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(data leak)RollingRank-7.0，赚钱

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liaozhaorun
2025-04-09 22:57:01 +08:00
parent dc1e62c77c
commit 8aad47ce33
10 changed files with 3689 additions and 3701 deletions
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184
code/data/update/update_stk_limit.ipynb
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@@ -2,58 +2,31 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "500802dc-7a20-48b7-a470-a4bae3ec534b",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-06T15:34:34.020485Z",
"start_time": "2025-04-06T15:34:33.497731Z"
"end_time": "2025-04-08T13:37:12.814092Z",
"start_time": "2025-04-08T13:37:11.953133Z"
}
},
"outputs": [],
"source": [
"import tushare as ts\n",
"\n",
"ts.set_token('3a0741c702ee7e5e5f2bf1f0846bafaafe4e320833240b2a7e4a685f')\n",
"pro = ts.pro_api()"
]
],
"outputs": [],
"execution_count": 1
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5a84bc9da6d54868",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-06T15:34:46.227924Z",
"start_time": "2025-04-06T15:34:34.042810Z"
"end_time": "2025-04-08T13:37:35.724923Z",
"start_time": "2025-04-08T13:37:12.820096Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"4717 600285.SH 20250403\n",
"4718 600287.SH 20250403\n",
"4719 600288.SH 20250403\n",
"4708 600273.SH 20250403\n",
"5309 601121.SH 20250403\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 10301468 entries, 0 to 28272\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 235.8+ MB\n",
"None\n",
"20250403\n",
"20250407\n"
]
}
],
"source": [
"import pandas as pd\n",
"import time\n",
@@ -73,37 +46,45 @@
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(start_date)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-06T15:34:48.652346Z",
"start_time": "2025-04-06T15:34:46.236695Z"
},
"scrolled": true
},
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250417 完成\n",
"任务 20250418 完成\n",
"任务 20250416 完成\n",
"任务 20250415 完成\n",
"任务 20250414 完成\n",
"任务 20250411 完成\n",
"任务 20250410 完成\n",
"任务 20250409 完成\n",
"任务 20250408 完成\n",
"任务 20250407 完成\n"
" ts_code trade_date\n",
"4721 600284.SH 20250408\n",
"4722 600285.SH 20250408\n",
"4723 600287.SH 20250408\n",
"4712 600272.SH 20250408\n",
"5 000008.SZ 20250408\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 10315620 entries, 0 to 14151\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 236.1+ MB\n",
"None\n",
"20250408\n",
"20250409\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "code",
"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-04-08T13:37:36.896959Z",
"start_time": "2025-04-08T13:37:35.931558Z"
}
},
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
@@ -134,58 +115,63 @@
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96a81aa5890ea3c3",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-06T15:34:48.680504Z",
"start_time": "2025-04-06T15:34:48.665530Z"
}
},
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ trade_date ts_code up_limit down_limit\n",
"0 20250408 000001.SZ 11.77 9.63\n",
"1 20250408 000002.SZ 7.26 5.94\n",
"2 20250408 000004.SZ 9.72 7.96\n",
"3 20250408 000006.SZ 6.90 5.64\n",
"4 20250408 000007.SZ 6.14 5.02\n",
"... ... ... ... ...\n",
"7072 20250408 920108.BJ 25.33 13.65\n",
"7073 20250408 920111.BJ 29.38 15.82\n",
"7074 20250408 920116.BJ 96.40 51.92\n",
"7075 20250408 920118.BJ 30.16 16.24\n",
"7076 20250408 920128.BJ 34.15 18.39\n",
"\n",
"[7077 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250407 000001.SZ 12.47 10.21\n",
"1 20250407 000002.SZ 7.85 6.43\n",
"2 20250407 000004.SZ 10.80 8.84\n",
"3 20250407 000006.SZ 7.67 6.27\n",
"4 20250407 000007.SZ 6.82 5.58\n",
"... ... ... ... ...\n",
"7070 20250407 920108.BJ 31.72 17.08\n",
"7071 20250407 920111.BJ 36.85 19.85\n",
"7072 20250407 920116.BJ 116.05 62.49\n",
"7073 20250407 920118.BJ 38.07 20.51\n",
"7074 20250407 920128.BJ 43.38 23.36\n",
"\n",
"[7075 rows x 4 columns]]\n"
"任务 20250418 完成\n",
"任务 20250417 完成\n",
"任务 20250416 完成\n",
"任务 20250415 完成\n",
"任务 20250414 完成\n",
"任务 20250411 完成\n",
"任务 20250409 完成\n",
"任务 20250410 完成\n"
]
}
],
"execution_count": 3
},
{
"cell_type": "code",
"id": "96a81aa5890ea3c3",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-08T13:37:37.699901Z",
"start_time": "2025-04-08T13:37:36.909744Z"
}
},
"source": [
"print(all_daily_data)\n",
"# 将所有数据合并为一个 DataFrame\n",
"all_daily_data_df = pd.concat(all_daily_data, ignore_index=True)"
]
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n"
]
},
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[4], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28mprint\u001B[39m(all_daily_data)\n\u001B[0;32m 2\u001B[0m \u001B[38;5;66;03m# 将所有数据合并为一个 DataFrame\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m all_daily_data_df \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mconcat(all_daily_data, ignore_index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:382\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m 379\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m copy \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write():\n\u001B[0;32m 380\u001B[0m copy \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m--> 382\u001B[0m op \u001B[38;5;241m=\u001B[39m _Concatenator(\n\u001B[0;32m 383\u001B[0m objs,\n\u001B[0;32m 384\u001B[0m axis\u001B[38;5;241m=\u001B[39maxis,\n\u001B[0;32m 385\u001B[0m ignore_index\u001B[38;5;241m=\u001B[39mignore_index,\n\u001B[0;32m 386\u001B[0m join\u001B[38;5;241m=\u001B[39mjoin,\n\u001B[0;32m 387\u001B[0m keys\u001B[38;5;241m=\u001B[39mkeys,\n\u001B[0;32m 388\u001B[0m levels\u001B[38;5;241m=\u001B[39mlevels,\n\u001B[0;32m 389\u001B[0m names\u001B[38;5;241m=\u001B[39mnames,\n\u001B[0;32m 390\u001B[0m verify_integrity\u001B[38;5;241m=\u001B[39mverify_integrity,\n\u001B[0;32m 391\u001B[0m copy\u001B[38;5;241m=\u001B[39mcopy,\n\u001B[0;32m 392\u001B[0m sort\u001B[38;5;241m=\u001B[39msort,\n\u001B[0;32m 393\u001B[0m )\n\u001B[0;32m 395\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:445\u001B[0m, in \u001B[0;36m_Concatenator.__init__\u001B[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001B[0m\n\u001B[0;32m 442\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mverify_integrity \u001B[38;5;241m=\u001B[39m verify_integrity\n\u001B[0;32m 443\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcopy \u001B[38;5;241m=\u001B[39m copy\n\u001B[1;32m--> 445\u001B[0m objs, keys \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_clean_keys_and_objs(objs, keys)\n\u001B[0;32m 447\u001B[0m \u001B[38;5;66;03m# figure out what our result ndim is going to be\u001B[39;00m\n\u001B[0;32m 448\u001B[0m ndims \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_ndims(objs)\n",
"File \u001B[1;32mE:\\Python\\anaconda\\envs\\new_trader\\Lib\\site-packages\\pandas\\core\\reshape\\concat.py:507\u001B[0m, in \u001B[0;36m_Concatenator._clean_keys_and_objs\u001B[1;34m(self, objs, keys)\u001B[0m\n\u001B[0;32m 504\u001B[0m objs_list \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(objs)\n\u001B[0;32m 506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(objs_list) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 507\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo objects to concatenate\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 509\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m keys \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 510\u001B[0m objs_list \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(com\u001B[38;5;241m.\u001B[39mnot_none(\u001B[38;5;241m*\u001B[39mobjs_list))\n",
"\u001B[1;31mValueError\u001B[0m: No objects to concatenate"
]
}
],
"execution_count": 4
},
{
"cell_type": "code",
@@ -193,7 +179,7 @@
"id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-06T15:34:48.966102Z",
"end_time": "2025-04-08T13:37:37.748574900Z",
"start_time": "2025-04-06T15:34:48.693158Z"
}
},
@@ -221,7 +207,7 @@
"id": "7e777f1f-4d54-4a74-b916-691ede6af055",
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-06T15:34:48.980659Z",
"end_time": "2025-04-08T13:37:37.762102Z",
"start_time": "2025-04-06T15:34:48.977771Z"
}
},