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liaozhaorun
2025-03-31 23:08:03 +08:00
parent ee35513935
commit 01092b8cae
14 changed files with 5561 additions and 2922 deletions
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258
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": "2025-02-11T15:18:36.892437Z",
"start_time": "2025-02-11T15:18:36.020822Z"
"end_time": "2025-03-30T16:42:39.056767Z",
"start_time": "2025-03-30T16:42:37.817887Z"
}
},
"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": "2025-02-11T15:20:12.573607Z",
"start_time": "2025-02-11T15:20:00.110127Z"
"end_time": "2025-03-30T16:42:59.784780Z",
"start_time": "2025-03-30T16:42:39.056767Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ts_code trade_date\n",
"4682 600310.SH 20250211\n",
"4683 600312.SH 20250211\n",
"4684 600313.SH 20250211\n",
"4673 600299.SH 20250211\n",
"0 000001.SZ 20250211\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 10040878 entries, 0 to 10040877\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: 229.8+ MB\n",
"None\n",
"20250211\n",
"20250212\n"
]
}
],
"source": [
"import pandas as pd\n",
"import time\n",
@@ -68,39 +41,50 @@
" max_date = df['trade_date'].max()\n",
"\n",
"print(max_date)\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250220')\n",
"trade_cal = pro.trade_cal(exchange='', start_date='20170101', end_date='20250420')\n",
"trade_cal = trade_cal[trade_cal['is_open'] == 1] # 只保留交易日\n",
"trade_dates = trade_cal[trade_cal['cal_date'] > max_date]['cal_date'].tolist()\n",
"start_date = min(trade_dates)\n",
"print(start_date)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:21:27.831699Z",
"start_time": "2025-02-11T15:21:26.665039Z"
},
"scrolled": true
},
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250219 完成\n",
"任务 20250220 完成\n",
"任务 20250217 完成\n",
"任务 20250218 完成\n",
"任务 20250214 完成\n",
"任务 20250213 完成\n",
"任务 20250212 完成\n"
" ts_code trade_date\n",
"4705 600289.SH 20250321\n",
"4706 600292.SH 20250321\n",
"4707 600293.SH 20250321\n",
"4696 600279.SH 20250321\n",
"7051 920116.BJ 20250321\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 10237887 entries, 0 to 35266\n",
"Data columns (total 2 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object\n",
" 1 trade_date object\n",
"dtypes: object(2)\n",
"memory usage: 234.3+ MB\n",
"None\n",
"20250321\n",
"20250324\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "code",
"id": "bb3191de-27a2-4c89-a3b5-32a0d7b9496f",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-03-30T16:43:03.372001Z",
"start_time": "2025-03-30T16:43:00.012140Z"
}
},
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
@@ -131,69 +115,143 @@
" except Exception as e:\n",
" print(f\"获取 {trade_date} 数据时出错: {e}\")\n",
"\n"
]
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"任务 20250418 完成\n",
"任务 20250417 完成\n",
"任务 20250416 完成\n",
"任务 20250415 完成\n",
"任务 20250411 完成\n",
"任务 20250414 完成\n",
"任务 20250409 完成\n",
"任务 20250410 完成\n",
"任务 20250408 完成\n",
"任务 20250407 完成\n",
"任务 20250403 完成\n",
"任务 20250402 完成\n",
"任务 20250401 完成\n",
"任务 20250331 完成\n",
"任务 20250327 完成\n",
"任务 20250328 完成\n",
"任务 20250326 完成\n",
"任务 20250325 完成\n",
"任务 20250324 完成\n"
]
}
],
"execution_count": 3
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96a81aa5890ea3c3",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:21:29.294283Z",
"start_time": "2025-02-11T15:21:29.247112Z"
"end_time": "2025-03-30T16:43:03.397757Z",
"start_time": "2025-03-30T16:43:03.384786Z"
}
},
"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 20250213 000001.SZ 12.56 10.28\n",
"1 20250213 000002.SZ 8.76 7.16\n",
"2 20250213 000004.SZ 15.40 12.60\n",
"3 20250213 000006.SZ 7.92 6.48\n",
"4 20250213 000007.SZ 7.39 6.05\n",
"0 20250327 000001.SZ 12.52 10.24\n",
"1 20250327 000002.SZ 7.92 6.48\n",
"2 20250327 000004.SZ 11.40 9.32\n",
"3 20250327 000006.SZ 7.44 6.08\n",
"4 20250327 000007.SZ 7.00 5.72\n",
"... ... ... ... ...\n",
"7014 20250213 920108.BJ 27.22 14.66\n",
"7015 20250213 920111.BJ 35.98 19.38\n",
"7016 20250213 920116.BJ 80.44 43.32\n",
"7017 20250213 920118.BJ 34.46 18.56\n",
"7018 20250213 920128.BJ 39.84 21.46\n",
"7059 20250327 920108.BJ 33.56 18.08\n",
"7060 20250327 920111.BJ 40.57 21.85\n",
"7061 20250327 920116.BJ 126.29 68.01\n",
"7062 20250327 920118.BJ 44.14 23.78\n",
"7063 20250327 920128.BJ 47.35 25.51\n",
"\n",
"[7019 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250212 000001.SZ 12.56 10.28\n",
"1 20250212 000002.SZ 7.96 6.52\n",
"2 20250212 000004.SZ 15.07 12.33\n",
"3 20250212 000006.SZ 7.74 6.34\n",
"4 20250212 000007.SZ 7.40 6.06\n",
"[7064 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250328 000001.SZ 12.53 10.25\n",
"1 20250328 000002.SZ 7.89 6.45\n",
"2 20250328 000004.SZ 11.19 9.15\n",
"3 20250328 000006.SZ 8.18 6.70\n",
"4 20250328 000007.SZ 6.99 5.72\n",
"... ... ... ... ...\n",
"7014 20250212 920108.BJ 27.41 14.77\n",
"7015 20250212 920111.BJ 34.51 18.59\n",
"7016 20250212 920116.BJ 79.66 42.90\n",
"7017 20250212 920118.BJ 34.81 18.75\n",
"7018 20250212 920128.BJ 38.98 21.00\n",
"7060 20250328 920108.BJ 31.03 16.71\n",
"7061 20250328 920111.BJ 39.65 21.35\n",
"7062 20250328 920116.BJ 115.67 62.29\n",
"7063 20250328 920118.BJ 41.00 22.08\n",
"7064 20250328 920128.BJ 44.83 24.15\n",
"\n",
"[7019 rows x 4 columns]]\n"
"[7065 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250326 000001.SZ 12.57 10.29\n",
"1 20250326 000002.SZ 7.91 6.47\n",
"2 20250326 000004.SZ 11.28 9.23\n",
"3 20250326 000006.SZ 7.17 5.87\n",
"4 20250326 000007.SZ 6.67 5.45\n",
"... ... ... ... ...\n",
"7056 20250326 920108.BJ 33.96 18.30\n",
"7057 20250326 920111.BJ 41.92 22.58\n",
"7058 20250326 920116.BJ 133.64 71.96\n",
"7059 20250326 920118.BJ 41.93 22.59\n",
"7060 20250326 920128.BJ 49.40 26.60\n",
"\n",
"[7061 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250325 000001.SZ 12.52 10.24\n",
"1 20250325 000002.SZ 7.90 6.46\n",
"2 20250325 000004.SZ 11.55 9.45\n",
"3 20250325 000006.SZ 7.13 5.83\n",
"4 20250325 000007.SZ 6.60 5.40\n",
"... ... ... ... ...\n",
"7055 20250325 920108.BJ 33.30 17.94\n",
"7056 20250325 920111.BJ 39.97 21.53\n",
"7057 20250325 920116.BJ 137.78 74.20\n",
"7058 20250325 920118.BJ 39.52 21.28\n",
"7059 20250325 920128.BJ 46.22 24.90\n",
"\n",
"[7060 rows x 4 columns], trade_date ts_code up_limit down_limit\n",
"0 20250324 000001.SZ 12.56 10.28\n",
"1 20250324 000002.SZ 8.10 6.62\n",
"2 20250324 000004.SZ 12.82 10.49\n",
"3 20250324 000006.SZ 7.44 6.08\n",
"4 20250324 000007.SZ 6.89 5.63\n",
"... ... ... ... ...\n",
"7053 20250324 920108.BJ 34.84 18.76\n",
"7054 20250324 920111.BJ 40.41 21.77\n",
"7055 20250324 920116.BJ 134.55 72.45\n",
"7056 20250324 920118.BJ 38.67 20.83\n",
"7057 20250324 920128.BJ 45.86 24.70\n",
"\n",
"[7058 rows x 4 columns]]\n"
]
}
],
"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)"
]
"execution_count": 4
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ad9733a1-2f42-43ee-a98c-0bf699304c21",
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-11T15:20:37.999493Z",
"start_time": "2025-02-11T15:20:37.375220Z"
"end_time": "2025-03-30T16:43:03.696614Z",
"start_time": "2025-03-30T16:43:03.411036Z"
}
},
"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",
@@ -203,22 +261,20 @@
]
}
],
"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！\")"
]
"execution_count": 5
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e777f1f-4d54-4a74-b916-691ede6af055",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:43:03.713628Z",
"start_time": "2025-03-30T16:43:03.711521Z"
}
},
"source": [],
"outputs": [],
"source": []
"execution_count": null
}
],
"metadata": {
@@ -237,7 +293,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.19"
"version": "3.11.11"
}
},
"nbformat": 4,