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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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104
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"
},