From b783a6f9685684727bb98c48c3c239a045b24ed7 Mon Sep 17 00:00:00 2001 From: liaozhaorun Date: Tue, 6 May 2025 23:42:40 +0800 Subject: [PATCH] Classify2 --- main/data/daily_basic.ipynb | 439 +- main/data/finance.ipynb | 5920 ++++++++++++++++- main/data/index_and_industry.ipynb | 104 +- main/data/name_change.ipynb | 214 +- main/data/update/cyq-perf.ipynb | 145 +- main/data/update/sw_daily.ipynb | 145 +- main/data/update/update_daily_basic.ipynb | 386 +- main/data/update/update_daily_data.ipynb | 1146 ++-- main/data/update/update_is_st.ipynb | 222 +- main/data/update/update_money_flow.ipynb | 167 +- main/data/update/update_stk_limit.ipynb | 215 +- main/train/Classify2.ipynb | 744 ++- .../catboost_info/catboost_training.json | 302 +- .../catboost_info/learn/events.out.tfevents | Bin 14392 -> 22556 bytes main/train/catboost_info/learn_error.tsv | 302 +- .../catboost_info/test/events.out.tfevents | Bin 14392 -> 22556 bytes main/train/catboost_info/test_error.tsv | 302 +- main/train/catboost_info/time_left.tsv | 302 +- main/train/predictions_test.tsv | 1109 +-- 19 files changed, 9390 insertions(+), 2774 deletions(-) diff --git a/main/data/daily_basic.ipynb b/main/data/daily_basic.ipynb index 6ee2828..6fb2619 100644 --- a/main/data/daily_basic.ipynb +++ b/main/data/daily_basic.ipynb @@ -79,6 +79,25 @@ " name_change_dict[ts_code] = filter_rows(st_data)" ] }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b151990c", + "metadata": {}, + "outputs": [ + { + "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" + ] + } + ], + "source": [ + "print(name_change_dict['002848.SZ'])" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -123,8 +142,8 @@ "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 20250320 完成\n", "任务 20250318 完成\n", "任务 20250317 完成\n", "任务 20250314 完成\n", @@ -137,8 +156,8 @@ "任务 20250305 完成\n", "任务 20250304 完成\n", "任务 20250303 完成\n", - "任务 20250227 完成\n", "任务 20250228 完成\n", + "任务 20250227 完成\n", "任务 20250226 完成\n", "任务 20250225 完成\n", "任务 20250224 完成\n", @@ -149,8 +168,8 @@ "任务 20250217 完成\n", "任务 20250214 完成\n", "任务 20250213 完成\n", - "任务 20250212 完成\n", "任务 20250211 完成\n", + "任务 20250212 完成\n", "任务 20250210 完成\n", "任务 20250207 完成\n", "任务 20250206 完成\n", @@ -158,33 +177,33 @@ "任务 20250127 完成\n", "任务 20250124 完成\n", "任务 20250123 完成\n", - "任务 20250121 完成\n", "任务 20250122 完成\n", + "任务 20250121 完成\n", "任务 20250120 完成\n", "任务 20250117 完成\n", "任务 20250116 完成\n", "任务 20250115 完成\n", - "任务 20250113 完成\n", "任务 20250114 完成\n", + "任务 20250113 完成\n", "任务 20250110 完成\n", "任务 20250109 完成\n", "任务 20250108 完成\n", "任务 20250107 完成\n", "任务 20250106 完成\n", - "任务 20250103 完成\n", "任务 20250102 完成\n", + "任务 20250103 完成\n", "任务 20241231 完成\n", "任务 20241230 完成\n", - "任务 20241226 完成\n", "任务 20241227 完成\n", - "任务 20241224 完成\n", + "任务 20241226 完成\n", "任务 20241225 完成\n", + "任务 20241224 完成\n", "任务 20241223 完成\n", "任务 20241220 完成\n", - "任务 20241218 完成\n", "任务 20241219 完成\n", - "任务 20241216 完成\n", + "任务 20241218 完成\n", "任务 20241217 完成\n", + "任务 20241216 完成\n", "任务 20241213 完成\n", "任务 20241212 完成\n", "任务 20241211 完成\n", @@ -192,10 +211,10 @@ "任务 20241209 完成\n", "任务 20241206 完成\n", "任务 20241205 完成\n", - "任务 20241203 完成\n", "任务 20241204 完成\n", - "任务 20241129 完成\n", + "任务 20241203 完成\n", "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", @@ -205,8 +224,8 @@ "任务 20241120 完成\n", "任务 20241119 完成\n", "任务 20241118 完成\n", - "任务 20241114 完成\n", "任务 20241115 完成\n", + "任务 20241114 完成\n", "任务 20241113 完成\n", "任务 20241112 完成\n", "任务 20241111 完成\n", @@ -233,32 +252,32 @@ "任务 20241011 完成\n", "任务 20241010 完成\n", "任务 20241009 完成\n", - "任务 20241008 完成\n", "任务 20240930 完成\n", + "任务 20241008 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 20240923 完成\n", - "任务 20240919 完成\n", "任务 20240920 完成\n", + "任务 20240923 完成\n", "任务 20240918 完成\n", - "任务 20240913 完成\n", + "任务 20240919 完成\n", "任务 20240912 完成\n", + "任务 20240913 完成\n", "任务 20240911 完成\n", "任务 20240910 完成\n", "任务 20240909 完成\n", "任务 20240906 完成\n", - "任务 20240904 完成\n", "任务 20240905 完成\n", + "任务 20240904 完成\n", "任务 20240903 完成\n", "任务 20240902 完成\n", "任务 20240830 完成\n", "任务 20240829 完成\n", "任务 20240828 完成\n", "任务 20240827 完成\n", - "任务 20240823 完成\n", "任务 20240826 完成\n", + "任务 20240823 完成\n", "任务 20240822 完成\n", "任务 20240821 完成\n", "任务 20240820 完成\n", @@ -274,8 +293,8 @@ "任务 20240806 完成\n", "任务 20240805 完成\n", "任务 20240802 完成\n", - "任务 20240731 完成\n", "任务 20240801 完成\n", + "任务 20240731 完成\n", "任务 20240730 完成\n", "任务 20240729 完成\n", "任务 20240726 完成\n", @@ -295,16 +314,16 @@ "任务 20240708 完成\n", "任务 20240705 完成\n", "任务 20240704 完成\n", - "任务 20240702 完成\n", "任务 20240703 完成\n", + "任务 20240702 完成\n", "任务 20240701 完成\n", "任务 20240628 完成\n", "任务 20240627 完成\n", "任务 20240626 完成\n", - "任务 20240625 完成\n", "任务 20240624 完成\n", - "任务 20240621 完成\n", + "任务 20240625 完成\n", "任务 20240620 完成\n", + "任务 20240621 完成\n", "任务 20240619 完成\n", "任务 20240618 完成\n", "任务 20240617 完成\n", @@ -316,15 +335,15 @@ "任务 20240606 完成\n", "任务 20240605 完成\n", "任务 20240604 完成\n", - "任务 20240531 完成\n", "任务 20240603 完成\n", + "任务 20240531 完成\n", "任务 20240530 完成\n", "任务 20240529 完成\n", "任务 20240528 完成\n", "任务 20240527 完成\n", "任务 20240524 完成\n", - "任务 20240522 完成\n", "任务 20240523 完成\n", + "任务 20240522 完成\n", "任务 20240521 完成\n", "任务 20240520 完成\n", "任务 20240517 完成\n", @@ -335,10 +354,10 @@ "任务 20240510 完成\n", "任务 20240509 完成\n", "任务 20240508 完成\n", - "任务 20240506 完成\n", "任务 20240507 完成\n", - "任务 20240429 完成\n", + "任务 20240506 完成\n", "任务 20240430 完成\n", + "任务 20240429 完成\n", "任务 20240426 完成\n", "任务 20240425 完成\n", "任务 20240424 完成\n", @@ -349,8 +368,8 @@ "任务 20240417 完成\n", "任务 20240416 完成\n", "任务 20240415 完成\n", - "任务 20240411 完成\n", "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", @@ -359,10 +378,10 @@ "任务 20240401 完成\n", "任务 20240329 完成\n", "任务 20240328 完成\n", - "任务 20240326 完成\n", "任务 20240327 完成\n", - "任务 20240325 完成\n", + "任务 20240326 完成\n", "任务 20240322 完成\n", + "任务 20240325 完成\n", "任务 20240321 完成\n", "任务 20240320 完成\n", "任务 20240319 完成\n", @@ -375,8 +394,8 @@ "任务 20240308 完成\n", "任务 20240307 完成\n", "任务 20240306 完成\n", - "任务 20240305 完成\n", "任务 20240304 完成\n", + "任务 20240305 完成\n", "任务 20240301 完成\n", "任务 20240229 完成\n", "任务 20240228 完成\n", @@ -413,18 +432,18 @@ "任务 20240108 完成\n", "任务 20240105 完成\n", "任务 20240104 完成\n", - "任务 20240103 完成\n", "任务 20240102 完成\n", + "任务 20240103 完成\n", "任务 20231229 完成\n", "任务 20231228 完成\n", "任务 20231227 完成\n", - "任务 20231225 完成\n", "任务 20231226 完成\n", + "任务 20231225 完成\n", "任务 20231222 完成\n", - "任务 20231221 完成\n", "任务 20231220 完成\n", - "任务 20231219 完成\n", + "任务 20231221 完成\n", "任务 20231218 完成\n", + "任务 20231219 完成\n", "任务 20231215 完成\n", "任务 20231214 完成\n", "任务 20231213 完成\n", @@ -434,8 +453,8 @@ "任务 20231207 完成\n", "任务 20231206 完成\n", "任务 20231205 完成\n", - "任务 20231201 完成\n", "任务 20231204 完成\n", + "任务 20231201 完成\n", "任务 20231130 完成\n", "任务 20231129 完成\n", "任务 20231128 完成\n", @@ -456,8 +475,8 @@ "任务 20231107 完成\n", "任务 20231106 完成\n", "任务 20231103 完成\n", - "任务 20231101 完成\n", "任务 20231102 完成\n", + "任务 20231101 完成\n", "任务 20231031 完成\n", "任务 20231030 完成\n", "任务 20231027 完成\n", @@ -470,29 +489,29 @@ "任务 20231018 完成\n", "任务 20231017 完成\n", "任务 20231016 完成\n", - "任务 20231012 完成\n", "任务 20231013 完成\n", + "任务 20231012 完成\n", "任务 20231011 完成\n", "任务 20231010 完成\n", - "任务 20231009 完成\n", "任务 20230928 完成\n", + "任务 20231009 完成\n", + "任务 20230926 完成\n", "任务 20230927 完成\n", "任务 20230925 完成\n", - "任务 20230926 完成\n", "任务 20230922 完成\n", "任务 20230921 完成\n", "任务 20230920 完成\n", "任务 20230919 完成\n", "任务 20230918 完成\n", "任务 20230915 完成\n", - "任务 20230913 完成\n", "任务 20230914 完成\n", + "任务 20230913 完成\n", "任务 20230912 完成\n", "任务 20230911 完成\n", "任务 20230908 完成\n", "任务 20230907 完成\n", - "任务 20230905 完成\n", "任务 20230906 完成\n", + "任务 20230905 完成\n", "任务 20230904 完成\n", "任务 20230901 完成\n", "任务 20230831 完成\n", @@ -502,8 +521,8 @@ "任务 20230825 完成\n", "任务 20230824 完成\n", "任务 20230823 完成\n", - "任务 20230822 完成\n", "任务 20230821 完成\n", + "任务 20230822 完成\n", "任务 20230818 完成\n", "任务 20230817 完成\n", "任务 20230816 完成\n", @@ -512,8 +531,8 @@ "任务 20230811 完成\n", "任务 20230810 完成\n", "任务 20230809 完成\n", - "任务 20230807 完成\n", "任务 20230808 完成\n", + "任务 20230807 完成\n", "任务 20230804 完成\n", "任务 20230803 完成\n", "任务 20230802 完成\n", @@ -526,11 +545,11 @@ "任务 20230724 完成\n", "任务 20230721 完成\n", "任务 20230720 完成\n", - "任务 20230718 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 20230713 完成\n", - "任务 20230714 完成\n", "任务 20230712 完成\n", "任务 20230711 完成\n", "任务 20230710 完成\n", @@ -561,20 +580,20 @@ "任务 20230601 完成\n", "任务 20230531 完成\n", "任务 20230530 完成\n", - "任务 20230526 完成\n", "任务 20230529 完成\n", - "任务 20230524 完成\n", + "任务 20230526 完成\n", "任务 20230525 完成\n", + "任务 20230524 完成\n", "任务 20230523 完成\n", - "任务 20230522 完成\n", "任务 20230519 完成\n", + "任务 20230522 完成\n", "任务 20230518 完成\n", "任务 20230517 完成\n", "任务 20230516 完成\n", "任务 20230515 完成\n", "任务 20230512 完成\n", - "任务 20230510 完成\n", "任务 20230511 完成\n", + "任务 20230510 完成\n", "任务 20230509 完成\n", "任务 20230508 完成\n", "任务 20230505 完成\n", @@ -586,8 +605,8 @@ "任务 20230424 完成\n", "任务 20230421 完成\n", "任务 20230420 完成\n", - "任务 20230419 完成\n", "任务 20230418 完成\n", + "任务 20230419 完成\n", "任务 20230417 完成\n", "任务 20230414 完成\n", "任务 20230413 完成\n", @@ -599,23 +618,23 @@ "任务 20230404 完成\n", "任务 20230403 完成\n", "任务 20230331 完成\n", + "任务 20230330 完成\n", "任务 20230329 完成\n", "任务 20230328 完成\n", "任务 20230327 完成\n", - "任务 20230330 完成\n", "任务 20230324 完成\n", - "任务 20230323 完成\n", "任务 20230322 完成\n", + "任务 20230323 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 20230310 完成\n", "任务 20230313 完成\n", - "任务 20230308 完成\n", + "任务 20230310 完成\n", "任务 20230309 完成\n", + "任务 20230308 完成\n", "任务 20230307 完成\n", "任务 20230306 完成\n", "任务 20230303 完成\n", @@ -625,12 +644,12 @@ "任务 20230227 完成\n", "任务 20230224 完成\n", "任务 20230223 完成\n", - "任务 20230221 完成\n", "任务 20230222 完成\n", + "任务 20230221 完成\n", "任务 20230220 完成\n", "任务 20230217 完成\n", - "任务 20230215 完成\n", "任务 20230216 完成\n", + "任务 20230215 完成\n", "任务 20230214 完成\n", "任务 20230213 完成\n", "任务 20230210 完成\n", @@ -641,8 +660,8 @@ "任务 20230203 完成\n", "任务 20230202 完成\n", "任务 20230201 完成\n", - "任务 20230130 完成\n", "任务 20230131 完成\n", + "任务 20230130 完成\n", "任务 20230120 完成\n", "任务 20230119 完成\n", "任务 20230118 完成\n", @@ -681,8 +700,8 @@ "任务 20221201 完成\n", "任务 20221130 完成\n", "任务 20221129 完成\n", - "任务 20221125 完成\n", "任务 20221128 完成\n", + "任务 20221125 完成\n", "任务 20221124 完成\n", "任务 20221123 完成\n", "任务 20221122 完成\n", @@ -692,8 +711,8 @@ "任务 20221116 完成\n", "任务 20221115 完成\n", "任务 20221114 完成\n", - "任务 20221110 完成\n", "任务 20221111 完成\n", + "任务 20221110 完成\n", "任务 20221109 完成\n", "任务 20221108 完成\n", "任务 20221107 完成\n", @@ -706,10 +725,10 @@ "任务 20221027 完成\n", "任务 20221026 完成\n", "任务 20221025 完成\n", - "任务 20221021 完成\n", "任务 20221024 完成\n", - "任务 20221019 完成\n", + "任务 20221021 完成\n", "任务 20221020 完成\n", + "任务 20221019 完成\n", "任务 20221018 完成\n", "任务 20221017 完成\n", "任务 20221014 完成\n", @@ -720,10 +739,10 @@ "任务 20220930 完成\n", "任务 20220929 完成\n", "任务 20220928 完成\n", - "任务 20220927 完成\n", - "任务 20220923 完成\n", "任务 20220926 完成\n", + "任务 20220927 完成\n", "任务 20220922 完成\n", + "任务 20220923 完成\n", "任务 20220921 完成\n", "任务 20220920 完成\n", "任务 20220919 完成\n", @@ -732,14 +751,14 @@ "任务 20220914 完成\n", "任务 20220913 完成\n", "任务 20220909 完成\n", - "任务 20220907 完成\n", "任务 20220908 完成\n", + "任务 20220907 完成\n", "任务 20220906 完成\n", "任务 20220905 完成\n", "任务 20220902 完成\n", "任务 20220901 完成\n", - "任务 20220831 完成\n", "任务 20220830 完成\n", + "任务 20220831 完成\n", "任务 20220829 完成\n", "任务 20220826 完成\n", "任务 20220825 完成\n", @@ -752,26 +771,26 @@ "任务 20220816 完成\n", "任务 20220815 完成\n", "任务 20220812 完成\n", - "任务 20220811 完成\n", "任务 20220810 完成\n", - "任务 20220808 完成\n", + "任务 20220811 完成\n", "任务 20220809 完成\n", - "任务 20220805 完成\n", + "任务 20220808 完成\n", "任务 20220804 完成\n", + "任务 20220805 完成\n", "任务 20220803 完成\n", "任务 20220802 完成\n", - "任务 20220729 完成\n", "任务 20220801 完成\n", + "任务 20220729 完成\n", "任务 20220728 完成\n", "任务 20220727 完成\n", - "任务 20220725 完成\n", "任务 20220726 完成\n", + "任务 20220725 完成\n", "任务 20220722 完成\n", "任务 20220721 完成\n", - "任务 20220720 完成\n", "任务 20220719 完成\n", - "任务 20220715 完成\n", + "任务 20220720 完成\n", "任务 20220718 完成\n", + "任务 20220715 完成\n", "任务 20220714 完成\n", "任务 20220713 完成\n", "任务 20220712 完成\n", @@ -794,8 +813,8 @@ "任务 20220617 完成\n", "任务 20220616 完成\n", "任务 20220615 完成\n", - "任务 20220614 完成\n", "任务 20220613 完成\n", + "任务 20220614 完成\n", "任务 20220610 完成\n", "任务 20220609 完成\n", "任务 20220608 完成\n", @@ -807,20 +826,20 @@ "任务 20220530 完成\n", "任务 20220527 完成\n", "任务 20220526 完成\n", - "任务 20220524 完成\n", "任务 20220525 完成\n", - "任务 20220520 完成\n", + "任务 20220524 完成\n", "任务 20220523 完成\n", + "任务 20220520 完成\n", "任务 20220519 完成\n", "任务 20220518 完成\n", "任务 20220517 完成\n", "任务 20220516 完成\n", - "任务 20220512 完成\n", "任务 20220513 完成\n", - "任务 20220510 完成\n", + "任务 20220512 完成\n", "任务 20220511 完成\n", - "任务 20220506 完成\n", + "任务 20220510 完成\n", "任务 20220509 完成\n", + "任务 20220506 完成\n", "任务 20220505 完成\n", "任务 20220429 完成\n", "任务 20220428 完成\n", @@ -862,8 +881,8 @@ "任务 20220307 完成\n", "任务 20220304 完成\n", "任务 20220303 完成\n", - "任务 20220301 完成\n", "任务 20220302 完成\n", + "任务 20220301 完成\n", "任务 20220228 完成\n", "任务 20220225 完成\n", "任务 20220224 完成\n", @@ -889,16 +908,16 @@ "任务 20220120 完成\n", "任务 20220119 完成\n", "任务 20220118 完成\n", - "任务 20220114 完成\n", "任务 20220117 完成\n", - "任务 20220112 完成\n", + "任务 20220114 完成\n", "任务 20220113 完成\n", + "任务 20220112 完成\n", "任务 20220111 完成\n", "任务 20220110 完成\n", "任务 20220107 完成\n", "任务 20220106 完成\n", - "任务 20220104 完成\n", "任务 20220105 完成\n", + "任务 20220104 完成\n", "任务 20211231 完成\n", "任务 20211230 完成\n", "任务 20211229 完成\n", @@ -921,8 +940,8 @@ "任务 20211206 完成\n", "任务 20211203 完成\n", "任务 20211202 完成\n", - "任务 20211130 完成\n", "任务 20211201 完成\n", + "任务 20211130 完成\n", "任务 20211129 完成\n", "任务 20211126 完成\n", "任务 20211125 完成\n", @@ -932,21 +951,21 @@ "任务 20211119 完成\n", "任务 20211118 完成\n", "任务 20211117 完成\n", - "任务 20211116 完成\n", "任务 20211115 完成\n", + "任务 20211116 完成\n", + "任务 20211111 完成\n", "任务 20211112 完成\n", "任务 20211110 完成\n", - "任务 20211111 完成\n", "任务 20211109 完成\n", "任务 20211108 完成\n", - "任务 20211104 完成\n", "任务 20211105 完成\n", + "任务 20211104 完成\n", "任务 20211103 完成\n", "任务 20211102 完成\n", "任务 20211101 完成\n", "任务 20211029 完成\n", - "任务 20211028 完成\n", "任务 20211027 完成\n", + "任务 20211028 完成\n", "任务 20211026 完成\n", "任务 20211025 完成\n", "任务 20211022 完成\n", @@ -959,18 +978,18 @@ "任务 20211013 完成\n", "任务 20211012 完成\n", "任务 20211011 完成\n", - "任务 20210930 完成\n", "任务 20211008 完成\n", + "任务 20210930 完成\n", "任务 20210929 完成\n", "任务 20210928 完成\n", "任务 20210927 完成\n", "任务 20210924 完成\n", "任务 20210923 完成\n", "任务 20210922 完成\n", - "任务 20210916 完成\n", "任务 20210917 完成\n", - "任务 20210914 完成\n", + "任务 20210916 完成\n", "任务 20210915 完成\n", + "任务 20210914 完成\n", "任务 20210913 完成\n", "任务 20210910 完成\n", "任务 20210909 完成\n", @@ -1009,8 +1028,8 @@ "任务 20210726 完成\n", "任务 20210723 完成\n", "任务 20210722 完成\n", - "任务 20210720 完成\n", "任务 20210721 完成\n", + "任务 20210720 完成\n", "任务 20210719 完成\n", "任务 20210716 完成\n", "任务 20210715 完成\n", @@ -1041,12 +1060,12 @@ "任务 20210609 完成\n", "任务 20210608 完成\n", "任务 20210607 完成\n", - "任务 20210603 完成\n", "任务 20210604 完成\n", + "任务 20210603 完成\n", "任务 20210602 完成\n", "任务 20210601 完成\n", - "任务 20210528 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 20210525 完成\n", @@ -1059,20 +1078,20 @@ "任务 20210514 完成\n", "任务 20210513 完成\n", "任务 20210512 完成\n", - "任务 20210510 完成\n", "任务 20210511 完成\n", + "任务 20210510 完成\n", "任务 20210507 完成\n", "任务 20210506 完成\n", "任务 20210430 完成\n", "任务 20210429 完成\n", "任务 20210428 完成\n", "任务 20210427 完成\n", - "任务 20210426 完成\n", "任务 20210423 完成\n", - "任务 20210421 完成\n", + "任务 20210426 完成\n", "任务 20210422 完成\n", - "任务 20210419 完成\n", + "任务 20210421 完成\n", "任务 20210420 完成\n", + "任务 20210419 完成\n", "任务 20210416 完成\n", "任务 20210415 完成\n", "任务 20210414 完成\n", @@ -1083,8 +1102,8 @@ "任务 20210407 完成\n", "任务 20210406 完成\n", "任务 20210402 完成\n", - "任务 20210401 完成\n", "任务 20210331 完成\n", + "任务 20210401 完成\n", "任务 20210330 完成\n", "任务 20210329 完成\n", "任务 20210326 完成\n", @@ -1098,8 +1117,8 @@ "任务 20210316 完成\n", "任务 20210315 完成\n", "任务 20210312 完成\n", - "任务 20210310 完成\n", "任务 20210311 完成\n", + "任务 20210310 完成\n", "任务 20210309 完成\n", "任务 20210308 完成\n", "任务 20210305 完成\n", @@ -1111,8 +1130,8 @@ "任务 20210225 完成\n", "任务 20210224 完成\n", "任务 20210223 完成\n", - "任务 20210222 完成\n", "任务 20210219 完成\n", + "任务 20210222 完成\n", "任务 20210218 完成\n", "任务 20210210 完成\n", "任务 20210209 完成\n", @@ -1121,10 +1140,10 @@ "任务 20210204 完成\n", "任务 20210203 完成\n", "任务 20210202 完成\n", - "任务 20210201 完成\n", "任务 20210129 完成\n", - "任务 20210127 完成\n", + "任务 20210201 完成\n", "任务 20210128 完成\n", + "任务 20210127 完成\n", "任务 20210126 完成\n", "任务 20210125 完成\n", "任务 20210122 完成\n", @@ -1132,8 +1151,8 @@ "任务 20210120 完成\n", "任务 20210119 完成\n", "任务 20210118 完成\n", - "任务 20210114 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 20210111 完成\n", @@ -1155,8 +1174,8 @@ "任务 20201217 完成\n", "任务 20201216 完成\n", "任务 20201215 完成\n", - "任务 20201214 完成\n", "任务 20201211 完成\n", + "任务 20201214 完成\n", "任务 20201210 完成\n", "任务 20201209 完成\n", "任务 20201208 完成\n", @@ -1166,14 +1185,14 @@ "任务 20201202 完成\n", "任务 20201201 完成\n", "任务 20201130 完成\n", - "任务 20201126 完成\n", "任务 20201127 完成\n", + "任务 20201126 完成\n", "任务 20201125 完成\n", "任务 20201124 完成\n", - "任务 20201120 完成\n", "任务 20201123 完成\n", - "任务 20201118 完成\n", + "任务 20201120 完成\n", "任务 20201119 完成\n", + "任务 20201118 完成\n", "任务 20201117 完成\n", "任务 20201116 完成\n", "任务 20201113 完成\n", @@ -1191,8 +1210,8 @@ "任务 20201028 完成\n", "任务 20201027 完成\n", "任务 20201026 完成\n", - "任务 20201022 完成\n", "任务 20201023 完成\n", + "任务 20201022 完成\n", "任务 20201021 完成\n", "任务 20201020 完成\n", "任务 20201019 完成\n", @@ -1208,16 +1227,16 @@ "任务 20200925 完成\n", "任务 20200924 完成\n", "任务 20200923 完成\n", - "任务 20200921 完成\n", "任务 20200922 完成\n", + "任务 20200921 完成\n", "任务 20200918 完成\n", "任务 20200917 完成\n", "任务 20200916 完成\n", "任务 20200915 完成\n", "任务 20200914 完成\n", "任务 20200911 完成\n", - "任务 20200909 完成\n", "任务 20200910 完成\n", + "任务 20200909 完成\n", "任务 20200908 完成\n", "任务 20200907 完成\n", "任务 20200904 完成\n", @@ -1228,22 +1247,22 @@ "任务 20200828 完成\n", "任务 20200827 完成\n", "任务 20200826 完成\n", - "任务 20200824 完成\n", "任务 20200825 完成\n", - "任务 20200820 完成\n", + "任务 20200824 完成\n", "任务 20200821 完成\n", + "任务 20200820 完成\n", "任务 20200819 完成\n", "任务 20200818 完成\n", "任务 20200817 完成\n", "任务 20200814 完成\n", - "任务 20200812 完成\n", "任务 20200813 完成\n", - "任务 20200810 完成\n", + "任务 20200812 完成\n", "任务 20200811 完成\n", + "任务 20200810 完成\n", "任务 20200807 完成\n", "任务 20200806 完成\n", - "任务 20200804 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 20200730 完成\n", @@ -1270,20 +1289,20 @@ "任务 20200701 完成\n", "任务 20200630 完成\n", "任务 20200629 完成\n", - "任务 20200623 完成\n", "任务 20200624 完成\n", + "任务 20200623 完成\n", "任务 20200622 完成\n", "任务 20200619 完成\n", "任务 20200618 完成\n", "任务 20200617 完成\n", "任务 20200616 完成\n", "任务 20200615 完成\n", - "任务 20200611 完成\n", "任务 20200612 完成\n", + "任务 20200611 完成\n", "任务 20200610 完成\n", "任务 20200609 完成\n", - "任务 20200605 完成\n", "任务 20200608 完成\n", + "任务 20200605 完成\n", "任务 20200604 完成\n", "任务 20200603 完成\n", "任务 20200602 完成\n", @@ -1319,12 +1338,12 @@ "任务 20200416 完成\n", "任务 20200415 完成\n", "任务 20200414 完成\n", - "任务 20200413 完成\n", "任务 20200410 完成\n", + "任务 20200413 完成\n", "任务 20200409 完成\n", "任务 20200408 完成\n", - "任务 20200403 完成\n", "任务 20200407 完成\n", + "任务 20200403 完成\n", "任务 20200402 完成\n", "任务 20200401 完成\n", "任务 20200331 完成\n", @@ -1337,8 +1356,8 @@ "任务 20200320 完成\n", "任务 20200319 完成\n", "任务 20200318 完成\n", - "任务 20200316 完成\n", "任务 20200317 完成\n", + "任务 20200316 完成\n", "任务 20200313 完成\n", "任务 20200312 完成\n", "任务 20200311 完成\n", @@ -1351,36 +1370,36 @@ "任务 20200302 完成\n", "任务 20200228 完成\n", "任务 20200227 完成\n", - "任务 20200225 完成\n", "任务 20200226 完成\n", - "任务 20200221 完成\n", + "任务 20200225 完成\n", "任务 20200224 完成\n", - "任务 20200220 完成\n", + "任务 20200221 完成\n", "任务 20200219 完成\n", + "任务 20200220 完成\n", "任务 20200218 完成\n", "任务 20200217 完成\n", - "任务 20200214 完成\n", "任务 20200213 完成\n", + "任务 20200214 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 20200206 完成\n", "任务 20200205 完成\n", - "任务 20200203 完成\n", "任务 20200204 完成\n", + "任务 20200203 完成\n", "任务 20200123 完成\n", "任务 20200122 完成\n", "任务 20200121 完成\n", "任务 20200120 完成\n", - "任务 20200116 完成\n", "任务 20200117 完成\n", + "任务 20200116 完成\n", "任务 20200115 完成\n", "任务 20200114 完成\n", "任务 20200113 完成\n", "任务 20200110 完成\n", - "任务 20200108 完成\n", "任务 20200109 完成\n", + "任务 20200108 完成\n", "任务 20200107 完成\n", "任务 20200106 完成\n", "任务 20200103 完成\n", @@ -1394,8 +1413,8 @@ "任务 20191223 完成\n", "任务 20191220 完成\n", "任务 20191219 完成\n", - "任务 20191217 完成\n", "任务 20191218 完成\n", + "任务 20191217 完成\n", "任务 20191216 完成\n", "任务 20191213 完成\n", "任务 20191212 完成\n", @@ -1430,8 +1449,8 @@ "任务 20191101 完成\n", "任务 20191031 完成\n", "任务 20191030 完成\n", - "任务 20191028 完成\n", "任务 20191029 完成\n", + "任务 20191028 完成\n", "任务 20191025 完成\n", "任务 20191024 完成\n", "任务 20191023 完成\n", @@ -1440,18 +1459,18 @@ "任务 20191018 完成\n", "任务 20191017 完成\n", "任务 20191016 完成\n", - "任务 20191014 完成\n", "任务 20191015 完成\n", + "任务 20191014 完成\n", "任务 20191011 完成\n", "任务 20191010 完成\n", "任务 20191009 完成\n", "任务 20191008 完成\n", "任务 20190930 完成\n", + "任务 20190926 完成\n", "任务 20190927 完成\n", "任务 20190925 完成\n", - "任务 20190926 完成\n", - "任务 20190923 完成\n", "任务 20190924 完成\n", + "任务 20190923 完成\n", "任务 20190920 完成\n", "任务 20190919 完成\n", "任务 20190918 完成\n", @@ -1468,8 +1487,8 @@ "任务 20190902 完成\n", "任务 20190830 完成\n", "任务 20190829 完成\n", - "任务 20190827 完成\n", "任务 20190828 完成\n", + "任务 20190827 完成\n", "任务 20190826 完成\n", "任务 20190823 完成\n", "任务 20190822 完成\n", @@ -1494,15 +1513,15 @@ "任务 20190726 完成\n", "任务 20190725 完成\n", "任务 20190724 完成\n", - "任务 20190722 完成\n", "任务 20190723 完成\n", - "任务 20190718 完成\n", + "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 20190712 完成\n", "任务 20190715 完成\n", "任务 20190711 完成\n", + "任务 20190712 完成\n", "任务 20190710 完成\n", "任务 20190709 完成\n", "任务 20190708 完成\n", @@ -1525,8 +1544,8 @@ "任务 20190613 完成\n", "任务 20190612 完成\n", "任务 20190611 完成\n", - "任务 20190606 完成\n", "任务 20190610 完成\n", + "任务 20190606 完成\n", "任务 20190605 完成\n", "任务 20190604 完成\n", "任务 20190603 完成\n", @@ -1553,8 +1572,8 @@ "任务 20190430 完成\n", "任务 20190429 完成\n", "任务 20190426 完成\n", - "任务 20190424 完成\n", "任务 20190425 完成\n", + "任务 20190424 完成\n", "任务 20190423 完成\n", "任务 20190422 完成\n", "任务 20190419 完成\n", @@ -1601,16 +1620,16 @@ "任务 20190220 完成\n", "任务 20190219 完成\n", "任务 20190218 完成\n", - "任务 20190214 完成\n", "任务 20190215 完成\n", + "任务 20190214 完成\n", "任务 20190213 完成\n", "任务 20190212 完成\n", "任务 20190211 完成\n", "任务 20190201 完成\n", "任务 20190131 完成\n", "任务 20190130 完成\n", - "任务 20190128 完成\n", "任务 20190129 完成\n", + "任务 20190128 完成\n", "任务 20190125 完成\n", "任务 20190124 完成\n", "任务 20190123 完成\n", @@ -1619,14 +1638,14 @@ "任务 20190118 完成\n", "任务 20190117 完成\n", "任务 20190116 完成\n", - "任务 20190114 完成\n", "任务 20190115 完成\n", + "任务 20190114 完成\n", "任务 20190111 完成\n", "任务 20190110 完成\n", "任务 20190109 完成\n", "任务 20190108 完成\n", - "任务 20190104 完成\n", "任务 20190107 完成\n", + "任务 20190104 完成\n", "任务 20190103 完成\n", "任务 20190102 完成\n", "任务 20181228 完成\n", @@ -1642,12 +1661,12 @@ "任务 20181214 完成\n", "任务 20181213 完成\n", "任务 20181212 完成\n", - "任务 20181210 完成\n", "任务 20181211 完成\n", + "任务 20181210 完成\n", "任务 20181207 完成\n", "任务 20181206 完成\n", - "任务 20181205 完成\n", "任务 20181204 完成\n", + "任务 20181205 完成\n", "任务 20181203 完成\n", "任务 20181130 完成\n", "任务 20181129 完成\n", @@ -1660,12 +1679,12 @@ "任务 20181120 完成\n", "任务 20181119 完成\n", "任务 20181116 完成\n", - "任务 20181114 完成\n", "任务 20181115 完成\n", + "任务 20181114 完成\n", "任务 20181113 完成\n", "任务 20181112 完成\n", - "任务 20181108 完成\n", "任务 20181109 完成\n", + "任务 20181108 完成\n", "任务 20181107 完成\n", "任务 20181106 完成\n", "任务 20181105 完成\n", @@ -1681,8 +1700,8 @@ "任务 20181022 完成\n", "任务 20181019 完成\n", "任务 20181018 完成\n", - "任务 20181016 完成\n", "任务 20181017 完成\n", + "任务 20181016 完成\n", "任务 20181015 完成\n", "任务 20181012 完成\n", "任务 20181011 完成\n", @@ -1721,16 +1740,16 @@ "任务 20180817 完成\n", "任务 20180816 完成\n", "任务 20180815 完成\n", - "任务 20180813 完成\n", "任务 20180814 完成\n", + "任务 20180813 完成\n", "任务 20180810 完成\n", "任务 20180809 完成\n", "任务 20180808 完成\n", "任务 20180807 完成\n", "任务 20180806 完成\n", "任务 20180803 完成\n", - "任务 20180801 完成\n", "任务 20180802 完成\n", + "任务 20180801 完成\n", "任务 20180731 完成\n", "任务 20180730 完成\n", "任务 20180727 完成\n", @@ -1756,8 +1775,8 @@ "任务 20180629 完成\n", "任务 20180628 完成\n", "任务 20180627 完成\n", - "任务 20180626 完成\n", "任务 20180625 完成\n", + "任务 20180626 完成\n", "任务 20180622 完成\n", "任务 20180621 完成\n", "任务 20180620 完成\n", @@ -1771,8 +1790,8 @@ "任务 20180607 完成\n", "任务 20180606 完成\n", "任务 20180605 完成\n", - "任务 20180601 完成\n", "任务 20180604 完成\n", + "任务 20180601 完成\n", "任务 20180531 完成\n", "任务 20180530 完成\n", "任务 20180529 完成\n", @@ -1800,8 +1819,8 @@ "任务 20180425 完成\n", "任务 20180424 完成\n", "任务 20180423 完成\n", - "任务 20180419 完成\n", "任务 20180420 完成\n", + "任务 20180419 完成\n", "任务 20180418 完成\n", "任务 20180417 完成\n", "任务 20180416 完成\n", @@ -1811,12 +1830,12 @@ "任务 20180410 完成\n", "任务 20180409 完成\n", "任务 20180404 完成\n", - "任务 20180402 完成\n", "任务 20180403 完成\n", + "任务 20180402 完成\n", "任务 20180330 完成\n", "任务 20180329 完成\n", - "任务 20180327 完成\n", "任务 20180328 完成\n", + "任务 20180327 完成\n", "任务 20180326 完成\n", "任务 20180323 完成\n", "任务 20180322 完成\n", @@ -1837,8 +1856,8 @@ "任务 20180301 完成\n", "任务 20180228 完成\n", "任务 20180227 完成\n", - "任务 20180223 完成\n", "任务 20180226 完成\n", + "任务 20180223 完成\n", "任务 20180222 完成\n", "任务 20180214 完成\n", "任务 20180213 完成\n", @@ -1859,15 +1878,15 @@ "任务 20180123 完成\n", "任务 20180122 完成\n", "任务 20180119 完成\n", - "任务 20180118 完成\n", "任务 20180117 完成\n", + "任务 20180118 完成\n", "任务 20180116 完成\n", "任务 20180115 完成\n", "任务 20180112 完成\n", "任务 20180111 完成\n", "任务 20180110 完成\n", - "任务 20180108 完成\n", "任务 20180109 完成\n", + "任务 20180108 完成\n", "任务 20180105 完成\n", "任务 20180104 完成\n", "任务 20180103 完成\n", @@ -1939,8 +1958,8 @@ "任务 20170925 完成\n", "任务 20170922 完成\n", "任务 20170921 完成\n", - "任务 20170920 完成\n", "任务 20170919 完成\n", + "任务 20170920 完成\n", "任务 20170918 完成\n", "任务 20170915 完成\n", "任务 20170914 完成\n", @@ -1950,10 +1969,10 @@ "任务 20170908 完成\n", "任务 20170907 完成\n", "任务 20170906 完成\n", - "任务 20170904 完成\n", "任务 20170905 完成\n", - "任务 20170831 完成\n", + "任务 20170904 完成\n", "任务 20170901 完成\n", + "任务 20170831 完成\n", "任务 20170830 完成\n", "任务 20170829 完成\n", "任务 20170828 完成\n", @@ -1988,8 +2007,8 @@ "任务 20170718 完成\n", "任务 20170717 完成\n", "任务 20170714 完成\n", - "任务 20170713 完成\n", "任务 20170712 完成\n", + "任务 20170713 完成\n", "任务 20170711 完成\n", "任务 20170710 完成\n", "任务 20170707 完成\n", @@ -2004,8 +2023,8 @@ "任务 20170626 完成\n", "任务 20170623 完成\n", "任务 20170622 完成\n", - "任务 20170620 完成\n", "任务 20170621 完成\n", + "任务 20170620 完成\n", "任务 20170619 完成\n", "任务 20170616 完成\n", "任务 20170615 完成\n", @@ -2023,8 +2042,8 @@ "任务 20170526 完成\n", "任务 20170525 完成\n", "任务 20170524 完成\n", - "任务 20170523 完成\n", "任务 20170522 完成\n", + "任务 20170523 完成\n", "任务 20170519 完成\n", "任务 20170518 完成\n", "任务 20170517 完成\n", @@ -2048,14 +2067,14 @@ "任务 20170420 完成\n", "任务 20170419 完成\n", "任务 20170418 完成\n", - "任务 20170414 完成\n", "任务 20170417 完成\n", - "任务 20170412 完成\n", + "任务 20170414 完成\n", "任务 20170413 完成\n", + "任务 20170412 完成\n", "任务 20170411 完成\n", "任务 20170410 完成\n", - "任务 20170406 完成\n", "任务 20170407 完成\n", + "任务 20170406 完成\n", "任务 20170405 完成\n", "任务 20170331 完成\n", "任务 20170330 完成\n", @@ -2070,18 +2089,18 @@ "任务 20170317 完成\n", "任务 20170316 完成\n", "任务 20170315 完成\n", - "任务 20170313 完成\n", "任务 20170314 完成\n", + "任务 20170313 完成\n", "任务 20170310 完成\n", "任务 20170309 完成\n", - "任务 20170308 完成\n", "任务 20170307 完成\n", + "任务 20170308 完成\n", "任务 20170306 完成\n", "任务 20170303 完成\n", "任务 20170302 完成\n", "任务 20170301 完成\n", - "任务 20170227 完成\n", "任务 20170228 完成\n", + "任务 20170227 完成\n", "任务 20170224 完成\n", "任务 20170223 完成\n", "任务 20170222 完成\n", @@ -2094,12 +2113,12 @@ "任务 20170213 完成\n", "任务 20170210 完成\n", "任务 20170209 完成\n", - "任务 20170207 完成\n", "任务 20170208 完成\n", + "任务 20170207 完成\n", "任务 20170206 完成\n", "任务 20170203 完成\n", - "任务 20170125 完成\n", "任务 20170126 完成\n", + "任务 20170125 完成\n", "任务 20170124 完成\n", "任务 20170123 完成\n", "任务 20170120 完成\n", @@ -2114,8 +2133,8 @@ "任务 20170109 完成\n", "任务 20170106 完成\n", "任务 20170105 完成\n", - "任务 20170104 完成\n", - "任务 20170103 完成\n" + "任务 20170103 完成\n", + "任务 20170104 完成\n" ] } ], @@ -2200,11 +2219,11 @@ "3 834639.BJ 20250430 8.37 6.1158 7.8866 \n", "4 000909.SZ 20250430 5.72 0.6104 1.0424 \n", "... ... ... ... ... ... \n", - "8594006 600708.SH 20170103 9.03 0.7694 1.0169 \n", - "8594007 600712.SH 20170103 10.29 0.5859 0.8028 \n", - "8594008 001872.SZ 20170103 19.33 1.0970 5.4258 \n", - "8594009 001914.SZ 20170103 12.37 3.2627 6.6991 \n", - "8594010 302132.SZ 20170103 23.28 0.4912 1.5149 \n", + "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", "\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", @@ -2213,39 +2232,39 @@ "3 0.87 70.0984 215.1863 2.0171 0.8405 0.8329 \n", "4 0.55 NaN NaN 2.3539 7.7727 8.2925 \n", "... ... ... ... ... ... ... \n", - "8594006 0.85 23.3367 22.2458 1.4847 0.9613 0.9248 \n", - "8594007 0.67 202.4855 287.1454 5.1852 2.3682 2.5386 \n", - "8594008 0.77 23.6158 23.1883 2.7052 6.6556 6.5584 \n", - "8594009 1.02 20.5631 15.1595 2.1186 1.4950 1.2600 \n", - "8594010 0.74 91.3908 84.6980 6.9391 8.9531 8.8570 \n", + "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", "\n", - " dv_ratio dv_ttm total_share float_share free_share total_mv \\\n", - "0 2.0904 2.0904 40391.1511 40240.6511 4634.6511 5.800169e+05 \n", - "1 0.0000 NaN 63973.2569 63922.1969 51122.1969 1.042764e+06 \n", - "2 3.7471 3.7471 47382.5333 46932.3226 14014.3219 7.533823e+05 \n", - "3 NaN NaN 20160.0000 11721.5883 9089.7537 1.687392e+05 \n", - "4 0.0000 NaN 43771.4245 43771.0570 25634.2299 2.503725e+05 \n", - "... ... ... ... ... ... ... \n", - "8594006 1.1074 1.1074 131871.9966 75088.9215 56812.2811 1.190804e+06 \n", - "8594007 0.1555 0.1555 54465.5360 53795.9475 39266.3119 5.604504e+05 \n", - "8594008 2.1211 2.1211 64476.3730 46486.6050 9398.8050 1.246328e+06 \n", - "8594009 0.4042 0.4042 66696.1416 66678.0666 32475.1786 8.250313e+05 \n", - "8594010 0.2291 0.2291 39384.0333 30419.3588 9862.3809 9.168603e+05 \n", + " 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", "\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", - "8594006 6.780530e+05 False \n", - "8594007 5.535603e+05 False \n", - "8594008 8.985861e+05 False \n", - "8594009 8.248077e+05 False \n", - "8594010 7.081627e+05 False \n", + " 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", "\n", - "[8594011 rows x 19 columns]\n" + "[8599138 rows x 19 columns]\n" ] } ], diff --git a/main/data/finance.ipynb b/main/data/finance.ipynb index 9f7c404..33423e3 100644 --- a/main/data/finance.ipynb +++ b/main/data/finance.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "f403cc963e1d39b", "metadata": { "ExecuteTime": { @@ -359,6 +359,5791 @@ "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 0.97 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", + "成功获取 000819.SZ 的数据\n", + "成功获取 000820.SZ 的数据\n", + "成功获取 000821.SZ 的数据\n", + "成功获取 000822.SZ 的数据\n", + "成功获取 000823.SZ 的数据\n", + "成功获取 000825.SZ 的数据\n", + "成功获取 000826.SZ 的数据\n", + "成功获取 000828.SZ 的数据\n", + "成功获取 000829.SZ 的数据\n", + "成功获取 000830.SZ 的数据\n", + "成功获取 000831.SZ 的数据\n", + "成功获取 000833.SZ 的数据\n", + "成功获取 000837.SZ 的数据\n", + "成功获取 000838.SZ 的数据\n", + "成功获取 000839.SZ 的数据\n", + "成功获取 000848.SZ 的数据\n", + "成功获取 000850.SZ 的数据\n", + "成功获取 000851.SZ 的数据\n", + "成功获取 000852.SZ 的数据\n", + "成功获取 000856.SZ 的数据\n", + "成功获取 000858.SZ 的数据\n", + "成功获取 000859.SZ 的数据\n", + "成功获取 000860.SZ 的数据\n", + "成功获取 000862.SZ 的数据\n", + "成功获取 000863.SZ 的数据\n", + "成功获取 000868.SZ 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In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " all_cashflow = pd.concat(cashflow_list, ignore_index=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "所有日线数据已保存到 cashflow.h5\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", + "stocks_df = pd.read_csv('../../stocks_list.csv', encoding='utf-8-sig')\n", + "\n", + "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", + "cashflow_list = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "# 循环遍历每个股票代码并获取数据\n", + "for idx, row in stocks_df.iterrows():\n", + " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", + " try:\n", + " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", + " cashflow = pro.cashflow(ts_code=ts_code)\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if not cashflow.empty:\n", + " cashflow['ts_code'] = ts_code\n", + " cashflow_list.append(cashflow)\n", + " print(f\"成功获取 {ts_code} 的数据\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 300 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + "# 合并所有获取到的日线数据\n", + "if cashflow_list:\n", + " all_cashflow = pd.concat(cashflow_list, ignore_index=True)\n", + " all_cashflow.to_hdf('../../data/cashflow.h5', key='cashflow', mode='w', format='table')\n", + " print(\"所有日线数据已保存到 cashflow.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "85bdf760cb83fbd3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T15:47:42.761559200Z", + "start_time": 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的数据\n", @@ -659,6 +6444,7 @@ "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 5.16 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", @@ -959,6 +6745,7 @@ "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 0.03 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", @@ -1559,7 +7346,7 @@ "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 1.88 秒以满足速率限制...\n", + "已调用300次API，等待 3.26 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", @@ -2160,7 +7947,6 @@ "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 0.89 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", @@ -2461,7 +8247,7 @@ "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 2.56 秒以满足速率限制...\n", + "已调用300次API，等待 9.20 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", @@ -2762,7 +8548,6 @@ "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 3.25 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", @@ -3963,6 +9748,7 @@ "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 0.90 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", @@ -4263,7 +10049,7 @@ "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 3.10 秒以满足速率限制...\n", + "已调用300次API，等待 4.00 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", @@ -4564,7 +10350,7 @@ "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 1.72 秒以满足速率限制...\n", + "已调用300次API，等待 5.85 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", @@ -4865,7 +10651,7 @@ "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 1.17 秒以满足速率限制...\n", + "已调用300次API，等待 14.28 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", @@ -5166,7 +10952,7 @@ "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 0.58 秒以满足速率限制...\n", + "已调用300次API，等待 13.31 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", @@ -5467,6 +11253,7 @@ "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 12.71 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", @@ -5758,83 +11545,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_16220\\823660297.py:40: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " all_fina_indicator = pd.concat(cashflow_list, ignore_index=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "所有日线数据已保存到 cashflow.h5\n" - ] - } - ], - "source": [ - "\n", - "\n", - "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", - "stocks_df = pd.read_csv('../../stocks_list.csv', encoding='utf-8-sig')\n", - "\n", - "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", - "cashflow_list = []\n", - "\n", - "# API 调用计数和时间控制变量\n", - "api_call_count = 0\n", - "batch_start_time = time.time()\n", - "\n", - "# 循环遍历每个股票代码并获取数据\n", - "for idx, row in stocks_df.iterrows():\n", - " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", - " try:\n", - " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", - " cashflow = pro.cashflow(ts_code=ts_code)\n", - " # 如果返回数据不为空，则添加一列标识股票代码\n", - " if not cashflow.empty:\n", - " cashflow['ts_code'] = ts_code\n", - " cashflow_list.append(cashflow)\n", - " print(f\"成功获取 {ts_code} 的数据\")\n", - " except Exception as e:\n", - " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", - "\n", - " # 计数一次 API 调用\n", - " api_call_count += 1\n", - "\n", - " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", - " if api_call_count % 300 == 0:\n", - " elapsed = time.time() - batch_start_time\n", - " if elapsed < 60:\n", - " sleep_time = 60 - elapsed\n", - " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", - " time.sleep(sleep_time)\n", - " # 重置批次起始时间\n", - " batch_start_time = time.time()\n", - "\n", - "# 合并所有获取到的日线数据\n", - "if cashflow_list:\n", - " all_cashflow = pd.concat(cashflow_list, ignore_index=True)\n", - " all_cashflow.to_hdf('../../data/cashflow.h5', key='cashflow', mode='w', format='table')\n", - " print(\"所有日线数据已保存到 cashflow.h5\")\n", - "else:\n", - " print(\"未获取到任何日线数据。\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "85bdf760cb83fbd3", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T15:47:42.761559200Z", - "start_time": "2025-02-07T16:24:09.366158Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_27304\\2498023504.py:3: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_23876\\3311990243.py:40: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " all_fina_indicator = pd.concat(fina_indicator_list, ignore_index=True)\n" ] }, @@ -5897,7 +11608,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "83403f50", "metadata": {}, "outputs": [ @@ -6505,6 +12216,7 @@ "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 4.07 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", @@ -7405,6 +13117,7 @@ "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 10.83 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", @@ -7705,6 +13418,7 @@ "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 0.77 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", @@ -8005,6 +13719,7 @@ "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 4.82 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", @@ -8305,7 +14020,7 @@ "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 3.29 秒以满足速率限制...\n", + "已调用300次API，等待 15.11 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", @@ -8606,7 +14321,7 @@ "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 2.17 秒以满足速率限制...\n", + "已调用300次API，等待 17.18 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", @@ -8907,6 +14622,7 @@ "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 7.70 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", @@ -9807,6 +15523,7 @@ "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 0.50 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", @@ -10107,7 +15824,7 @@ "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 0.04 秒以满足速率限制...\n", + "已调用300次API，等待 9.64 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", @@ -10408,6 +16125,7 @@ "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 12.57 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", @@ -10708,7 +16426,7 @@ "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 2.28 秒以满足速率限制...\n", + "已调用300次API，等待 16.15 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", @@ -11009,7 +16727,7 @@ "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 3.96 秒以满足速率限制...\n", + "已调用300次API，等待 7.15 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", @@ -11310,7 +17028,7 @@ "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 2.74 秒以满足速率限制...\n", + "已调用300次API，等待 13.13 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", @@ -11602,7 +17320,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_16220\\3217625028.py:40: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_23876\\3217625028.py:40: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " all_balancesheet = pd.concat(balancesheet_list, ignore_index=True)\n" ] }, @@ -11665,7 +17383,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "b27bb37b", "metadata": {}, "outputs": [ @@ -11679,12 +17397,12 @@ "3 5.272164e+12\n", "4 5.243822e+12\n", " ... \n", - "372441 1.150050e+09\n", - "372442 9.132874e+08\n", - "372443 4.395018e+08\n", - "372444 4.792661e+08\n", - "372445 3.738566e+08\n", - "Name: total_liab, Length: 372446, dtype: float64\n" + "372450 1.150050e+09\n", + "372451 9.132874e+08\n", + "372452 4.395018e+08\n", + "372453 4.792661e+08\n", + "372454 3.738566e+08\n", + "Name: total_liab, Length: 372455, dtype: float64\n" ] } ], diff --git a/main/data/index_and_industry.ipynb b/main/data/index_and_industry.ipynb index 743ffda..d92be32 100644 --- a/main/data/index_and_industry.ipynb +++ b/main/data/index_and_industry.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -9,6 +10,7 @@ "start_time": "2025-04-09T14:57:26.124592Z" } }, + "outputs": [], "source": [ "from operator import index\n", "\n", @@ -18,12 +20,11 @@ "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "f448da220816bf98", "metadata": { "ExecuteTime": { @@ -31,6 +32,23 @@ "start_time": "2025-04-09T14:57:27.392846Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "数据已经成功存储到index_data.h5文件中\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_28220\\1832869062.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " final_df = pd.concat(all_data, ignore_index=True)\n" + ] + } + ], "source": [ "# 定义四个指数\n", "index_list = ['399300.SH', '000905.SH', '000852.SH', '399006.SZ']\n", @@ -50,28 +68,11 @@ "final_df.to_hdf('../../data/index_data.h5', key='index_data', mode='w')\n", "\n", "print(\"数据已经成功存储到index_data.h5文件中\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "数据已经成功存储到index_data.h5文件中\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_15500\\3209233630.py:13: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " final_df = pd.concat(all_data, ignore_index=True)\n" - ] - } - ], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "907f732d3c397bf", "metadata": { "ExecuteTime": { @@ -79,54 +80,53 @@ "start_time": "2025-04-09T14:57:37.695917Z" } }, - "source": [ - "h5_filename = '../../data/index_data.h5'\n", - "key = '/index_data'\n", - "with pd.HDFStore(h5_filename, mode='r') as store:\n", - " df = store[key]\n", - " print(df)\n" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date close open high low \\\n", - "0 000905.SH 20250409 5439.7716 5249.6841 5465.1449 5135.9655 \n", - "1 000905.SH 20250408 5326.9140 5279.7566 5371.1834 5249.2318 \n", - "2 000905.SH 20250407 5287.0333 5523.9636 5587.8502 5212.6773 \n", - "3 000905.SH 20250403 5845.5045 5842.6167 5906.7057 5817.9662 \n", - "4 000905.SH 20250402 5899.0865 5884.8925 5936.6467 5884.1126 \n", + "0 000905.SH 20250506 5740.3338 5668.8762 5740.3338 5666.4698 \n", + "1 000905.SH 20250430 5631.8249 5604.6537 5647.7821 5603.1718 \n", + "2 000905.SH 20250429 5604.9057 5583.7186 5622.0220 5571.2363 \n", + "3 000905.SH 20250428 5598.2951 5624.4166 5628.0778 5587.7857 \n", + "4 000905.SH 20250425 5627.1804 5613.1407 5661.5869 5596.5266 \n", "... ... ... ... ... ... ... \n", - "13444 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", - "13445 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", - "13446 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", - "13447 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", - "13448 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", + "13492 399006.SZ 20100607 1069.4680 1005.0280 1075.2250 1001.7020 \n", + "13493 399006.SZ 20100604 1027.6810 989.6810 1027.6810 986.5040 \n", + "13494 399006.SZ 20100603 998.3940 1002.3550 1026.7020 997.7750 \n", + "13495 399006.SZ 20100602 997.1190 967.6090 997.1190 952.6110 \n", + "13496 399006.SZ 20100601 973.2330 986.0150 994.7930 948.1180 \n", "\n", " pre_close change pct_chg vol amount \n", - "0 5326.9140 112.8576 2.1186 2.451180e+08 2.882574e+08 \n", - "1 5287.0333 39.8807 0.7543 2.238407e+08 2.618753e+08 \n", - "2 5845.5045 -558.4712 -9.5539 2.365227e+08 2.673974e+08 \n", - "3 5899.0865 -53.5820 -0.9083 1.349386e+08 1.736621e+08 \n", - "4 5892.8502 6.2363 0.1058 1.121600e+08 1.406421e+08 \n", + "0 5631.8249 108.5089 1.9267 1.627736e+08 2.170600e+08 \n", + "1 5604.9057 26.9192 0.4803 1.383866e+08 1.816166e+08 \n", + "2 5598.2951 6.6106 0.1181 1.267429e+08 1.580330e+08 \n", + "3 5627.1804 -28.8853 -0.5133 1.362181e+08 1.676163e+08 \n", + "4 5605.8796 21.3008 0.3800 1.400008e+08 1.719338e+08 \n", "... ... ... ... ... ... \n", - "13444 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", - "13445 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", - "13446 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", - "13447 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", - "13448 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", + "13492 1027.6810 41.7870 4.0661 2.655275e+06 9.106095e+06 \n", + "13493 998.3940 29.2870 2.9334 1.500295e+06 5.269441e+06 \n", + "13494 997.1190 1.2750 0.1279 1.616805e+06 6.240835e+06 \n", + "13495 973.2330 23.8860 2.4543 1.074628e+06 4.001206e+06 \n", + "13496 1000.0000 -26.7670 -2.6767 1.356285e+06 4.924177e+06 \n", "\n", - "[13449 rows x 11 columns]\n" + "[13497 rows x 11 columns]\n" ] } ], - "execution_count": 3 + "source": [ + "h5_filename = '../../data/index_data.h5'\n", + "key = '/index_data'\n", + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key]\n", + " print(df)\n" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/data/name_change.ipynb b/main/data/name_change.ipynb index 367c985..4d94328 100644 --- a/main/data/name_change.ipynb +++ b/main/data/name_change.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "94412ea8-aad7-47fb-8597-d80adef21a8b", "metadata": { "ExecuteTime": { @@ -9,70 +10,24 @@ "start_time": "2025-03-01T09:19:23.930364Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "9067006f-6352-4fe6-9295-22208f40f235", "metadata": { - "scrolled": true, "ExecuteTime": { "end_time": "2025-03-01T09:56:42.369757Z", "start_time": "2025-03-01T09:19:24.709524Z" - } + }, + "scrolled": true }, - "source": [ - "from tqdm import tqdm\n", - "import pandas as pd\n", - "import time\n", - "\n", - "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", - "stocks_df = pd.read_csv('../../stocks_list.csv', encoding='utf-8-sig')\n", - "\n", - "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", - "name_change_data_list = []\n", - "\n", - "# API 调用计数和时间控制变量\n", - "api_call_count = 0\n", - "batch_start_time = time.time()\n", - "\n", - "# 循环遍历每个股票代码并获取数据\n", - "for idx, row in stocks_df.iterrows():\n", - " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", - " try:\n", - " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", - " name_change_data = pro.namechange(ts_code=ts_code, fields='ts_code,name,start_date,end_date,change_reason')\n", - " # 如果返回数据不为空，则添加一列标识股票代码\n", - " if not name_change_data.empty:\n", - " name_change_data_list.append(name_change_data)\n", - " print(f\"成功获取 {ts_code} 的数据\")\n", - " except Exception as e:\n", - " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", - "\n", - " # 计数一次 API 调用\n", - " api_call_count += 1\n", - "\n", - " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", - " if api_call_count % 150 == 0:\n", - " elapsed = time.time() - batch_start_time\n", - " if elapsed < 60:\n", - " sleep_time = 60 - elapsed\n", - " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", - " time.sleep(sleep_time)\n", - " # 重置批次起始时间\n", - " batch_start_time = time.time()\n", - "\n", - "name_change_df = pd.concat(name_change_data_list, ignore_index=True)\n", - "# 输出部分结果\n", - "print(name_change_df.head())\n", - "print(f\"名称变化记录总数: {len(name_change_df)}\")\n" - ], "outputs": [ { "name": "stdout", @@ -228,7 +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 41.14 秒以满足速率限制...\n", + "已调用300次API，等待 38.79 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", @@ -379,7 +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 40.78 秒以满足速率限制...\n", + "已调用300次API，等待 38.14 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", @@ -530,7 +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 40.77 秒以满足速率限制...\n", + "已调用300次API，等待 38.70 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", @@ -681,7 +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 40.70 秒以满足速率限制...\n", + "已调用300次API，等待 38.23 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", @@ -832,7 +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 40.20 秒以满足速率限制...\n", + "已调用300次API，等待 38.48 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", @@ -983,7 +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 40.84 秒以满足速率限制...\n", + "已调用300次API，等待 38.28 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", @@ -1134,7 +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 41.66 秒以满足速率限制...\n", + "已调用300次API，等待 38.10 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", @@ -1285,7 +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 40.74 秒以满足速率限制...\n", + "已调用300次API，等待 39.07 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", @@ -1436,7 +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 41.14 秒以满足速率限制...\n", + "已调用300次API，等待 38.58 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", @@ -1587,7 +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 40.57 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 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", @@ -1738,7 +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 41.05 秒以满足速率限制...\n", + "已调用300次API，等待 38.05 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", @@ -1889,7 +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 40.69 秒以满足速率限制...\n", + "已调用300次API，等待 38.83 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", @@ -2040,7 +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 40.51 秒以满足速率限制...\n", + "已调用300次API，等待 38.36 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", @@ -2191,7 +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 39.15 秒以满足速率限制...\n", + "已调用300次API，等待 39.00 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", @@ -2342,7 +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 38.87 秒以满足速率限制...\n", + "已调用300次API，等待 39.10 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", @@ -2493,7 +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 40.54 秒以满足速率限制...\n", + "已调用300次API，等待 38.14 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", @@ -2644,7 +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 41.03 秒以满足速率限制...\n", + "已调用300次API，等待 38.08 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", @@ -2795,7 +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 40.99 秒以满足速率限制...\n", + "已调用300次API，等待 38.67 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", @@ -2946,7 +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 41.17 秒以满足速率限制...\n", + "已调用300次API，等待 39.59 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", @@ -3097,7 +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 40.74 秒以满足速率限制...\n", + "已调用300次API，等待 38.63 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", @@ -3248,7 +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 41.16 秒以满足速率限制...\n", + "已调用300次API，等待 38.00 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", @@ -3399,7 +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 40.45 秒以满足速率限制...\n", + "已调用300次API，等待 36.61 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", @@ -3550,7 +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 41.00 秒以满足速率限制...\n", + "已调用300次API，等待 38.88 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", @@ -3701,7 +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 41.08 秒以满足速率限制...\n", + "已调用300次API，等待 38.49 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", @@ -3852,7 +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 41.02 秒以满足速率限制...\n", + "已调用300次API，等待 38.62 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", @@ -4003,7 +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 40.67 秒以满足速率限制...\n", + "已调用300次API，等待 38.79 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", @@ -4154,7 +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 41.24 秒以满足速率限制...\n", + "已调用300次API，等待 39.75 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", @@ -4305,7 +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 40.73 秒以满足速率限制...\n", + "已调用300次API，等待 38.97 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", @@ -4456,7 +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 41.30 秒以满足速率限制...\n", + "已调用300次API，等待 39.13 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", @@ -4607,7 +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 40.75 秒以满足速率限制...\n", + "已调用300次API，等待 39.25 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", @@ -4758,7 +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 41.17 秒以满足速率限制...\n", + "已调用300次API，等待 38.88 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", @@ -4909,7 +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 41.28 秒以满足速率限制...\n", + "已调用300次API，等待 35.24 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", @@ -5060,7 +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 41.23 秒以满足速率限制...\n", + "已调用300次API，等待 37.62 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", @@ -5211,7 +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 40.17 秒以满足速率限制...\n", + "已调用300次API，等待 39.35 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", @@ -5362,7 +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 41.36 秒以满足速率限制...\n", + "已调用300次API，等待 39.39 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", @@ -5513,7 +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 40.98 秒以满足速率限制...\n", + "已调用300次API，等待 38.64 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", @@ -5664,7 +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 40.90 秒以满足速率限制...\n", + "已调用300次API，等待 39.83 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", @@ -5806,14 +5761,60 @@ "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: 31934\n" + "名称变化记录总数: 32258\n" ] } ], - "execution_count": 2 + "source": [ + "from tqdm import tqdm\n", + "import pandas as pd\n", + "import time\n", + "\n", + "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", + "stocks_df = pd.read_csv('../../stocks_list.csv', encoding='utf-8-sig')\n", + "\n", + "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", + "name_change_data_list = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "# 循环遍历每个股票代码并获取数据\n", + "for idx, row in stocks_df.iterrows():\n", + " ts_code = row['ts_code'] # 假设股票代码列名为 ts_code\n", + " try:\n", + " # 调用 tushare 接口获取该股票自 2017 年以来的日线数据\n", + " name_change_data = pro.namechange(ts_code=ts_code, fields='ts_code,name,start_date,end_date,change_reason')\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if not name_change_data.empty:\n", + " name_change_data_list.append(name_change_data)\n", + " print(f\"成功获取 {ts_code} 的数据\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + "\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 150 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n", + "\n", + "name_change_df = pd.concat(name_change_data_list, ignore_index=True)\n", + "# 输出部分结果\n", + "print(name_change_df.head())\n", + "print(f\"名称变化记录总数: {len(name_change_df)}\")\n" + ] }, { "cell_type": "code", + "execution_count": 3, "id": "4d5524b8-2a90-44bb-b5ef-e59cfa232ff0", "metadata": { "ExecuteTime": { @@ -5821,14 +5822,6 @@ "start_time": "2025-03-01T09:56:42.431891Z" } }, - "source": [ - "# 合并所有获取到的日线数据\n", - "if True:\n", - " name_change_df.to_hdf('../../data/name_change.h5', key='name_change', mode='w', format='table')\n", - " print(\"所有日线数据已保存到 daily_data.h5\")\n", - "else:\n", - " print(\"未获取到任何日线数据。\")" - ], "outputs": [ { "name": "stdout", @@ -5838,10 +5831,18 @@ ] } ], - "execution_count": 3 + "source": [ + "# 合并所有获取到的日线数据\n", + "if True:\n", + " name_change_df.to_hdf('../../data/name_change.h5', key='name_change', mode='w', format='table')\n", + " print(\"所有日线数据已保存到 daily_data.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] }, { "cell_type": "code", + "execution_count": 4, "id": "1e920791-e8de-4a51-a39b-283f54132b44", "metadata": { "ExecuteTime": { @@ -5849,9 +5850,6 @@ "start_time": "2025-03-01T09:56:42.545392Z" } }, - "source": [ - "print(name_change_df.head())" - ], "outputs": [ { "name": "stdout", @@ -5866,10 +5864,13 @@ ] } ], - "execution_count": 4 + "source": [ + "print(name_change_df.head())" + ] }, { "cell_type": "code", + "execution_count": null, "id": "4f5651f7-0910-4df5-9c3f-79d6ce033d53", "metadata": { "ExecuteTime": { @@ -5877,14 +5878,13 @@ "start_time": "2025-03-01T09:56:42.569013Z" } }, - "source": [], "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, @@ -5898,7 +5898,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.19" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/main/data/update/cyq-perf.ipynb b/main/data/update/cyq-perf.ipynb index 62a7ad6..7bb4859 100644 --- a/main/data/update/cyq-perf.ipynb +++ b/main/data/update/cyq-perf.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { @@ -9,16 +10,16 @@ "start_time": "2025-04-09T14:57:33.903794Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", "metadata": { "ExecuteTime": { @@ -26,6 +27,30 @@ "start_time": "2025-04-09T14:57:34.666469Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "0 000001.SZ 20250312\n", + "1 000002.SZ 20250312\n", + "2 000004.SZ 20250312\n", + "3 000006.SZ 20250312\n", + "4 000007.SZ 20250312\n", + "... ... ...\n", + "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", + "\n", + "[7648931 rows x 2 columns]\n", + "20250430\n", + "start_date: 20250506\n" + ] + } + ], "source": [ "import pandas as pd\n", "import time\n", @@ -39,40 +64,16 @@ " 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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n", "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", "start_date = min(trade_dates)\n", "print(f'start_date: {start_date}')" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ts_code trade_date\n", - "0 000001.SZ 20250312\n", - "1 000002.SZ 20250312\n", - "2 000004.SZ 20250312\n", - "3 000006.SZ 20250312\n", - "4 000007.SZ 20250312\n", - "... ... ...\n", - "5387 920108.BJ 20250408\n", - "5388 920111.BJ 20250408\n", - "5389 920116.BJ 20250408\n", - "5390 920118.BJ 20250408\n", - "5391 920128.BJ 20250408\n", - "\n", - "[7562721 rows x 2 columns]\n", - "20250408\n", - "start_date: 20250409\n" - ] - } - ], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "747acc47-0884-4f76-90fb-276f6494e31d", "metadata": { "ExecuteTime": { @@ -80,6 +81,47 @@ "start_time": "2025-04-09T14:57:42.232250Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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 20250616 完成\n", + "任务 20250611 完成\n", + "任务 20250612 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 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 20250516 完成\n", + "任务 20250515 完成\n", + "任务 20250514 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" + ] + } + ], "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -109,27 +151,11 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250418 完成\n", - "任务 20250417 完成\n", - "任务 20250416 完成\n", - "任务 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" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "c6765638-481f-40d8-a259-2e7b25362618", "metadata": { "ExecuteTime": { @@ -137,16 +163,6 @@ "start_time": "2025-04-09T14:57:45.698824Z" } }, - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "\n", - "# 将所有数据合并为一个 DataFrame\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -156,12 +172,21 @@ ] } ], - "execution_count": 4 + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/data/update/sw_daily.ipynb b/main/data/update/sw_daily.ipynb index c65e73b..ec0f74d 100644 --- a/main/data/update/sw_daily.ipynb +++ b/main/data/update/sw_daily.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "f74ce078-f7e8-4733-a14c-14d8815a3626", "metadata": { "ExecuteTime": { @@ -9,16 +10,16 @@ "start_time": "2025-04-09T14:57:34.837095Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "44dd8d87-e60b-49e5-aed9-efaa7f92d4fe", "metadata": { "ExecuteTime": { @@ -26,6 +27,30 @@ "start_time": "2025-04-09T14:57:35.854308Z" } }, + "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", + "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", + "\n", + "[1065026 rows x 2 columns]\n", + "20250430\n", + "start_date: 20250506\n" + ] + } + ], "source": [ "import pandas as pd\n", "import time\n", @@ -39,40 +64,16 @@ " 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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\n", "trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n", "trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n", "start_date = min(trade_dates)\n", "print(f'start_date: {start_date}')" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ts_code trade_date\n", - "0 801001.SI 20250221\n", - "1 801002.SI 20250221\n", - "2 801003.SI 20250221\n", - "3 801005.SI 20250221\n", - "4 801010.SI 20250221\n", - ".. ... ...\n", - "434 859811.SI 20250408\n", - "435 859821.SI 20250408\n", - "436 859822.SI 20250408\n", - "437 859852.SI 20250408\n", - "438 859951.SI 20250408\n", - "\n", - "[1058002 rows x 2 columns]\n", - "20250408\n", - "start_date: 20250409\n" - ] - } - ], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "747acc47-0884-4f76-90fb-276f6494e31d", "metadata": { "ExecuteTime": { @@ -80,6 +81,47 @@ "start_time": "2025-04-09T14:57:38.104541Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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 20250616 完成\n", + "任务 20250613 完成\n", + "任务 20250611 完成\n", + "任务 20250612 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 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 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 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" + ] + } + ], "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -109,27 +151,11 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250417 完成\n", - "任务 20250418 完成\n", - "任务 20250415 完成\n", - "任务 20250416 完成\n", - "任务 20250414 完成\n", - "任务 20250411 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "c6765638-481f-40d8-a259-2e7b25362618", "metadata": { "ExecuteTime": { @@ -137,16 +163,6 @@ "start_time": "2025-04-09T14:57:40.773783Z" } }, - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "\n", - "# 将所有数据合并为一个 DataFrame\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -156,12 +172,21 @@ ] } ], - "execution_count": 4 + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key=key, mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/data/update/update_daily_basic.ipynb b/main/data/update/update_daily_basic.ipynb index 1fa5342..6704db1 100644 --- a/main/data/update/update_daily_basic.ipynb +++ b/main/data/update/update_daily_basic.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "18d1d622-b083-4cc4-a6f8-7c1ed2d0edd2", "metadata": { "ExecuteTime": { @@ -9,16 +10,16 @@ "start_time": "2025-04-09T14:57:36.159612Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "14671a7f72de2564", "metadata": { "ExecuteTime": { @@ -26,6 +27,7 @@ "start_time": "2025-04-09T14:57:36.918051Z" } }, + "outputs": [], "source": [ "from datetime import datetime\n", "import pandas as pd\n", @@ -70,15 +72,15 @@ "name_change_dict = {}\n", "for ts_code, group in name_change_df.groupby('ts_code'):\n", " # 只保留 'ST' 和 '*ST' 的记录\n", - " st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", + " # st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", + " st_data = group[group['name'].str.contains('ST')]\n", " if not st_data.empty:\n", " name_change_dict[ts_code] = filter_rows(st_data)" - ], - "outputs": [], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "e7f8cce2f80e2f20", "metadata": { "ExecuteTime": { @@ -86,6 +88,26 @@ "start_time": "2025-04-09T14:57:39.339423Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 8599138 entries, 0 to 8599137\n", + "Data columns (total 2 columns):\n", + " # Column Dtype \n", + "--- ------ ----- \n", + " 0 ts_code object\n", + " 1 trade_date object\n", + "dtypes: object(2)\n", + "memory usage: 196.8+ MB\n", + "None\n", + "20250430\n", + "20250506\n" + ] + } + ], "source": [ "import time\n", "from concurrent.futures import ThreadPoolExecutor, as_completed\n", @@ -99,44 +121,85 @@ " 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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\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(start_date)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-09T14:58:16.817010Z", + "start_time": "2025-04-09T14:58:09.326485Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Index: 8512911 entries, 0 to 5391\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 194.8+ MB\n", - "None\n", - "20250408\n", - "20250409\n" + "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 20250714 完成\n", + "任务 20250709 完成\n", + "任务 20250710 完成\n", + "任务 20250707 完成\n", + "任务 20250708 完成\n", + "任务 20250704 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", + "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 20250618 完成\n", + "任务 20250617 完成\n", + "任务 20250616 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 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 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 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" ] } ], - "execution_count": 3 - }, - { - "cell_type": "code", - "id": "553cfb36-f560-4cc4-b2bc-68323ccc5072", - "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-04-09T14:58:16.817010Z", - "start_time": "2025-04-09T14:58:09.326485Z" - } - }, "source": [ "\n", "\n", @@ -186,27 +249,11 @@ " # 重置批次起始时间\n", " batch_start_time = time.time()\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250418 完成\n", - "任务 20250417 完成\n", - "任务 20250416 完成\n", - "任务 20250415 完成\n", - "任务 20250414 完成\n", - "任务 20250411 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n" - ] - } - ], - "execution_count": 4 + ] }, { "cell_type": "code", + "execution_count": 5, "id": "919023c693d7a47a", "metadata": { "ExecuteTime": { @@ -214,75 +261,75 @@ "start_time": "2025-04-09T14:58:16.855084Z" } }, - "source": [ - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", - "print(all_daily_data_df)" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "0 300285.SZ 20250409 16.61 2.1086 2.2506 \n", - "1 300458.SZ 20250409 44.48 9.9286 11.7046 \n", - "2 605090.SH 20250409 23.81 0.6834 1.1888 \n", - "3 688686.SH 20250409 69.52 1.6005 5.7492 \n", - "4 002057.SZ 20250409 7.18 4.7461 7.1088 \n", - "... ... ... ... ... ... \n", - "5390 301511.SZ 20250409 12.23 3.4040 4.6900 \n", - "5391 688355.SH 20250409 15.84 1.4154 4.4898 \n", - "5392 600019.SH 20250409 6.83 0.4729 1.2898 \n", - "5393 603507.SH 20250409 22.00 30.8936 42.4775 \n", - "5394 600886.SH 20250409 14.58 0.7795 2.4989 \n", + " 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", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "0 1.11 29.0985 27.1266 2.5144 4.2913 4.1010 0.6020 \n", - "1 1.54 168.9309 168.9309 9.3966 12.3119 12.3119 0.3364 \n", - "2 1.00 11.8377 9.0427 1.7135 0.5819 0.6421 3.2226 \n", - "3 1.18 43.8690 61.1222 2.9105 9.0031 9.2377 NaN \n", - "4 1.35 19.8304 29.3370 1.7625 1.9656 2.0487 3.2191 \n", - "... ... ... ... ... ... ... ... \n", - "5390 1.36 58.1209 NaN 1.9116 1.1803 1.1129 0.3212 \n", - "5391 1.31 133.9017 29.7427 1.8103 3.6805 3.1067 NaN \n", - "5392 1.28 12.5281 15.7915 0.7518 0.4344 0.4503 4.4796 \n", - "5393 2.89 22.7537 22.7537 1.6401 1.0276 1.0276 1.3553 \n", - "5394 1.04 17.4059 16.1402 1.8424 2.0579 1.9930 3.1604 \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", "\n", - " dv_ttm total_share float_share free_share total_mv \\\n", - "0 0.6020 9.970483e+04 8.039498e+04 75323.2612 1.656097e+06 \n", - "1 0.3364 6.332851e+04 5.179696e+04 43937.3622 2.816852e+06 \n", - "2 3.2226 6.492580e+04 6.426965e+04 36946.4646 1.545883e+06 \n", - "3 NaN 1.222355e+04 1.222355e+04 3402.7889 8.497809e+05 \n", - "4 3.2191 7.584828e+04 7.501396e+04 50081.8345 5.445906e+05 \n", - "... ... ... ... ... ... \n", - "5390 0.3212 6.303220e+04 3.736720e+04 27120.6014 7.708838e+05 \n", - "5391 NaN 1.239561e+04 1.239561e+04 3907.6756 1.963464e+05 \n", - "5392 4.4796 2.190864e+06 2.178208e+06 798651.6922 1.496360e+07 \n", - "5393 1.3553 1.843013e+04 1.843013e+04 13404.1045 4.054629e+05 \n", - "5394 3.1604 8.004494e+05 7.454180e+05 232532.2636 1.167055e+07 \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 NaN 8000.0000 1577.6325 1577.6325 4.200000e+05 \n", + "4 NaN 16177.0306 8763.6153 7897.4398 2.097028e+06 \n", + "... ... ... ... ... ... \n", + "5381 0.5650 40394.4205 40263.2044 25893.0990 5.004869e+05 \n", + "5382 2.1767 110992.3600 105986.8113 36194.3684 3.518458e+05 \n", + "5383 NaN 38728.0800 38728.0800 26808.2764 2.563799e+05 \n", + "5384 NaN 240794.5408 159659.3800 107284.6868 1.341226e+06 \n", + "5385 NaN 79641.0841 77768.6667 73609.4256 4.502110e+06 \n", "\n", " circ_mv is_st \n", - "0 1.335361e+06 False \n", - "1 2.303929e+06 False \n", - "2 1.530260e+06 False \n", - "3 8.497809e+05 False \n", - "4 5.386002e+05 False \n", + "0 6.446022e+05 False \n", + "1 6.083115e+05 False \n", + "2 8.562331e+05 False \n", + "3 8.282571e+04 False \n", + "4 1.136027e+06 False \n", "... ... ... \n", - "5390 4.570009e+05 False \n", - "5391 1.963464e+05 False \n", - "5392 1.487716e+07 False \n", - "5393 4.054629e+05 False \n", - "5394 1.086819e+07 False \n", + "5381 4.988611e+05 False \n", + "5382 3.359782e+05 False \n", + "5383 2.563799e+05 False \n", + "5384 8.893027e+05 False \n", + "5385 4.396263e+06 False \n", "\n", - "[5395 rows x 19 columns]\n" + "[5386 rows x 19 columns]\n" ] } ], - "execution_count": 5 + "source": [ + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n", + "print(all_daily_data_df)" + ] }, { "cell_type": "code", + "execution_count": 6, "id": "28cb78d032671b20", "metadata": { "ExecuteTime": { @@ -290,74 +337,74 @@ "start_time": "2025-04-09T14:58:16.871184Z" } }, - "source": [ - "print(all_daily_data_df[all_daily_data_df['is_st']])" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date close turnover_rate turnover_rate_f \\\n", - "85 002822.SZ 20250409 3.11 1.8467 1.9219 \n", - "123 603959.SH 20250409 3.27 1.7568 2.2420 \n", - "181 688282.SH 20250409 42.59 2.5546 3.0570 \n", - "259 600777.SH 20250409 2.66 1.9331 2.4597 \n", - "283 002052.SZ 20250409 6.15 1.5326 2.5481 \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", - "5286 002602.SZ 20250409 5.93 3.0376 3.5162 \n", - "5345 002501.SZ 20250409 1.89 4.3252 5.5834 \n", - "5364 600387.SH 20250409 2.34 0.0904 0.1163 \n", - "5366 002656.SZ 20250409 1.95 2.7047 3.0210 \n", - "5378 300013.SZ 20250409 3.57 2.8370 3.1107 \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", "\n", - " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", - "85 2.59 NaN NaN 1.2023 0.5923 0.7314 0.0 \n", - "123 2.22 NaN NaN 4.3282 0.7749 1.1811 0.0 \n", - "181 1.07 NaN NaN 2.9277 172.3150 21.9335 NaN \n", - "259 0.96 6.9694 7.6204 0.8381 2.0443 2.0567 0.0 \n", - "283 0.74 NaN NaN NaN 19.5551 17.1988 0.0 \n", - "... ... ... ... ... ... ... ... \n", - "5286 3.30 84.3318 49.2129 1.6993 3.3267 2.3228 0.0 \n", - "5345 1.75 NaN NaN 7.0441 14.0701 19.7111 0.0 \n", - "5364 1.33 NaN NaN 0.3818 0.5148 0.8454 0.0 \n", - "5366 1.75 NaN NaN 3.8456 4.7986 5.9354 0.0 \n", - "5378 0.90 NaN NaN 8.2438 4.8281 4.2666 0.0 \n", + " volume_ratio pe pe_ttm pb ps ps_ttm dv_ratio \\\n", + "23 3.99 NaN NaN 9.0141 10.6452 13.5427 0.0 \n", + "33 0.77 NaN NaN 28.5038 19.4588 19.2499 0.0 \n", + "82 1.96 NaN NaN 7.4242 9.3299 11.0451 NaN \n", + "105 1.34 NaN NaN NaN 4.1337 4.1261 0.0 \n", + "114 1.22 NaN NaN 120.9449 2.9900 3.1074 0.0 \n", + "... ... ... ... ... ... ... ... \n", + "5259 0.73 NaN NaN 1.6685 4.5071 4.6210 0.0 \n", + "5264 1.52 NaN NaN 15.5269 2.9812 3.6083 0.0 \n", + "5294 2.91 NaN NaN 24.3171 17.6463 26.1361 0.0 \n", + "5324 0.99 NaN NaN 2.7137 3.2758 3.8102 0.0 \n", + 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"\n", + "print(\"所有每日基础数据获取并保存完毕！\")\n" + ] }, { "cell_type": "code", + "execution_count": 8, "id": "d7a773fc20293477", "metadata": { "ExecuteTime": { @@ -391,18 +438,13 @@ "start_time": "2025-04-09T14:58:17.816332Z" } }, - "source": [ - "with pd.HDFStore(h5_filename, mode='r') as store:\n", - " df = store[key][['ts_code', 'trade_date', 'is_st']]\n", - " print(df.info())" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Index: 8518306 entries, 0 to 5394\n", + "Index: 8604524 entries, 0 to 5385\n", "Data columns (total 3 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -410,17 +452,21 @@ " 1 trade_date object\n", " 2 is_st bool \n", "dtypes: bool(1), object(2)\n", - "memory usage: 203.1+ MB\n", + "memory usage: 205.1+ MB\n", "None\n" ] } ], - "execution_count": 8 + "source": [ + "with pd.HDFStore(h5_filename, mode='r') as store:\n", + " df = store[key][['ts_code', 'trade_date', 'is_st']]\n", + " print(df.info())" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/data/update/update_daily_data.ipynb b/main/data/update/update_daily_data.ipynb index 6a777ba..f9e0207 100644 --- a/main/data/update/update_daily_data.ipynb +++ b/main/data/update/update_daily_data.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "f294ba92-512a-48e6-bbaa-e19401c691ba", "metadata": { "ExecuteTime": { @@ -9,6 +10,7 @@ "start_time": "2025-04-09T15:12:26.421811Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "import pandas as pd\n", @@ -18,12 +20,11 @@ "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "d31855fab4f8b1bc", "metadata": { "ExecuteTime": { @@ -31,6 +32,26 @@ "start_time": "2025-04-09T15:12:27.379199Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 8670620 entries, 0 to 80410\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", + "None\n", + "20250430\n", + "20250506\n" + ] + } + ], "source": [ "h5_filename = '../../../data/daily_data.h5'\n", "key = '/daily_data'\n", @@ -41,90 +62,24 @@ " 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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250620')\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(start_date)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 8584848 entries, 0 to 5374\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 196.5+ MB\n", - "None\n", - "20250408\n", - "20250409\n" - ] - } - ], - "execution_count": 2 + ] }, { "cell_type": "code", + "execution_count": 3, "id": "f403cc963e1d39b", "metadata": { - "scrolled": true, "ExecuteTime": { "end_time": "2025-04-09T15:25:31.420128Z", "start_time": "2025-04-09T15:12:34.587041Z" - } + }, + "scrolled": true }, - "source": [ - "from concurrent.futures import ThreadPoolExecutor, as_completed\n", - "\n", - "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", - "stocks_df = pd.read_csv('../../../stocks_list.csv', encoding='utf-8-sig')\n", - "\n", - "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", - "daily_data_list = []\n", - "\n", - "# API 调用计数和时间控制变量\n", - "api_call_count = 0\n", - "batch_start_time = time.time()\n", - "\n", - "\n", - "def get_data(ts_code):\n", - " daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date=start_date)\n", - " # 如果返回数据不为空，则添加一列标识股票代码\n", - " if daily_data is not None and not daily_data.empty:\n", - " return daily_data\n", - "\n", - "\n", - "# 循环遍历每个股票代码并获取数据\n", - "with ThreadPoolExecutor(max_workers=2) as executor:\n", - " future_to_date = {executor.submit(get_data, row['ts_code']): row['ts_code'] for idx, row in stocks_df.iterrows()}\n", - "\n", - " for future in as_completed(future_to_date):\n", - " ts_code = future_to_date[future]\n", - " try:\n", - " result = future.result()\n", - " daily_data_list.append(result)\n", - " print(f\"任务 {ts_code} 完成\")\n", - " except Exception as e:\n", - " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", - " # 计数一次 API 调用\n", - " api_call_count += 1\n", - "\n", - " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", - " if api_call_count % 300 == 0:\n", - " elapsed = time.time() - batch_start_time\n", - " if elapsed < 60:\n", - " sleep_time = 60 - 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完成\n", "任务 300739.SZ 完成\n", - "任务 300740.SZ 完成\n", "任务 300741.SZ 完成\n", + "任务 300740.SZ 完成\n", "任务 300743.SZ 完成\n", "任务 300745.SZ 完成\n", "任务 300746.SZ 完成\n", "任务 300747.SZ 完成\n", - "任务 300748.SZ 完成\n", "任务 300749.SZ 完成\n", + "任务 300748.SZ 完成\n", "任务 300750.SZ 完成\n", "任务 300751.SZ 完成\n", "任务 300752.SZ 完成\n", @@ -2331,8 +2286,8 @@ "任务 300758.SZ 完成\n", "任务 300759.SZ 完成\n", "任务 300760.SZ 完成\n", - "任务 300761.SZ 完成\n", "任务 300762.SZ 完成\n", + "任务 300761.SZ 完成\n", "任务 300763.SZ 完成\n", "任务 300765.SZ 完成\n", "任务 300766.SZ 完成\n", @@ -2392,22 +2347,22 @@ "任务 300822.SZ 完成\n", "任务 300823.SZ 完成\n", "任务 300824.SZ 完成\n", - "任务 300825.SZ 完成\n", "任务 300826.SZ 完成\n", + "任务 300825.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 300831.SZ 完成\n", "任务 300832.SZ 完成\n", + "任务 300831.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", "任务 300836.SZ 完成\n", "任务 300837.SZ 完成\n", "任务 300838.SZ 完成\n", - "任务 300840.SZ 完成\n", "任务 300839.SZ 完成\n", + "任务 300840.SZ 完成\n", "任务 300841.SZ 完成\n", "任务 300842.SZ 完成\n", "任务 300843.SZ 完成\n", @@ -2454,8 +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 300888.SZ 完成\n", "任务 300889.SZ 完成\n", + "任务 300888.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", @@ -2469,22 +2424,22 @@ "任务 300900.SZ 完成\n", "任务 300901.SZ 完成\n", "任务 300902.SZ 完成\n", - "任务 300904.SZ 完成\n", "任务 300903.SZ 完成\n", + "任务 300904.SZ 完成\n", "任务 300905.SZ 完成\n", "任务 300906.SZ 完成\n", - "任务 300908.SZ 完成\n", "任务 300907.SZ 完成\n", + "任务 300908.SZ 完成\n", "任务 300909.SZ 完成\n", "任务 300910.SZ 完成\n", "任务 300911.SZ 完成\n", "任务 300912.SZ 完成\n", "任务 300913.SZ 完成\n", "任务 300915.SZ 完成\n", - "任务 300917.SZ 完成\n", "任务 300916.SZ 完成\n", - "任务 300919.SZ 完成\n", + "任务 300917.SZ 完成\n", "任务 300918.SZ 完成\n", + "任务 300919.SZ 完成\n", "任务 300920.SZ 完成\n", "任务 300921.SZ 完成\n", "任务 300922.SZ 完成\n", @@ -2500,8 +2455,8 @@ "任务 300933.SZ 完成\n", "任务 300935.SZ 完成\n", "任务 300936.SZ 完成\n", - "任务 300937.SZ 完成\n", "任务 300938.SZ 完成\n", + "任务 300937.SZ 完成\n", "任务 300939.SZ 完成\n", "任务 300940.SZ 完成\n", "任务 300941.SZ 完成\n", @@ -2533,16 +2488,16 @@ "任务 300969.SZ 完成\n", "任务 300970.SZ 完成\n", "任务 300971.SZ 完成\n", - "任务 300972.SZ 完成\n", "任务 300973.SZ 完成\n", + "任务 300972.SZ 完成\n", "任务 300975.SZ 完成\n", "任务 300976.SZ 完成\n", - "任务 300977.SZ 完成\n", "任务 300978.SZ 完成\n", - "任务 300979.SZ 完成\n", + "任务 300977.SZ 完成\n", "任务 300980.SZ 完成\n", - "任务 300981.SZ 完成\n", + "任务 300979.SZ 完成\n", "任务 300982.SZ 完成\n", + "任务 300981.SZ 完成\n", "任务 300983.SZ 完成\n", "任务 300984.SZ 完成\n", "任务 300985.SZ 完成\n", @@ -2551,12 +2506,12 @@ "任务 300988.SZ 完成\n", "任务 300989.SZ 完成\n", "任务 300990.SZ 完成\n", - "任务 300991.SZ 完成\n", "任务 300992.SZ 完成\n", + "任务 300991.SZ 完成\n", "任务 300993.SZ 完成\n", "任务 300994.SZ 完成\n", - "任务 300995.SZ 完成\n", "任务 300996.SZ 完成\n", + "任务 300995.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", @@ -2607,29 +2562,29 @@ "任务 301047.SZ 完成\n", "任务 301048.SZ 完成\n", "任务 301049.SZ 完成\n", - "任务 301050.SZ 完成\n", "任务 301051.SZ 完成\n", + "任务 301050.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", "任务 301056.SZ 完成\n", "任务 301057.SZ 完成\n", "任务 301058.SZ 完成\n", - "任务 301060.SZ 完成\n", "任务 301059.SZ 完成\n", - "任务 301061.SZ 完成\n", + "任务 301060.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 301067.SZ 完成\n", - "任务 301069.SZ 完成\n", "任务 301068.SZ 完成\n", - "任务 301071.SZ 完成\n", + "任务 301067.SZ 完成\n", "任务 301070.SZ 完成\n", - "任务 301073.SZ 完成\n", + "任务 301069.SZ 完成\n", "任务 301072.SZ 完成\n", + "任务 301071.SZ 完成\n", "任务 301075.SZ 完成\n", + "任务 301073.SZ 完成\n", "任务 301076.SZ 完成\n", "任务 301077.SZ 完成\n", "任务 301078.SZ 完成\n", @@ -2638,8 +2593,8 @@ "任务 301081.SZ 完成\n", "任务 301082.SZ 完成\n", "任务 301083.SZ 完成\n", - "任务 301085.SZ 完成\n", "任务 301086.SZ 完成\n", + "任务 301085.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", @@ -2651,8 +2606,8 @@ "任务 301096.SZ 完成\n", "任务 301097.SZ 完成\n", "任务 301098.SZ 完成\n", - "任务 301100.SZ 完成\n", "任务 301099.SZ 完成\n", + "任务 301100.SZ 完成\n", "任务 301101.SZ 完成\n", "任务 301102.SZ 完成\n", "任务 301103.SZ 完成\n", @@ -2665,8 +2620,8 @@ "任务 301111.SZ 完成\n", "任务 301112.SZ 完成\n", "任务 301113.SZ 完成\n", - "任务 301116.SZ 完成\n", "任务 301115.SZ 完成\n", + "任务 301116.SZ 完成\n", "任务 301117.SZ 完成\n", "任务 301118.SZ 完成\n", "任务 301119.SZ 完成\n", @@ -2676,12 +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 301127.SZ 完成\n", "任务 301128.SZ 完成\n", + "任务 301127.SZ 完成\n", "任务 301129.SZ 完成\n", "任务 301130.SZ 完成\n", - "任务 301131.SZ 完成\n", "任务 301132.SZ 完成\n", + "任务 301131.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", @@ -2700,30 +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 301160.SZ 完成\n", "任务 301161.SZ 完成\n", + "任务 301160.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 301166.SZ 完成\n", "任务 301167.SZ 完成\n", + "任务 301166.SZ 完成\n", "任务 301168.SZ 完成\n", "任务 301169.SZ 完成\n", - "任务 301171.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 301175.SZ 完成\n", - "任务 301176.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 301180.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 301187.SZ 完成\n", "任务 301188.SZ 完成\n", + "任务 301187.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", @@ -2764,8 +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 301233.SZ 完成\n", "任务 301234.SZ 完成\n", + "任务 301233.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", @@ -2777,7 +2733,6 @@ "任务 301252.SZ 完成\n", "任务 301255.SZ 完成\n", "任务 301256.SZ 完成\n", - "任务 301185.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", @@ -2821,10 +2776,10 @@ "任务 301303.SZ 完成\n", "任务 301305.SZ 完成\n", "任务 301306.SZ 完成\n", - "任务 301307.SZ 完成\n", "任务 301308.SZ 完成\n", - "任务 301310.SZ 完成\n", + "任务 301307.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", "任务 301312.SZ 完成\n", "任务 301313.SZ 完成\n", @@ -2833,22 +2788,22 @@ "任务 301316.SZ 完成\n", "任务 301317.SZ 完成\n", "任务 301318.SZ 完成\n", - "任务 301319.SZ 完成\n", "任务 301320.SZ 完成\n", + "任务 301319.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", "任务 301325.SZ 完成\n", "任务 301326.SZ 完成\n", "任务 301327.SZ 完成\n", - "任务 301329.SZ 完成\n", "任务 301328.SZ 完成\n", - "任务 301331.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 301333.SZ 完成\n", - "任务 301336.SZ 完成\n", "任务 301335.SZ 完成\n", + "任务 301336.SZ 完成\n", "任务 301337.SZ 完成\n", "任务 301338.SZ 完成\n", "任务 301339.SZ 完成\n", @@ -2892,12 +2847,12 @@ "任务 301393.SZ 完成\n", "任务 301395.SZ 完成\n", "任务 301396.SZ 完成\n", - "任务 301397.SZ 完成\n", "任务 301398.SZ 完成\n", - "任务 301399.SZ 完成\n", + "任务 301397.SZ 完成\n", "任务 301408.SZ 完成\n", - "任务 301413.SZ 完成\n", + "任务 301399.SZ 完成\n", "任务 301418.SZ 完成\n", + "任务 301413.SZ 完成\n", "任务 301419.SZ 完成\n", "任务 301421.SZ 完成\n", "任务 301428.SZ 完成\n", @@ -2916,8 +2871,8 @@ "任务 301489.SZ 完成\n", "任务 301498.SZ 完成\n", "任务 301499.SZ 完成\n", - "任务 301500.SZ 完成\n", "任务 301502.SZ 完成\n", + "任务 301500.SZ 完成\n", "任务 301503.SZ 完成\n", "任务 301505.SZ 完成\n", "任务 301507.SZ 完成\n", @@ -2934,10 +2889,10 @@ "任务 301520.SZ 完成\n", "任务 301522.SZ 完成\n", "任务 301525.SZ 完成\n", - "任务 301526.SZ 完成\n", "任务 301528.SZ 完成\n", - "任务 301529.SZ 完成\n", + "任务 301526.SZ 完成\n", "任务 301533.SZ 完成\n", + "任务 301529.SZ 完成\n", "任务 301536.SZ 完成\n", "任务 301538.SZ 完成\n", "任务 301539.SZ 完成\n", @@ -2948,8 +2903,8 @@ "任务 301555.SZ 完成\n", "任务 301556.SZ 完成\n", "任务 301558.SZ 完成\n", - "任务 301559.SZ 完成\n", "任务 301565.SZ 完成\n", + "任务 301559.SZ 完成\n", "任务 301566.SZ 完成\n", "任务 301567.SZ 完成\n", "任务 301568.SZ 完成\n", @@ -2978,16 +2933,16 @@ "任务 301613.SZ 完成\n", "任务 301617.SZ 完成\n", "任务 301618.SZ 完成\n", - "任务 301622.SZ 完成\n", "任务 301626.SZ 完成\n", - "任务 301628.SZ 完成\n", + "任务 301622.SZ 完成\n", "任务 301631.SZ 完成\n", + "任务 301628.SZ 完成\n", "任务 301633.SZ 完成\n", "任务 430017.BJ 完成\n", "任务 430047.BJ 完成\n", "任务 430090.BJ 完成\n", - "任务 430139.BJ 完成\n", "任务 430198.BJ 完成\n", + "任务 430139.BJ 完成\n", "任务 430300.BJ 完成\n", "任务 430418.BJ 完成\n", "任务 430425.BJ 完成\n", @@ -3029,12 +2984,12 @@ "任务 600035.SH 完成\n", "任务 600036.SH 完成\n", "任务 600037.SH 完成\n", - "任务 600039.SH 完成\n", "任务 600038.SH 完成\n", + "任务 600039.SH 完成\n", "任务 600048.SH 完成\n", "任务 600050.SH 完成\n", - "任务 600052.SH 完成\n", "任务 600051.SH 完成\n", + "任务 600052.SH 完成\n", "任务 600053.SH 完成\n", "任务 600054.SH 完成\n", "任务 600055.SH 完成\n", @@ -3072,70 +3027,70 @@ "任务 600098.SH 完成\n", "任务 600099.SH 完成\n", "任务 600100.SH 完成\n", - "任务 600103.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 600105.SH 完成\n", "任务 600106.SH 完成\n", "任务 600107.SH 完成\n", "任务 600108.SH 完成\n", "任务 600109.SH 完成\n", - "任务 600111.SH 完成\n", "任务 600110.SH 完成\n", + "任务 600111.SH 完成\n", "任务 600113.SH 完成\n", "任务 600114.SH 完成\n", "任务 600115.SH 完成\n", "任务 600116.SH 完成\n", - "任务 600118.SH 完成\n", "任务 600117.SH 完成\n", + "任务 600118.SH 完成\n", "任务 600119.SH 完成\n", "任务 600120.SH 完成\n", "任务 600121.SH 完成\n", "任务 600123.SH 完成\n", "任务 600125.SH 完成\n", "任务 600126.SH 完成\n", - "任务 600128.SH 完成\n", "任务 600127.SH 完成\n", - "任务 600130.SH 完成\n", + "任务 600128.SH 完成\n", "任务 600129.SH 完成\n", + "任务 600130.SH 完成\n", "任务 600131.SH 完成\n", "任务 600132.SH 完成\n", - "任务 600135.SH 完成\n", "任务 600133.SH 完成\n", - "任务 600137.SH 完成\n", + "任务 600135.SH 完成\n", "任务 600136.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", - "任务 600148.SH 完成\n", "任务 600143.SH 完成\n", - "任务 600150.SH 完成\n", + "任务 600148.SH 完成\n", "任务 600149.SH 完成\n", + "任务 600150.SH 完成\n", "任务 600151.SH 完成\n", "任务 600152.SH 完成\n", "任务 600153.SH 完成\n", "任务 600155.SH 完成\n", "任务 600156.SH 完成\n", "任务 600157.SH 完成\n", - "任务 600159.SH 完成\n", "任务 600158.SH 完成\n", + "任务 600159.SH 完成\n", "任务 600160.SH 完成\n", "任务 600161.SH 完成\n", "任务 600162.SH 完成\n", "任务 600163.SH 完成\n", "任务 600165.SH 完成\n", "任务 600166.SH 完成\n", - "任务 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"\n", + "# 读取本地保存的股票列表 CSV 文件（假设文件名为 stocks_data.csv）\n", + "stocks_df = pd.read_csv('../../../stocks_list.csv', encoding='utf-8-sig')\n", + "\n", + "# 用于存放所有股票的日线数据（每次获取的 DataFrame）\n", + "daily_data_list = []\n", + "\n", + "# API 调用计数和时间控制变量\n", + "api_call_count = 0\n", + "batch_start_time = time.time()\n", + "\n", + "\n", + "def get_data(ts_code):\n", + " daily_data = ts.pro_bar(ts_code=ts_code, adj='hfq', start_date=start_date)\n", + " # 如果返回数据不为空，则添加一列标识股票代码\n", + " if daily_data is not None and not daily_data.empty:\n", + " return daily_data\n", + "\n", + "\n", + "# 循环遍历每个股票代码并获取数据\n", + "with ThreadPoolExecutor(max_workers=2) as executor:\n", + " future_to_date = {executor.submit(get_data, row['ts_code']): row['ts_code'] for idx, row in stocks_df.iterrows()}\n", + "\n", + " for future in as_completed(future_to_date):\n", + " ts_code = future_to_date[future]\n", + " try:\n", + " result = future.result()\n", + " daily_data_list.append(result)\n", + " print(f\"任务 {ts_code} 完成\")\n", + " except Exception as e:\n", + " print(f\"获取 {ts_code} 数据时出错: {e}\")\n", + " # 计数一次 API 调用\n", + " api_call_count += 1\n", + "\n", + " # 每调用300次，检查时间是否少于1分钟，如果少于则等待剩余时间\n", + " if api_call_count % 300 == 0:\n", + " elapsed = time.time() - batch_start_time\n", + " if elapsed < 60:\n", + " sleep_time = 60 - elapsed\n", + " print(f\"已调用300次API，等待 {sleep_time:.2f} 秒以满足速率限制...\")\n", + " time.sleep(sleep_time)\n", + " # 重置批次起始时间\n", + " batch_start_time = time.time()\n" + ] }, { "cell_type": "code", + "execution_count": 4, "id": "35d605a189a83c1f", "metadata": { "ExecuteTime": { @@ -5829,49 +5830,49 @@ "start_time": "2025-04-09T15:25:31.461200Z" } }, - "source": [ - "all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n", - "print(all_daily_data)" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ts_code trade_date open high low close pre_close \\\n", - "0 000001.SZ 20250409 1371.12 1383.90 1362.18 1380.07 1382.62 \n", - "1 000002.SZ 20250409 1217.42 1302.82 1202.88 1282.83 1235.59 \n", - "2 000004.SZ 20250409 34.91 36.29 31.70 36.09 35.19 \n", - "3 000006.SZ 20250409 222.54 238.04 204.66 235.26 226.92 \n", - "4 000007.SZ 20250409 44.32 45.89 40.34 45.56 44.49 \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", "... ... ... ... ... ... ... ... \n", - "5356 873706.BJ 20250409 16.57 21.81 16.57 21.81 16.79 \n", - "5357 873726.BJ 20250409 20.96 24.75 20.33 24.19 21.68 \n", - "5358 873806.BJ 20250409 12.64 14.34 12.35 14.25 12.78 \n", - "5359 873833.BJ 20250409 18.37 21.47 17.66 20.86 19.24 \n", - "5360 689009.SH 20250409 52.65 56.90 52.11 54.86 54.70 \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", "\n", - " change pct_chg vol amount \n", - "0 -2.55 -0.18 1064394.05 1145238.958 \n", - "1 47.24 3.82 1853079.77 1284483.339 \n", - "2 0.90 2.56 73354.00 61248.520 \n", - "3 8.34 3.68 685967.13 386042.322 \n", - "4 1.07 2.41 61500.00 32136.135 \n", - "... ... ... ... ... \n", - "5356 5.02 29.90 101369.40 178514.657 \n", - "5357 2.51 11.58 34407.94 75592.009 \n", - "5358 1.47 11.50 92646.26 124407.734 \n", - "5359 1.62 8.42 82933.72 145386.999 \n", - "5360 0.16 0.29 102228.24 556103.751 \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", "\n", - "[5361 rows x 11 columns]\n" + "[5355 rows x 11 columns]\n" ] } ], - "execution_count": 4 + "source": [ + "all_daily_data = pd.concat(daily_data_list, ignore_index=True)\n", + "print(all_daily_data)" + ] }, { "cell_type": "code", + "execution_count": 5, "id": "446aeba1a846b34", "metadata": { "ExecuteTime": { @@ -5879,16 +5880,6 @@ "start_time": "2025-04-09T15:25:31.660508Z" } }, - "source": [ - "\n", - "# 合并所有获取到的日线数据\n", - "if daily_data_list:\n", - " # 使用 HDF5 格式保存到本地文件（文件名：daily_data.h5，key设为 'daily_data'）\n", - " all_daily_data.to_hdf(h5_filename, key='daily_data', mode='a', append=True, data_columns=True, format='table')\n", - " print(\"所有日线数据已保存到 daily_data.h5\")\n", - "else:\n", - " print(\"未获取到任何日线数据。\")" - ], "outputs": [ { "name": "stdout", @@ -5898,12 +5889,21 @@ ] } ], - "execution_count": 5 + "source": [ + "\n", + "# 合并所有获取到的日线数据\n", + "if daily_data_list:\n", + " # 使用 HDF5 格式保存到本地文件（文件名：daily_data.h5，key设为 'daily_data'）\n", + " all_daily_data.to_hdf(h5_filename, key='daily_data', mode='a', append=True, data_columns=True, format='table')\n", + " print(\"所有日线数据已保存到 daily_data.h5\")\n", + "else:\n", + " print(\"未获取到任何日线数据。\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/data/update/update_is_st.ipynb b/main/data/update/update_is_st.ipynb index eee37fe..e9fa1d9 100644 --- a/main/data/update/update_is_st.ipynb +++ b/main/data/update/update_is_st.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 3, "id": "17cc645336d4eb18", "metadata": { "ExecuteTime": { @@ -9,73 +10,57 @@ "start_time": "2025-02-08T16:55:18.958639Z" } }, + "outputs": [], "source": [ "import pandas as pd\n", "import tushare as ts" - ], - "outputs": [], - "execution_count": 1 + ] }, { + "cell_type": "code", + "execution_count": 8, + "id": "48ae71ed02d61819", "metadata": { "ExecuteTime": { "end_time": "2025-02-08T16:55:27.578361Z", "start_time": "2025-02-08T16:55:19.882313Z" } }, - "cell_type": "code", - "source": [ - "daily_basic = pd.read_hdf('../../data/daily_basic.h5', key='daily_basic', columns=['ts_code', 'trade_date '])\n", - "name_change_df = pd.read_hdf('../../data/name_change.h5', key='name_change')\n", - "name_change_df = name_change_df.drop_duplicates(keep='first')\n", - "\n", - "# 确保 name_change_df 的日期格式正确\n", - "name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n", - "name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n", - "name_change_df = name_change_df[name_change_df.name.str.contains('ST')]\n" - ], - "id": "48ae71ed02d61819", "outputs": [], - "execution_count": 2 + "source": [ + "daily_basic = pd.read_hdf('../../../data/daily_basic.h5', key='daily_basic')\n" + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "e6606a96e5728b8", "metadata": { "ExecuteTime": { "end_time": "2025-02-08T16:55:27.938078Z", "start_time": "2025-02-08T16:55:27.584226Z" } }, - "cell_type": "code", - "source": [ - "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", - " if not st_data.empty:\n", - " name_change_dict[ts_code] = st_data" - ], - "id": "e6606a96e5728b8", "outputs": [], - "execution_count": 3 - }, - { - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2025-02-08T16:59:20.537632Z", - "start_time": "2025-02-08T16:55:27.971219Z" - } - }, - "cell_type": "code", "source": [ "from datetime import datetime\n", - "import pandas as pd\n", + "import warnings\n", "\n", + "warnings.filterwarnings(\"ignore\")\n", + "def filter_rows(df):\n", + " # 按照 name 和 start_date 分组\n", + " def select_row(group):\n", + " # 如果有 end_date 不为 NaT 的行，优先保留这些行\n", + " valid_rows = group[group['end_date'].notna()]\n", + " if not valid_rows.empty:\n", + " return valid_rows.iloc[0] # 返回第一个有效行\n", + " else:\n", + " return group.iloc[0] # 如果没有有效行，返回第一行\n", + "\n", + " filtered_df = df.groupby(['name', 'start_date'], group_keys=False).apply(select_row)\n", + " filtered_df = filtered_df.reset_index(drop=True)\n", + " return filtered_df\n", "\n", - "# 判断股票是否为 ST 的函数\n", - "#stock_code = 'xxxxxx.SH'\n", - "#target_date = '20200830'\n", - "#若为ST，返回True；否则返回False\n", "def is_st(name_change_dict, stock_code, target_date):\n", " target_date = datetime.strptime(target_date, '%Y%m%d')\n", " if stock_code not in name_change_dict.keys():\n", @@ -84,15 +69,129 @@ " for i in range(len(df)):\n", " sds = df.iloc[i, 2]\n", " eds = df.iloc[i, 3]\n", - " # sd = datetime.strptime(sds, '%Y%m%d')\n", - " if eds == None:\n", - " ed = datetime.now()\n", - " # else:\n", - " # ed = datetime.strptime(eds, '%Y%m%d')\n", + " if eds is None or eds is pd.NaT:\n", + " eds = datetime.now()\n", " if (target_date - sds).days >= 0 and (target_date - eds).days <= 0:\n", " return True\n", " return False\n", "\n", + "name_change_df = pd.read_hdf('../../../data/name_change.h5', key='name_change')\n", + "name_change_df = name_change_df.drop_duplicates(keep='first')\n", + "\n", + "# 确保 name_change_df 的日期格式正确\n", + "name_change_df['start_date'] = pd.to_datetime(name_change_df['start_date'], format='%Y%m%d')\n", + "name_change_df['end_date'] = pd.to_datetime(name_change_df['end_date'], format='%Y%m%d', errors='coerce')\n", + "name_change_df = name_change_df[name_change_df.name.str.contains('ST')]\n", + "name_change_dict = {}\n", + "for ts_code, group in name_change_df.groupby('ts_code'):\n", + " # 只保留 'ST' 和 '*ST' 的记录\n", + " # st_data = group[(group['change_reason'] == 'ST') | (group['change_reason'] == '*ST')]\n", + " st_data = group[group['name'].str.contains('ST')]\n", + " if not st_data.empty:\n", + " name_change_dict[ts_code] = filter_rows(st_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "41bc125d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date close turnover_rate turnover_rate_f \\\n", + "0 603848.SH 20250430 14.36 0.5401 4.6897 \n", + "1 300290.SZ 20250430 16.30 2.8540 3.5686 \n", + "2 603877.SH 20250430 15.90 0.3794 1.2707 \n", + "3 834639.BJ 20250430 8.37 6.1158 7.8866 \n", + "4 000909.SZ 20250430 5.72 0.6104 1.0424 \n", + "... ... ... ... ... ... \n", + "8594006 600708.SH 20170103 9.03 0.7694 1.0169 \n", + "8594007 600712.SH 20170103 10.29 0.5859 0.8028 \n", + "8594008 001872.SZ 20170103 19.33 1.0970 5.4258 \n", + "8594009 001914.SZ 20170103 12.37 3.2627 6.6991 \n", + "8594010 302132.SZ 20170103 23.28 0.4912 1.5149 \n", + "\n", + " volume_ratio pe pe_ttm pb ps ps_ttm \\\n", + "0 1.31 23.3421 25.6176 2.3433 3.7254 3.8065 \n", + "1 1.00 NaN NaN 13.1076 13.5867 13.5756 \n", + "2 0.98 29.1494 33.6975 1.6522 1.1075 1.1304 \n", + "3 0.87 70.0984 215.1863 2.0171 0.8405 0.8329 \n", + "4 0.55 NaN NaN 2.3539 7.7727 8.2925 \n", + "... ... ... ... ... ... ... \n", + "8594006 0.85 23.3367 22.2458 1.4847 0.9613 0.9248 \n", + "8594007 0.67 202.4855 287.1454 5.1852 2.3682 2.5386 \n", + "8594008 0.77 23.6158 23.1883 2.7052 6.6556 6.5584 \n", + "8594009 1.02 20.5631 15.1595 2.1186 1.4950 1.2600 \n", + "8594010 0.74 91.3908 84.6980 6.9391 8.9531 8.8570 \n", + "\n", + " dv_ratio dv_ttm total_share float_share free_share total_mv \\\n", + "0 2.0904 2.0904 40391.1511 40240.6511 4634.6511 5.800169e+05 \n", + "1 0.0000 NaN 63973.2569 63922.1969 51122.1969 1.042764e+06 \n", + "2 3.7471 3.7471 47382.5333 46932.3226 14014.3219 7.533823e+05 \n", + "3 NaN NaN 20160.0000 11721.5883 9089.7537 1.687392e+05 \n", + "4 0.0000 NaN 43771.4245 43771.0570 25634.2299 2.503725e+05 \n", + "... ... ... ... ... ... ... \n", + "8594006 1.1074 1.1074 131871.9966 75088.9215 56812.2811 1.190804e+06 \n", + "8594007 0.1555 0.1555 54465.5360 53795.9475 39266.3119 5.604504e+05 \n", + "8594008 2.1211 2.1211 64476.3730 46486.6050 9398.8050 1.246328e+06 \n", + "8594009 0.4042 0.4042 66696.1416 66678.0666 32475.1786 8.250313e+05 \n", + "8594010 0.2291 0.2291 39384.0333 30419.3588 9862.3809 9.168603e+05 \n", + "\n", + " circ_mv is_st \n", + "0 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", + "8594006 6.780530e+05 False \n", + "8594007 5.535603e+05 False \n", + "8594008 8.985861e+05 False \n", + "8594009 8.248077e+05 False \n", + "8594010 7.081627e+05 False \n", + "\n", + "[8594011 rows x 19 columns]\n" + ] + } + ], + "source": [ + "print(daily_basic)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-08T16:59:20.537632Z", + "start_time": "2025-02-08T16:55:27.971219Z" + }, + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "is st...\n", + " ts_code trade_date is_st\n", + "0 603848.SH 20250430 False\n", + "1 300290.SZ 20250430 False\n", + "2 603877.SH 20250430 False\n", + "3 834639.BJ 20250430 False\n", + "4 000909.SZ 20250430 True\n" + ] + } + ], + "source": [ + "from datetime import datetime\n", + "import pandas as pd\n", + "\n", + "\n", "\n", "print('is st...')\n", "# 创建一个新的列 is_st，判断每只股票是否是 ST\n", @@ -101,47 +200,30 @@ ")\n", "\n", "# 保存结果到新的 HDF5 文件\n", - "daily_basic.to_hdf('../../data/daily_basic_with_st.h5', key='daily_basic_with_st', mode='w', format='table')\n", + "daily_basic.to_hdf('../../../data/daily_basic.h5', key='daily_basic', mode='w', format='table')\n", "\n", "# 输出部分结果\n", "print(daily_basic[['ts_code', 'trade_date', 'is_st']].head())\n" - ], - "id": "initial_id", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "is st...\n", - " ts_code trade_date is_st\n", - "0 603429.SH 20250127 False\n", - "1 300917.SZ 20250127 False\n", - "2 301266.SZ 20250127 False\n", - "3 688399.SH 20250127 False\n", - "4 603737.SH 20250127 False\n" - ] - } - ], - "execution_count": 4 + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "new_trader", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.11.11" } }, "nbformat": 4, diff --git a/main/data/update/update_money_flow.ipynb b/main/data/update/update_money_flow.ipynb index 0058ae4..47a0781 100644 --- a/main/data/update/update_money_flow.ipynb +++ b/main/data/update/update_money_flow.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "b94bb1f2-5332-485e-ae1b-eea01f938106", "metadata": { "ExecuteTime": { @@ -9,17 +10,17 @@ "start_time": "2025-04-09T14:57:39.137312Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "742c29d453b9bb38", "metadata": { "ExecuteTime": { @@ -27,6 +28,26 @@ "start_time": "2025-04-09T14:57:40.190466Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 8435700 entries, 0 to 40956\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", + "None\n", + "20250430\n", + "start_date: 20250506\n" + ] + } + ], "source": [ "import pandas as pd\n", "import time\n", @@ -40,44 +61,85 @@ " 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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\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": "679ce40e-8d62-4887-970c-e1d8cbdeee6b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-09T14:58:17.197319Z", + "start_time": "2025-04-09T14:58:10.724923Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Index: 8353711 entries, 0 to 5126\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 191.2+ MB\n", - "None\n", - "20250408\n", - "start_date: 20250409\n" + "任务 20250717 完成\n", + "任务 20250718 完成\n", + "任务 20250715 完成\n", + "任务 20250716 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 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", + "任务 20250701 完成\n", + "任务 20250630 完成\n", + "任务 20250627 完成\n", + "任务 20250626 完成\n", + "任务 20250625 完成\n", + "任务 20250624 完成\n", + "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 20250616 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 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 20250526 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", + "任务 20250515 完成\n", + "任务 20250514 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" ] } ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b", - "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-04-09T14:58:17.197319Z", - "start_time": "2025-04-09T14:58:10.724923Z" - } - }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -107,27 +169,11 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250417 完成\n", - "任务 20250418 完成\n", - "任务 20250416 完成\n", - "任务 20250415 完成\n", - "任务 20250411 完成\n", - "任务 20250414 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "9af80516849d4e80", "metadata": { "ExecuteTime": { @@ -135,14 +181,14 @@ "start_time": "2025-04-09T14:58:17.210734Z" } }, + "outputs": [], "source": [ "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)\n" - ], - "outputs": [], - "execution_count": 4 + ] }, { "cell_type": "code", + "execution_count": 5, "id": "a2b05187-437f-4053-bc43-bd80d4cf8b0e", "metadata": { "ExecuteTime": { @@ -150,15 +196,6 @@ "start_time": "2025-04-09T14:58:17.229837Z" } }, - "source": [ - "\n", - "# 将所有数据合并为一个 DataFrame\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -168,12 +205,20 @@ ] } ], - "execution_count": 5 + "source": [ + "\n", + "# 将所有数据合并为一个 DataFrame\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key='money_flow', mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/data/update/update_stk_limit.ipynb b/main/data/update/update_stk_limit.ipynb index bbc1476..b2a6ca8 100644 --- a/main/data/update/update_stk_limit.ipynb +++ b/main/data/update/update_stk_limit.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "500802dc-7a20-48b7-a470-a4bae3ec534b", "metadata": { "ExecuteTime": { @@ -9,17 +10,17 @@ "start_time": "2025-04-09T14:57:40.584930Z" } }, + "outputs": [], "source": [ "import tushare as ts\n", "\n", "ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n", "pro = ts.pro_api()" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": 2, "id": "5a84bc9da6d54868", "metadata": { "ExecuteTime": { @@ -27,6 +28,32 @@ "start_time": "2025-04-09T14:57:41.540345Z" } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ts_code trade_date\n", + "4745 600276.SH 20250506\n", + "4746 600278.SH 20250506\n", + "4747 600279.SH 20250506\n", + "4736 600262.SH 20250506\n", + "281 000791.SZ 20250506\n", + "\n", + "Index: 10436295 entries, 0 to 113592\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: 238.9+ MB\n", + "None\n", + "20250506\n", + "20250507\n" + ] + } + ], "source": [ "import pandas as pd\n", "import time\n", @@ -41,50 +68,84 @@ " 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 = pro.trade_cal(exchange='', start_date='20170101', end_date='20250720')\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(start_date)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-09T14:58:09.342522Z", + "start_time": "2025-04-09T14:58:05.259974Z" + }, + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ts_code trade_date\n", - "4721 600284.SH 20250408\n", - "4722 600285.SH 20250408\n", - "4723 600287.SH 20250408\n", - "4712 600272.SH 20250408\n", - "5 000008.SZ 20250408\n", - "\n", - "Index: 10315620 entries, 0 to 14151\n", - "Data columns (total 2 columns):\n", - " # Column Dtype \n", - "--- ------ ----- \n", - " 0 ts_code object\n", - " 1 trade_date object\n", - "dtypes: object(2)\n", - "memory usage: 236.1+ MB\n", - "None\n", - "20250408\n", - "20250409\n" + "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 20250714 完成\n", + "任务 20250711 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 20250703 完成\n", + "任务 20250704 完成\n", + "任务 20250701 完成\n", + "任务 20250702 完成\n", + "任务 20250630 完成\n", + "任务 20250627 完成\n", + "任务 20250626 完成\n", + "任务 20250625 完成\n", + "任务 20250624 完成\n", + "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 20250616 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 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 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 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" ] } ], - "execution_count": 2 - }, - { - "cell_type": "code", - "id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f", - "metadata": { - "scrolled": true, - "ExecuteTime": { - "end_time": "2025-04-09T14:58:09.342522Z", - "start_time": "2025-04-09T14:58:05.259974Z" - } - }, "source": [ "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", @@ -115,27 +176,11 @@ " except Exception as e:\n", " print(f\"获取 {trade_date} 数据时出错: {e}\")\n", "\n" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "任务 20250417 完成\n", - "任务 20250418 完成\n", - "任务 20250416 完成\n", - "任务 20250415 完成\n", - "任务 20250414 完成\n", - "任务 20250410 完成\n", - "任务 20250409 完成\n", - "任务 20250411 完成\n" - ] - } - ], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": 4, "id": "96a81aa5890ea3c3", "metadata": { "ExecuteTime": { @@ -143,37 +188,38 @@ "start_time": "2025-04-09T14:58:09.346528Z" } }, - "source": [ - "print(all_daily_data)\n", - "# 将所有数据合并为一个 DataFrame\n", - "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)" - ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ trade_date ts_code up_limit down_limit\n", - "0 20250409 000001.SZ 11.90 9.74\n", - "1 20250409 000002.SZ 7.48 6.12\n", - "2 20250409 000004.SZ 9.53 7.79\n", - "3 20250409 000006.SZ 6.28 5.14\n", - "4 20250409 000007.SZ 5.91 4.83\n", - "... ... ... ... ...\n", - "7077 20250409 920108.BJ 26.55 14.31\n", - "7078 20250409 920111.BJ 30.84 16.62\n", - "7079 20250409 920116.BJ 100.29 54.01\n", - "7080 20250409 920118.BJ 31.62 17.04\n", - "7081 20250409 920128.BJ 35.26 19.00\n", - "\n", - "[7082 rows x 4 columns]]\n" + "[]\n" + ] + }, + { + "ename": "ValueError", + "evalue": "No objects to concatenate", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(all_daily_data)\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m all_daily_data_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat(all_daily_data, ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\u001b[0m, in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[0;32m 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 382\u001b[0m op \u001b[38;5;241m=\u001b[39m _Concatenator(\n\u001b[0;32m 383\u001b[0m objs,\n\u001b[0;32m 384\u001b[0m axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m 385\u001b[0m ignore_index\u001b[38;5;241m=\u001b[39mignore_index,\n\u001b[0;32m 386\u001b[0m join\u001b[38;5;241m=\u001b[39mjoin,\n\u001b[0;32m 387\u001b[0m keys\u001b[38;5;241m=\u001b[39mkeys,\n\u001b[0;32m 388\u001b[0m levels\u001b[38;5;241m=\u001b[39mlevels,\n\u001b[0;32m 389\u001b[0m names\u001b[38;5;241m=\u001b[39mnames,\n\u001b[0;32m 390\u001b[0m verify_integrity\u001b[38;5;241m=\u001b[39mverify_integrity,\n\u001b[0;32m 391\u001b[0m copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m 392\u001b[0m sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m 393\u001b[0m )\n\u001b[0;32m 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:445\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverify_integrity \u001b[38;5;241m=\u001b[39m verify_integrity\n\u001b[0;32m 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;241m=\u001b[39m copy\n\u001b[1;32m--> 445\u001b[0m objs, keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clean_keys_and_objs(objs, keys)\n\u001b[0;32m 447\u001b[0m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[0;32m 448\u001b[0m ndims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ndims(objs)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:507\u001b[0m, in \u001b[0;36m_Concatenator._clean_keys_and_objs\u001b[1;34m(self, objs, keys)\u001b[0m\n\u001b[0;32m 504\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[0;32m 506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 507\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 510\u001b[0m objs_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs_list))\n", + "\u001b[1;31mValueError\u001b[0m: No objects to concatenate" ] } ], - "execution_count": 4 + "source": [ + "print(all_daily_data)\n", + "# 将所有数据合并为一个 DataFrame\n", + "all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)" + ] }, { "cell_type": "code", + "execution_count": null, "id": "ad9733a1-2f42-43ee-a98c-0bf699304c21", "metadata": { "ExecuteTime": { @@ -181,14 +227,6 @@ "start_time": "2025-04-09T14:58:09.366441Z" } }, - "source": [ - "\n", - "\n", - "# 将数据保存为 HDF5 文件（table 格式）\n", - "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n", - "\n", - "print(\"所有每日基础数据获取并保存完毕！\")" - ], "outputs": [ { "name": "stdout", @@ -198,10 +236,18 @@ ] } ], - "execution_count": 5 + "source": [ + "\n", + "\n", + "# 将数据保存为 HDF5 文件（table 格式）\n", + "all_daily_data_df.to_hdf(h5_filename, key='stk_limit', mode='a', format='table', append=True, data_columns=True)\n", + "\n", + "print(\"所有每日基础数据获取并保存完毕！\")" + ] }, { "cell_type": "code", + "execution_count": null, "id": "7e777f1f-4d54-4a74-b916-691ede6af055", "metadata": { "ExecuteTime": { @@ -209,14 +255,13 @@ "start_time": "2025-04-09T14:58:09.686524Z" } }, - "source": [], "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "new_trader", "language": "python", "name": "python3" }, diff --git a/main/train/Classify2.ipynb b/main/train/Classify2.ipynb index 34a5dba..b7e8b93 100644 --- a/main/train/Classify2.ipynb +++ b/main/train/Classify2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 53, "id": "79a7758178bafdd3", "metadata": { "ExecuteTime": { @@ -18,6 +18,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", "e:\\PyProject\\NewStock\\main\\train\n" ] } @@ -44,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 54, "id": "a79cafb06a7e0e43", "metadata": { "ExecuteTime": { @@ -68,7 +70,7 @@ "cyq perf\n", "left merge on ['ts_code', 'trade_date']\n", "\n", - "RangeIndex: 8509852 entries, 0 to 8509851\n", + "RangeIndex: 8595791 entries, 0 to 8595790\n", "Data columns (total 32 columns):\n", " # Column Dtype \n", "--- ------ ----- \n", @@ -143,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 55, "id": "cac01788dac10678", "metadata": { "ExecuteTime": { @@ -158,6 +160,30 @@ "text": [ "industry\n" ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[55], line 45\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m 44\u001b[0m \u001b[38;5;66;03m# 使用示例\u001b[39;00m\n\u001b[1;32m---> 45\u001b[0m df \u001b[38;5;241m=\u001b[39m merge_with_industry_data(df, industry_df)\n", + "Cell \u001b[1;32mIn[55], line 19\u001b[0m, in \u001b[0;36mmerge_with_industry_data\u001b[1;34m(df, industry_df)\u001b[0m\n\u001b[0;32m 16\u001b[0m df_sorted \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39msort_values([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrade_date\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts_code\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# 使用 merge_asof 进行向后合并\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m merged \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mmerge_asof(\n\u001b[0;32m 20\u001b[0m df_sorted,\n\u001b[0;32m 21\u001b[0m industry_df_sorted,\n\u001b[0;32m 22\u001b[0m by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts_code\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# 按 ts_code 分组\u001b[39;00m\n\u001b[0;32m 23\u001b[0m left_on\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrade_date\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 24\u001b[0m right_on\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124min_date\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 25\u001b[0m direction\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 26\u001b[0m )\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# 获取每个 ts_code 的最早 in_date 记录\u001b[39;00m\n\u001b[0;32m 29\u001b[0m min_in_date_per_ts \u001b[38;5;241m=\u001b[39m (industry_df_sorted\n\u001b[0;32m 30\u001b[0m \u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts_code\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 31\u001b[0m \u001b[38;5;241m.\u001b[39mfirst()\n\u001b[0;32m 32\u001b[0m \u001b[38;5;241m.\u001b[39mreset_index()[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts_code\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml2_code\u001b[39m\u001b[38;5;124m'\u001b[39m]])\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:708\u001b[0m, in \u001b[0;36mmerge_asof\u001b[1;34m(left, right, on, left_on, right_on, left_index, right_index, by, left_by, right_by, suffixes, tolerance, allow_exact_matches, direction)\u001b[0m\n\u001b[0;32m 456\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;124;03mPerform a merge by key distance.\u001b[39;00m\n\u001b[0;32m 458\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;124;03m4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN\u001b[39;00m\n\u001b[0;32m 690\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 691\u001b[0m op \u001b[38;5;241m=\u001b[39m _AsOfMerge(\n\u001b[0;32m 692\u001b[0m left,\n\u001b[0;32m 693\u001b[0m right,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 706\u001b[0m direction\u001b[38;5;241m=\u001b[39mdirection,\n\u001b[0;32m 707\u001b[0m )\n\u001b[1;32m--> 708\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\\merge.py:1946\u001b[0m, in \u001b[0;36m_OrderedMerge.get_result\u001b[1;34m(self, copy)\u001b[0m\n\u001b[0;32m 1943\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1944\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;124mfill_method must be \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mffill\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or None\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1946\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_and_concat(\n\u001b[0;32m 1947\u001b[0m join_index, left_join_indexer, right_join_indexer, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m 1948\u001b[0m )\n\u001b[0;32m 1949\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_add_join_keys(result, left_indexer, right_indexer)\n\u001b[0;32m 1951\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py:879\u001b[0m, in \u001b[0;36m_MergeOperation._reindex_and_concat\u001b[1;34m(self, join_index, left_indexer, right_indexer, copy)\u001b[0m\n\u001b[0;32m 877\u001b[0m left\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m llabels\n\u001b[0;32m 878\u001b[0m right\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m rlabels\n\u001b[1;32m--> 879\u001b[0m result \u001b[38;5;241m=\u001b[39m concat([left, right], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:395\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 380\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;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[1;32m (...)\u001b[0m\n\u001b[0;32m 392\u001b[0m sort\u001b[38;5;241m=\u001b[39msort,\n\u001b[0;32m 393\u001b[0m )\n\u001b[1;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:684\u001b[0m, in \u001b[0;36m_Concatenator.get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 680\u001b[0m indexers[ax] \u001b[38;5;241m=\u001b[39m obj_labels\u001b[38;5;241m.\u001b[39mget_indexer(new_labels)\n\u001b[0;32m 682\u001b[0m mgrs_indexers\u001b[38;5;241m.\u001b[39mappend((obj\u001b[38;5;241m.\u001b[39m_mgr, indexers))\n\u001b[1;32m--> 684\u001b[0m new_data \u001b[38;5;241m=\u001b[39m concatenate_managers(\n\u001b[0;32m 685\u001b[0m mgrs_indexers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnew_axes, concat_axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbm_axis, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy\n\u001b[0;32m 686\u001b[0m )\n\u001b[0;32m 687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m using_copy_on_write():\n\u001b[0;32m 688\u001b[0m new_data\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\internals\\concat.py:131\u001b[0m, in \u001b[0;36mconcatenate_managers\u001b[1;34m(mgrs_indexers, axes, concat_axis, copy)\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[38;5;66;03m# Assertions disabled for performance\u001b[39;00m\n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# for tup in mgrs_indexers:\u001b[39;00m\n\u001b[0;32m 126\u001b[0m \u001b[38;5;66;03m# # caller is responsible for ensuring this\u001b[39;00m\n\u001b[0;32m 127\u001b[0m \u001b[38;5;66;03m# indexers = tup[1]\u001b[39;00m\n\u001b[0;32m 128\u001b[0m \u001b[38;5;66;03m# assert concat_axis not in indexers\u001b[39;00m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m concat_axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 131\u001b[0m mgrs \u001b[38;5;241m=\u001b[39m _maybe_reindex_columns_na_proxy(axes, mgrs_indexers, needs_copy)\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m mgrs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mconcat_horizontal(mgrs, axes)\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mgrs_indexers) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m mgrs_indexers[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mnblocks \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\internals\\concat.py:230\u001b[0m, in \u001b[0;36m_maybe_reindex_columns_na_proxy\u001b[1;34m(axes, mgrs_indexers, needs_copy)\u001b[0m\n\u001b[0;32m 220\u001b[0m mgr \u001b[38;5;241m=\u001b[39m mgr\u001b[38;5;241m.\u001b[39mreindex_indexer(\n\u001b[0;32m 221\u001b[0m axes[i],\n\u001b[0;32m 222\u001b[0m indexers[i],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 227\u001b[0m use_na_proxy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;66;03m# only relevant for i==0\u001b[39;00m\n\u001b[0;32m 228\u001b[0m )\n\u001b[0;32m 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m needs_copy \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m indexers:\n\u001b[1;32m--> 230\u001b[0m mgr \u001b[38;5;241m=\u001b[39m mgr\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 232\u001b[0m new_mgrs\u001b[38;5;241m.\u001b[39mappend(mgr)\n\u001b[0;32m 233\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_mgrs\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:604\u001b[0m, in \u001b[0;36mBaseBlockManager.copy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m 601\u001b[0m res\u001b[38;5;241m.\u001b[39m_blklocs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 603\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[1;32m--> 604\u001b[0m res\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n\u001b[0;32m 605\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1791\u001b[0m, in \u001b[0;36mBlockManager._consolidate_inplace\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1789\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_consolidated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 1790\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_known_consolidated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m-> 1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rebuild_blknos_and_blklocs()\n", + "File \u001b[1;32minternals.pyx:755\u001b[0m, in \u001b[0;36mpandas._libs.internals.BlockManager._rebuild_blknos_and_blklocs\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\internals\\base.py:84\u001b[0m, in \u001b[0;36mDataManager.shape\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshape\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Shape:\n\u001b[1;32m---> 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mlen\u001b[39m(ax) \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", + "File \u001b[1;32me:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\internals\\base.py:84\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshape\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Shape:\n\u001b[1;32m---> 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mlen\u001b[39m(ax) \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -211,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "c4e9e1d31da6dba6", "metadata": { "ExecuteTime": { @@ -303,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a735bc02ceb4d872", "metadata": { "ExecuteTime": { @@ -319,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "53f86ddc0677a6d7", "metadata": { "ExecuteTime": { @@ -382,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "dbe2fd8021b9417f", "metadata": { "ExecuteTime": { @@ -410,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "85c3e3d0235ffffa", "metadata": { "ExecuteTime": { @@ -433,25 +459,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "a1c5858e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['ts_code', 'ann_date', 'money_cap', 'total_liab'], dtype='object')\n" - ] - } - ], - "source": [ - "print(balancesheet_df.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "92d84ce15a562ec6", "metadata": { "ExecuteTime": { @@ -464,6 +472,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", + "使用 'ann_date' 作为财务数据生效日期。\n", + "警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n", "开始计算因子: AR, BR (原地修改)...\n", "因子 AR, BR 计算成功。\n", "因子 AR, BR 计算流程结束。\n", @@ -479,31 +491,62 @@ " 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n", " 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n", " 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n", - " 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", + " 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR',\n", " 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n", " 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n", - " 'future_return', 'cat_up_limit', 'label', 'lg_elg_net_buy_vol',\n", - " 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n", - " 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n", - " 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n", - " 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n", - " 'floating_chip_proxy', 'cost_support_15pct_change',\n", - " 'cat_winner_price_zone', 'flow_chip_consistency',\n", - " 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n", - " 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n", - " '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n", - " 'return_skew', 'return_kurtosis', 'volume_change_rate',\n", + " 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol',\n", + " 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol',\n", + " '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", " 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n", " 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n", " 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n", " dtype='object')\n", + "Calculating lg_flow_mom_corr_20_60...\n", + "Finished lg_flow_mom_corr_20_60.\n", + "Calculating lg_flow_accel...\n", + "Finished lg_flow_accel.\n", + "Calculating profit_pressure...\n", + "Finished profit_pressure.\n", + "Calculating underwater_resistance...\n", + "Finished underwater_resistance.\n", + "Calculating cost_conc_std_20...\n", + "Finished cost_conc_std_20.\n", + "Calculating profit_decay_20...\n", + "Finished profit_decay_20.\n", + "Calculating vol_amp_loss_20...\n", + "Finished vol_amp_loss_20.\n", + "Calculating vol_drop_profit_cnt_5...\n", + "Finished vol_drop_profit_cnt_5.\n", + "Calculating lg_flow_vol_interact_20...\n", + "Finished lg_flow_vol_interact_20.\n", + "Calculating cost_break_confirm_cnt_5...\n", + "Finished cost_break_confirm_cnt_5.\n", + "Calculating atr_norm_channel_pos_14...\n", + "Finished atr_norm_channel_pos_14.\n", + "Calculating turnover_diff_skew_20...\n", + "Finished turnover_diff_skew_20.\n", + "Calculating lg_sm_flow_diverge_20...\n", + "Finished lg_sm_flow_diverge_20.\n", + "Calculating pullback_strong_20_20...\n", + "Finished pullback_strong_20_20.\n", + "Calculating vol_wgt_hist_pos_20...\n", + "Finished vol_wgt_hist_pos_20.\n", + "Calculating vol_adj_roc_20...\n", + "Finished vol_adj_roc_20.\n", "\n", - "Index: 4450396 entries, 0 to 4450395\n", - "Columns: 118 entries, ts_code to mv_growth\n", - "dtypes: bool(5), datetime64[ns](1), float64(106), int32(4), object(2)\n", - "memory usage: 3.7+ GB\n", + "Index: 4502216 entries, 0 to 4502215\n", + "Columns: 157 entries, ts_code to vol_adj_roc_20\n", + "dtypes: bool(10), datetime64[ns](1), float64(141), int32(3), object(2)\n", + "memory usage: 5.0+ GB\n", "None\n", - "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'future_return', 'cat_up_limit', 'label', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', '_ema_5', '_ema_13', '_ema_20', '_ema_60', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', '_stddev_close', '_rank_stddev', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth']\n" + "['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', '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', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', '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']\n" ] } ], @@ -539,23 +582,46 @@ "df = variance_n(df, n=20)\n", "df = bbi_ratio_factor(df)\n", "df, _ = get_rolling_factor(df)\n", + "df, _ = get_simple_factor(df)\n", "\n", - "# lg_flow_mom_corr(df, N=20, M=60)\n", - "# lg_flow_accel(df)\n", - "# profit_pressure(df)\n", - "# underwater_resistance(df)\n", - "# cost_conc_std(df, N=20)\n", - "# profit_decay(df, N=20)\n", - "# vol_amp_loss(df, N=20)\n", - "# vol_drop_profit_cnt(df, N=20, M=5)\n", - "# lg_flow_vol_interact(df, N=20)\n", - "# cost_break_confirm_cnt(df, M=5)\n", - "# atr_norm_channel_pos(df, N=14)\n", - "# turnover_diff_skew(df, N=20)\n", - "# lg_sm_flow_diverge(df, N=20)\n", - "# pullback_strong(df, N=20, M=20)\n", - "# vol_wgt_hist_pos(df, N=20)\n", - "# vol_adj_roc(df, N=20)\n", + "lg_flow_mom_corr(df, N=20, M=60)\n", + "lg_flow_accel(df)\n", + "profit_pressure(df)\n", + "underwater_resistance(df)\n", + "cost_conc_std(df, N=20)\n", + "profit_decay(df, N=20)\n", + "vol_amp_loss(df, N=20)\n", + "vol_drop_profit_cnt(df, N=20, M=5)\n", + "lg_flow_vol_interact(df, N=20)\n", + "cost_break_confirm_cnt(df, M=5)\n", + "atr_norm_channel_pos(df, N=14)\n", + "turnover_diff_skew(df, N=20)\n", + "lg_sm_flow_diverge(df, N=20)\n", + "pullback_strong(df, N=20, M=20)\n", + "vol_wgt_hist_pos(df, N=20)\n", + "vol_adj_roc(df, N=20)\n", + "\n", + "calculate_complex_factor(df)\n", + "cs_rank_net_lg_flow_val(df)\n", + "cs_rank_flow_divergence(df)\n", + "cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n", + "cs_rank_elg_buy_ratio(df)\n", + "cs_rank_rel_profit_margin(df)\n", + "cs_rank_cost_breadth(df)\n", + "cs_rank_dist_to_upper_cost(df)\n", + "cs_rank_winner_rate(df)\n", + "cs_rank_intraday_range(df)\n", + "cs_rank_close_pos_in_range(df)\n", + "cs_rank_opening_gap(df) # Needs pre_close\n", + "cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n", + "cs_rank_vol_x_profit_margin(df)\n", + "cs_rank_lg_flow_price_concordance(df)\n", + "cs_rank_turnover_per_winner(df)\n", + "cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n", + "cs_rank_volume_ratio(df) # Needs volume_ratio\n", + "cs_rank_elg_buy_sell_sm_ratio(df)\n", + "cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n", + "cs_rank_size(df) # Needs circ_mv\n", "\n", "df = df.rename(columns={'l1_code': 'cat_l1_code'})\n", "df = df.rename(columns={'l2_code': 'cat_l2_code'})\n", @@ -568,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "b87b938028afa206", "metadata": { "ExecuteTime": { @@ -606,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "f4f16d63ad18d1bc", "metadata": { "ExecuteTime": { @@ -838,8 +904,8 @@ " # 使用当日中位数填充该特征列的 NaN 值\n", " # inplace=True 会直接修改 group DataFrame\n", " group[feature_col].fillna(median_val, inplace=True)\n", - " else:\n", - " print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", + " # else:\n", + " # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n", "\n", " return group\n", "\n", @@ -852,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "40e6b68a91b30c79", "metadata": { "ExecuteTime": { @@ -1143,7 +1209,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "47c12bb34062ae7a", "metadata": { "ExecuteTime": { @@ -1177,7 +1243,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "b76ea08a", "metadata": {}, "outputs": [ @@ -1189,7 +1255,7 @@ "0 000001.SZ 2019-01-02 16.574219\n", "2738 000001.SZ 2019-01-03 16.583965\n", "5477 000001.SZ 2019-01-04 16.633371\n", - "['undist_profit_ps', 'AR_BR', 'pe_ttm', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'cashflow_to_ev_factor', 'ocfps', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor']\n", + "['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', '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", "去除极值\n", "开始截面 MAD 去极值处理 (k=3.0)...\n" ] @@ -1198,7 +1264,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 24/24 [00:04<00:00, 5.00it/s]\n" + "MAD Filtering: 100%|██████████| 114/114 [00:22<00:00, 5.04it/s]\n" ] }, { @@ -1213,7 +1279,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "MAD Filtering: 100%|██████████| 24/24 [00:03<00:00, 6.27it/s]\n" + "MAD Filtering: 100%|██████████| 114/114 [00:18<00:00, 6.16it/s]\n" ] }, { @@ -1251,15 +1317,15 @@ "output_type": "stream", "text": [ "截面 MAD 去极值处理完成。\n", - "feature_columns: ['undist_profit_ps', 'AR_BR', 'pe_ttm', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'cat_up_limit', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'cashflow_to_ev_factor', 'ocfps', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor']\n", + "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', '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: 2025-04-09\n", - "2058298\n", + "df最大日期: 2025-05-06\n", + "2058185\n", "train_data最小日期: 2020-01-02\n", "train_data最大日期: 2022-12-30\n", - "1678529\n", + "1730349\n", "test_data最小日期: 2023-01-03\n", - "test_data最大日期: 2025-04-09\n", + "test_data最大日期: 2025-05-06\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", @@ -1287,55 +1353,81 @@ "# train_data, _ = create_deviation_within_dates(train_data, feature_columns)\n", "# test_data, _ = create_deviation_within_dates(test_data, feature_columns)\n", "\n", - "feature_columns = [\n", - " 'undist_profit_ps', \n", - " 'AR_BR', \n", - " 'pe_ttm',\n", - " 'alpha_22_improved', \n", - " 'alpha_003', \n", - " 'alpha_007', \n", - " 'alpha_013', \n", - " 'cat_up_limit', \n", - " 'cat_down_limit', \n", - " 'up_limit_count_10d', \n", - " 'down_limit_count_10d', \n", - " 'consecutive_up_limit', \n", - " 'vol_break', \n", - " 'weight_roc5', \n", - " 'price_cost_divergence', \n", - " 'smallcap_concentration', \n", - " 'cost_stability', \n", - " 'high_cost_break_days', \n", - " 'liquidity_risk', \n", - " 'turnover_std', \n", - " 'mv_volatility', \n", - " 'volume_growth', \n", - " 'mv_growth', \n", - " 'lg_flow_mom_corr_20_60', \n", - " 'lg_flow_accel', \n", - " 'profit_pressure', \n", - " 'underwater_resistance', \n", - " 'cost_conc_std_20', \n", - " 'profit_decay_20', \n", - " 'vol_amp_loss_20', \n", - " 'vol_drop_profit_cnt_5', \n", - " 'lg_flow_vol_interact_20', \n", - " 'cost_break_confirm_cnt_5', \n", - " 'atr_norm_channel_pos_14', \n", - " 'turnover_diff_skew_20', \n", - " 'lg_sm_flow_diverge_20', \n", - " 'pullback_strong_20_20', \n", - " 'vol_wgt_hist_pos_20', \n", - " 'vol_adj_roc_20',\n", - " 'cashflow_to_ev_factor',\n", - " 'ocfps',\n", - " 'book_to_price_ratio',\n", - " 'turnover_rate_mean_5',\n", - " 'variance_20',\n", - " 'bbi_ratio_factor'\n", - "]\n", - "feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "# feature_columns = [\n", + "# 'undist_profit_ps', \n", + "# 'AR_BR', \n", + "# 'pe_ttm',\n", + "# 'alpha_22_improved', \n", + "# 'alpha_003', \n", + "# 'alpha_007', \n", + "# 'alpha_013', \n", + "# 'cat_up_limit', \n", + "# 'cat_down_limit', \n", + "# 'up_limit_count_10d', \n", + "# 'down_limit_count_10d', \n", + "# 'consecutive_up_limit', \n", + "# 'vol_break', \n", + "# 'weight_roc5', \n", + "# 'price_cost_divergence', \n", + "# 'smallcap_concentration', \n", + "# 'cost_stability', \n", + "# 'high_cost_break_days', \n", + "# 'liquidity_risk', \n", + "# 'turnover_std', \n", + "# 'mv_volatility', \n", + "# 'volume_growth', \n", + "# 'mv_growth', \n", + "# 'lg_flow_mom_corr_20_60', \n", + "# 'lg_flow_accel', \n", + "# 'profit_pressure', \n", + "# 'underwater_resistance', \n", + "# 'cost_conc_std_20', \n", + "# 'profit_decay_20', \n", + "# 'vol_amp_loss_20', \n", + "# 'vol_drop_profit_cnt_5', \n", + "# 'lg_flow_vol_interact_20', \n", + "# 'cost_break_confirm_cnt_5', \n", + "# 'atr_norm_channel_pos_14', \n", + "# 'turnover_diff_skew_20', \n", + "# 'lg_sm_flow_diverge_20', \n", + "# 'pullback_strong_20_20', \n", + "# 'vol_wgt_hist_pos_20', \n", + "# 'vol_adj_roc_20',\n", + "# 'cashflow_to_ev_factor',\n", + "# 'ocfps',\n", + "# 'book_to_price_ratio',\n", + "# 'turnover_rate_mean_5',\n", + "# 'variance_20',\n", + "# 'bbi_ratio_factor'\n", + "# ]\n", + "# feature_columns = [col for col in feature_columns if col in train_data.columns]\n", + "# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n", + "\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", @@ -1344,16 +1436,16 @@ "train_data = fill_nan_with_daily_median(train_data, feature_columns)\n", "test_data = fill_nan_with_daily_median(test_data, feature_columns)\n", "\n", - "train_data = train_data.dropna(subset=feature_columns)\n", + "train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", "train_data = train_data.dropna(subset=['label'])\n", "train_data = train_data.reset_index(drop=True)\n", "# print(test_data.tail())\n", - "test_data = test_data.dropna(subset=feature_columns)\n", + "test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n", "# test_data = test_data.dropna(subset=['label'])\n", "test_data = test_data.reset_index(drop=True)\n", "\n", "transform_feature_columns = feature_columns\n", - "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat')]\n", + "transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n", "# transform_feature_columns.remove('undist_profit_ps')\n", "print('去除极值')\n", "cs_mad_filter(train_data, transform_feature_columns)\n", @@ -1366,7 +1458,7 @@ "# cs_neutralize_industry_cap(test_data, transform_feature_columns)\n", "# cs_zscore_standardize(test_data, transform_feature_columns)\n", "\n", - "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat')]\n", + "mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n", "cs_mad_filter(train_data, mad_filter_feature_columns)\n", "cs_mad_filter(test_data, mad_filter_feature_columns)\n", "\n", @@ -1393,64 +1485,64 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "e23d1759", "metadata": {}, "outputs": [], "source": [ - "feature_columns = [\n", - " 'undist_profit_ps', \n", - " 'AR_BR', \n", - " 'pe_ttm',\n", - " 'alpha_22_improved', \n", - " 'alpha_003', \n", - " 'alpha_007', \n", - " 'alpha_013', \n", - " 'cat_up_limit', \n", - " 'cat_down_limit', \n", - " 'up_limit_count_10d', \n", - " 'down_limit_count_10d', \n", - " 'consecutive_up_limit', \n", - " 'vol_break', \n", - " 'weight_roc5', \n", - " 'price_cost_divergence', \n", - " 'smallcap_concentration', \n", - " 'cost_stability', \n", - " 'high_cost_break_days', \n", - " 'liquidity_risk', \n", - " 'turnover_std', \n", - " 'mv_volatility', \n", - " 'volume_growth', \n", - " 'mv_growth', \n", - " 'lg_flow_mom_corr_20_60', \n", - " 'lg_flow_accel', \n", - " 'profit_pressure', \n", - " 'underwater_resistance', \n", - " 'cost_conc_std_20', \n", - " 'profit_decay_20', \n", - " 'vol_amp_loss_20', \n", - " 'vol_drop_profit_cnt_5', \n", - " 'lg_flow_vol_interact_20', \n", - " 'cost_break_confirm_cnt_5', \n", - " 'atr_norm_channel_pos_14', \n", - " 'turnover_diff_skew_20', \n", - " 'lg_sm_flow_diverge_20', \n", - " 'pullback_strong_20_20', \n", - " 'vol_wgt_hist_pos_20', \n", - " 'vol_adj_roc_20',\n", - " 'cashflow_to_ev_factor',\n", - " 'ocfps',\n", - " 'book_to_price_ratio',\n", - " 'turnover_rate_mean_5',\n", - " 'variance_20',\n", - " 'bbi_ratio_factor'\n", - "]\n", - "feature_columns = [col for col in feature_columns if col in train_data.columns]" + "# feature_columns = [\n", + "# 'undist_profit_ps', \n", + "# 'AR_BR', \n", + "# 'pe_ttm',\n", + "# 'alpha_22_improved', \n", + "# 'alpha_003', \n", + "# 'alpha_007', \n", + "# 'alpha_013', \n", + "# 'cat_up_limit', \n", + "# 'cat_down_limit', \n", + "# 'up_limit_count_10d', \n", + "# 'down_limit_count_10d', \n", + "# 'consecutive_up_limit', \n", + "# 'vol_break', \n", + "# 'weight_roc5', \n", + "# 'price_cost_divergence', \n", + "# 'smallcap_concentration', \n", + "# 'cost_stability', \n", + "# 'high_cost_break_days', \n", + "# 'liquidity_risk', \n", + "# 'turnover_std', \n", + "# 'mv_volatility', \n", + "# 'volume_growth', \n", + "# 'mv_growth', \n", + "# 'lg_flow_mom_corr_20_60', \n", + "# 'lg_flow_accel', \n", + "# 'profit_pressure', \n", + "# 'underwater_resistance', \n", + "# 'cost_conc_std_20', \n", + "# 'profit_decay_20', \n", + "# 'vol_amp_loss_20', \n", + "# 'vol_drop_profit_cnt_5', \n", + "# 'lg_flow_vol_interact_20', \n", + "# 'cost_break_confirm_cnt_5', \n", + "# 'atr_norm_channel_pos_14', \n", + "# 'turnover_diff_skew_20', \n", + "# 'lg_sm_flow_diverge_20', \n", + "# 'pullback_strong_20_20', \n", + "# 'vol_wgt_hist_pos_20', \n", + "# 'vol_adj_roc_20',\n", + "# 'cashflow_to_ev_factor',\n", + "# 'ocfps',\n", + "# 'book_to_price_ratio',\n", + "# 'turnover_rate_mean_5',\n", + "# 'variance_20',\n", + "# 'bbi_ratio_factor'\n", + "# ]\n", + "# feature_columns = [col for col in feature_columns if col in train_data.columns]" ] }, { "cell_type": "code", - "execution_count": 133, + "execution_count": null, "id": "8f134d435f71e9e2", "metadata": { "ExecuteTime": { @@ -1553,7 +1645,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": null, "id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81", "metadata": { "ExecuteTime": { @@ -1568,13 +1660,13 @@ "text": [ "train data size: 36400\n", "原始样本数: 36400, 去除标签为空后样本数: 36400\n", - "cat_features: [7]\n" + "cat_features: [27, 30, 37, 39, 41, 80, 86, 87, 88, 100, 102, 125]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4301941223d144c89c9302df7bb676ce", + "model_id": "a830a64e308a436b919bfc0dc59eb9a7", "version_major": 2, "version_minor": 0 }, @@ -1589,10 +1681,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.6421053\ttest: 0.1200000\tbest: 0.1200000 (0)\ttotal: 98.9ms\tremaining: 49.3s\n", - "bestTest = 0.8\n", - "bestIteration = 17\n", - "Shrink model to first 18 iterations.\n" + "0:\tlearn: 0.6527197\ttest: 0.3052632\tbest: 0.3052632 (0)\ttotal: 170ms\tremaining: 1m 25s\n", + "bestTest = 0.4869565217\n", + "bestIteration = 83\n", + "Shrink model to first 84 iterations.\n" ] } ], @@ -1604,12 +1696,12 @@ "# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n", "# light_params['feature_contri'] = feature_contri\n", "# print(f'feature_contri: {feature_contri}')\n", - "model, scaler, pca = train_model(train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')), feature_columns)\n" + "model, scaler, pca = train_model(train_data.dropna(subset=['label']).groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left'), feature_columns)\n" ] }, { "cell_type": "code", - "execution_count": 139, + "execution_count": null, "id": "5d1522a7538db91b", "metadata": { "ExecuteTime": { @@ -1629,6 +1721,8 @@ "score_df = test_data\n", "score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n", "score_df = score_df[score_df['pe_ttm'] > 0]\n", + "score_df = score_df.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", @@ -1643,44 +1737,32 @@ ").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", - "# " + "save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n" ] }, { "cell_type": "code", - "execution_count": 140, - "id": "340a82b9", + "execution_count": null, + "id": "09b1799e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "53077\n", - "0 2.98\n", - "1 8.26\n", - "2 10.03\n", - "3 6.06\n", - "4 6.06\n", - " ... \n", - "168084 5.03\n", - "168085 -0.98\n", - "168086 5.44\n", - "168087 3.01\n", - "168088 -0.24\n", - "Name: pct_chg, Length: 168089, dtype: float64\n" + "174\n", + "['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', '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" ] } ], "source": [ - "print(len(score_df[score_df['label'] == 1]))\n", - "print(score_df['pct_chg'])" + "print(len(feature_columns))\n", + "print(feature_columns)" ] }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "id": "7e9023cc", "metadata": {}, "outputs": [], @@ -1877,6 +1959,254 @@ "# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n", "# print(factor_analysis_report)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0000d75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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 172470 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", + "circ_mv_bin 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 \\\n", + "future_return_bin ... \n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "... .. .. .. .. .. .. .. .. .. .. ... .. .. .. \n", + "95 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "96 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "97 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "98 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n", + "\n", + "circ_mv_bin 93 94 95 96 97 98 99 \n", + "future_return_bin \n", + "0 NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN \n", + "... .. .. .. .. .. .. .. \n", + "95 NaN NaN NaN NaN NaN NaN NaN \n", + "96 NaN NaN NaN NaN NaN NaN NaN \n", + "97 NaN NaN NaN NaN NaN NaN NaN \n", + "98 NaN NaN NaN NaN NaN NaN NaN \n", + "99 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "[100 rows x 100 columns]\n", + "生成热力图...\n", + "分析完成。\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy.stats import spearmanr\n", + "from tqdm import tqdm # 用于显示进度条 (可选)\n", + "\n", + "# 设置 Matplotlib/Seaborn 样式 (可选)\n", + "sns.set_theme(style=\"whitegrid\")\n", + "plt.rcParams['font.sans-serif'] = ['SimHei'] # 或者其他支持中文的字体\n", + "plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题\n", + "\n", + "def analyze_score_performance_2d(score_df: pd.DataFrame,\n", + " score_col: str = 'score',\n", + " label_col: str = 'label',\n", + " condition1_col: str = 'circ_mv',\n", + " condition2_col: str = 'future_return',\n", + " n_bins: int = 100,\n", + " min_samples_per_bin: int = 30): # 每个格子最少样本数\n", + " \"\"\"\n", + " 分析 score 在两个条件下 (如市值、未来收益) 的二维分箱表现。\n", + "\n", + " Args:\n", + " score_df (pd.DataFrame): 包含分数、标签和条件列的 DataFrame。\n", + " score_col (str): 预测分数所在的列名。\n", + " label_col (str): 目标标签所在的列名 (应为数值或可排序类别)。\n", + " condition1_col (str): 第一个条件列名 (例如 'circ_mv')。\n", + " condition2_col (str): 第二个条件列名 (例如 'future_return')。\n", + " n_bins (int): 每个条件划分的箱数 (分位数数量)。\n", + " min_samples_per_bin (int): 计算指标所需的最小样本数，小于此数目的格子结果将被屏蔽。\n", + "\n", + " Returns:\n", + " tuple: 包含 (performance_pivot, count_pivot, fig)\n", + " performance_pivot: 以二维分箱为索引/列的 Spearman 相关系数矩阵。\n", + " count_pivot: 每个二维分箱的样本数量矩阵。\n", + " fig: 生成的热力图 Matplotlib Figure 对象。\n", + " \"\"\"\n", + " print(f\"开始分析 '{score_col}' 在 '{condition1_col}' 和 '{condition2_col}' 下的表现...\")\n", + "\n", + " required_cols = [score_col, label_col, condition1_col, condition2_col]\n", + " if not all(col in score_df.columns for col in required_cols):\n", + " missing = [col for col in required_cols if col not in score_df.columns]\n", + " raise ValueError(f\"输入 DataFrame 缺少必需列: {missing}\")\n", + "\n", + " # --- 1. 数据准备和清洗 ---\n", + " print(\"准备数据，处理 NaN 值...\")\n", + " # 只保留需要的列，并移除包含 NaN 的行，避免影响分箱和计算\n", + " analysis_df = score_df[required_cols].dropna().copy()\n", + " n_original = len(score_df)\n", + " n_after_drop = len(analysis_df)\n", + " print(f\"原始数据 {n_original} 行，移除 NaN 后剩余 {n_after_drop} 行用于分析。\")\n", + "\n", + " if n_after_drop < min_samples_per_bin * n_bins: # 检查数据量是否过少\n", + " print(f\"警告: 清理 NaN 后数据量 ({n_after_drop}) 可能不足以支持 {n_bins}x{n_bins} 的精细分箱分析。\")\n", + " if n_after_drop < min_samples_per_bin:\n", + " print(\"错误: 有效数据过少，无法进行分析。\")\n", + " return None, None, None\n", + "\n", + " # --- 2. 二维分箱 ---\n", + " print(f\"对 '{condition1_col}' 和 '{condition2_col}' 进行 {n_bins} 分位数分箱...\")\n", + " bin1_col = f'{condition1_col}_bin'\n", + " bin2_col = f'{condition2_col}_bin'\n", + "\n", + " try:\n", + " # 使用 qcut 进行分位数分箱，labels=False 返回 0 到 n_bins-1 的整数标签\n", + " # duplicates='drop' 会丢弃导致边界不唯一的重复值所在的箱子，可能导致某些箱号缺失\n", + " # 对于可视化，这通常可以接受，但如果需要严格的等分，需先 rank\n", + " analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col], q=n_bins, labels=False, duplicates='drop')\n", + " analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col], q=n_bins, labels=False, duplicates='drop')\n", + " except Exception as e:\n", + " print(f\"错误: 分箱失败，请检查数据分布或减少 n_bins。错误信息: {e}\")\n", + " # 可以尝试先 rank 再 qcut\n", + " # analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n", + " # analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n", + " return None, None, None\n", + "\n", + " # --- 3. 分组计算表现指标 (Spearman Rank IC) ---\n", + " print(\"按二维分箱分组计算 Spearman Rank IC...\")\n", + "\n", + " def safe_spearmanr(x, y):\n", + " \"\"\"安全计算 Spearman 相关性，处理数据量过少的情况\"\"\"\n", + " if len(x) < max(2, min_samples_per_bin): # 要求至少有 min_samples_per_bin 个点才计算\n", + " return np.nan\n", + " corr, p_value = spearmanr(x, y)\n", + " return corr if not np.isnan(corr) else np.nan # 确保返回 NaN 而不是 None 或其他\n", + "\n", + " # 按两个分箱列分组\n", + " grouped = analysis_df.groupby([bin1_col, bin2_col])\n", + "\n", + " # 计算每个格子的 Spearman 相关系数\n", + " # apply 可能较慢，但计算相关性通常需要 apply\n", + " performance_series = grouped.apply(lambda sub: safe_spearmanr(sub[score_col], sub[label_col]))\n", + "\n", + " # 计算每个格子的样本数量\n", + " count_series = grouped.size()\n", + "\n", + " # --- 4. 结果整理成 Pivot Table (用于绘图) ---\n", + " print(\"整理结果用于绘图...\")\n", + " try:\n", + " # 将 performance_series 转换成二维矩阵\n", + " # index 为 condition1_bin, columns 为 condition2_bin\n", + " performance_pivot = performance_series.unstack(level=0) # level=0 对应第一个 groupby key (bin1_col)\n", + " count_pivot = count_series.unstack(level=0)\n", + "\n", + " # 可选：按列和索引排序，确保顺序正确\n", + " performance_pivot = performance_pivot.sort_index(axis=0).sort_index(axis=1)\n", + " count_pivot = count_pivot.sort_index(axis=0).sort_index(axis=1)\n", + " \n", + " print(performance_pivot)\n", + "\n", + " except Exception as e:\n", + " print(f\"错误: 无法将结果转换为二维矩阵，可能因为分箱不均匀或数据问题: {e}\")\n", + " return None, None, None\n", + "\n", + " # --- 5. 可视化：绘制热力图 ---\n", + " print(\"生成热力图...\")\n", + " fig, ax = plt.subplots(figsize=(16, 12)) # 调整图像大小\n", + "\n", + " # 使用 count_pivot 创建一个 mask，屏蔽掉样本量过小的格子\n", + " mask = count_pivot < min_samples_per_bin\n", + "\n", + " # 绘制热力图\n", + " sns.heatmap(performance_pivot,\n", + " annot=False, # 100x100 个格子加注释会太密集\n", + " fmt=\".2f\",\n", + " cmap=\"viridis\", # 选择颜色映射, 'viridis', 'coolwarm', 'RdYlGn' 等都不错\n", + " linewidths=.5,\n", + " linecolor='lightgray',\n", + " # mask=mask, # 应用 mask\n", + " ax=ax,\n", + " cbar_kws={'label': f'Spearman Rank IC ({score_col} vs {label_col})'}) # 颜色条标签\n", + "\n", + " # 设置标题和轴标签\n", + " ax.set_title(f'{score_col} 表现分析 (Rank IC vs {label_col})\\n基于 {condition1_col} 和 {condition2_col} {n_bins}x{n_bins} 分箱', fontsize=16)\n", + " ax.set_xlabel(f'{condition1_col} 分位数 (0 -> 高)', fontsize=12)\n", + " ax.set_ylabel(f'{condition2_col} 分位数 (0 -> 高)', fontsize=12)\n", + "\n", + " # 可选：调整刻度标签，避免显示所有 100 个刻度\n", + " if n_bins > 20:\n", + " tick_interval = n_bins // 10 # 大约显示 10 个刻度\n", + " ax.set_xticks(np.arange(0, n_bins, tick_interval) + 0.5)\n", + " ax.set_yticks(np.arange(0, n_bins, tick_interval) + 0.5)\n", + " ax.set_xticklabels(np.arange(0, n_bins, tick_interval))\n", + " ax.set_yticklabels(np.arange(0, n_bins, tick_interval))\n", + "\n", + " plt.xticks(rotation=45, ha='right')\n", + " plt.yticks(rotation=0)\n", + " plt.tight_layout() # 调整布局\n", + "\n", + " print(\"分析完成。\")\n", + " return performance_pivot, count_pivot, fig\n", + "\n", + "# --- 如何使用 ---\n", + "# 假设你的包含预测结果和所需列的 DataFrame 是 final_predictions_df\n", + "# 确保它包含 'score', 'label', 'circ_mv', 'future_return'\n", + "\n", + "# # 示例调用 (你需要有实际的 score_df)\n", + "try:\n", + " # 确保数据类型正确\n", + " cols_to_numeric = ['score', 'label', 'circ_mv', 'future_return']\n", + " for col in cols_to_numeric:\n", + " if col in score_df.columns:\n", + " score_df[col] = pd.to_numeric(score_df[col], errors='coerce')\n", + "\n", + " # 调用分析函数\n", + " performance_matrix, count_matrix, heatmap_figure = analyze_score_performance_2d(\n", + " score_df,\n", + " n_bins=100, # 你要求的100分箱\n", + " min_samples_per_bin=50 # 每个格子至少需要50个样本才显示IC，可以调整\n", + " )\n", + "\n", + " # 显示图像\n", + " if heatmap_figure:\n", + " plt.show()\n", + "\n", + " # 可以查看具体的 performance_matrix 和 count_matrix\n", + " # print(\"\\nPerformance Matrix (Spearman IC):\")\n", + " # print(performance_matrix)\n", + " # print(\"\\nCount Matrix:\")\n", + " # print(count_matrix)\n", + "\n", + "except ValueError as ve:\n", + " print(f\"数据错误: {ve}\")\n", + "except Exception as e:\n", + " print(f\"发生未知错误: {e}\")" + ] } ], "metadata": { diff --git a/main/train/catboost_info/catboost_training.json b/main/train/catboost_info/catboost_training.json index 21d11a3..b72fdf8 100644 --- a/main/train/catboost_info/catboost_training.json +++ b/main/train/catboost_info/catboost_training.json @@ -1,122 +1,188 @@ { 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