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Classify2

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liaozhaorun
2025-05-06 23:42:40 +08:00
parent 721e72c599
commit b783a6f968
19 changed files with 9390 additions and 2774 deletions
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main/data/daily_basic.ipynb
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main/data/finance.ipynb
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main/data/index_and_industry.ipynb
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@@ -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"
},

214
main/data/name_change.ipynb
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@@ -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,

145
main/data/update/cyq-perf.ipynb
View File

@@ -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"
},

145
main/data/update/sw_daily.ipynb
View File

@@ -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"
},

386
main/data/update/update_daily_basic.ipynb
View File

@@ -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": [
"<class 'pandas.core.frame.DataFrame'>\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": [
"<class 'pandas.core.frame.DataFrame'>\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",
"5345 0.05 30.7156 NaN 1.2351 1.3543 1.7858 0.0 \n",
"\n",
" dv_ttm total_share float_share free_share total_mv \\\n",
"85 NaN 73467.1821 56245.3696 54046.3738 2.284829e+05 \n",
"123 NaN 49029.8992 49029.8992 38419.3842 1.603278e+05 \n",
"181 NaN 8800.0000 3652.0000 3051.8414 3.747920e+05 \n",
"259 NaN 680049.5825 636615.2391 500325.8436 1.808932e+06 \n",
"283 NaN 74595.9694 74595.5944 44867.2806 4.587652e+05 \n",
"... ... ... ... ... ... \n",
"5286 NaN 745255.6968 687870.8273 594244.1179 4.419366e+06 \n",
"5345 NaN 355000.0000 354999.9006 274999.9006 6.709500e+05 \n",
"5364 NaN 46814.4464 40404.8492 31411.4405 1.095458e+05 \n",
"5366 NaN 71251.9844 60945.7555 54564.8212 1.389414e+05 \n",
"5378 NaN 55835.8894 44606.0865 40680.8215 1.993341e+05 \n",
" dv_ttm total_share float_share free_share total_mv circ_mv \\\n",
"23 NaN 64362.0201 29403.1899 28611.4718 131298.5210 59982.5074 \n",
"33 NaN 69559.6569 57572.5450 40880.9749 227460.0781 188262.2222 \n",
"82 NaN 6699.9900 4689.3344 4093.0077 48574.9275 33997.6744 \n",
"105 NaN 150196.5923 147183.9203 131325.6306 274859.7639 269346.5741 \n",
"114 NaN 82986.8769 78987.6719 73061.8561 151036.1160 143757.5629 \n",
"... ... ... ... ... ... ... \n",
"5259 NaN 43885.0000 43885.0000 36485.0000 106640.5500 106640.5500 \n",
"5264 NaN 119867.5082 104974.0608 49171.2582 281688.6443 246689.0429 \n",
"5294 NaN 76386.9228 76375.7508 73182.1277 252840.7145 252803.7351 \n",
"5324 NaN 28854.9669 27000.9948 23150.5534 125807.6557 117724.3373 \n",
"5345 NaN 71215.1832 71087.9480 58808.3718 216494.1569 216107.3619 \n",
"\n",
" circ_mv is_st \n",
"85 1.749231e+05 True \n",
"123 1.603278e+05 True \n",
"181 1.555387e+05 True \n",
"259 1.693397e+06 True \n",
"283 4.587629e+05 True \n",
"... ... ... \n",
"5286 4.079074e+06 True \n",
"5345 6.709498e+05 True \n",
"5364 9.454735e+04 True \n",
"5366 1.188442e+05 True \n",
"5378 1.592437e+05 True \n",
" is_st \n",
"23 True \n",
"33 True \n",
"82 True \n",
"105 True \n",
"114 True \n",
"... ... \n",
"5259 True \n",
"5264 True \n",
"5294 True \n",
"5324 True \n",
"5345 True \n",
"\n",
"[106 rows x 19 columns]\n"
"[196 rows x 19 columns]\n"
]
}
],
"execution_count": 6
"source": [
"print(all_daily_data_df[all_daily_data_df['is_st']])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "692b58674b7462c9",
"metadata": {
"ExecuteTime": {
@@ -365,12 +412,6 @@
"start_time": "2025-04-09T14:58:16.903459Z"
}
},
"source": [
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='daily_basic', 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！\")\n"
],
"outputs": [
{
"name": "stdout",
@@ -380,10 +421,16 @@
]
}
],
"execution_count": 7
"source": [
"# 将数据保存为 HDF5 文件（table 格式）\n",
"all_daily_data_df.to_hdf(h5_filename, key='daily_basic', 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！\")\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": [
"<class 'pandas.core.frame.DataFrame'>\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"
},

1146
main/data/update/update_daily_data.ipynb
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File diff suppressed because it is too large Load Diff

222
main/data/update/update_is_st.ipynb
View File

@@ -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,

167
main/data/update/update_money_flow.ipynb
View File

@@ -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": [
"<class 'pandas.core.frame.DataFrame'>\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": [
"<class 'pandas.core.frame.DataFrame'>\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"
},

215
main/data/update/update_stk_limit.ipynb
View File

@@ -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",
"<class 'pandas.core.frame.DataFrame'>\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",
"<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"
"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": {
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1 iter Precision CrossEntropy
2 0 0.6421052632 0.6527196653 0.6541943716 0.6539506586
3 1 0.8571428571 0.7857142857 0.6220139285 0.62
4 2 0.9473684211 0.7876106195 0.5916510322 0.5895233377
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6 4 0.890625 0.8421052632 0.5410338375 0.5384506159
7 5 0.8 0.8854166667 0.5196276588 0.5166076215
8 6 0.8518518519 0.9069767442 0.5010221829 0.4981064667
9 7 0.8764044944 0.8910891089 0.4849534885 0.4814881886
10 8 0.8804347826 0.8727272727 0.4703366289 0.466400298
11 9 0.8875 0.8679245283 0.457428825 0.4533062343
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21 19 0.8876404494 0.9275362319 0.383472642 0.3780131586
22 20 0.8850574713 0.9310344828 0.3796266965 0.3741117915
23 21 0.9 0.9361702128 0.3761398694 0.3698772169
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iter Precision CrossEntropy
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104 0.44 0.3838380534
105 0.4186046512 0.3838293186
106 0.4108527132 0.384031901
107 0.421875 0.3838463542
108 0.4351145038 0.3837952474
109 0.4263565891 0.3839005263
110 0.4453125 0.3837520616
111 0.44 0.3839232585
112 0.4444444444 0.3838974609
113 0.4461538462 0.3840630968
114 0.4552238806 0.3841141493
115 0.4552238806 0.3842514648
116 0.4718309859 0.3843915473
117 0.4620689655 0.3844533963
118 0.4557823129 0.3845418023
119 0.4657534247 0.3844443359
120 0.46 0.3846325955
121 0.4630872483 0.3846319987
122 0.4630872483 0.3846895616
123 0.4557823129 0.3846195747
124 0.4765100671 0.3847169596
125 0.4709677419 0.3849635688
126 0.4585987261 0.3850583767
127 0.4545454545 0.3852940538
128 0.4575163399 0.3853448079
129 0.474025974 0.3852395562
130 0.464516129 0.3851817763
131 0.461038961 0.385196913
132 0.4545454545 0.3849804145
133 0.4551282051 0.3849924859
134 0.4575163399 0.3848855252
135 0.4539473684 0.3850203451
136 0.4539473684 0.3852070584
137 0.4503311258 0.385253852
138 0.4520547945 0.3853320041
139 0.4585987261 0.3856692166
140 0.4615384615 0.3857099067
141 0.464516129 0.3856298286
142 0.4591194969 0.3853765734
143 0.4782608696 0.3855229221
144 0.4691358025 0.3854374729
145 0.472392638 0.3854031033
146 0.4785276074 0.3854896105
147 0.4720496894 0.385499566
148 0.472392638 0.3856041667
149 0.472392638 0.3857845323
150 0.472392638 0.385792589
151 0.4764705882 0.3857090115
152 0.4698795181 0.3857729221
153 0.4610778443 0.3858970812
154 0.4545454545 0.3860140516
155 0.4375 0.3861292318
156 0.4472049689 0.386065918
157 0.4625 0.386123291
158 0.4556962025 0.3860105252
159 0.4625 0.3859614258
160 0.4528301887 0.3859398058
161 0.4534161491 0.3861076118
162 0.4567901235 0.3861334093
163 0.4634146341 0.3860480957
164 0.4666666667 0.3861162109
165 0.4666666667 0.3862133789
166 0.4596273292 0.3863245985
167 0.450617284 0.3864905328
168 0.4573170732 0.3863822971
169 0.4545454545 0.3864398872
170 0.4578313253 0.3864295519
171 0.4561403509 0.3866640354
172 0.4534883721 0.3867054579
173 0.4444444444 0.3866737196
174 0.4425287356 0.3867723253
175 0.4540229885 0.3868446181
176 0.4488636364 0.3868221571
177 0.450867052 0.3870137804
178 0.450867052 0.3871307237
179 0.4593023256 0.3872641602
180 0.4488636364 0.3872341309
181 0.4425287356 0.3873837619
182 0.4431818182 0.3874805773
183 0.4413407821 0.3874806315
1 iter Precision CrossEntropy
2 0 0.12 0.3052631579 0.6594913737 0.6606884766
3 1 0 0.3103448276 0.6304362522 0.6303273112
4 2 0 0.3035714286 0.6035512695 0.6038824327
5 3 0.3333333333 0.5795352105 0.5790144314
6 4 0.5263157895 0.3333333333 0.5591463216 0.5570897895
7 5 0.3333333333 0.275862069 0.540188151 0.5386381836
8 6 0.2222222222 0.2666666667 0.5237671441 0.5219863824
9 7 0.3333333333 0.2972972973 0.5084826931 0.5076717122
10 8 0.3125 0.325 0.4955966797 0.4940914171
11 9 0.25 0.3333333333 0.4841406793 0.4827998589
12 10 0.1666666667 0.3181818182 0.4736386176 0.4726039497
13 11 0 0.2950819672 0.4643907878 0.4643194444
14 12 0.125 0.328358209 0.4563280165 0.4554793837
15 13 0.5555555556 0.3333333333 0.4490027941 0.4476724718
16 14 0.7142857143 0.3243243243 0.4425872938 0.4417778863
17 15 0.6 0.2898550725 0.4368164334 0.4361890734
18 16 0.75 0.3529411765 0.4317668457 0.4313453776
19 17 0.8 0.3636363636 0.4277447374 0.4262438965
20 18 0.3846153846 0.3333333333 0.4239844835 0.4224629449
21 19 0.4615384615 0.3580246914 0.4201444227 0.4182514106
22 20 0.4166666667 0.325 0.416756429 0.4148546821
23 21 0.3333333333 0.3582089552 0.4137484809 0.4119712728
24 22 0.4285714286 0.350877193 0.4109760471 0.4090849338
25 23 0.375 0.3620689655 0.4086826986 0.4063893229
26 24 0.5 0.3269230769 0.4065253906 0.4043189833
27 25 0.5 0.3043478261 0.4043982476 0.4021975098
28 26 0.375 0.3191489362 0.4028764106 0.4007753364
29 27 0.4285714286 0.2941176471 0.4015186632 0.3991743435
30 28 0.5 0.3529411765 0.3997085232 0.3976788194
31 29 0.5 0.34375 0.3982244466 0.3960837131
32 30 0.4285714286 0.3442622951 0.3970369737 0.3951454536
33 31 0.375 0.3928571429 0.3956321886 0.3936280653
34 32 0.375 0.3859649123 0.3947504883 0.3925985514
35 33 0.375 0.3888888889 0.3937069499 0.3917349718
36 34 0.3333333333 0.387755102 0.3932520888 0.3910504015
37 35 0.3333333333 0.46 0.3928204481 0.3901231011
38 36 0.3684210526 0.4666666667 0.3921121691 0.3894119737
39 37 0.3333333333 0.4285714286 0.3916260851 0.3887729763
40 38 0.3333333333 0.4 0.3911342231 0.388536594
41 39 0.380952381 0.4821428571 0.3904462891 0.3876555447
42 40 0.380952381 0.4259259259 0.390167806 0.3868581814
43 41 0.3846153846 0.4210526316 0.3895910102 0.3866296929
44 42 0.4137931034 0.4482758621 0.3890837131 0.3860138346
45 43 0.4137931034 0.3818181818 0.3885024414 0.3857928331
46 44 0.4137931034 0.4 0.3879628092 0.3855309787
47 45 0.4 0.3962264151 0.3877528754 0.3852399902
48 46 0.3636363636 0.4333333333 0.3875401747 0.3847372504
49 47 0.3513513514 0.3846153846 0.3873875597 0.384755995
50 48 0.3488372093 0.358490566 0.3871630859 0.3844680718
51 49 0.3863636364 0.358490566 0.3873189833 0.3843583713
52 50 0.3913043478 0.3653846154 0.3872931315 0.3842032064
53 51 0.3571428571 0.3653846154 0.3872769097 0.3838373752
54 52 0.3720930233 0.3191489362 0.387190701 0.383723036
55 53 0.3617021277 0.3265306122 0.3868662923 0.3835469564
56 54 0.3863636364 0.3333333333 0.3872360297 0.3837113173
57 55 0.3863636364 0.3492063492 0.3871567383 0.3836729058
58 56 0.3720930233 0.3442622951 0.3870482042 0.3837430556
59 57 0.3695652174 0.380952381 0.3868121745 0.3837650011
60 58 0.3617021277 0.4179104478 0.3867534451 0.3837915853
61 59 0.3617021277 0.4090909091 0.3865920139 0.383500217
62 60 0.3333333333 0.4153846154 0.3864595269 0.38315389
63 61 0.3846153846 0.4029850746 0.3865724826 0.383358724
64 62 0.3859649123 0.3943661972 0.3864565701 0.3832910699
65 63 0.4035087719 0.4027777778 0.386326199 0.3833045247
66 64 0.4230769231 0.4303797468 0.3862506782 0.3834173448
67 65 0.4230769231 0.4285714286 0.3862313368 0.3834139811
68 66 0.4230769231 0.4285714286 0.385976888 0.3833682997
69 67 0.387755102 0.4186046512 0.386124566 0.3834855143
70 68 0.38 0.4137931034 0.3858969184 0.3837184516
71 69 0.38 0.404494382 0.3858519151 0.3835157335
72 70 0.38 0.4090909091 0.3859381239 0.3835523275
73 71 0.387755102 0.4137931034 0.3860583496 0.3835948351
74 72 0.3725490196 0.4090909091 0.3857856988 0.3834436578
75 73 0.3846153846 0.4137931034 0.3859902344 0.383366428
76 74 0.3773584906 0.4 0.3859985623 0.3833662109
77 75 0.3818181818 0.4210526316 0.3859373101 0.3832851291
78 76 0.3818181818 0.4141414141 0.3858856608 0.3835284288
79 77 0.3818181818 0.4537037037 0.386057373 0.3835838759
80 78 0.4032258065 0.4545454545 0.3860869683 0.3836141493
81 79 0.393442623 0.4587155963 0.3860341254 0.3835327148
82 80 0.3870967742 0.4818181818 0.3861741265 0.3835458442
83 81 0.380952381 0.4821428571 0.3866146918 0.3835291612
84 82 0.390625 0.4774774775 0.3866864692 0.3835062391
85 83 0.3823529412 0.4869565217 0.3868192817 0.3834068197
86 84 0.3880597015 0.4833333333 0.3866136882 0.3833978136
87 85 0.4078947368 0.4836065574 0.3865292697 0.3834318576
88 86 0.4230769231 0.4833333333 0.3863948568 0.3833043077
89 87 0.4197530864 0.4833333333 0.3865127767 0.3831566026
90 88 0.4197530864 0.475 0.3865546061 0.3831979167
91 89 0.4197530864 0.4615384615 0.3864517144 0.3832379286
92 90 0.4024390244 0.4705882353 0.386520752 0.3833047689
93 91 0.4 0.4615384615 0.3865970052 0.3834944661
94 92 0.4 0.4628099174 0.3865461155 0.3836822645
95 93 0.4 0.4634146341 0.3866591254 0.3837473145
96 94 0.4022988506 0.4634146341 0.3867526584 0.383617079
97 95 0.4175824176 0.4596774194 0.3869412435 0.3836049805
98 96 0.4222222222 0.4621848739 0.3870782606 0.3837751194
99 97 0.4183673469 0.4758064516 0.3872464464 0.3837325846
100 98 0.4183673469 0.4758064516 0.3871889377 0.3837513021
101 99 0.4329896907 0.472 0.387300944 0.3836912435
102 100 0.4270833333 0.4761904762 0.3874012316 0.3834313151
103 101 0.4226804124 0.4596774194 0.387583686 0.3834144423
104 102 0.4141414141 0.4462809917 0.3879282498 0.3839306912
105 103 0.4141414141 0.44 0.3878271484 0.3839262967
106 104 0.387755102 0.44 0.387887424 0.3838380534
107 105 0.4257425743 0.4186046512 0.3880032281 0.3838293186
108 106 0.4285714286 0.4108527132 0.3881157769 0.384031901
109 107 0.41 0.421875 0.3882778863 0.3838463542
110 108 0.40625 0.4351145038 0.3884315592 0.3837952474
111 109 0.4174757282 0.4263565891 0.3883866102 0.3839005263
112 110 0.4 0.4453125 0.3882190755 0.3837520616
113 111 0.4117647059 0.44 0.3881155599 0.3839232585
114 112 0.4077669903 0.4444444444 0.3880919868 0.3838974609
115 113 0.4245283019 0.4461538462 0.388188151 0.3840630968
116 114 0.4509803922 0.4552238806 0.3881395128 0.3841141493
117 115 0.4347826087 0.4552238806 0.3886143934 0.3842514648
118 116 0.4226804124 0.4718309859 0.388562066 0.3843915473
119 117 0.4387755102 0.4620689655 0.3886107856 0.3844533963
120 118 0.4557823129 0.3845418023
121 119 0.4657534247 0.3844443359
122 120 0.46 0.3846325955
123 121 0.4630872483 0.3846319987
124 122 0.4630872483 0.3846895616
125 123 0.4557823129 0.3846195747
126 124 0.4765100671 0.3847169596
127 125 0.4709677419 0.3849635688
128 126 0.4585987261 0.3850583767
129 127 0.4545454545 0.3852940538
130 128 0.4575163399 0.3853448079
131 129 0.474025974 0.3852395562
132 130 0.464516129 0.3851817763
133 131 0.461038961 0.385196913
134 132 0.4545454545 0.3849804145
135 133 0.4551282051 0.3849924859
136 134 0.4575163399 0.3848855252
137 135 0.4539473684 0.3850203451
138 136 0.4539473684 0.3852070584
139 137 0.4503311258 0.385253852
140 138 0.4520547945 0.3853320041
141 139 0.4585987261 0.3856692166
142 140 0.4615384615 0.3857099067
143 141 0.464516129 0.3856298286
144 142 0.4591194969 0.3853765734
145 143 0.4782608696 0.3855229221
146 144 0.4691358025 0.3854374729
147 145 0.472392638 0.3854031033
148 146 0.4785276074 0.3854896105
149 147 0.4720496894 0.385499566
150 148 0.472392638 0.3856041667
151 149 0.472392638 0.3857845323
152 150 0.472392638 0.385792589
153 151 0.4764705882 0.3857090115
154 152 0.4698795181 0.3857729221
155 153 0.4610778443 0.3858970812
156 154 0.4545454545 0.3860140516
157 155 0.4375 0.3861292318
158 156 0.4472049689 0.386065918
159 157 0.4625 0.386123291
160 158 0.4556962025 0.3860105252
161 159 0.4625 0.3859614258
162 160 0.4528301887 0.3859398058
163 161 0.4534161491 0.3861076118
164 162 0.4567901235 0.3861334093
165 163 0.4634146341 0.3860480957
166 164 0.4666666667 0.3861162109
167 165 0.4666666667 0.3862133789
168 166 0.4596273292 0.3863245985
169 167 0.450617284 0.3864905328
170 168 0.4573170732 0.3863822971
171 169 0.4545454545 0.3864398872
172 170 0.4578313253 0.3864295519
173 171 0.4561403509 0.3866640354
174 172 0.4534883721 0.3867054579
175 173 0.4444444444 0.3866737196
176 174 0.4425287356 0.3867723253
177 175 0.4540229885 0.3868446181
178 176 0.4488636364 0.3868221571
179 177 0.450867052 0.3870137804
180 178 0.450867052 0.3871307237
181 179 0.4593023256 0.3872641602
182 180 0.4488636364 0.3872341309
183 181 0.4425287356 0.3873837619
184 182 0.4431818182 0.3874805773
185 183 0.4413407821 0.3874806315

302
main/train/catboost_info/time_left.tsv
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@@ -1,119 +1,185 @@
iter Passed Remaining
0 98 49344
1 126 31532
2 223 37075
3 318 39520
4 418 41447
5 507 41751
6 596 42000
7 689 42384
8 779 42504
9 868 42547
10 960 42709
11 1015 41302
12 1109 41578
13 1200 41659
14 1292 41806
15 1384 41867
16 1476 41963
17 1572 42116
18 1668 42242
19 1760 42262
20 1854 42293
21 1946 42282
22 2038 42267
23 2134 42338
24 2228 42338
25 2323 42355
26 2415 42315
27 2507 42272
28 2598 42203
29 2690 42158
30 2781 42083
31 2871 41998
32 2963 41939
33 3055 41879
34 3145 41796
35 3237 41726
36 3328 41650
37 3420 41586
38 3515 41551
39 3605 41461
40 3698 41406
41 3788 41317
42 3879 41230
43 3968 41132
44 4062 41075
45 4152 40987
46 4245 40921
47 4337 40843
48 4426 40746
49 4519 40676
50 4610 40589
51 4703 40519
52 4795 40441
53 4891 40402
54 4985 40333
55 5078 40267
56 5172 40203
57 5264 40117
58 5358 40049
59 5450 39968
60 5542 39888
61 5634 39804
62 5725 39715
63 5817 39629
64 5909 39548
65 6000 39460
66 6098 39411
67 6192 39339
68 6290 39292
69 6387 39237
70 6480 39155
71 6573 39073
72 6664 38982
73 6754 38885
74 6846 38799
75 6935 38695
76 7027 38604
77 7122 38533
78 7213 38440
79 7306 38358
80 7394 38250
81 7490 38183
82 7581 38088
83 7672 37995
84 7767 37922
85 7857 37826
86 7951 37747
87 8041 37647
88 8132 37554
89 8223 37463
90 8314 37369
91 8405 37274
92 8431 36899
93 8526 36829
94 8621 36754
95 8713 36667
96 8803 36574
97 8896 36493
98 8990 36417
99 9086 36347
100 9176 36252
101 9267 36161
102 9363 36088
103 9470 36060
104 9563 35975
105 9654 35885
106 9746 35797
107 9839 35713
108 9933 35632
109 10026 35547
110 10120 35467
111 10209 35370
112 10301 35278
113 10398 35208
114 10489 35118
115 10581 35029
116 10675 34945
117 10766 34853
0 170 85040
1 295 73474
2 427 70840
3 554 68721
4 672 66544
5 793 65357
6 916 64579
7 1038 63868
8 1158 63207
9 1278 62650
10 1399 62198
11 1520 61838
12 1651 61874
13 1776 61654
14 1897 61359
15 2022 61178
16 2145 60944
17 2268 60750
18 2391 60543
19 2516 60401
20 2637 60151
21 2765 60087
22 2893 60009
23 3022 59947
24 3143 59732
25 3264 59508
26 3386 59332
27 3508 59149
28 3636 59059
29 3758 58877
30 3878 58683
31 3999 58488
32 4121 58331
33 4241 58129
34 4361 57941
35 4483 57789
36 4603 57608
37 4722 57416
38 4842 57245
39 4977 57241
40 5095 57042
41 5214 56866
42 5338 56736
43 5465 56643
44 5589 56513
45 5712 56380
46 5840 56294
47 5966 56182
48 6087 56033
49 6209 55881
50 6331 55739
51 6457 55632
52 6584 55531
53 6705 55385
54 6838 55329
55 6965 55229
56 7087 55084
57 7210 54949
58 7335 54830
59 7463 54735
60 7587 54607
61 7708 54454
62 7824 54277
63 7945 54130
64 8066 53984
65 8191 53865
66 8312 53720
67 8432 53572
68 8554 53435
69 8672 53276
70 8798 53165
71 8924 53050
72 9041 52886
73 9165 52765
74 9291 52650
75 9408 52490
76 9528 52345
77 9661 52271
78 9783 52137
79 9907 52015
80 10032 51895
81 10152 51753
82 10275 51625
83 10401 51513
84 10529 51408
85 10646 51249
86 10772 51137
87 10901 51037
88 11024 50910
89 11146 50779
90 11264 50627
91 11384 50488
92 11507 50359
93 11629 50229
94 11747 50082
95 11873 49966
96 11998 49848
97 12116 49702
98 12238 49572
99 12357 49431
100 12484 49321
101 12607 49193
102 12729 49063
103 12855 48948
104 12978 48823
105 13102 48703
106 13229 48591
107 13353 48467
108 13476 48341
109 13602 48227
110 13723 48093
111 13843 47957
112 13964 47823
113 14087 47699
114 14206 47562
115 14330 47438
116 14452 47308
117 14574 47181
118 14701 47069
119 14825 46947
120 14946 46816
121 15076 46713
122 15196 46578
123 15317 46447
124 15438 46316
125 15561 46190
126 15686 46070
127 15806 45936
128 15926 45805
129 16051 45683
130 16169 45546
131 16291 45419
132 16417 45301
133 16544 45189
134 16663 45053
135 16784 44923
136 16910 44806
137 17035 44687
138 17155 44555
139 17277 44428
140 17398 44297
141 17521 44172
142 17645 44052
143 17764 43917
144 17887 43792
145 18007 43661
146 18133 43544
147 18256 43421
148 18376 43288
149 18501 43169
150 18622 43041
151 18752 42934
152 18872 42803
153 19010 42710
154 19140 42601
155 19263 42477
156 19387 42355
157 19514 42240
158 19639 42120
159 19763 41997
160 19889 41879
161 20012 41753
162 20138 41636
163 20260 41509
164 20381 41379
165 20500 41248
166 20624 41124
167 20754 41015
168 20876 40888
169 20995 40755
170 21122 40638
171 21246 40516
172 21372 40396
173 21494 40270
174 21619 40150
175 21747 40035
176 21872 39914
177 21995 39789
178 22119 39666
179 22242 39542
180 22362 39412
181 22490 39296
182 22611 39167
183 22731 39038
1 iter Passed Remaining
2 0 98 170 49344 85040
3 1 126 295 31532 73474
4 2 223 427 37075 70840
5 3 318 554 39520 68721
6 4 418 672 41447 66544
7 5 507 793 41751 65357
8 6 596 916 42000 64579
9 7 689 1038 42384 63868
10 8 779 1158 42504 63207
11 9 868 1278 42547 62650
12 10 960 1399 42709 62198
13 11 1015 1520 41302 61838
14 12 1109 1651 41578 61874
15 13 1200 1776 41659 61654
16 14 1292 1897 41806 61359
17 15 1384 2022 41867 61178
18 16 1476 2145 41963 60944
19 17 1572 2268 42116 60750
20 18 1668 2391 42242 60543
21 19 1760 2516 42262 60401
22 20 1854 2637 42293 60151
23 21 1946 2765 42282 60087
24 22 2038 2893 42267 60009
25 23 2134 3022 42338 59947
26 24 2228 3143 42338 59732
27 25 2323 3264 42355 59508
28 26 2415 3386 42315 59332
29 27 2507 3508 42272 59149
30 28 2598 3636 42203 59059
31 29 2690 3758 42158 58877
32 30 2781 3878 42083 58683
33 31 2871 3999 41998 58488
34 32 2963 4121 41939 58331
35 33 3055 4241 41879 58129
36 34 3145 4361 41796 57941
37 35 3237 4483 41726 57789
38 36 3328 4603 41650 57608
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185 183 22731 39038

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