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多线程rank6.0，赚钱，回撤略微减小

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liaozhaorun
2025-04-08 20:32:51 +08:00
parent e0087aa6e1
commit dc1e62c77c
9 changed files with 3737 additions and 4668 deletions
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147
code/data/update/update_money_flow.ipynb
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@@ -2,31 +2,52 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b94bb1f2-5332-485e-ae1b-eea01f938106",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:42:37.847407Z",
"start_time": "2025-03-30T16:42:36.773187Z"
"end_time": "2025-04-06T15:34:19.686298Z",
"start_time": "2025-04-06T15:34:19.679462Z"
}
},
"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": {
"end_time": "2025-03-30T16:42:59.016187Z",
"start_time": "2025-03-30T16:42:37.850022Z"
"end_time": "2025-04-06T15:34:29.569406Z",
"start_time": "2025-04-06T15:34:19.711970Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8343458 entries, 0 to 20511\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: 191.0+ MB\n",
"None\n",
"20250403\n",
"start_date: 20250407\n"
]
}
],
"source": [
"import pandas as pd\n",
"import time\n",
@@ -45,39 +66,37 @@
"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": "2025-04-06T15:34:32.842166Z",
"start_time": "2025-04-06T15:34:29.601368Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8297316 entries, 0 to 30724\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: 189.9+ MB\n",
"None\n",
"20250321\n",
"start_date: 20250324\n"
"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 20250409 完成\n",
"任务 20250410 完成\n",
"任务 20250408 完成\n",
"任务 20250407 完成\n"
]
}
],
"execution_count": 2
},
{
"cell_type": "code",
"id": "679ce40e-8d62-4887-970c-e1d8cbdeee6b",
"metadata": {
"scrolled": true,
"ExecuteTime": {
"end_time": "2025-03-30T16:43:03.168764Z",
"start_time": "2025-03-30T16:42:59.422934Z"
}
},
"source": [
"from concurrent.futures import ThreadPoolExecutor, as_completed\n",
"\n",
@@ -107,69 +126,33 @@
" 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 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",
"任务 20250408 完成\n",
"任务 20250407 完成\n",
"任务 20250403 完成\n",
"任务 20250402 完成\n",
"任务 20250401 完成\n",
"任务 20250331 完成\n",
"任务 20250328 完成\n",
"任务 20250327 完成\n",
"任务 20250326 完成\n",
"任务 20250325 完成\n",
"任务 20250324 完成\n"
]
}
],
"execution_count": 3
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9af80516849d4e80",
"metadata": {
"ExecuteTime": {
"end_time": "2025-03-30T16:43:03.181032Z",
"start_time": "2025-03-30T16:43:03.173867Z"
"end_time": "2025-04-06T15:34:32.851075Z",
"start_time": "2025-04-06T15:34:32.844866Z"
}
},
"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": {
"end_time": "2025-03-30T16:43:05.401668Z",
"start_time": "2025-03-30T16:43:03.197033Z"
"end_time": "2025-04-06T15:34:35.261741Z",
"start_time": "2025-04-06T15:34:32.864789Z"
}
},
"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",
@@ -179,7 +162,15 @@
]
}
],
"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": {