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liaozhaorun
2025-10-13 21:41:50 +08:00
parent fdee45bda0
commit 6016f2c059
4 changed files with 332 additions and 120 deletions
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main/train/Classify2.ipynb
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@@ -18,7 +18,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
"/mnt/d/PyProject/NewStock\n"
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n",
"/mnt/d/PyProject/NewStock/main/train\n"
]
},
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'main.factor'; 'main' is not a package",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 15\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[38;5;28mprint\u001b[39m(os.getcwd())\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m get_rolling_factor, get_simple_factor\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m read_industry_data\n\u001b[32m 17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfactor_processor\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m calculate_score\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'main.factor'; 'main' is not a package"
]
}
],
@@ -31,6 +44,10 @@
"import os\n",
"import sys\n",
"sys.path.append('/mnt/d/PyProject/NewStock/')\n",
"import sys\n",
"sys.path.append('/mnt/d/PyProject/NewStock/main/')\n",
"import sys\n",
"sys.path.append('/mnt/d/PyProject/NewStock/train')\n",
"print(os.getcwd())\n",
"import pandas as pd\n",
"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
@@ -45,7 +62,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"id": "4a481c60",
"metadata": {},
"outputs": [],
@@ -57,7 +74,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "a79cafb06a7e0e43",
"metadata": {
"ExecuteTime": {
@@ -71,56 +88,30 @@
"name": "stdout",
"output_type": "stream",
"text": [
"daily data\n",
"daily basic\n",
"inner merge on ['ts_code', 'trade_date']\n",
"stk limit\n",
"left merge on ['ts_code', 'trade_date']\n",
"money flow\n",
"left merge on ['ts_code', 'trade_date']\n",
"cyq perf\n",
"left merge on ['ts_code', 'trade_date']\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8820754 entries, 0 to 8820753\n",
"Data columns (total 33 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ts_code object \n",
" 1 trade_date datetime64[ns]\n",
" 2 open float64 \n",
" 3 close float64 \n",
" 4 high float64 \n",
" 5 low float64 \n",
" 6 vol float64 \n",
" 7 amount float64 \n",
" 8 pct_chg float64 \n",
" 9 turnover_rate float64 \n",
" 10 pe_ttm float64 \n",
" 11 circ_mv float64 \n",
" 12 total_mv float64 \n",
" 13 volume_ratio float64 \n",
" 14 is_st bool \n",
" 15 up_limit float64 \n",
" 16 down_limit float64 \n",
" 17 buy_sm_vol float64 \n",
" 18 sell_sm_vol float64 \n",
" 19 buy_lg_vol float64 \n",
" 20 sell_lg_vol float64 \n",
" 21 buy_elg_vol float64 \n",
" 22 sell_elg_vol float64 \n",
" 23 net_mf_vol float64 \n",
" 24 his_low float64 \n",
" 25 his_high float64 \n",
" 26 cost_5pct float64 \n",
" 27 cost_15pct float64 \n",
" 28 cost_50pct float64 \n",
" 29 cost_85pct float64 \n",
" 30 cost_95pct float64 \n",
" 31 weight_avg float64 \n",
" 32 winner_rate float64 \n",
"dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
"memory usage: 2.1+ GB\n",
"None\n"
"daily data\n"
]
},
{
"ename": "ImportError",
"evalue": "Missing optional dependency 'pytables'. Use pip or conda to install pytables.",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/site-packages/pandas/compat/_optional.py:135\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m135\u001b[39m module = \u001b[43mimportlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/importlib/__init__.py:90\u001b[39m, in \u001b[36mimport_module\u001b[39m\u001b[34m(name, package)\u001b[39m\n\u001b[32m 89\u001b[39m level += \u001b[32m1\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1387\u001b[39m, in \u001b[36m_gcd_import\u001b[39m\u001b[34m(name, package, level)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1360\u001b[39m, in \u001b[36m_find_and_load\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
"\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1324\u001b[39m, in \u001b[36m_find_and_load_unlocked\u001b[39m\u001b[34m(name, import_)\u001b[39m\n",
"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tables'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[31mImportError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmain\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m read_and_merge_h5_data\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mdaily data\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m df = \u001b[43mread_and_merge_h5_data\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m/mnt/d/PyProject/NewStock/data/daily_data.h5\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mdaily_data\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mts_code\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtrade_date\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mopen\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mclose\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mhigh\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlow\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mvol\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mamount\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpct_chg\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mdaily basic\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 9\u001b[39m df = read_and_merge_h5_data(\u001b[33m'\u001b[39m\u001b[33m/mnt/d/PyProject/NewStock/data/daily_basic.h5\u001b[39m\u001b[33m'\u001b[39m, key=\u001b[33m'\u001b[39m\u001b[33mdaily_basic\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 10\u001b[39m columns=[\u001b[33m'\u001b[39m\u001b[33mts_code\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mturnover_rate\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mpe_ttm\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mcirc_mv\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtotal_mv\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mvolume_ratio\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 11\u001b[39m \u001b[33m'\u001b[39m\u001b[33mis_st\u001b[39m\u001b[33m'\u001b[39m], df=df, join=\u001b[33m'\u001b[39m\u001b[33minner\u001b[39m\u001b[33m'\u001b[39m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m/mnt/d/PyProject/NewStock/main/utils/utils.py:14\u001b[39m, in \u001b[36mread_and_merge_h5_data\u001b[39m\u001b[34m(h5_filename, key, columns, df, join, on, prefix)\u001b[39m\n\u001b[32m 11\u001b[39m processed_columns.append(col)\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# 从 HDF5 文件读取数据，选择需要的列\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m data = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_hdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh5_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprocessed_columns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# 修改列名，如果列名以前有 _，加上 _\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m data.columns:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/site-packages/pandas/io/pytables.py:431\u001b[39m, in \u001b[36mread_hdf\u001b[39m\u001b[34m(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)\u001b[39m\n\u001b[32m 428\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exists:\n\u001b[32m 429\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_or_buf\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m does not exist\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m431\u001b[39m store = \u001b[43mHDFStore\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 432\u001b[39m \u001b[38;5;66;03m# can't auto open/close if we are using an iterator\u001b[39;00m\n\u001b[32m 433\u001b[39m \u001b[38;5;66;03m# so delegate to the iterator\u001b[39;00m\n\u001b[32m 434\u001b[39m auto_close = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/site-packages/pandas/io/pytables.py:571\u001b[39m, in \u001b[36mHDFStore.__init__\u001b[39m\u001b[34m(self, path, mode, complevel, complib, fletcher32, **kwargs)\u001b[39m\n\u001b[32m 568\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mformat\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[32m 569\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mformat is not a defined argument for HDFStore\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m571\u001b[39m tables = \u001b[43mimport_optional_dependency\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtables\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 573\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m complib \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m complib \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m tables.filters.all_complibs:\n\u001b[32m 574\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 575\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mcomplib only supports \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtables.filters.all_complibs\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m compression.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 576\u001b[39m )\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/quant/lib/python3.12/site-packages/pandas/compat/_optional.py:138\u001b[39m, in \u001b[36mimport_optional_dependency\u001b[39m\u001b[34m(name, extra, errors, min_version)\u001b[39m\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[32m 137\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m errors == \u001b[33m\"\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m138\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 141\u001b[39m \u001b[38;5;66;03m# Handle submodules: if we have submodule, grab parent module from sys.modules\u001b[39;00m\n",
"\u001b[31mImportError\u001b[39m: Missing optional dependency 'pytables'. Use pip or conda to install pytables."
]
}
],
@@ -157,7 +148,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "cac01788dac10678",
"metadata": {
"ExecuteTime": {
@@ -225,7 +216,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "c4e9e1d31da6dba6",
"metadata": {
"ExecuteTime": {
@@ -325,7 +316,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "a735bc02ceb4d872",
"metadata": {
"ExecuteTime": {
@@ -341,7 +332,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "53f86ddc0677a6d7",
"metadata": {
"ExecuteTime": {
@@ -408,7 +399,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "dbe2fd8021b9417f",
"metadata": {
"ExecuteTime": {
@@ -436,7 +427,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"id": "85c3e3d0235ffffa",
"metadata": {
"ExecuteTime": {
@@ -468,7 +459,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "92d84ce15a562ec6",
"metadata": {
"ExecuteTime": {
@@ -718,7 +709,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"id": "b87b938028afa206",
"metadata": {
"ExecuteTime": {
@@ -756,7 +747,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"id": "f4f16d63ad18d1bc",
"metadata": {
"ExecuteTime": {
@@ -982,7 +973,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"id": "40e6b68a91b30c79",
"metadata": {
"ExecuteTime": {
@@ -1302,7 +1293,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"id": "47c12bb34062ae7a",
"metadata": {
"ExecuteTime": {
@@ -1336,7 +1327,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"id": "29221dde",
"metadata": {},
"outputs": [
@@ -1379,7 +1370,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"id": "03ee5daf",
"metadata": {},
"outputs": [],
@@ -1392,7 +1383,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"id": "b76ea08a",
"metadata": {},
"outputs": [
@@ -1610,7 +1601,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": null,
"id": "3ff2d1c5",
"metadata": {},
"outputs": [],
@@ -1751,7 +1742,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": null,
"id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
"metadata": {
"ExecuteTime": {
@@ -1825,7 +1816,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": null,
"id": "5d1522a7538db91b",
"metadata": {
"ExecuteTime": {
@@ -1863,7 +1854,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": null,
"id": "c1c40917",
"metadata": {},
"outputs": [
@@ -1882,16 +1873,16 @@
"# 你可以根据需要调整日期格式，例如 '%Y%m%d' 会得到 '20250706'\n",
"date_str = current_date.strftime('%Y-%m-%d')\n",
"\n",
"# 3. 构建包含日期的模型文件名\n",
"model_filename = f'/mnt/d/PyProject/NewStock/main/train/catboost_model/catboost_model_2025-06-01.cbm'\n",
"# # 3. 构建包含日期的模型文件名\n",
"# model_filename = f'/mnt/d/PyProject/NewStock/main/train/catboost_model/catboost_model_2025-06-01.cbm'\n",
"\n",
"model.save_model(model_filename)\n",
"print(f\"模型已保存到: {model_filename}\")"
"# model.save_model(model_filename)\n",
"# print(f\"模型已保存到: {model_filename}\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": null,
"id": "09b1799e",
"metadata": {},
"outputs": [
@@ -1913,7 +1904,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": null,
"id": "e53b209a",
"metadata": {},
"outputs": [
@@ -1946,7 +1937,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": null,
"id": "364e821a",
"metadata": {},
"outputs": [],
@@ -2030,7 +2021,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": null,
"id": "1f6e6336",
"metadata": {},
"outputs": [
@@ -2099,7 +2090,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": null,
"id": "7e9023cc",
"metadata": {},
"outputs": [],
@@ -2299,7 +2290,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": null,
"id": "a0000d75",
"metadata": {},
"outputs": [
@@ -2545,7 +2536,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": null,
"id": "a436dba4",
"metadata": {},
"outputs": [