - "任务 20250506 完成\n" + "任务 20250507 完成\n" ] } ], diff --git a/main/data/update/index_data.ipynb b/main/data/update/index_data.ipynb deleted file mode 100644 index f32b914..0000000 --- a/main/data/update/index_data.ipynb +++ /dev/null @@ -1,194 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", - "metadata": {}, - "outputs": [], - "source": [ - "import tushare as ts\n", - "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", - "pro = ts.pro_api()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ts_code trade_date\n", - "0 801001.SI 20250221\n", - "1 801002.SI 20250221\n", - "2 801003.SI 20250221\n", - "3 801005.SI 20250221\n", - "4 801010.SI 20250221\n", - "... ... ...\n", - "1044388 857344.SI 20170103\n", - "1044389 857411.SI 20170103\n", - "1044390 857421.SI 20170103\n", - "1044391 857431.SI 20170103\n", - "1044392 858811.SI 20170103\n", - "\n", - "[1044393 rows x 2 columns]\n", - "20250221\n", - "start_date: 20250224\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import time\n", - "\n", - "h5_filename = '../../../data/sw_daily.h5'\n", - "key = '/sw_daily'\n", - "max_date = None\n", - "with pd.HDFStore(h5_filename, mode='r') as store:\n", - " df = store[key][['ts_code', 'trade_date']]\n", - " print(df)\n", - " max_date = df['trade_date'].max()\n", - "\n", - "print(max_date)\n", - "trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n", - "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", - "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", - "start_date = min(trade_dates)\n", - "print(f'start_date: {start_date}')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "747acc47-0884-4f76-90fb-276f6494e31d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250417 完成\n", - "任务 20250418 完成\n", - "任务 20250416 完成\n", - "任务 20250415 完成\n", - "任务 20250411 完成\n", - "任务 20250414 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250408 完成\n", - "任务 20250403 完成\n", - "任务 20250407 完成\n", - "任务 20250402 完成\n", - "任务 20250401 完成\n", - "任务 20250331 完成\n", - "任务 20250328 完成\n", - "任务 20250327 完成\n", - "任务 20250326 完成\n", - "任务 20250325 完成\n", - "任务 20250324 完成\n", - "任务 20250321 完成\n", - "任务 20250320 完成\n", - "任务 20250319 完成\n", - "任务 20250317 完成\n", - "任务 20250314 完成\n", - "任务 20250318 完成\n", - "任务 20250313 完成\n", - "任务 20250312 完成\n", - "任务 20250311 完成\n", - "任务 20250310 完成\n", - "任务 20250307 完成\n", - "任务 20250306 完成\n", - "任务 20250305 完成\n", - "任务 20250304 完成\n", - "任务 20250303 完成\n", - "任务 20250228 完成\n", - "任务 20250227 完成\n", - "任务 20250226 完成\n", - "任务 20250225 完成\n", - "任务 20250224 完成\n" - ] - } - ], - "source": [ - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "\n", - "all_daily_data = []\n", - "\n", - "# API 调用计数和时间控制变量\n", - "api_call_count = 0\n", - "batch_start_time = time.time()\n", - "\n", - "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n", - "def get_data(trade_date):\n", - " time.sleep(0.1)\n", - " data = pro.sw_daily(trade_date=trade_date)\n", - " if data is not None and not data.empty:\n", - " return data\n", - "\n", - "\n", - "with ThreadPoolExecutor(max_workers=2) as executor:\n", - " future_to_date = {executor.submit(get_data, td): td for td in trade_dates}\n", - "\n", - " for future in as_completed(future_to_date):\n", - " trade_date = future_to_date[future] # 获取对应的交易日期\n", - " try:\n", - " result = future.result() # 获取任务执行的结果\n", - " all_daily_data.append(result)\n", - " print(f\"任务 {trade_date} 完成\")\n", - " except Exception as e:\n", - " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c6765638-481f-40d8-a259-2e7b25362618", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "所有每日基础数据获取并保存完毕！\n" - ] - } - ], - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "\n", - "# 将所有数据合并为一个 DataFrame\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/main/data/update/sw_daily.ipynb b/main/data/update/sw_daily.ipynb index ec0f74d..35b637d 100644 --- a/main/data/update/sw_daily.ipynb +++ b/main/data/update/sw_daily.ipynb @@ -32,22 +32,22 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date\n", - "0 801001.SI 20250221\n", - "1 801002.SI 20250221\n", - "2 801003.SI 20250221\n", - "3 801005.SI 20250221\n", - "4 801010.SI 20250221\n", - "... ... ...\n", - "3507 859811.SI 20250421\n", - "3508 859821.SI 20250421\n", - "3509 859822.SI 20250421\n", - "3510 859852.SI 20250421\n", - "3511 859951.SI 20250421\n", + " ts_code trade_date\n", + "0 801001.SI 20250221\n", + "1 801002.SI 20250221\n", + "2 801003.SI 20250221\n", + "3 801005.SI 20250221\n", + "4 801010.SI 20250221\n", + ".. ... ...\n", + "434 859811.SI 20250506\n", + "435 859821.SI 20250506\n", + "436 859822.SI 20250506\n", + "437 859852.SI 20250506\n", + "438 859951.SI 20250506\n", "\n", - "[1065026 rows x 2 columns]\n", - "20250430\n", - "start_date: 20250506\n" + "[1065465 rows x 2 columns]\n", + "20250506\n", + "start_date: 20250507\n" ] } ], @@ -92,33 +92,32 @@ "任务 20250617 完成\n", "任务 20250616 完成\n", "任务 20250613 完成\n", - "任务 20250611 完成\n", "任务 20250612 完成\n", + "任务 20250611 完成\n", "任务 20250610 完成\n", "任务 20250609 完成\n", "任务 20250606 完成\n", "任务 20250605 完成\n", - "任务 20250603 完成\n", "任务 20250604 完成\n", + "任务 20250603 完成\n", "任务 20250530 完成\n", - "任务 20250529 完成\n", "任务 20250528 完成\n", - "任务 20250527 完成\n", + "任务 20250529 完成\n", "任务 20250526 完成\n", + "任务 20250527 完成\n", "任务 20250523 完成\n", "任务 20250522 完成\n", "任务 20250521 完成\n", "任务 20250520 完成\n", "任务 20250519 完成\n", "任务 20250516 完成\n", - "任务 20250515 完成\n", "任务 20250514 完成\n", + "任务 20250515 完成\n", "任务 20250513 完成\n", "任务 20250512 完成\n", "任务 20250509 完成\n", "任务 20250508 完成\n", - "任务 20250507 完成\n", - "任务 20250506 完成\n" + "任务 20250507 完成\n" ] } ], diff --git a/main/data/update/update_daily_basic.ipynb b/main/data/update/update_daily_basic.ipynb index 6704db1..1610dd4 100644 --- a/main/data/update/update_daily_basic.ipynb +++ b/main/data/update/update_daily_basic.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "14671a7f72de2564", "metadata": { "ExecuteTime": { @@ -68,19 +68,19 @@ "# 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_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['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", - " st_data = group[group['name'].str.contains('ST')]\n", + " st_data = group[(group['name'].str.contains('ST')) | (group['name'].str.contains('退'))]\n", " if not st_data.empty:\n", " name_change_dict[ts_code] = filter_rows(st_data)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "e7f8cce2f80e2f20", "metadata": { "ExecuteTime": { @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", "metadata": { "ExecuteTime": { @@ -146,14 +146,14 @@ "text": [ "任务 20250718 完成\n", "任务 20250717 完成\n", - "任务 20250715 完成\n", "任务 20250716 完成\n", - "任务 20250711 完成\n", + "任务 20250715 完成\n", "任务 20250714 完成\n", - "任务 20250709 完成\n", + "任务 20250711 完成\n", "任务 20250710 完成\n", - "任务 20250707 完成\n", + "任务 20250709 完成\n", "任务 20250708 完成\n", + "任务 20250707 完成\n", "任务 20250704 完成\n", "任务 20250703 完成\n", "任务 20250702 完成\n", @@ -164,8 +164,8 @@ "任务 20250625 完成\n", "任务 20250624 完成\n", "任务 20250623 完成\n", - "任务 20250619 完成\n", "任务 20250620 完成\n", + "任务 20250619 完成\n", "任务 20250618 完成\n", "任务 20250617 完成\n", "任务 20250616 完成\n", @@ -178,14 +178,14 @@ "任务 20250605 完成\n", "任务 20250604 完成\n", "任务 20250603 完成\n", - "任务 20250530 完成\n", "任务 20250529 完成\n", - "任务 20250528 完成\n", + "任务 20250530 完成\n", "任务 20250527 完成\n", + "任务 20250528 完成\n", "任务 20250526 完成\n", "任务 20250523 完成\n", - "任务 20250522 完成\n", "任务 20250521 完成\n", + "任务 20250522 完成\n", "任务 20250520 完成\n", "任务 20250519 完成\n", "任务 20250516 完成\n", @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "919023c693d7a47a", "metadata": { "ExecuteTime": { @@ -266,59 +266,59 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "0 002390.SZ 20250506 3.48 0.7696 1.3833 \n", - "1 300708.SZ 20250506 11.64 2.8994 3.2217 \n", - "2 301171.SZ 20250506 27.73 9.9120 10.7228 \n", - "3 301662.SZ 20250506 52.50 17.0926 17.0926 \n", - "4 001309.SZ 20250506 129.63 5.7123 6.3388 \n", - "... ... ... ... ... ... \n", - "5381 000551.SZ 20250506 12.39 2.0213 3.1432 \n", - "5382 600792.SH 20250506 3.17 0.8036 2.3531 \n", - "5383 300176.SZ 20250506 6.62 1.7530 2.5325 \n", - "5384 000016.SZ 20250506 5.57 13.9545 20.7669 \n", - "5385 300339.SZ 20250506 56.53 11.3184 11.9579 \n", + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "0 301261.SZ 20250507 97.25 15.5042 19.6511 \n", + "1 002643.SZ 20250507 11.12 1.3481 2.3303 \n", + "2 001211.SZ 20250507 22.11 3.5506 6.1239 \n", + "3 002466.SZ 20250507 28.98 1.0588 1.5771 \n", + "4 603005.SH 20250507 29.32 5.1961 6.1690 \n", + "... ... ... ... ... ... \n", + "10769 000551.SZ 20250506 12.39 2.0213 3.1432 \n", + "10770 600792.SH 20250506 3.17 0.8036 2.3531 \n", + "10771 300176.SZ 20250506 6.62 1.7530 2.5325 \n", + "10772 000016.SZ 20250506 5.57 13.9545 20.7669 \n", + "10773 300339.SZ 20250506 56.53 11.3184 11.9579 \n", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 1.02 66.7242 80.7223 1.0020 1.1214 1.1483 2.5321 \n", - "1 1.14 40.4767 37.8935 2.9328 2.8689 2.7390 1.3334 \n", - "2 0.95 56.4451 55.0565 3.6159 5.1380 4.3691 0.4867 \n", - "3 0.79 20.2143 23.5423 2.7909 2.0091 2.2310 NaN \n", - "4 1.02 59.8205 243.9150 8.6523 4.3939 4.0221 0.0702 \n", - "... ... ... ... ... ... ... ... \n", - "5381 1.20 19.9692 18.7030 1.8602 1.1939 1.1927 0.5650 \n", - "5382 0.89 NaN NaN 1.1995 0.5271 0.5777 2.1767 \n", - "5383 1.12 92.1443 96.5538 2.7208 1.4839 1.4627 0.0000 \n", - "5384 3.66 NaN NaN 5.6643 1.2067 1.1979 0.0000 \n", - "5385 2.40 279.4392 270.1037 12.8967 13.2445 13.0061 0.0000 \n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "0 0.84 122.6810 146.2352 5.5730 8.2774 8.3189 0.4627 \n", + "1 0.79 41.9902 45.3885 1.4569 2.8000 2.8594 2.6982 \n", + "2 0.83 56.0080 58.9563 1.8078 1.1637 1.1399 0.0000 \n", + "3 0.92 NaN NaN 1.1380 3.6409 3.6410 4.6569 \n", + "4 1.35 75.6520 71.1174 4.4020 16.9225 16.2060 0.1570 \n", + "... ... ... ... ... ... ... ... \n", + "10769 1.20 19.9692 18.7030 1.8602 1.1939 1.1927 0.5650 \n", + "10770 0.89 NaN NaN 1.1995 0.5271 0.5777 2.1767 \n", + "10771 1.12 92.1443 96.5538 2.7208 1.4839 1.4627 0.0000 \n", + "10772 3.66 NaN NaN 5.6643 1.2067 1.1979 0.0000 \n", + "10773 2.40 279.4392 270.1037 12.8967 13.2445 13.0061 0.0000 \n", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 2.5321 194385.1868 185230.5076 103045.2550 6.764605e+05 \n", - "1 1.3003 68015.2346 52260.4413 47031.2918 7.916973e+05 \n", - "2 0.4867 47188.5905 30877.5025 28542.8345 1.308540e+06 \n", - "3 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@@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "28cb78d032671b20", "metadata": { "ExecuteTime": { @@ -342,59 +342,59 @@ "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "23 000820.SZ 20250506 2.04 11.8279 12.1552 \n", - "33 300506.SZ 20250506 3.27 0.6104 0.8597 \n", - "82 839680.BJ 20250506 7.25 34.6648 39.7153 \n", - "105 300159.SZ 20250506 1.83 3.6351 4.0740 \n", - "114 300301.SZ 20250506 1.82 1.3707 1.4819 \n", - "... ... ... ... ... ... \n", - "5259 600243.SH 20250506 2.43 6.7484 8.1172 \n", - "5264 002528.SZ 20250506 2.35 2.0592 4.3961 \n", - "5294 300044.SZ 20250506 3.31 12.8866 13.4490 \n", - "5324 300097.SZ 20250506 4.36 2.5814 3.0107 \n", - "5345 600200.SH 20250506 3.04 0.2013 0.2433 \n", + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "8 300147.SZ 20250507 6.58 5.3209 6.8857 \n", + "19 002501.SZ 20250507 2.10 2.8874 3.7273 \n", + "52 600238.SH 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-430,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "d7a773fc20293477", "metadata": { "ExecuteTime": { @@ -444,7 +444,7 @@ "output_type": "stream", "text": [ "\n", - "Index: 8604524 entries, 0 to 5385\n", + "Index: 8609912 entries, 0 to 10773\n", "Data columns (total 3 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -452,7 +452,7 @@ " 1 trade_date object\n", " 2 is_st bool \n", "dtypes: bool(1), object(2)\n", - "memory usage: 205.1+ MB\n", + "memory usage: 205.3+ MB\n", "None\n" ] } diff --git a/main/data/update/update_daily_data.ipynb b/main/data/update/update_daily_data.ipynb index f9e0207..5e868b1 100644 --- a/main/data/update/update_daily_data.ipynb +++ b/main/data/update/update_daily_data.ipynb @@ -38,17 +38,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8670620 entries, 0 to 80410\n", + "Index: 8675975 entries, 0 to 5354\n", "Data columns (total 2 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object\n", " 1 trade_date object\n", "dtypes: object(2)\n", - "memory usage: 198.5+ MB\n", + "memory usage: 198.6+ MB\n", "None\n", - "20250430\n", - "20250506\n" + "20250506\n", + "20250507\n" ] } ], @@ -85,16 +85,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 000001.SZ 完成\n", "任务 000002.SZ 完成\n", - "任务 000004.SZ 完成\n", + "任务 000001.SZ 完成\n", "任务 000006.SZ 完成\n", + "任务 000004.SZ 完成\n", "任务 000007.SZ 完成\n", "任务 000008.SZ 完成\n", "任务 000009.SZ 完成\n", "任务 000010.SZ 完成\n", - "任务 000011.SZ 完成\n", "任务 000012.SZ 完成\n", + "任务 000011.SZ 完成\n", "任务 000014.SZ 完成\n", "任务 000016.SZ 完成\n", "任务 000017.SZ 完成\n", @@ -103,12 +103,12 @@ "任务 000021.SZ 完成\n", "任务 000025.SZ 完成\n", "任务 000026.SZ 完成\n", - "任务 000027.SZ 完成\n", "任务 000028.SZ 完成\n", - "任务 000029.SZ 完成\n", + "任务 000027.SZ 完成\n", "任务 000030.SZ 完成\n", - "任务 000031.SZ 完成\n", + "任务 000029.SZ 完成\n", "任务 000032.SZ 完成\n", + "任务 000031.SZ 完成\n", "任务 000034.SZ 完成\n", "任务 000035.SZ 完成\n", "任务 000036.SZ 完成\n", @@ -130,12 +130,12 @@ 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300820.SZ 完成\n", - "任务 300821.SZ 完成\n", "任务 300822.SZ 完成\n", + "任务 300821.SZ 完成\n", "任务 300823.SZ 完成\n", "任务 300824.SZ 完成\n", - "任务 300826.SZ 完成\n", "任务 300825.SZ 完成\n", + "任务 300826.SZ 完成\n", "任务 300827.SZ 完成\n", "任务 300828.SZ 完成\n", "任务 300829.SZ 完成\n", "任务 300830.SZ 完成\n", - "任务 300832.SZ 完成\n", "任务 300831.SZ 完成\n", + "任务 300832.SZ 完成\n", "任务 300833.SZ 完成\n", "任务 300834.SZ 完成\n", "任务 300835.SZ 完成\n", @@ -2388,14 +2388,14 @@ "任务 300863.SZ 完成\n", "任务 300864.SZ 完成\n", "任务 300865.SZ 完成\n", - "任务 300866.SZ 完成\n", "任务 300867.SZ 完成\n", + "任务 300866.SZ 完成\n", "任务 300868.SZ 完成\n", "任务 300869.SZ 完成\n", "任务 300870.SZ 完成\n", "任务 300871.SZ 完成\n", - "任务 300872.SZ 完成\n", "任务 300873.SZ 完成\n", + "任务 300872.SZ 完成\n", "任务 300875.SZ 完成\n", "任务 300876.SZ 完成\n", "任务 300877.SZ 完成\n", @@ -2409,8 +2409,8 @@ "任务 300885.SZ 完成\n", "任务 300886.SZ 完成\n", "任务 300887.SZ 完成\n", - "任务 300889.SZ 完成\n", "任务 300888.SZ 完成\n", + "任务 300889.SZ 完成\n", "任务 300890.SZ 完成\n", "任务 300891.SZ 完成\n", "任务 300892.SZ 完成\n", @@ 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"任务 300990.SZ 完成\n", - "任务 300992.SZ 完成\n", "任务 300991.SZ 完成\n", + "任务 300992.SZ 完成\n", "任务 300993.SZ 完成\n", "任务 300994.SZ 完成\n", - "任务 300996.SZ 完成\n", "任务 300995.SZ 完成\n", + "任务 300996.SZ 完成\n", "任务 300997.SZ 完成\n", "任务 300998.SZ 完成\n", "任务 300999.SZ 完成\n", @@ -2520,8 +2520,8 @@ "任务 301002.SZ 完成\n", "任务 301003.SZ 完成\n", "任务 301004.SZ 完成\n", - "任务 301005.SZ 完成\n", "任务 301006.SZ 完成\n", + "任务 301005.SZ 完成\n", "任务 301007.SZ 完成\n", "任务 301008.SZ 完成\n", "任务 301009.SZ 完成\n", @@ -2562,8 +2562,8 @@ "任务 301047.SZ 完成\n", "任务 301048.SZ 完成\n", "任务 301049.SZ 完成\n", - "任务 301051.SZ 完成\n", "任务 301050.SZ 完成\n", + "任务 301051.SZ 完成\n", "任务 301052.SZ 完成\n", "任务 301053.SZ 完成\n", "任务 301055.SZ 完成\n", @@ -2572,33 +2572,33 @@ "任务 301058.SZ 完成\n", "任务 301059.SZ 完成\n", "任务 301060.SZ 完成\n", + "任务 301061.SZ 完成\n", "任务 301062.SZ 完成\n", "任务 301063.SZ 完成\n", - "任务 301061.SZ 完成\n", "任务 301065.SZ 完成\n", "任务 301066.SZ 完成\n", - "任务 301068.SZ 完成\n", "任务 301067.SZ 完成\n", - "任务 301070.SZ 完成\n", + "任务 301068.SZ 完成\n", "任务 301069.SZ 完成\n", - "任务 301072.SZ 完成\n", + "任务 301070.SZ 完成\n", "任务 301071.SZ 完成\n", - "任务 301075.SZ 完成\n", + "任务 301072.SZ 完成\n", "任务 301073.SZ 完成\n", - "任务 301076.SZ 完成\n", + "任务 301075.SZ 完成\n", "任务 301077.SZ 完成\n", + "任务 301076.SZ 完成\n", "任务 301078.SZ 完成\n", "任务 301079.SZ 完成\n", "任务 301080.SZ 完成\n", "任务 301081.SZ 完成\n", "任务 301082.SZ 完成\n", "任务 301083.SZ 完成\n", - "任务 301086.SZ 完成\n", "任务 301085.SZ 完成\n", + "任务 301086.SZ 完成\n", "任务 301087.SZ 完成\n", "任务 301088.SZ 完成\n", - "任务 301089.SZ 完成\n", "任务 301090.SZ 完成\n", + "任务 301089.SZ 完成\n", "任务 301091.SZ 完成\n", "任务 301092.SZ 完成\n", "任务 301093.SZ 完成\n", @@ -2631,12 +2631,12 @@ "任务 301123.SZ 完成\n", "任务 301125.SZ 完成\n", "任务 301126.SZ 完成\n", - "任务 301128.SZ 完成\n", "任务 301127.SZ 完成\n", + "任务 301128.SZ 完成\n", "任务 301129.SZ 完成\n", "任务 301130.SZ 完成\n", - "任务 301132.SZ 完成\n", "任务 301131.SZ 完成\n", + "任务 301132.SZ 完成\n", "任务 301133.SZ 完成\n", "任务 301135.SZ 完成\n", "任务 301136.SZ 完成\n", @@ -2655,31 +2655,31 @@ "任务 301157.SZ 完成\n", "任务 301158.SZ 完成\n", "任务 301159.SZ 完成\n", - "任务 301161.SZ 完成\n", "任务 301160.SZ 完成\n", + "任务 301161.SZ 完成\n", "任务 301162.SZ 完成\n", "任务 301163.SZ 完成\n", "任务 301165.SZ 完成\n", - "任务 301167.SZ 完成\n", "任务 301166.SZ 完成\n", + "任务 301167.SZ 完成\n", "任务 301168.SZ 完成\n", "任务 301169.SZ 完成\n", "任务 301170.SZ 完成\n", - "任务 301171.SZ 完成\n", "任务 301172.SZ 完成\n", + "任务 301171.SZ 完成\n", "任务 301175.SZ 完成\n", - "任务 301177.SZ 完成\n", "任务 301176.SZ 完成\n", "任务 301178.SZ 完成\n", - "任务 301179.SZ 完成\n", + "任务 301177.SZ 完成\n", "任务 301180.SZ 完成\n", + "任务 301179.SZ 完成\n", "任务 301181.SZ 完成\n", "任务 301182.SZ 完成\n", "任务 301183.SZ 完成\n", "任务 301185.SZ 完成\n", "任务 301186.SZ 完成\n", - "任务 301188.SZ 完成\n", "任务 301187.SZ 完成\n", + "任务 301188.SZ 完成\n", "任务 301189.SZ 完成\n", "任务 301190.SZ 完成\n", "任务 301191.SZ 完成\n", @@ -2720,8 +2720,8 @@ "任务 301230.SZ 完成\n", "任务 301231.SZ 完成\n", "任务 301232.SZ 完成\n", - "任务 301234.SZ 完成\n", "任务 301233.SZ 完成\n", + "任务 301234.SZ 完成\n", "任务 301235.SZ 完成\n", "任务 301236.SZ 完成\n", "任务 301237.SZ 完成\n", @@ -2730,12 +2730,12 @@ "任务 301246.SZ 完成\n", "任务 301248.SZ 完成\n", "任务 301251.SZ 完成\n", - "任务 301252.SZ 完成\n", "任务 301255.SZ 完成\n", + "任务 301252.SZ 完成\n", "任务 301256.SZ 完成\n", "任务 301257.SZ 完成\n", - "任务 301258.SZ 完成\n", "任务 301259.SZ 完成\n", + "任务 301258.SZ 完成\n", "任务 301260.SZ 完成\n", "任务 301261.SZ 完成\n", "任务 301262.SZ 完成\n", @@ -2776,8 +2776,8 @@ "任务 301303.SZ 完成\n", "任务 301305.SZ 完成\n", "任务 301306.SZ 完成\n", - "任务 301308.SZ 完成\n", "任务 301307.SZ 完成\n", + "任务 301308.SZ 完成\n", "任务 301309.SZ 完成\n", "任务 301310.SZ 完成\n", "任务 301311.SZ 完成\n", @@ -2788,8 +2788,8 @@ "任务 301316.SZ 完成\n", "任务 301317.SZ 完成\n", "任务 301318.SZ 完成\n", - "任务 301320.SZ 完成\n", "任务 301319.SZ 完成\n", + "任务 301320.SZ 完成\n", "任务 301321.SZ 完成\n", "任务 301322.SZ 完成\n", "任务 301323.SZ 完成\n", @@ -2799,8 +2799,8 @@ "任务 301328.SZ 完成\n", "任务 301329.SZ 完成\n", "任务 301330.SZ 完成\n", - "任务 301331.SZ 完成\n", "任务 301332.SZ 完成\n", + "任务 301331.SZ 完成\n", "任务 301333.SZ 完成\n", "任务 301335.SZ 完成\n", "任务 301336.SZ 完成\n", @@ -2835,8 +2835,8 @@ "任务 301379.SZ 完成\n", "任务 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430510.BJ 完成\n", "任务 430556.BJ 完成\n", + "任务 430510.BJ 完成\n", "任务 430564.BJ 完成\n", "任务 430685.BJ 完成\n", "任务 430718.BJ 完成\n", @@ -2970,16 +2970,16 @@ "任务 600019.SH 完成\n", "任务 600020.SH 完成\n", "任务 600021.SH 完成\n", - "任务 600022.SH 完成\n", "任务 600023.SH 完成\n", + "任务 600022.SH 完成\n", "任务 600025.SH 完成\n", "任务 600026.SH 完成\n", "任务 600027.SH 完成\n", "任务 600028.SH 完成\n", "任务 600029.SH 完成\n", "任务 600030.SH 完成\n", - "任务 600031.SH 完成\n", "任务 600032.SH 完成\n", + "任务 600031.SH 完成\n", "任务 600033.SH 完成\n", "任务 600035.SH 完成\n", "任务 600036.SH 完成\n", @@ -2994,8 +2994,8 @@ "任务 600054.SH 完成\n", "任务 600055.SH 完成\n", "任务 600056.SH 完成\n", - "任务 600057.SH 完成\n", "任务 600058.SH 完成\n", + "任务 600057.SH 完成\n", "任务 600059.SH 完成\n", "任务 600060.SH 完成\n", "任务 600061.SH 完成\n", @@ -3022,14 +3022,14 @@ "任务 600089.SH 完成\n", "任务 600094.SH 完成\n", "任务 600095.SH 完成\n", - "任务 600096.SH 完成\n", "任务 600097.SH 完成\n", - "任务 600098.SH 完成\n", + "任务 600096.SH 完成\n", "任务 600099.SH 完成\n", + "任务 600098.SH 完成\n", "任务 600100.SH 完成\n", "任务 600101.SH 完成\n", - "任务 600103.SH 完成\n", "任务 600104.SH 完成\n", + "任务 600103.SH 完成\n", "任务 600105.SH 完成\n", "任务 600106.SH 完成\n", "任务 600107.SH 完成\n", @@ -3056,8 +3056,8 @@ "任务 600131.SH 完成\n", "任务 600132.SH 完成\n", "任务 600133.SH 完成\n", - "任务 600135.SH 完成\n", "任务 600136.SH 完成\n", + "任务 600135.SH 完成\n", "任务 600137.SH 完成\n", "任务 600138.SH 完成\n", "任务 600141.SH 完成\n", @@ -3139,14 +3139,14 @@ "任务 600235.SH 完成\n", "任务 600236.SH 完成\n", "任务 600237.SH 完成\n", - "任务 600238.SH 完成\n", "任务 600239.SH 完成\n", - "任务 600241.SH 完成\n", + "任务 600238.SH 完成\n", "任务 600243.SH 完成\n", - "任务 600246.SH 完成\n", + "任务 600241.SH 完成\n", "任务 600248.SH 完成\n", - "任务 600249.SH 完成\n", + "任务 600246.SH 完成\n", "任务 600250.SH 完成\n", + "任务 600249.SH 完成\n", "任务 600251.SH 完成\n", "任务 600252.SH 完成\n", "任务 600255.SH 完成\n", @@ -3239,10 +3239,10 @@ "任务 600370.SH 完成\n", "任务 600371.SH 完成\n", "任务 600372.SH 完成\n", - "任务 600375.SH 完成\n", "任务 600373.SH 完成\n", - "任务 600377.SH 完成\n", + "任务 600375.SH 完成\n", "任务 600376.SH 完成\n", + "任务 600377.SH 完成\n", "任务 600378.SH 完成\n", "任务 600379.SH 完成\n", "任务 600380.SH 完成\n", @@ -3282,17 +3282,17 @@ "任务 600429.SH 完成\n", "任务 600433.SH 完成\n", "任务 600435.SH 完成\n", - "任务 600438.SH 完成\n", "任务 600436.SH 完成\n", - "任务 600444.SH 完成\n", "任务 600439.SH 完成\n", - "任务 600448.SH 完成\n", + "任务 600438.SH 完成\n", + "任务 600444.SH 完成\n", "任务 600446.SH 完成\n", + "任务 600448.SH 完成\n", "任务 600449.SH 完成\n", "任务 600452.SH 完成\n", "任务 600455.SH 完成\n", - "任务 600456.SH 完成\n", "任务 600458.SH 完成\n", + "任务 600456.SH 完成\n", "任务 600459.SH 完成\n", "任务 600460.SH 完成\n", "任务 600461.SH 完成\n", @@ -3329,8 +3329,8 @@ "任务 600503.SH 完成\n", "任务 600505.SH 完成\n", "任务 600506.SH 完成\n", - "任务 600507.SH 完成\n", "任务 600508.SH 完成\n", + "任务 600507.SH 完成\n", "任务 600509.SH 完成\n", "任务 600510.SH 完成\n", "任务 600511.SH 完成\n", @@ -3352,8 +3352,8 @@ "任务 600529.SH 完成\n", "任务 600530.SH 完成\n", "任务 600531.SH 完成\n", - "任务 600535.SH 完成\n", "任务 600533.SH 完成\n", + "任务 600535.SH 完成\n", "任务 600536.SH 完成\n", "任务 600537.SH 完成\n", "任务 600538.SH 完成\n", @@ -3364,18 +3364,18 @@ 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600766.SH 完成\n", "任务 600767.SH 完成\n", "任务 600772.SH 完成\n", "任务 600781.SH 完成\n", @@ -5739,20 +5739,20 @@ "任务 600788.SH 完成\n", "任务 600799.SH 完成\n", "任务 600806.SH 完成\n", - "任务 600813.SH 完成\n", "任务 600823.SH 完成\n", - "任务 600832.SH 完成\n", + "任务 600813.SH 完成\n", "任务 600836.SH 完成\n", + "任务 600832.SH 完成\n", "任务 600840.SH 完成\n", "任务 600842.SH 完成\n", "任务 600852.SH 完成\n", "任务 600856.SH 完成\n", "任务 600870.SH 完成\n", "任务 600878.SH 完成\n", - "任务 600890.SH 完成\n", "任务 600891.SH 完成\n", - "任务 600899.SH 完成\n", + "任务 600890.SH 完成\n", "任务 600896.SH 完成\n", + "任务 600899.SH 完成\n", "任务 600978.SH 完成\n", "任务 600991.SH 完成\n", "任务 601258.SH 完成\n", @@ -5761,8 +5761,8 @@ "任务 601558.SH 完成\n", "任务 603133.SH 完成\n", "任务 603157.SH 完成\n", - "任务 603555.SH 完成\n", "任务 603603.SH 完成\n", + "任务 603555.SH 完成\n", "任务 603996.SH 完成\n", "任务 688086.SH 完成\n", "任务 688555.SH 完成\n", @@ -5836,32 +5836,32 @@ "output_type": "stream", "text": [ " ts_code trade_date open high low close pre_close \\\n", - "0 000001.SZ 20250506 1395.40 1405.63 1391.57 1400.51 1394.12 \n", - "1 000002.SZ 20250506 1233.77 1242.86 1231.95 1241.04 1237.40 \n", - "2 000004.SZ 20250506 30.76 30.76 30.76 30.76 32.39 \n", - "3 000006.SZ 20250506 251.16 256.72 251.16 256.72 252.35 \n", - "4 000007.SZ 20250506 55.92 57.49 55.42 56.50 55.67 \n", + "0 000002.SZ 20250507 1277.38 1282.83 1250.12 1253.76 1241.04 \n", + "1 000001.SZ 20250507 1406.90 1410.74 1401.79 1409.46 1400.51 \n", + "2 000006.SZ 20250507 260.30 264.67 257.91 260.69 256.72 \n", + "3 000004.SZ 20250507 29.22 32.11 29.22 31.41 30.76 \n", + "4 000007.SZ 20250507 57.49 58.32 55.75 56.17 56.50 \n", "... ... ... ... ... ... ... ... \n", - "5350 920116.BJ 20250506 105.54 108.67 104.18 108.67 104.03 \n", - "5351 920111.BJ 20250506 27.88 28.89 27.88 28.87 27.74 \n", - "5352 920118.BJ 20250506 26.65 27.50 26.33 27.46 26.44 \n", - "5353 920128.BJ 20250506 31.72 31.72 30.46 31.40 30.43 \n", - "5354 689009.SH 20250506 63.53 64.94 62.36 64.49 63.19 \n", + "5336 873706.BJ 20250507 23.44 23.50 22.24 22.66 23.41 \n", + "5337 873726.BJ 20250507 32.22 32.85 30.64 31.42 33.24 \n", + "5338 873806.BJ 20250507 15.68 16.65 15.39 15.68 15.44 \n", + "5339 873833.BJ 20250507 25.47 26.57 24.55 25.13 25.75 \n", + "5340 689009.SH 20250507 65.15 66.08 61.74 63.12 64.49 \n", "\n", - " change pct_chg vol amount \n", - "0 6.39 0.46 854671.27 935212.114 \n", - "1 3.64 0.29 721363.73 491928.533 \n", - "2 -1.63 -5.03 9181.00 6950.017 \n", - "3 4.37 1.73 239431.89 153531.506 \n", - "4 0.83 1.49 106041.66 72322.522 \n", - "... ... ... ... ... \n", - "5350 4.64 4.46 39848.66 304340.713 \n", - "5351 1.13 4.07 23637.81 67284.097 \n", - "5352 1.02 3.86 9807.68 26421.193 \n", - "5353 0.97 3.19 13050.28 40500.102 \n", - "5354 1.30 2.06 78559.76 497655.377 \n", + " change pct_chg vol amount \n", + "0 12.72 1.02 1354623.92 942344.427 \n", + "1 8.95 0.64 1126270.01 1240098.147 \n", + "2 3.97 1.55 318051.41 209069.577 \n", + "3 0.65 2.11 291124.98 212008.744 \n", + "4 -0.33 -0.58 89759.76 61497.633 \n", + "... ... ... ... ... \n", + "5336 -0.75 -3.20 59911.85 120264.684 \n", + "5337 -1.82 -5.48 62793.43 190415.775 \n", + "5338 0.24 1.55 98266.88 154524.253 \n", + "5339 -0.62 -2.41 79927.42 180493.607 \n", + "5340 -1.37 -2.12 78871.31 499935.095 \n", "\n", - "[5355 rows x 11 columns]\n" + "[5341 rows x 11 columns]\n" ] } ], diff --git a/main/data/update/update_money_flow.ipynb b/main/data/update/update_money_flow.ipynb index 47a0781..47a7867 100644 --- a/main/data/update/update_money_flow.ipynb +++ b/main/data/update/update_money_flow.ipynb @@ -34,17 +34,17 @@ "output_type": "stream", "text": [ "\n", - "Index: 8435700 entries, 0 to 40956\n", + "Index: 8440821 entries, 0 to 5120\n", "Data columns (total 2 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object\n", " 1 trade_date object\n", "dtypes: object(2)\n", - "memory usage: 193.1+ MB\n", + "memory usage: 193.2+ MB\n", "None\n", - "20250430\n", - "start_date: 20250506\n" + "20250506\n", + "start_date: 20250507\n" ] } ], @@ -84,18 +84,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "任务 20250717 完成\n", "任务 20250718 完成\n", - "任务 20250715 完成\n", + "任务 20250717 完成\n", "任务 20250716 完成\n", + "任务 20250715 完成\n", "任务 20250714 完成\n", "任务 20250711 完成\n", - "任务 20250710 完成\n", "任务 20250709 完成\n", + "任务 20250710 完成\n", "任务 20250708 完成\n", "任务 20250707 完成\n", - "任务 20250704 完成\n", "任务 20250703 完成\n", + "任务 20250704 完成\n", "任务 20250702 完成\n", "任务 20250701 完成\n", "任务 20250630 完成\n", @@ -106,8 +106,8 @@ "任务 20250623 完成\n", "任务 20250620 完成\n", "任务 20250619 完成\n", - "任务 20250618 完成\n", "任务 20250617 完成\n", + "任务 20250618 完成\n", "任务 20250616 完成\n", "任务 20250613 完成\n", "任务 20250612 完成\n", @@ -120,14 +120,14 @@ "任务 20250603 完成\n", "任务 20250530 完成\n", "任务 20250529 完成\n", - "任务 20250527 完成\n", "任务 20250528 完成\n", - "任务 20250523 完成\n", + "任务 20250527 完成\n", "任务 20250526 完成\n", - "任务 20250521 完成\n", + "任务 20250523 完成\n", "任务 20250522 完成\n", - "任务 20250520 完成\n", + "任务 20250521 完成\n", "任务 20250519 完成\n", + "任务 20250520 完成\n", "任务 20250516 完成\n", "任务 20250515 完成\n", "任务 20250514 完成\n", @@ -135,8 +135,7 @@ "任务 20250512 完成\n", "任务 20250509 完成\n", "任务 20250508 完成\n", - "任务 20250507 完成\n", - "任务 20250506 完成\n" + "任务 20250507 完成\n" ] } ], diff --git a/main/data/update/update_stk_limit.ipynb b/main/data/update/update_stk_limit.ipynb index b2a6ca8..ff92d04 100644 --- a/main/data/update/update_stk_limit.ipynb +++ b/main/data/update/update_stk_limit.ipynb @@ -97,14 +97,14 @@ "任务 20250716 完成\n", "任务 20250714 完成\n", "任务 20250711 完成\n", - "任务 20250709 完成\n", "任务 20250710 完成\n", + "任务 20250709 完成\n", "任务 20250708 完成\n", "任务 20250707 完成\n", - "任务 20250703 完成\n", "任务 20250704 完成\n", - "任务 20250701 完成\n", + "任务 20250703 完成\n", "任务 20250702 完成\n", + "任务 20250701 完成\n", "任务 20250630 完成\n", "任务 20250627 完成\n", "任务 20250626 完成\n", @@ -123,10 +123,10 @@ "任务 20250609 完成\n", "任务 20250606 完成\n", "任务 20250605 完成\n", - "任务 20250604 完成\n", "任务 20250603 完成\n", - "任务 20250530 完成\n", + "任务 20250604 完成\n", "任务 20250529 完成\n", + "任务 20250530 完成\n", "任务 20250528 完成\n", "任务 20250527 完成\n", "任务 20250526 完成\n", @@ -193,21 +193,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n" - ] - }, - { - "ename": "ValueError", - "evalue": "No objects to concatenate", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[4], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(all_daily_data)\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m all_daily_data_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(all_daily_data, ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[0;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m _Concatenator(\n\u001b[0;32m 383\u001b[0m objs,\n\u001b[0;32m 384\u001b[0m axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m 385\u001b[0m ignore_index\u001b[38;5;241m=\u001b[39mignore_index,\n\u001b[0;32m 386\u001b[0m join\u001b[38;5;241m=\u001b[39mjoin,\n\u001b[0;32m 387\u001b[0m keys\u001b[38;5;241m=\u001b[39mkeys,\n\u001b[0;32m 388\u001b[0m levels\u001b[38;5;241m=\u001b[39mlevels,\n\u001b[0;32m 389\u001b[0m names\u001b[38;5;241m=\u001b[39mnames,\n\u001b[0;32m 390\u001b[0m verify_integrity\u001b[38;5;241m=\u001b[39mverify_integrity,\n\u001b[0;32m 391\u001b[0m copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m 392\u001b[0m sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m 393\u001b[0m )\n\u001b[0;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", - "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[0;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[1;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clean_keys_and_objs(objs, keys)\n\u001b[0;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[0;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", - "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[1;34m(self, objs, keys)\u001b[0m\n\u001b[0;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[0;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", - "\u001b[1;31mValueError\u001b[0m: No objects to concatenate" + "[ trade_date ts_code up_limit down_limit\n", + "0 20250507 000001.SZ 12.06 9.86\n", + "1 20250507 000002.SZ 7.51 6.15\n", + "2 20250507 000004.SZ 7.95 7.19\n", + "3 20250507 000006.SZ 7.11 5.81\n", + "4 20250507 000007.SZ 7.50 6.14\n", + "... ... ... ... ...\n", + "7107 20250507 920445.BJ 13.42 7.24\n", + "7108 20250507 920489.BJ 31.69 17.07\n", + "7109 20250507 920682.BJ 16.41 8.85\n", + "7110 20250507 920799.BJ 78.58 42.32\n", + "7111 20250507 920819.BJ 5.82 3.14\n", + "\n", + "[7112 rows x 4 columns]]\n" ] } ], @@ -219,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ad9733a1-2f42-43ee-a98c-0bf699304c21", "metadata": { "ExecuteTime": { diff --git a/main/test.txt b/main/test.txt new file mode 100644 index 0000000..db117dc --- /dev/null +++ b/main/test.txt @@ -0,0 +1,27 @@ + ts_code trade_date log_circ_mv +0 600306.SH 2020-01-02 11.552040 +1 603269.SH 2020-01-02 11.324801 +2 002633.SZ 2020-01-02 11.759023 +3 603991.SH 2020-01-02 11.181150 +4 000691.SZ 2020-01-02 11.677910 +... ... ... ... +36395 600615.SH 2022-12-30 12.027909 +36396 603829.SH 2022-12-30 12.034572 +36397 603037.SH 2022-12-30 12.035767 +36398 002767.SZ 2022-12-30 11.896427 +36399 600561.SH 2022-12-30 11.858571 + + +train data size: 36400 + ts_code trade_date log_circ_mv +0 600306.SH 2020-01-02 11.552040 +1 603269.SH 2020-01-02 11.324801 +2 002633.SZ 2020-01-02 11.759023 +3 603991.SH 2020-01-02 11.181150 +4 000691.SZ 2020-01-02 11.677910 +... ... ... ... +36395 600615.SH 2022-12-30 12.027909 +36396 603829.SH 2022-12-30 12.034572 +36397 603037.SH 2022-12-30 12.035767 +36398 002767.SZ 2022-12-30 11.896427 +36399 600561.SH 2022-12-30 11.858571 \ No newline at end of file diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb index ebde1f1..cfc562f 100644 --- a/main/train/Classify2.ipynb +++ b/main/train/Classify2.ipynb @@ -58,13 +58,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "daily data\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "daily data\n", "daily basic\n", "inner merge on ['ts_code', 'trade_date']\n", "stk limit\n", @@ -74,7 +68,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8595791 entries, 0 to 8595790\n", + "RangeIndex: 8601132 entries, 0 to 8601131\n", "Data columns (total 32 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -561,7 +555,7 @@ "Calculating cs_rank_size...\n", "Finished cs_rank_size.\n", "\n", - "Index: 4502216 entries, 0 to 4502215\n", + "Index: 4503567 entries, 0 to 4503566\n", "Columns: 177 entries, ts_code to cs_rank_size\n", "dtypes: bool(10), datetime64[ns](1), float64(161), int32(3), object(2)\n", "memory usage: 5.6+ GB\n", @@ -1228,7 +1222,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "id": "47c12bb34062ae7a", "metadata": { "ExecuteTime": { @@ -1238,7 +1232,7 @@ }, "outputs": [], "source": [ - "days = 2\n", + "days = 5\n", "validation_days = 120\n", "\n", "import gc\n", @@ -1262,7 +1256,45 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, + "id": "29221dde", + "metadata": {}, + "outputs": [], + "source": [ + "feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n", + "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", + " 'ts_code',\n", + " 'label']]\n", + "feature_columns = [col for col in feature_columns if 'future' not in col]\n", + "feature_columns = [col for col in feature_columns if 'label' not in col]\n", + "feature_columns = [col for col in feature_columns if 'score' not in col]\n", + "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", + "feature_columns = [col for col in feature_columns if 'is_st' not in col]\n", + "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n", + "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n", + "feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n", + "feature_columns = [col for col in feature_columns if 'code' not in col]\n", + "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", + "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", + "feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n", + " 'cap_neutral_cost_metric', \n", + " 'hurst_net_mf_vol_60', \n", + " 'complex_factor_deap_1', \n", + " 'lg_buy_consolidation_20',\n", + " 'cs_rank_ind_cap_neutral_pe',\n", + " 'cs_rank_opening_gap',\n", + " 'cs_rank_ind_adj_lg_flow']]\n", + "\n", + "# df = fill_nan_with_daily_median(df, feature_columns)\n", + "for feature_col in [col for col in feature_columns if col in df.columns]:\n", + " median_val = df[feature_col].median()\n", + " df[feature_col].fillna(0, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "id": "b76ea08a", "metadata": {}, "outputs": [ @@ -1283,7 +1315,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 130/130 [00:30<00:00, 4.28it/s]\n" + "MAD Filtering: 100%|██████████| 130/130 [00:28<00:00, 4.62it/s]\n" ] }, { @@ -1298,7 +1330,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 130/130 [00:23<00:00, 5.44it/s]\n" + "MAD Filtering: 100%|██████████| 130/130 [00:23<00:00, 5.55it/s]\n" ] }, { @@ -1338,13 +1370,13 @@ "截面 MAD 去极值处理完成。\n", "feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', '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', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n", "df最小日期: 2019-01-02\n", - "df最大日期: 2025-05-06\n", - "2058185\n", + "df最大日期: 2025-05-07\n", + "2057678\n", "train_data最小日期: 2020-01-02\n", "train_data最大日期: 2022-12-30\n", - "1730349\n", + "1730630\n", "test_data最小日期: 2023-01-03\n", - "test_data最大日期: 2025-05-06\n", + "test_data最大日期: 2025-05-07\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", @@ -1374,7 +1406,7 @@ "\n", "# feature_columns = [\n", "# 'undist_profit_ps', \n", - "# 'AR_BR', \n", + "# 'AR_BR',\n", "# 'pe_ttm',\n", "# 'alpha_22_improved', \n", "# 'alpha_003', \n", @@ -1422,38 +1454,13 @@ "# 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", "\n", - "feature_columns = [col for col in test_data.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n", - "feature_columns = [col for col in feature_columns if col not in ['trade_date',\n", - " 'ts_code',\n", - " 'label']]\n", - "feature_columns = [col for col in feature_columns if 'future' not in col]\n", - "feature_columns = [col for col in feature_columns if 'label' not in col]\n", - "feature_columns = [col for col in feature_columns if 'score' not in col]\n", - "feature_columns = [col for col in feature_columns if 'gen' not in col]\n", - "feature_columns = [col for col in feature_columns if 'is_st' not in col]\n", - "feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n", - "# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n", - "feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n", - "feature_columns = [col for col in feature_columns if 'code' not in col]\n", - "feature_columns = [col for col in feature_columns if col not in origin_columns]\n", - "feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", - "# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n", - "feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n", - " 'cap_neutral_cost_metric', \n", - " 'hurst_net_mf_vol_60', \n", - " 'complex_factor_deap_1', \n", - " 'lg_buy_consolidation_20',\n", - " 'cs_rank_ind_cap_neutral_pe',\n", - " 'cs_rank_opening_gap',\n", - " 'cs_rank_ind_adj_lg_flow']]\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", "# 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", + "# 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=[col for col in feature_columns if col in train_data.columns])\n", "train_data = train_data.dropna(subset=['label'])\n", @@ -1504,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "id": "e23d1759", "metadata": {}, "outputs": [], @@ -1561,7 +1568,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { @@ -1591,7 +1598,7 @@ " target_column='label'): # 增加目标列参数\n", "\n", " print('train data size: ', len(train_data_df))\n", - "\n", + " print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n", " # 确保数据按时间排序\n", " train_data_df = train_data_df.sort_values(by='trade_date')\n", "\n", @@ -1605,8 +1612,6 @@ " 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", @@ -1621,6 +1626,7 @@ " X_val = val_data_split[feature_columns]\n", " y_val = val_data_split['label']\n", "\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", @@ -1638,15 +1644,15 @@ "\n", " params = {\n", " 'loss_function': 'Logloss', # 适用于二分类\n", - " 'eval_metric': 'Precision', # 评估指标\n", - " 'iterations': 500,\n", + " 'eval_metric': 'Logloss', # 评估指标\n", + " 'iterations': 1500,\n", " 'learning_rate': 0.01,\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': 300,\n", - " # 'one_hot_max_size': 50,\n", - " # 'class_weights': [0.6, 1.2]\n", + " 'l2_leaf_reg': 5, # L2 正则化\n", + " 'verbose': 5000,\n", + " 'early_stopping_rounds': 3000,\n", + " 'one_hot_max_size': 50,\n", + " 'class_weights': [0.6, 1.2],\n", " 'task_type': 'GPU',\n", " 'has_time': True,\n", " 'random_seed': 7\n", @@ -1658,7 +1664,10 @@ "\n", " model = CatBoostClassifier(**params)\n", " model.fit(train_pool,\n", - " eval_set=val_pool, plot=True, use_best_model=True)\n", + " eval_set=val_pool, \n", + " plot=True, \n", + " use_best_model=True\n", + " )\n", "\n", "\n", " return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象" @@ -1666,7 +1675,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 18, "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "metadata": { "ExecuteTime": { @@ -1680,6 +1689,20 @@ "output_type": "stream", "text": [ "train data size: 36400\n", + " ts_code trade_date log_circ_mv\n", + "0 600306.SH 2020-01-02 11.552040\n", + "1 603269.SH 2020-01-02 11.324801\n", + "2 002633.SZ 2020-01-02 11.759023\n", + "3 603991.SH 2020-01-02 11.181150\n", + "4 000691.SZ 2020-01-02 11.677910\n", + "... ... ... ...\n", + "36395 600615.SH 2022-12-30 12.027909\n", + "36396 603829.SH 2022-12-30 12.034572\n", + "36397 603037.SH 2022-12-30 12.035767\n", + "36398 002767.SZ 2022-12-30 11.896427\n", + "36399 600561.SH 2022-12-30 11.858571\n", + "\n", + "[36400 rows x 3 columns]\n", "原始样本数: 36400, 去除标签为空后样本数: 36400\n", "cat_features: [27, 30, 37, 39, 41, 80, 86, 87, 88, 100, 102, 141]\n" ] @@ -1687,7 +1710,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "66258c1f2f874fd0bfd05d169b09cc51", + "model_id": "73e4fa876f004bafb847cea54620f732", "version_major": 2, "version_minor": 0 }, @@ -1702,11 +1725,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.6593407\ttest: 0.2558140\tbest: 0.2558140 (0)\ttotal: 118ms\tremaining: 58.9s\n", - "499:\tlearn: 0.9223881\ttest: 0.3881579\tbest: 0.4090909 (449)\ttotal: 43.8s\tremaining: 0us\n", - "bestTest = 0.4090909091\n", - "bestIteration = 449\n", - "Shrink model to first 450 iterations.\n" + "0:\tlearn: 0.6886803\ttest: 0.6892921\tbest: 0.6892921 (0)\ttotal: 141ms\tremaining: 3m 32s\n", + "1499:\tlearn: 0.3359066\ttest: 0.5174742\tbest: 0.5163721 (873)\ttotal: 1m 51s\tremaining: 0us\n", + "bestTest = 0.5163720682\n", + "bestIteration = 873\n", + "Shrink model to first 874 iterations.\n" ] } ], @@ -1727,7 +1750,66 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 19, + "id": "ec189398", + "metadata": {}, + "outputs": [], + "source": [ + "# if True:\n", + "# train_data_df = train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv'))\n", + "# # 识别数值型特征列\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=['label'])\n", + "\n", + "\n", + "# # 提取特征和标签，只取数值型特征用于线性回归\n", + " \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", + "# val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n", + " \n", + "\n", + "# score_df = val_data_split\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.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", + "# score_df = score_df.merge(index_data, on='trade_date', how='left')\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", + "# print(score_df.columns.tolist())\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", + "# import pandas as pd\n", + "# from sklearn.metrics import accuracy_score\n", + "\n", + "# # 假设 df 是你的 DataFrame\n", + "# # df = pd.read_csv('your_data.csv')\n", + "\n", + "# # 将预测分数转换为类别预测（例如：0.5 为阈值）\n", + "# save_df['pred'] = (save_df['score'] >= 0.5).astype(int)\n", + "\n", + "# # 计算准确率\n", + "# acc = accuracy_score(save_df['label'], save_df['pred'])\n", + "\n", + "# print(f\"准确率为：{acc:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "5d1522a7538db91b", "metadata": { "ExecuteTime": { @@ -1744,9 +1826,9 @@ "# 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 = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(500, 'total_mv'))\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.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n", "score_df = score_df.merge(index_data, on='trade_date', how='left')\n", "# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n", @@ -1763,12 +1845,13 @@ ").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 = save_df.sort_values(['trade_date', 'score'])\n", "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 32, "id": "09b1799e", "metadata": {}, "outputs": [ @@ -1788,7 +1871,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "id": "7e9023cc", "metadata": {}, "outputs": [], @@ -1988,7 +2071,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "id": "a0000d75", "metadata": {}, "outputs": [ @@ -1998,7 +2081,7 @@ "text": [ "开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n", "准备数据，处理 NaN 值...\n", - "原始数据 173284 行，移除 NaN 后剩余 172527 行用于分析。\n", + "原始数据 28200 行，移除 NaN 后剩余 27807 行用于分析。\n", "对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n", "按二维分箱分组计算 Spearman Rank IC...\n", "整理结果用于绘图...\n", @@ -2037,7 +2120,7 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -2233,6 +2316,26 @@ "except Exception as e:\n", " print(f\"发生未知错误: {e}\")" ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "a436dba4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty DataFrame\n", + "Columns: [ts_code, trade_date, is_st]\n", + "Index: []\n" + ] + } + ], + "source": [ + "print(df[(df['ts_code'] == '600242.SH') & (df['trade_date'] >= '2023-06-01')][['ts_code', 'trade_date', 'is_st']])" + ] } ], "metadata": { diff --git a/main/train/RollingRank.ipynb b/main/train/RollingRank.ipynb index 7a15122..644c477 100644 --- a/main/train/RollingRank.ipynb +++ b/main/train/RollingRank.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "id": "79a7758178bafdd3", "metadata": { "ExecuteTime": { @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { @@ -68,8 +68,8 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 5123740 entries, 0 to 5123739\n", - "Data columns (total 31 columns):\n", + "RangeIndex: 5209903 entries, 0 to 5209902\n", + "Data columns (total 32 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", " 0 ts_code object \n", @@ -83,27 +83,28 @@ " 8 turnover_rate float64 \n", " 9 pe_ttm float64 \n", " 10 circ_mv float64 \n", - " 11 volume_ratio float64 \n", - " 12 is_st bool \n", - " 13 up_limit float64 \n", - " 14 down_limit float64 \n", - " 15 buy_sm_vol float64 \n", - " 16 sell_sm_vol float64 \n", - " 17 buy_lg_vol float64 \n", - " 18 sell_lg_vol float64 \n", - " 19 buy_elg_vol float64 \n", - " 20 sell_elg_vol float64 \n", - " 21 net_mf_vol float64 \n", - " 22 his_low float64 \n", - " 23 his_high float64 \n", - " 24 cost_5pct float64 \n", - " 25 cost_15pct float64 \n", - " 26 cost_50pct float64 \n", - " 27 cost_85pct float64 \n", - " 28 cost_95pct float64 \n", - " 29 weight_avg float64 \n", - " 30 winner_rate float64 \n", - "dtypes: bool(1), datetime64[ns](1), float64(28), object(1)\n", + " 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: 1.2+ GB\n", "None\n" ] @@ -120,7 +121,7 @@ "\n", "print('daily basic')\n", "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n", - " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", + " columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n", " 'is_st'], df=df, join='inner')\n", "df = df[df['trade_date'] >= '2021-01-01']\n", "\n", @@ -144,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "id": "cac01788dac10678", "metadata": { "ExecuteTime": { @@ -226,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { @@ -318,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "id": "a735bc02ceb4d872", "metadata": { "ExecuteTime": { @@ -795,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "id": "53f86ddc0677a6d7", "metadata": { "ExecuteTime": { @@ -854,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { @@ -867,7 +868,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['ts_code', 'open', 'close', 'high', 'low', 'circ_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'l2_code', 'in_date']\n" + "['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', 'l2_code', 'in_date']\n" ] } ], @@ -882,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 28, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { @@ -896,21 +897,22 @@ "output_type": "stream", "text": [ "Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n", - " 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n", - " 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol',\n", - " 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol',\n", - " 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n", - " 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate',\n", - " 'l1_code', 'l2_code', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n", - " 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n", - " 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n", - " 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n", - " 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n", - " 'cost_support_15pct_change', 'cat_winner_price_zone',\n", - " 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n", - " '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n", - " '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n", - " 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate',\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', 'l1_code', 'l2_code', '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", @@ -957,10 +959,10 @@ "Error: 'hurst' library not installed. Cannot calculate factor.\n", "Finished hurst_net_mf_vol_60 (Placeholder).\n", "\n", - "Index: 3148690 entries, 0 to 3148689\n", - "Columns: 157 entries, ts_code to hurst_net_mf_vol_60\n", - "dtypes: bool(12), datetime64[ns](1), float64(137), int32(3), int64(1), object(3)\n", - "memory usage: 3.4+ GB\n", + "Index: 3166509 entries, 0 to 3166508\n", + "Columns: 158 entries, ts_code to hurst_net_mf_vol_60\n", + "dtypes: bool(12), datetime64[ns](1), float64(138), int32(3), int64(1), object(3)\n", + "memory usage: 3.5+ GB\n", "None\n" ] } @@ -1045,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "id": "f4f16d63ad18d1bc", "metadata": { "ExecuteTime": { @@ -1097,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 30, "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { @@ -1392,7 +1394,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 31, "id": "da2bb202843d9275", "metadata": { "ExecuteTime": { @@ -1501,7 +1503,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 32, "id": "ff19e3f1e051a489", "metadata": { "ExecuteTime": { @@ -1545,7 +1547,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 33, "id": "27dba27b2e108316", "metadata": { "ExecuteTime": { @@ -1558,7 +1560,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-04-09 00:00:00\n" + "2025-05-07 00:00:00\n" ] } ], @@ -1632,7 +1634,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 34, "id": "ca96fb81e17c4a90", "metadata": { "ExecuteTime": { @@ -1689,7 +1691,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 35, "id": "6746b3d1", "metadata": {}, "outputs": [ @@ -1699,130 +1701,130 @@ "text": [ "特征列分析:\n", "特征: vol 最大值: 56161348.41 最小值: 412.4 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: pct_chg 最大值: 11.94 最小值: -10.26 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: pct_chg 最大值: 10.64 最小值: -10.26 NaN 数量: 0 NaN 占比: 0.0\n", "特征: turnover_rate 最大值: 86.2547 最小值: 0.0238 NaN 数量: 0 NaN 占比: 0.0\n", "特征: volume_ratio 最大值: 147.72 最小值: 0.01 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: winner_rate 最大值: 100.59 最小值: 0.0 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: lg_elg_net_buy_vol 最大值: 6033873.0 最小值: -9918925.0 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: flow_lg_elg_intensity 最大值: 1.0001389979829125 最小值: -0.9999999999973489 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: sm_net_buy_vol 最大值: 6829932.0 最小值: -3215196.0 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: total_buy_vol 最大值: 39152290.0 最小值: 1.0 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: lg_elg_buy_prop 最大值: 0.999999999999989 最小值: 0.0 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: flow_struct_buy_change 最大值: 0.9999999999996841 最小值: -0.9999999999988995 NaN 数量: 8 NaN 占比: 6.759611322348965e-05\n", - "特征: lg_elg_net_buy_vol_change 最大值: 8937693.0 最小值: -15389140.0 NaN 数量: 8 NaN 占比: 6.759611322348965e-05\n", - "特征: flow_lg_elg_accel 最大值: 23486299.0 最小值: -20413545.0 NaN 数量: 12 NaN 占比: 0.00010139416983523447\n", - "特征: chip_concentration_range 最大值: 3.999999946666667 最小值: 1.8163778247505023e-05 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: chip_skewness 最大值: 2.549999872500006 最小值: -0.24642857134856214 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: floating_chip_proxy 最大值: 99.9998754669836 最小值: 0.0 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: cost_support_15pct_change 最大值: 414.28571428571433 最小值: -34.48275862068966 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: flow_chip_consistency 最大值: 202833.0 最小值: -366952.0 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: profit_taking_vs_absorb 最大值: 6033873.0 最小值: -9918925.0 NaN 数量: 4 NaN 占比: 3.3798056611744823e-05\n", - "特征: upside_vol 最大值: 764.4778008549366 最小值: 0.0165334933205142 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", - "特征: downside_vol 最大值: 33.318906704752486 最小值: 0.0 NaN 数量: 344 NaN 占比: 0.002906632868610055\n", + "特征: winner_rate 最大值: 100.59 最小值: 0.0 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: lg_elg_net_buy_vol 最大值: 6033873.0 最小值: -9918925.0 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: flow_lg_elg_intensity 最大值: 1.0001389979829125 最小值: -0.8916193146876132 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: sm_net_buy_vol 最大值: 6829932.0 最小值: -3215196.0 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: total_buy_vol 最大值: 39152290.0 最小值: 5.0 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: lg_elg_buy_prop 最大值: 0.999999999999989 最小值: 0.0 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: flow_struct_buy_change 最大值: 0.9999999999996841 最小值: -0.9999999999988995 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: lg_elg_net_buy_vol_change 最大值: 8937693.0 最小值: -15389140.0 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: flow_lg_elg_accel 最大值: 23486299.0 最小值: -20413545.0 NaN 数量: 10 NaN 占比: 8.271298593879239e-05\n", + "特征: chip_concentration_range 最大值: 2.528409090460155 最小值: 1.8163778247505023e-05 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: chip_skewness 最大值: 0.5899999941000001 最小值: -0.24642857134856214 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: floating_chip_proxy 最大值: 99.9998754669836 最小值: 0.0 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: cost_support_15pct_change 最大值: 362.06896551724145 最小值: -34.48275862068966 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: flow_chip_consistency 最大值: 202833.0 最小值: -366952.0 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: profit_taking_vs_absorb 最大值: 6033873.0 最小值: -9918925.0 NaN 数量: 4 NaN 占比: 3.3085194375516956e-05\n", + "特征: upside_vol 最大值: 764.4778008549366 最小值: 0.0165334933205142 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", + "特征: downside_vol 最大值: 13.530263354653558 最小值: 0.0 NaN 数量: 349 NaN 占比: 0.0028866832092638546\n", "特征: vol_ratio 最大值: 2247.2826795128544 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", "特征: return_skew 最大值: 2.236072323764041 最小值: -2.2360835630243616 NaN 数量: 0 NaN 占比: 0.0\n", "特征: return_kurtosis 最大值: 5.176755179819847 最小值: -3.3969791567236354 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: volume_change_rate 最大值: 3.6858958043807935 最小值: -0.9853275631981724 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", + "特征: volume_change_rate 最大值: 3.6858958043807935 最小值: -0.9853275631981724 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", "特征: turnover_deviation 最大值: 1.1547005383792526 最小值: -1.1547005383793087 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: avg_volume_ratio 最大值: 51.17666666666667 最小值: 0.016666666666666077 NaN 数量: 117 NaN 占比: 0.0009885931558935362\n", - "特征: vol_spike 最大值: 17304365.832 最小值: 2045.5939999999996 NaN 数量: 452 NaN 占比: 0.003819180397127165\n", - "特征: vol_std_5 最大值: 195.41495999867846 最小值: 0.02236335411041056 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: atr_14 最大值: 2724.9261989170695 最小值: 0.03221843258883346 NaN 数量: 354 NaN 占比: 0.002991128010139417\n", - "特征: atr_6 最大值: 3297.064666161383 最小值: 0.047258943138373116 NaN 数量: 67 NaN 占比: 0.0005661174482467259\n", + "特征: avg_volume_ratio 最大值: 51.17666666666667 最小值: 0.016666666666666077 NaN 数量: 117 NaN 占比: 0.000967741935483871\n", + "特征: vol_spike 最大值: 17304365.832 最小值: 2255.2485 NaN 数量: 468 NaN 占比: 0.003870967741935484\n", + "特征: vol_std_5 最大值: 195.41495999867848 最小值: 0.014991524185255834 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: atr_14 最大值: 2724.9261989170695 最小值: 0.03221843258883346 NaN 数量: 370 NaN 占比: 0.0030603804797353184\n", + "特征: atr_6 最大值: 3297.064666161383 最小值: 0.047258943138373116 NaN 数量: 71 NaN 占比: 0.000587262200165426\n", "特征: obv 最大值: 331434005.2600001 最小值: -65316063.65999998 NaN 数量: 0 NaN 占比: 0.0\n", "特征: maobv_6 最大值: 301175240.9416665 最小值: -63824730.77333332 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: rsi_3 最大值: 100.0 最小值: 3.3647855304150056 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: return_5 最大值: 5.2441983122362865 最小值: -0.02432244614315504 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: return_20 最大值: 5.826058201058201 最小值: -0.9763608087091757 NaN 数量: 470 NaN 占比: 0.003971271651880017\n", - "特征: std_return_5 最大值: 2.0453098693768377 最小值: 2.3779477130116365e-05 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: std_return_90 最大值: 0.48241224100991925 最小值: 0.006789942003203817 NaN 数量: 1722 NaN 占比: 0.014550063371356147\n", - "特征: std_return_90_2 最大值: 0.4824625874734999 最小值: 0.005959840082236695 NaN 数量: 1892 NaN 占比: 0.015986480777355302\n", - "特征: act_factor1 最大值: 1.7923765799991518 最小值: -1.7515748399531699 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: act_factor2 最大值: 2.22641899181911 最小值: -2.1386570901417015 NaN 数量: 329 NaN 占比: 0.002779890156316012\n", - "特征: act_factor3 最大值: 4.216399393047003 最小值: -3.977208595014822 NaN 数量: 470 NaN 占比: 0.003971271651880017\n", - "特征: act_factor4 最大值: 8.557135781567926 最小值: -7.2482094185227 NaN 数量: 1075 NaN 占比: 0.009083227714406422\n", - "特征: rank_act_factor1 最大值: 1.0 最小值: 0.00031959092361776926 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: rank_act_factor2 最大值: 1.0 最小值: 0.00031969309462915604 NaN 数量: 329 NaN 占比: 0.002779890156316012\n", - "特征: rank_act_factor3 最大值: 1.0 最小值: 0.0003197953309881676 NaN 数量: 470 NaN 占比: 0.003971271651880017\n", + "特征: rsi_3 最大值: 100.0 最小值: 20.533752609951414 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: return_5 最大值: 5.017205781142464 最小值: -0.02557183243382155 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: return_20 最大值: 5.826058201058201 最小值: -0.5717581910475312 NaN 数量: 487 NaN 占比: 0.004028122415219189\n", + "特征: std_return_5 最大值: 1.6582378585020816 最小值: 2.3779477130116365e-05 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: std_return_90 最大值: 0.39102330156826715 最小值: 0.006668710967306505 NaN 数量: 1770 NaN 占比: 0.014640198511166254\n", + "特征: std_return_90_2 最大值: 0.3907567413557471 最小值: 0.005959840082236695 NaN 数量: 1951 NaN 占比: 0.016137303556658395\n", + "特征: act_factor1 最大值: 1.7903251438438559 最小值: -1.4752711929884126 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: act_factor2 最大值: 2.2207580059881558 最小值: -1.7526892749536007 NaN 数量: 346 NaN 占比: 0.0028618693134822167\n", + "特征: act_factor3 最大值: 4.202337904307388 最小值: -3.3460071644261493 NaN 数量: 487 NaN 占比: 0.004028122415219189\n", + "特征: act_factor4 最大值: 8.541255729360902 最小值: -5.575401659184761 NaN 数量: 1146 NaN 占比: 0.009478908188585608\n", + "特征: rank_act_factor1 最大值: 0.9993552546744036 最小值: 0.0003222687721559781 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: rank_act_factor2 最大值: 0.9993455497382199 最小值: 0.0003223726627981947 NaN 数量: 346 NaN 占比: 0.0028618693134822167\n", + "特征: rank_act_factor3 最大值: 1.0 最小值: 0.0003224766204450177 NaN 数量: 487 NaN 占比: 0.004028122415219189\n", "特征: cov 最大值: 9211640500.850372 最小值: -1025351031.6684246 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: delta_cov 最大值: 9207544083.643394 最小值: -4494222099.3107605 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", - "特征: alpha_22_improved 最大值: 4494222099.3107605 最小值: -9207544083.643394 NaN 数量: 452 NaN 占比: 0.003819180397127165\n", + "特征: delta_cov 最大值: 9207544083.643394 最小值: -4494222099.3107605 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", + "特征: alpha_22_improved 最大值: 4494222099.3107605 最小值: -9207544083.643394 NaN 数量: 468 NaN 占比: 0.003870967741935484\n", "特征: alpha_003 最大值: 1.0 最小值: -1.0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: alpha_007 最大值: 1.0 最小值: 0.0003197953309881676 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: alpha_013 最大值: 1.0 最小值: 0.00032 NaN 数量: 452 NaN 占比: 0.003819180397127165\n", + "特征: alpha_007 最大值: 1.0 最小值: 0.0003223726627981947 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: alpha_013 最大值: 1.0 最小值: 0.0003225806451612903 NaN 数量: 468 NaN 占比: 0.003870967741935484\n", "特征: up_limit_count_10d 最大值: 10.0 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", "特征: down_limit_count_10d 最大值: 3.0 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", "特征: consecutive_up_limit 最大值: 17 最小值: 0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: weight_roc5 最大值: 4.360439560439561 最小值: -0.4019607843137255 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: smallcap_concentration 最大值: 31.905964365688405 最小值: 0.0 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: cost_stability 最大值: 0.9735383316685713 最小值: 0.0 NaN 数量: 1727 NaN 占比: 0.014592310942120828\n", + "特征: weight_roc5 最大值: 4.360439560439561 最小值: -0.4019607843137255 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: smallcap_concentration 最大值: 31.905964365688405 最小值: 0.0 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: cost_stability 最大值: 0.9735383316685713 最小值: 0.0 NaN 数量: 1510 NaN 占比: 0.01248966087675765\n", "特征: high_cost_break_days 最大值: 5.0 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: liquidity_risk 最大值: 0.02727568902683877 最小值: 1.5428810759666794e-08 NaN 数量: 1482 NaN 占比: 0.012522179974651458\n", - "特征: turnover_std 最大值: 29.89517932088497 最小值: 0.014570369174964438 NaN 数量: 452 NaN 占比: 0.003819180397127165\n", - "特征: mv_volatility 最大值: 2.394008567880757 最小值: 0.0008012062079915182 NaN 数量: 452 NaN 占比: 0.003819180397127165\n", - "特征: volume_growth 最大值: 293.7911250760605 最小值: -0.9934612815303644 NaN 数量: 470 NaN 占比: 0.003971271651880017\n", - "特征: mv_growth 最大值: 24.5789647232902 最小值: -0.08405345257185749 NaN 数量: 470 NaN 占比: 0.003971271651880017\n", - "特征: arbr 最大值: 100.95433748749065 最小值: -168097.89086977823 NaN 数量: 3225 NaN 占比: 0.027249683143219267\n", - "特征: momentum_factor 最大值: 4.193633393631664 最小值: -1.50529286642075 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", + "特征: liquidity_risk 最大值: 0.02727568902683877 最小值: 1.5428810759666794e-08 NaN 数量: 1252 NaN 占比: 0.010355665839536808\n", + "特征: turnover_std 最大值: 29.89517932088497 最小值: 0.014570369174964438 NaN 数量: 468 NaN 占比: 0.003870967741935484\n", + "特征: mv_volatility 最大值: 2.394008567880757 最小值: 0.0008012062079915182 NaN 数量: 468 NaN 占比: 0.003870967741935484\n", + "特征: volume_growth 最大值: 293.7911250760605 最小值: -0.9934612815303644 NaN 数量: 487 NaN 占比: 0.004028122415219189\n", + "特征: mv_growth 最大值: 24.5789647232902 最小值: -0.08328263450104802 NaN 数量: 487 NaN 占比: 0.004028122415219189\n", + "特征: arbr 最大值: 100.95433748749065 最小值: -168097.89086977823 NaN 数量: 3190 NaN 占比: 0.026385442514474774\n", + "特征: momentum_factor 最大值: 4.193633393631664 最小值: -1.50529286642075 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", "特征: resonance_factor 最大值: 932.1131999999999 最小值: -700.3989 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: log_close 最大值: 10.555153146092515 最小值: -0.916290731874155 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: log_close 最大值: 10.555153146092515 最小值: 0.5068176023684519 NaN 数量: 0 NaN 占比: 0.0\n", "特征: up 最大值: 0.20949720670391064 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", "特征: down 最大值: 0.1827803785874212 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: obv-maobv_6 最大值: 91437345.39833334 最小值: -27497457.346666686 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: std_return_5 / std_return_90 最大值: 4.303704383077534 最小值: 0.00044776171350482643 NaN 数量: 1722 NaN 占比: 0.014550063371356147\n", - "特征: std_return_90 - std_return_90_2 最大值: 0.20102002437102318 最小值: -0.12445401518813239 NaN 数量: 1892 NaN 占比: 0.015986480777355302\n", - "特征: act_factor5 最大值: 16.79229294033777 最小值: -15.078581389549012 NaN 数量: 1075 NaN 占比: 0.009083227714406422\n", - "特征: act_factor6 最大值: 1.4142127937678 最小值: -1.414213562373081 NaN 数量: 329 NaN 占比: 0.002779890156316012\n", - "特征: active_buy_volume_large 最大值: 46116.0 最小值: -39192.25 NaN 数量: 7 NaN 占比: 5.9146599070553444e-05\n", - "特征: active_buy_volume_big 最大值: 24021.0 最小值: -16309.69642857143 NaN 数量: 7 NaN 占比: 5.9146599070553444e-05\n", - "特征: active_buy_volume_small 最大值: 101231.0 最小值: -60896.75 NaN 数量: 7 NaN 占比: 5.9146599070553444e-05\n", - "特征: buy_lg_vol_minus_sell_lg_vol 最大值: 6315.5 最小值: -10406.0 NaN 数量: 7 NaN 占比: 5.9146599070553444e-05\n", - "特征: buy_elg_vol_minus_sell_elg_vol 最大值: 15169.0 最小值: -4364.428571428572 NaN 数量: 7 NaN 占比: 5.9146599070553444e-05\n", - "特征: ctrl_strength 最大值: 0.8142857142857142 最小值: 0.0 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: low_cost_dev 最大值: 159371.40000000023 最小值: -2.2444444444444476 NaN 数量: 1345 NaN 占比: 0.011364596535699198\n", - "特征: asymmetry 最大值: 44.00000000000096 最小值: 0.0 NaN 数量: 1345 NaN 占比: 0.011364596535699198\n", - "特征: lock_factor 最大值: 76.68641720430107 最小值: 0.008677083333333335 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: cost_atr_adj 最大值: 88.61860174093074 最小值: 0.0002894966414380616 NaN 数量: 1621 NaN 占比: 0.01369666244190959\n", + "特征: obv-maobv_6 最大值: 91437345.39833337 最小值: -27497457.346666686 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: std_return_5 / std_return_90 最大值: 4.303704383077534 最小值: 0.00044776171350482643 NaN 数量: 1770 NaN 占比: 0.014640198511166254\n", + "特征: std_return_90 - std_return_90_2 最大值: 0.11510243684648414 最小值: -0.05541087845752879 NaN 数量: 1951 NaN 占比: 0.016137303556658395\n", + "特征: act_factor5 最大值: 16.7546767835003 最小值: -11.517387087951299 NaN 数量: 1146 NaN 占比: 0.009478908188585608\n", + "特征: act_factor6 最大值: 1.4142127937678 最小值: -1.414213562373081 NaN 数量: 346 NaN 占比: 0.0028618693134822167\n", + "特征: active_buy_volume_large 最大值: 46116.0 最小值: -39192.25 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: active_buy_volume_big 最大值: 24021.0 最小值: -16309.69642857143 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: active_buy_volume_small 最大值: 101231.0 最小值: -60896.75 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: buy_lg_vol_minus_sell_lg_vol 最大值: 6315.5 最小值: -10406.0 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: buy_elg_vol_minus_sell_elg_vol 最大值: 15169.0 最小值: -4364.428571428572 NaN 数量: 7 NaN 占比: 5.789909015715467e-05\n", + "特征: ctrl_strength 最大值: 0.8142857142857142 最小值: 0.0 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: low_cost_dev 最大值: 159371.40000000023 最小值: -2.2444444444444476 NaN 数量: 1091 NaN 占比: 0.00902398676592225\n", + "特征: asymmetry 最大值: 44.00000000000096 最小值: 0.0 NaN 数量: 1091 NaN 占比: 0.00902398676592225\n", + "特征: lock_factor 最大值: 76.68641720430107 最小值: 0.008677083333333335 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: cost_atr_adj 最大值: 88.61860174093074 最小值: 0.0002894966414380616 NaN 数量: 1402 NaN 占比: 0.011596360628618694\n", "特征: mv_turnover_ratio 最大值: 7.519401708236303 最小值: 0.001323022901502634 NaN 数量: 0 NaN 占比: 0.0\n", "特征: mv_adjusted_volume 最大值: 3675084.7886439813 最小值: 33.57880521115875 NaN 数量: 0 NaN 占比: 0.0\n", "特征: mv_weighted_turnover 最大值: 7.519401708236303 最小值: 0.001323022901502634 NaN 数量: 0 NaN 占比: 0.0\n", "特征: nonlinear_mv_volume 最大值: 3675084.7886439813 最小值: 33.57880521115875 NaN 数量: 0 NaN 占比: 0.0\n", "特征: mv_volume_ratio 最大值: 11.5095150992187 最小值: 0.0007549215390555348 NaN 数量: 0 NaN 占比: 0.0\n", "特征: mv_momentum 最大值: 829.2617138502171 最小值: 5.7669216057474964e-05 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: lg_flow_mom_corr_20_60 最大值: 0.9968522209091177 最小值: -0.9776689270624704 NaN 数量: 6 NaN 占比: 5.0697084917617235e-05\n", - "特征: lg_flow_accel 最大值: 23486299.0 最小值: -20413545.0 NaN 数量: 12 NaN 占比: 0.00010139416983523447\n", - "特征: profit_pressure 最大值: 1219604.64459375 最小值: -32.29264392059554 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: underwater_resistance 最大值: 0.09745390693548084 最小值: -0.4039719240263924 NaN 数量: 1279 NaN 占比: 0.010806928601605408\n", - "特征: cost_conc_std_20 最大值: 0.7414146004910032 最小值: 0.0 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", + "特征: lg_flow_mom_corr_20_60 最大值: 0.9909560531477801 最小值: -0.9776689270625863 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: lg_flow_accel 最大值: 23486299.0 最小值: -20413545.0 NaN 数量: 10 NaN 占比: 8.271298593879239e-05\n", + "特征: profit_pressure 最大值: 1219604.64459375 最小值: -32.29264392059554 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: underwater_resistance 最大值: 0.09745390693548084 最小值: -0.4039719240263924 NaN 数量: 1034 NaN 占比: 0.008552522746071134\n", + "特征: cost_conc_std_20 最大值: 0.5663669515051302 最小值: 0.0 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", "特征: profit_decay_20 最大值: 63.63523419055014 最小值: -201.87032418942292 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: vol_amp_loss_20 最大值: 5.1986896779851355 最小值: 0.0 NaN 数量: 1482 NaN 占比: 0.012522179974651458\n", + "特征: vol_amp_loss_20 最大值: 5.1986896779851355 最小值: 0.0 NaN 数量: 1252 NaN 占比: 0.010355665839536808\n", "特征: vol_drop_profit_cnt_5 最大值: 3.0 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: lg_flow_vol_interact_20 最大值: 42.845555877280596 最小值: 0.018505969076852875 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", + "特征: lg_flow_vol_interact_20 最大值: 42.845555877280596 最小值: 0.018505969076852875 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", "特征: cost_break_confirm_cnt_5 最大值: 5.0 最小值: -4.0 NaN 数量: 0 NaN 占比: 0.0\n", "特征: atr_norm_channel_pos_14 最大值: 14.000000000000002 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: turnover_diff_skew_20 最大值: 4.459639535682866 最小值: -3.4969687384726353 NaN 数量: 235 NaN 占比: 0.0019856358259400086\n", - "特征: lg_sm_flow_diverge_20 最大值: 1.999775245257143 最小值: -0.41700592094659733 NaN 数量: 205 NaN 占比: 0.0017321504013519222\n", + "特征: turnover_diff_skew_20 最大值: 4.459639535682866 最小值: -3.4969687384726353 NaN 数量: 250 NaN 占比: 0.0020678246484698098\n", + "特征: lg_sm_flow_diverge_20 最大值: 1.999775245257143 最小值: -0.3913656926280843 NaN 数量: 218 NaN 占比: 0.0018031430934656741\n", "特征: pullback_strong_20_20 最大值: 253.95565410196681 最小值: -675.6615306894897 NaN 数量: 0 NaN 占比: 0.0\n", "特征: vol_wgt_hist_pos_20 最大值: 13.234987955447918 最小值: 0.0 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: vol_adj_roc_20 最大值: 1.538154444208913 最小值: -1.2754286421125485 NaN 数量: 0 NaN 占比: 0.0\n", - "特征: industry_obv 最大值: 33357180.0 最小值: -17512962.0 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_return_5 最大值: 0.524069457577581 最小值: -0.2267495964295888 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_return_20 最大值: 0.8652496476530473 最小值: -0.3099103385178408 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry__ema_5 最大值: 73493.7215643315 最小值: 378.62741122827816 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry__ema_13 最大值: 73322.71553688897 最小值: 379.5203678371781 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry__ema_20 最大值: 74222.1571225974 最小值: 381.359643019974 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry__ema_60 最大值: 74769.33183235777 最小值: 401.1538611414743 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_act_factor1 最大值: 1.6986610274753051 最小值: -1.5933252482654896 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_act_factor2 最大值: 2.0328341745548633 最小值: -1.7615460391418363 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_act_factor3 最大值: 3.7292424474002375 最小值: -3.0674619011216553 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_act_factor4 最大值: 6.509680946056449 最小值: -4.337297946027389 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_act_factor5 最大值: 13.628812636988545 最小值: -10.685144264799936 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_act_factor6 最大值: 1.4142134956701664 最小值: -1.4142135617846938 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_rank_act_factor1 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_rank_act_factor2 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_rank_act_factor3 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_return_5_percentile 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 474 NaN 占比: 0.004005069708491762\n", - "特征: industry_return_20_percentile 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 474 NaN 占比: 0.004005069708491762\n" + "特征: vol_adj_roc_20 最大值: 1.152339990791505 最小值: -0.3411549434026346 NaN 数量: 0 NaN 占比: 0.0\n", + "特征: industry_obv 最大值: 33399643.0 最小值: -17512962.0 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_return_5 最大值: 0.524069457577581 最小值: -0.2267495964295888 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_return_20 最大值: 0.8652496476530473 最小值: -0.3099103385178408 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry__ema_5 最大值: 73886.17770955434 最小值: 378.62741122827816 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry__ema_13 最大值: 73322.71553688897 最小值: 379.5203678371781 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry__ema_20 最大值: 74222.1571225974 最小值: 381.359643019974 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry__ema_60 最大值: 74769.33183235777 最小值: 401.1538611414743 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_act_factor1 最大值: 1.6986610274753051 最小值: -1.5833811847067532 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_act_factor2 最大值: 2.0328341745548633 最小值: -1.7615460391418363 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_act_factor3 最大值: 3.7292424474002375 最小值: -3.0674619011216553 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_act_factor4 最大值: 6.509680946056449 最小值: -4.337297946027389 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_act_factor5 最大值: 13.628812636988545 最小值: -10.685144264799936 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_act_factor6 最大值: 1.4142134956701664 最小值: -1.4142135617846938 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_rank_act_factor1 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_rank_act_factor2 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_rank_act_factor3 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_return_5_percentile 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n", + "特征: industry_return_20_percentile 最大值: 1.0 最小值: 0.002277904328018223 NaN 数量: 446 NaN 占比: 0.0036889991728701406\n" ] } ], @@ -1854,7 +1856,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 36, "id": "81d4570663ae21d7", "metadata": { "ExecuteTime": { @@ -1878,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 37, "id": "92428d543f4727ad", "metadata": { "ExecuteTime": { @@ -1893,7 +1895,7 @@ "0" ] }, - "execution_count": 92, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1934,7 +1936,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 38, "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { @@ -2029,6 +2031,13 @@ " score_df = test_data.copy()\n", " score_df['score'] = model.predict(score_df[final_feature_columns])\n", " # score_df = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\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", + " score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1, 'total_mv')).reset_index()\n", + " score_df = score_df.sort_values(['trade_date', 'score'])\n", " score_df = score_df[['trade_date', 'score', 'ts_code']]\n", " predictions_list.append(score_df)\n", " except Exception as e:\n", @@ -2047,7 +2056,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 39, "id": "777822bd", "metadata": {}, "outputs": [ @@ -2056,10 +2065,10 @@ "output_type": "stream", "text": [ "\n", - "Index: 118350 entries, 2240 to 82199\n", - "Columns: 180 entries, ts_code to industry_return_20_percentile\n", - "dtypes: bool(12), datetime64[ns](1), float64(161), int32(3), object(3)\n", - "memory usage: 152.6+ MB\n", + "Index: 120900 entries, 2237 to 120811\n", + "Columns: 181 entries, ts_code to industry_return_20_percentile\n", + "dtypes: bool(12), datetime64[ns](1), float64(162), int32(3), object(3)\n", + "memory usage: 156.8+ MB\n", "None\n" ] } @@ -2070,7 +2079,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 46, "id": "63235069-dc59-48fb-961a-e80373e41a61", "metadata": { "ExecuteTime": { @@ -2090,2358 +2099,2364 @@ "output_type": "stream", "text": [ "finish\n", - "train_data最大日期: 2022-01-10, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-11\n", - "划分后的训练集大小: 649, 验证集大小: 132\n", - "train_data最大日期: 2022-01-11, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-12\n", - "划分后的训练集大小: 661, 验证集大小: 136\n", - "train_data最大日期: 2022-01-12, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-13\n", - "划分后的训练集大小: 676, 验证集大小: 140\n", - "train_data最大日期: 2022-01-13, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-14\n", - "划分后的训练集大小: 680, 验证集大小: 136\n", - "train_data最大日期: 2022-01-14, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-17\n", - "划分后的训练集大小: 677, 验证集大小: 133\n", - "train_data最大日期: 2022-01-17, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-18\n", - "划分后的训练集大小: 674, 验证集大小: 129\n", - "train_data最大日期: 2022-01-18, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-19\n", - "划分后的训练集大小: 675, 验证集大小: 137\n", - "train_data最大日期: 2022-01-19, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-20\n", - "划分后的训练集大小: 674, 验证集大小: 139\n", - "train_data最大日期: 2022-01-20, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-21\n", - "划分后的训练集大小: 682, 验证集大小: 144\n", - "train_data最大日期: 2022-01-21, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-24\n", - "划分后的训练集大小: 692, 验证集大小: 143\n", - "train_data最大日期: 2022-01-24, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-25\n", - "划分后的训练集大小: 701, 验证集大小: 138\n", - "train_data最大日期: 2022-01-25, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-26\n", - "划分后的训练集大小: 702, 验证集大小: 138\n", - "train_data最大日期: 2022-01-26, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-27\n", - "划分后的训练集大小: 705, 验证集大小: 142\n", - "train_data最大日期: 2022-01-27, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-01-28\n", - "划分后的训练集大小: 698, 验证集大小: 137\n", - "train_data最大日期: 2022-01-28, 训练天数:5, feat size:116\n", - "test_data最大日期: 2022-02-07\n", - "划分后的训练集大小: 699, 验证集大小: 144\n", - "train_data最大日期: 2022-02-07, 训练天数:5, feat size:116\n", + "train_data最大日期: 2022-02-07, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-08\n", - "划分后的训练集大小: 705, 验证集大小: 144\n", - "train_data最大日期: 2022-02-08, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2752, 验证集大小: 145\n", + "train_data最大日期: 2022-02-08, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-09\n", - "划分后的训练集大小: 701, 验证集大小: 134\n", - "train_data最大日期: 2022-02-09, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2763, 验证集大小: 136\n", + "train_data最大日期: 2022-02-09, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-10\n", - "划分后的训练集大小: 688, 验证集大小: 129\n", - "train_data最大日期: 2022-02-10, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2767, 验证集大小: 131\n", + "train_data最大日期: 2022-02-10, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-11\n", - "划分后的训练集大小: 683, 验证集大小: 132\n", - "train_data最大日期: 2022-02-11, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2765, 验证集大小: 133\n", + "train_data最大日期: 2022-02-11, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-14\n", - "划分后的训练集大小: 673, 验证集大小: 134\n", - "train_data最大日期: 2022-02-14, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2762, 验证集大小: 135\n", + "train_data最大日期: 2022-02-14, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-15\n", - "划分后的训练集大小: 668, 验证集大小: 139\n", - "train_data最大日期: 2022-02-15, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2769, 验证集大小: 140\n", + "train_data最大日期: 2022-02-15, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-16\n", - "划分后的训练集大小: 678, 验证集大小: 144\n", - "train_data最大日期: 2022-02-16, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2776, 验证集大小: 144\n", + "train_data最大日期: 2022-02-16, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-17\n", - "划分后的训练集大小: 689, 验证集大小: 140\n", - "train_data最大日期: 2022-02-17, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2775, 验证集大小: 140\n", + "train_data最大日期: 2022-02-17, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-18\n", - "划分后的训练集大小: 698, 验证集大小: 141\n", - "train_data最大日期: 2022-02-18, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2779, 验证集大小: 142\n", + "train_data最大日期: 2022-02-18, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-21\n", - "划分后的训练集大小: 701, 验证集大小: 137\n", - "train_data最大日期: 2022-02-21, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2781, 验证集大小: 138\n", + "train_data最大日期: 2022-02-21, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-22\n", - "划分后的训练集大小: 701, 验证集大小: 139\n", - "train_data最大日期: 2022-02-22, 训练天数:5, feat size:116\n", + "划分后的训练集大小: 2790, 验证集大小: 139\n", + "train_data最大日期: 2022-02-22, 训练天数:20, feat size:116\n", "test_data最大日期: 2022-02-23\n", - "划分后的训练集大小: 681, 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days=days, validation_days=0, filter_index=filter_index, params=light_params)\n", "# final_predictions2 = rolling_train_predict(\n", "# pdf[(pdf['trade_date'] >= '2022-01-01') & (pdf['trade_date'] <= '2029-03-26')], 20, 1, feature_columns,\n", @@ -4469,7 +4484,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 41, "id": "e01fe33b-e30d-4bc6-bf40-de91e61862b4", "metadata": { "ExecuteTime": { @@ -4483,10 +4498,10 @@ "output_type": "stream", "text": [ "Empty DataFrame\n", - "Columns: [ts_code, trade_date, open, close, high, low, vol, pct_chg, turnover_rate, pe_ttm, circ_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_l1_code, cat_l2_code, 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, act_factor1, act_factor2, act_factor3, act_factor4, rank_act_factor1, rank_act_factor2, rank_act_factor3, log(circ_mv), cov, delta_cov, alpha_22_improved, alpha_003, alpha_007, alpha_013, cat_up_limit, cat_down_limit, up_limit_count_10d, down_limit_count_10d, consecutive_up_limit, vol_break, weight_roc5, price_cost_divergence, smallcap_concentration, cost_stability, high_cost_break_days, liquidity_risk, ...]\n", + "Columns: [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_l1_code, cat_l2_code, 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, act_factor1, act_factor2, act_factor3, act_factor4, rank_act_factor1, rank_act_factor2, rank_act_factor3, log(circ_mv), cov, delta_cov, alpha_22_improved, alpha_003, alpha_007, alpha_013, cat_up_limit, cat_down_limit, up_limit_count_10d, down_limit_count_10d, consecutive_up_limit, vol_break, weight_roc5, price_cost_divergence, smallcap_concentration, cost_stability, high_cost_break_days, ...]\n", "Index: []\n", "\n", - "[0 rows x 180 columns]\n" + "[0 rows x 181 columns]\n" ] } ], @@ -4496,7 +4511,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 42, "id": "0dc75517-c857-4f1d-8815-e807400a6d33", "metadata": { "ExecuteTime": { @@ -4554,7 +4569,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 43, "id": "8299a6f461097f14", "metadata": { "ExecuteTime": { @@ -4576,7 +4591,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 44, "id": 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ts_code='BK1184.DC') -print(df['total_liab']) \ No newline at end of file +print(df.sort_values('end_date')) \ No newline at end of file