2581 lines
287 KiB
Plaintext
2581 lines
287 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "code",
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||
"execution_count": 1,
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"id": "79a7758178bafdd3",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-03T12:46:06.987506Z",
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||
"start_time": "2025-04-03T12:46:06.259551Z"
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||
},
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||
"jupyter": {
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"source_hidden": true
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/mnt/d/PyProject/NewStock\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"# %load_ext cudf.pandas\n",
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"\n",
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"import gc\n",
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"import os\n",
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"import sys\n",
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"sys.path.append('/mnt/d/PyProject/NewStock/')\n",
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"print(os.getcwd())\n",
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"import pandas as pd\n",
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"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
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"from main.utils.factor import read_industry_data\n",
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"from main.utils.factor_processor import calculate_score\n",
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"from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
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"\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4a481c60",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 设置使用核心\n",
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"import os\n",
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"os.environ[\"MODIN_CPUS\"] = \"4\"\n"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"id": "a79cafb06a7e0e43",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-03T12:47:00.212859Z",
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||
"start_time": "2025-04-03T12:46:06.998047Z"
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||
},
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||
"scrolled": true
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||
},
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||
"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"daily data\n",
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"daily basic\n",
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"inner merge on ['ts_code', 'trade_date']\n",
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"stk limit\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"money flow\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"cyq perf\n",
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"left merge on ['ts_code', 'trade_date']\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 9162612 entries, 0 to 9162611\n",
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"Data columns (total 33 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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" 0 ts_code object \n",
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" 1 trade_date datetime64[ns]\n",
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" 2 open float64 \n",
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" 3 close float64 \n",
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" 4 high float64 \n",
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" 5 low float64 \n",
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" 6 vol float64 \n",
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" 7 amount float64 \n",
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" 8 pct_chg float64 \n",
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" 9 turnover_rate float64 \n",
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" 10 pe_ttm float64 \n",
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" 11 circ_mv float64 \n",
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" 12 total_mv float64 \n",
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" 13 volume_ratio float64 \n",
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" 14 is_st bool \n",
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" 15 up_limit float64 \n",
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" 16 down_limit float64 \n",
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" 17 buy_sm_vol float64 \n",
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" 18 sell_sm_vol float64 \n",
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" 19 buy_lg_vol float64 \n",
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" 20 sell_lg_vol float64 \n",
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" 21 buy_elg_vol float64 \n",
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" 22 sell_elg_vol float64 \n",
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" 23 net_mf_vol float64 \n",
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" 24 his_low float64 \n",
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" 25 his_high float64 \n",
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" 26 cost_5pct float64 \n",
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" 27 cost_15pct float64 \n",
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" 28 cost_50pct float64 \n",
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" 29 cost_85pct float64 \n",
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" 30 cost_95pct float64 \n",
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" 31 weight_avg float64 \n",
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" 32 winner_rate float64 \n",
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"dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
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"memory usage: 2.2+ GB\n",
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"None\n"
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]
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}
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||
],
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"source": [
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"from main.utils.utils import read_and_merge_h5_data\n",
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"\n",
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"print('daily data')\n",
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"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n",
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" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg'],\n",
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" df=None)\n",
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"\n",
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"print('daily basic')\n",
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"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_basic.h5', key='daily_basic',\n",
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" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n",
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" 'is_st'], df=df, join='inner')\n",
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"\n",
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"print('stk limit')\n",
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"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_limit.h5', key='stk_limit',\n",
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" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
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" df=df)\n",
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"print('money flow')\n",
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"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/money_flow.h5', key='money_flow',\n",
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" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
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" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
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" df=df)\n",
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"print('cyq perf')\n",
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"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cyq_perf.h5', key='cyq_perf',\n",
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" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
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" 'cost_50pct',\n",
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" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
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" df=df)\n",
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"print(df.info())"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "cac01788dac10678",
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||
"metadata": {
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||
"ExecuteTime": {
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||
"end_time": "2025-04-03T12:47:10.527104Z",
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||
"start_time": "2025-04-03T12:47:00.488715Z"
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||
}
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||
},
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||
"outputs": [
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{
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||
"name": "stdout",
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"output_type": "stream",
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||
"text": [
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"industry\n"
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||
]
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||
}
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||
],
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||
"source": [
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"print('industry')\n",
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"industry_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/industry_data.h5', key='industry_data',\n",
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" columns=['ts_code', 'l2_code', 'in_date'],\n",
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" df=None, on=['ts_code'], join='left')\n",
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"\n",
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"\n",
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"def merge_with_industry_data(df, industry_df):\n",
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" # 确保日期字段是 datetime 类型\n",
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" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
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" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
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"\n",
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" # 对 industry_df 按 ts_code 和 in_date 排序\n",
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" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
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"\n",
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" # 对原始 df 按 ts_code 和 trade_date 排序\n",
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" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
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"\n",
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" # 使用 merge_asof 进行向后合并\n",
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" merged = pd.merge_asof(\n",
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" df_sorted,\n",
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" industry_df_sorted,\n",
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" by='ts_code', # 按 ts_code 分组\n",
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" left_on='trade_date',\n",
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" right_on='in_date',\n",
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" direction='backward'\n",
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" )\n",
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"\n",
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" # 获取每个 ts_code 的最早 in_date 记录\n",
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" min_in_date_per_ts = (industry_df_sorted\n",
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" .groupby('ts_code')\n",
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" .first()\n",
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" .reset_index()[['ts_code', 'l2_code']])\n",
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"\n",
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" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
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" merged['l2_code'] = merged['l2_code'].fillna(\n",
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" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
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" )\n",
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"\n",
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" # 保留需要的列并重置索引\n",
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" result = merged.reset_index(drop=True)\n",
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" return result\n",
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"\n",
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"\n",
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"# 使用示例\n",
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"df = merge_with_industry_data(df, industry_df)\n",
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"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 5,
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||
"id": "c4e9e1d31da6dba6",
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||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.719252Z",
|
||
"start_time": "2025-04-03T12:47:10.541247Z"
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||
},
|
||
"jupyter": {
|
||
"source_hidden": true
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||
}
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"from main.factor.factor import *\n",
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"\n",
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"def calculate_indicators(df):\n",
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" \"\"\"\n",
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||
" 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n",
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" \"\"\"\n",
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" df = df.sort_values('trade_date')\n",
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" df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
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" # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
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" delta = df['close'].diff()\n",
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" gain = delta.where(delta > 0, 0)\n",
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||
" loss = -delta.where(delta < 0, 0)\n",
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||
" avg_gain = gain.rolling(window=14).mean()\n",
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||
" avg_loss = loss.rolling(window=14).mean()\n",
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" rs = avg_gain / avg_loss\n",
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" df['RSI'] = 100 - (100 / (1 + rs))\n",
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"\n",
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" # 计算MACD\n",
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" ema12 = df['close'].ewm(span=12, adjust=False).mean()\n",
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" ema26 = df['close'].ewm(span=26, adjust=False).mean()\n",
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||
" df['MACD'] = ema12 - ema26\n",
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" df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
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" df['MACD_hist'] = df['MACD'] - df['Signal_line']\n",
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"\n",
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" # 4. 情绪因子1：市场上涨比例（Up Ratio）\n",
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" df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n",
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" df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n",
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"\n",
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" # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n",
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" df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n",
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" df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n",
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"\n",
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" # 6. 情绪因子3：波动率（Volatility）\n",
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" df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n",
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"\n",
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" # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n",
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" df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n",
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" df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n",
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"\n",
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" # df = sentiment_panic_greed_index(df)\n",
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" # df = sentiment_market_breadth_proxy(df)\n",
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" # df = sentiment_reversal_indicator(df)\n",
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"\n",
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" return df\n",
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"\n",
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"\n",
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"def generate_index_indicators(h5_filename):\n",
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" df = pd.read_hdf(h5_filename, key='index_data')\n",
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" df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
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" df = df.sort_values('trade_date')\n",
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"\n",
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" # 计算每个ts_code的相关指标\n",
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" df_indicators = []\n",
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" for ts_code in df['ts_code'].unique():\n",
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" df_index = df[df['ts_code'] == ts_code].copy()\n",
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" df_index = calculate_indicators(df_index)\n",
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" df_indicators.append(df_index)\n",
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"\n",
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" # 合并所有指数的结果\n",
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" df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n",
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"\n",
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" # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n",
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" df_final = df_all_indicators.pivot_table(\n",
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" index='trade_date',\n",
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" columns='ts_code',\n",
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" values=['daily_return', \n",
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" 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
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" # 'sentiment_panic_greed_index',\n",
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" 'up_ratio_20d', 'volume_change_rate', 'volatility',\n",
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" 'amount_change_rate', 'amount_mean'],\n",
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" aggfunc='last'\n",
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" )\n",
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"\n",
|
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" df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n",
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" df_final = df_final.reset_index()\n",
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"\n",
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" return df_final\n",
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"\n",
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"\n",
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"# 使用函数\n",
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"h5_filename = '/mnt/d/PyProject/NewStock/data/index_data.h5'\n",
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"index_data = generate_index_indicators(h5_filename)\n",
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||
"index_data = index_data.dropna()\n"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "a735bc02ceb4d872",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.821169Z",
|
||
"start_time": "2025-04-03T12:47:10.751831Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import talib\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "53f86ddc0677a6d7",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:15.944254Z",
|
||
"start_time": "2025-04-03T12:47:10.826179Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from main.utils.factor import get_act_factor\n",
|
||
"\n",
|
||
"\n",
|
||
"def read_industry_data(h5_filename):\n",
|
||
" # 读取 H5 文件中所有的行业数据\n",
|
||
" industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[\n",
|
||
" 'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'\n",
|
||
" ]) # 假设 H5 文件的键是 'industry_data'\n",
|
||
" industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
" industry_data = industry_data.reindex()\n",
|
||
" industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')\n",
|
||
"\n",
|
||
" grouped = industry_data.groupby('ts_code', group_keys=False)\n",
|
||
" industry_data['obv'] = grouped.apply(\n",
|
||
" lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
|
||
" )\n",
|
||
" industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)\n",
|
||
" industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
|
||
"\n",
|
||
" industry_data = get_act_factor(industry_data, cat=False)\n",
|
||
" industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # # 计算每天每个 ts_code 的因子和当天所有 ts_code 的中位数的偏差\n",
|
||
" # factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列\n",
|
||
" # \n",
|
||
" # for factor in factor_columns:\n",
|
||
" # if factor in industry_data.columns:\n",
|
||
" # # 计算每天每个 ts_code 的因子值与当天所有 ts_code 的中位数的偏差\n",
|
||
" # industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(\n",
|
||
" # lambda x: x - x.mean())\n",
|
||
"\n",
|
||
" industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(\n",
|
||
" lambda x: x.rank(pct=True))\n",
|
||
" industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(\n",
|
||
" lambda x: x.rank(pct=True))\n",
|
||
"\n",
|
||
" # cs_rank_intraday_range(industry_data)\n",
|
||
" # cs_rank_close_pos_in_range(industry_data)\n",
|
||
"\n",
|
||
" industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(\n",
|
||
" columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})\n",
|
||
"\n",
|
||
" industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})\n",
|
||
" return industry_data\n",
|
||
"\n",
|
||
"\n",
|
||
"industry_df = read_industry_data('/mnt/d/PyProject/NewStock/data/sw_daily.h5')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "dbe2fd8021b9417f",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:15.969344Z",
|
||
"start_time": "2025-04-03T12:47:15.963327Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['ts_code', 'open', 'close', 'high', 'low', 'amount', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"origin_columns = df.columns.tolist()\n",
|
||
"origin_columns = [col for col in origin_columns if\n",
|
||
" col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n",
|
||
"origin_columns = [col for col in origin_columns if col not in index_data.columns]\n",
|
||
"origin_columns = [col for col in origin_columns if 'cyq' not in col]\n",
|
||
"print(origin_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "85c3e3d0235ffffa",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:16.089879Z",
|
||
"start_time": "2025-04-03T12:47:15.990101Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"fina_indicator_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/fina_indicator.h5', key='fina_indicator',\n",
|
||
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps', 'roa', 'roe'],\n",
|
||
" df=None)\n",
|
||
"cashflow_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cashflow.h5', key='cashflow',\n",
|
||
" columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
|
||
" df=None)\n",
|
||
"balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/balancesheet.h5', key='balancesheet',\n",
|
||
" columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
|
||
" df=None)\n",
|
||
"top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/top_list.h5', key='top_list',\n",
|
||
" columns=['ts_code', 'trade_date', 'reason'],\n",
|
||
" df=None)\n",
|
||
"\n",
|
||
"top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n",
|
||
"\n",
|
||
"stk_holdertrade_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5', key='stk_holdertrade',\n",
|
||
" columns=['ts_code', 'ann_date', 'in_de', 'change_ratio', 'after_ratio'],\n",
|
||
" df=None)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "92d84ce15a562ec6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:01.612695Z",
|
||
"start_time": "2025-04-03T12:47:16.121802Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"开始计算因子: AR, BR (原地修改)...\n",
|
||
"因子 AR, BR 计算成功。\n",
|
||
"因子 AR, BR 计算流程结束。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"计算 BBI...\n",
|
||
"--- 计算日级别偏离度 (使用 pct_chg) ---\n",
|
||
"--- 计算日级别动量基准 (使用 pct_chg) ---\n",
|
||
"日级别动量基准计算完成 (使用 pct_chg)。\n",
|
||
"日级别偏离度计算完成 (使用 pct_chg)。\n",
|
||
"--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n",
|
||
"--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n",
|
||
"错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
|
||
"错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
|
||
"Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
|
||
" 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv',\n",
|
||
" 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol',\n",
|
||
" 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol',\n",
|
||
" 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct',\n",
|
||
" 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg',\n",
|
||
" 'winner_rate', 'l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe',\n",
|
||
" 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor',\n",
|
||
" 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20',\n",
|
||
" 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol',\n",
|
||
" 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff',\n",
|
||
" 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop',\n",
|
||
" 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change',\n",
|
||
" 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness',\n",
|
||
" 'floating_chip_proxy', 'cost_support_15pct_change',\n",
|
||
" 'cat_winner_price_zone', 'flow_chip_consistency',\n",
|
||
" 'profit_taking_vs_absorb', '_is_positive', '_is_negative',\n",
|
||
" 'cat_is_positive', '_pos_returns', '_neg_returns', '_pos_returns_sq',\n",
|
||
" '_neg_returns_sq', 'upside_vol', 'downside_vol', 'vol_ratio',\n",
|
||
" 'return_skew', 'return_kurtosis', 'volume_change_rate',\n",
|
||
" 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike',\n",
|
||
" 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike',\n",
|
||
" 'vol_std_5', 'atr_14', 'atr_6', 'obv'],\n",
|
||
" dtype='object')\n",
|
||
"Calculating lg_flow_mom_corr_20_60...\n",
|
||
"Finished lg_flow_mom_corr_20_60.\n",
|
||
"Calculating lg_flow_accel...\n",
|
||
"Finished lg_flow_accel.\n",
|
||
"Calculating profit_pressure...\n",
|
||
"Finished profit_pressure.\n",
|
||
"Calculating underwater_resistance...\n",
|
||
"Finished underwater_resistance.\n",
|
||
"Calculating cost_conc_std_20...\n",
|
||
"Finished cost_conc_std_20.\n",
|
||
"Calculating profit_decay_20...\n",
|
||
"Finished profit_decay_20.\n",
|
||
"Calculating vol_amp_loss_20...\n",
|
||
"Finished vol_amp_loss_20.\n",
|
||
"Calculating vol_drop_profit_cnt_5...\n",
|
||
"Finished vol_drop_profit_cnt_5.\n",
|
||
"Calculating lg_flow_vol_interact_20...\n",
|
||
"Finished lg_flow_vol_interact_20.\n",
|
||
"Calculating cost_break_confirm_cnt_5...\n",
|
||
"Finished cost_break_confirm_cnt_5.\n",
|
||
"Calculating atr_norm_channel_pos_14...\n",
|
||
"Finished atr_norm_channel_pos_14.\n",
|
||
"Calculating turnover_diff_skew_20...\n",
|
||
"Finished turnover_diff_skew_20.\n",
|
||
"Calculating lg_sm_flow_diverge_20...\n",
|
||
"Finished lg_sm_flow_diverge_20.\n",
|
||
"Calculating pullback_strong_20_20...\n",
|
||
"Finished pullback_strong_20_20.\n",
|
||
"Calculating vol_wgt_hist_pos_20...\n",
|
||
"Finished vol_wgt_hist_pos_20.\n",
|
||
"Calculating vol_adj_roc_20...\n",
|
||
"Finished vol_adj_roc_20.\n",
|
||
"Calculating cs_rank_net_lg_flow_val...\n",
|
||
"Finished cs_rank_net_lg_flow_val.\n",
|
||
"Calculating cs_rank_flow_divergence...\n",
|
||
"Finished cs_rank_flow_divergence.\n",
|
||
"Calculating cs_rank_ind_adj_lg_flow...\n",
|
||
"Finished cs_rank_ind_adj_lg_flow.\n",
|
||
"Calculating cs_rank_elg_buy_ratio...\n",
|
||
"Finished cs_rank_elg_buy_ratio.\n",
|
||
"Calculating cs_rank_rel_profit_margin...\n",
|
||
"Finished cs_rank_rel_profit_margin.\n",
|
||
"Calculating cs_rank_cost_breadth...\n",
|
||
"Finished cs_rank_cost_breadth.\n",
|
||
"Calculating cs_rank_dist_to_upper_cost...\n",
|
||
"Finished cs_rank_dist_to_upper_cost.\n",
|
||
"Calculating cs_rank_winner_rate...\n",
|
||
"Finished cs_rank_winner_rate.\n",
|
||
"Calculating cs_rank_intraday_range...\n",
|
||
"Finished cs_rank_intraday_range.\n",
|
||
"Calculating cs_rank_close_pos_in_range...\n",
|
||
"Finished cs_rank_close_pos_in_range.\n",
|
||
"Calculating cs_rank_opening_gap...\n",
|
||
"Error calculating cs_rank_opening_gap: Missing 'pre_close' column. Assigning NaN.\n",
|
||
"Calculating cs_rank_pos_in_hist_range...\n",
|
||
"Finished cs_rank_pos_in_hist_range.\n",
|
||
"Calculating cs_rank_vol_x_profit_margin...\n",
|
||
"Finished cs_rank_vol_x_profit_margin.\n",
|
||
"Calculating cs_rank_lg_flow_price_concordance...\n",
|
||
"Finished cs_rank_lg_flow_price_concordance.\n",
|
||
"Calculating cs_rank_turnover_per_winner...\n",
|
||
"Finished cs_rank_turnover_per_winner.\n",
|
||
"Calculating cs_rank_ind_cap_neutral_pe (Placeholder - requires statsmodels)...\n",
|
||
"Finished cs_rank_ind_cap_neutral_pe (Placeholder).\n",
|
||
"Calculating cs_rank_volume_ratio...\n",
|
||
"Finished cs_rank_volume_ratio.\n",
|
||
"Calculating cs_rank_elg_buy_sell_sm_ratio...\n",
|
||
"Finished cs_rank_elg_buy_sell_sm_ratio.\n",
|
||
"Calculating cs_rank_cost_dist_vol_ratio...\n",
|
||
"Finished cs_rank_cost_dist_vol_ratio.\n",
|
||
"Calculating cs_rank_size...\n",
|
||
"Finished cs_rank_size.\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4819708 entries, 0 to 4819707\n",
|
||
"Columns: 181 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(165), int64(3), object(2)\n",
|
||
"memory usage: 6.2+ GB\n",
|
||
"None\n",
|
||
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'amount', 'pct_chg', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate', 'cat_l2_code', 'undist_profit_ps', 'ocfps', 'roa', 'roe', 'AR', 'BR', 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'price_cost_divergence', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_flow_divergence', 'cs_rank_ind_adj_lg_flow', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_opening_gap', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_ind_cap_neutral_pe', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"from main.factor.factor import *\n",
|
||
"from main.factor.money_factor import *\n",
|
||
"\n",
|
||
"\n",
|
||
"def filter_data(df):\n",
|
||
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
||
" # df = df[df['trade_date'] <= '2025-06-01']\n",
|
||
" df = df[~df['is_st']]\n",
|
||
" df = df[~df['ts_code'].str.endswith('BJ')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('30')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('68')]\n",
|
||
" df = df[~df['ts_code'].str.startswith('8')]\n",
|
||
" df = df[df['trade_date'] >= '2019-01-01']\n",
|
||
" if 'in_date' in df.columns:\n",
|
||
" df = df.drop(columns=['in_date'])\n",
|
||
" df = df.reset_index(drop=True)\n",
|
||
" return df\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"df = filter_data(df)\n",
|
||
"df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
"# df = price_minus_deduction_price(df, n=120)\n",
|
||
"# df = price_deduction_price_diff_ratio_to_sma(df, n=120)\n",
|
||
"# df = cat_price_vs_sma_vs_deduction_price(df, n=120)\n",
|
||
"# df = cat_reason(df, top_list_df)\n",
|
||
"# df = cat_is_on_top_list(df, top_list_df)\n",
|
||
"\n",
|
||
"# df = ts_turnover_rate_acceleration_5_20(df)\n",
|
||
"# df = ts_vol_sustain_10_30(df)\n",
|
||
"# df = cs_turnover_rate_relative_strength_20(df)\n",
|
||
"# df = cs_amount_outlier_10(df)\n",
|
||
"# df = holder_trade_factors(stk_holdertrade_df, df)\n",
|
||
"\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='undist_profit_ps')\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='ocfps')\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='roa')\n",
|
||
"df = add_financial_factor(df, fina_indicator_df, factor_value_col='roe')\n",
|
||
"\n",
|
||
"calculate_arbr(df, N=26)\n",
|
||
"df['log_circ_mv'] = np.log(df['circ_mv'])\n",
|
||
"df = calculate_cashflow_to_ev_factor(df, cashflow_df, balancesheet_df)\n",
|
||
"df = caculate_book_to_price_ratio(df, fina_indicator_df)\n",
|
||
"\n",
|
||
"df = turnover_rate_n(df, n=5)\n",
|
||
"df = variance_n(df, n=20)\n",
|
||
"df = bbi_ratio_factor(df)\n",
|
||
"df = daily_deviation(df)\n",
|
||
"df = daily_industry_deviation(df)\n",
|
||
"df, _ = get_rolling_factor(df)\n",
|
||
"df, _ = get_simple_factor(df)\n",
|
||
"\n",
|
||
"df = df.rename(columns={'l1_code': 'cat_l1_code'})\n",
|
||
"df = df.rename(columns={'l2_code': 'cat_l2_code'})\n",
|
||
"\n",
|
||
"lg_flow_mom_corr(df, N=20, M=60)\n",
|
||
"lg_flow_accel(df)\n",
|
||
"profit_pressure(df)\n",
|
||
"underwater_resistance(df)\n",
|
||
"cost_conc_std(df, N=20)\n",
|
||
"profit_decay(df, N=20)\n",
|
||
"vol_amp_loss(df, N=20)\n",
|
||
"vol_drop_profit_cnt(df, N=20, M=5)\n",
|
||
"lg_flow_vol_interact(df, N=20)\n",
|
||
"cost_break_confirm_cnt(df, M=5)\n",
|
||
"atr_norm_channel_pos(df, N=14)\n",
|
||
"turnover_diff_skew(df, N=20)\n",
|
||
"lg_sm_flow_diverge(df, N=20)\n",
|
||
"pullback_strong(df, N=20, M=20)\n",
|
||
"vol_wgt_hist_pos(df, N=20)\n",
|
||
"vol_adj_roc(df, N=20)\n",
|
||
"\n",
|
||
"cs_rank_net_lg_flow_val(df)\n",
|
||
"cs_rank_flow_divergence(df)\n",
|
||
"cs_rank_industry_adj_lg_flow(df) # Needs cat_l2_code\n",
|
||
"cs_rank_elg_buy_ratio(df)\n",
|
||
"cs_rank_rel_profit_margin(df)\n",
|
||
"cs_rank_cost_breadth(df)\n",
|
||
"cs_rank_dist_to_upper_cost(df)\n",
|
||
"cs_rank_winner_rate(df)\n",
|
||
"cs_rank_intraday_range(df)\n",
|
||
"cs_rank_close_pos_in_range(df)\n",
|
||
"cs_rank_opening_gap(df) # Needs pre_close\n",
|
||
"cs_rank_pos_in_hist_range(df) # Needs his_low, his_high\n",
|
||
"cs_rank_vol_x_profit_margin(df)\n",
|
||
"cs_rank_lg_flow_price_concordance(df)\n",
|
||
"cs_rank_turnover_per_winner(df)\n",
|
||
"cs_rank_ind_cap_neutral_pe(df) # Placeholder - needs external libraries\n",
|
||
"cs_rank_volume_ratio(df) # Needs volume_ratio\n",
|
||
"cs_rank_elg_buy_sell_sm_ratio(df)\n",
|
||
"cs_rank_cost_dist_vol_ratio(df) # Needs volume_ratio\n",
|
||
"cs_rank_size(df) # Needs circ_mv\n",
|
||
"\n",
|
||
"\n",
|
||
"# df = df.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"print(df.info())\n",
|
||
"print(df.columns.tolist())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "b87b938028afa206",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:03.658725Z",
|
||
"start_time": "2025-04-03T13:08:02.469611Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from scipy.stats import ks_2samp, wasserstein_distance\n",
|
||
"\n",
|
||
"\n",
|
||
"def remove_shifted_features(train_data, test_data, feature_columns, ks_threshold=0.05, wasserstein_threshold=0.1,\n",
|
||
" importance_threshold=0.05):\n",
|
||
" dropped_features = []\n",
|
||
"\n",
|
||
" # **统计数据漂移**\n",
|
||
" numeric_columns = train_data.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
" for feature in numeric_columns:\n",
|
||
" ks_stat, p_value = ks_2samp(train_data[feature], test_data[feature])\n",
|
||
" wasserstein_dist = wasserstein_distance(train_data[feature], test_data[feature])\n",
|
||
"\n",
|
||
" if p_value < ks_threshold or wasserstein_dist > wasserstein_threshold:\n",
|
||
" dropped_features.append(feature)\n",
|
||
"\n",
|
||
" print(f\"检测到 {len(dropped_features)} 个可能漂移的特征: {dropped_features}\")\n",
|
||
"\n",
|
||
" # **应用阈值进行最终筛选**\n",
|
||
" filtered_features = [f for f in feature_columns if f not in dropped_features]\n",
|
||
"\n",
|
||
" return filtered_features, dropped_features\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "f4f16d63ad18d1bc",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:03.670700Z",
|
||
"start_time": "2025-04-03T13:08:03.665739Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import statsmodels.api as sm # 用于中性化回归\n",
|
||
"from tqdm import tqdm # 可选，用于显示进度条\n",
|
||
"\n",
|
||
"# --- 常量 ---\n",
|
||
"epsilon = 1e-10 # 防止除零\n",
|
||
"\n",
|
||
"# --- 1. 中位数去极值 (MAD) ---\n",
|
||
"\n",
|
||
"def cs_mad_filter(df: pd.DataFrame,\n",
|
||
" features: list,\n",
|
||
" k: float = 3.0,\n",
|
||
" scale_factor: float = 1.4826):\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行截面 MAD 去极值处理 (原地修改)。\n",
|
||
"\n",
|
||
" 方法: 对每日截面数据，计算 median 和 MAD，\n",
|
||
" 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值\n",
|
||
" 替换为边界值 (Winsorization)。\n",
|
||
" scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
|
||
" features (list): 需要处理的特征列名列表。\n",
|
||
" k (float): MAD 的倍数，用于确定边界。默认为 3.0。\n",
|
||
" scale_factor (float): MAD 的缩放因子。默认为 1.4826。\n",
|
||
"\n",
|
||
" WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
|
||
" \"\"\"\n",
|
||
" print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n",
|
||
" if not all(col in df.columns for col in features):\n",
|
||
" missing = [col for col in features if col not in df.columns]\n",
|
||
" print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" grouped = df.groupby('trade_date')\n",
|
||
"\n",
|
||
" for col in tqdm(features, desc=\"MAD Filtering\"):\n",
|
||
" try:\n",
|
||
" # 计算截面中位数\n",
|
||
" median = grouped[col].transform('median')\n",
|
||
" # 计算截面 MAD (Median Absolute Deviation from Median)\n",
|
||
" mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median')\n",
|
||
"\n",
|
||
" # 计算上下边界\n",
|
||
" lower_bound = median - k * scale_factor * mad\n",
|
||
" upper_bound = median + k * scale_factor * mad\n",
|
||
"\n",
|
||
" # 原地应用 clip\n",
|
||
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
|
||
"\n",
|
||
" except KeyError:\n",
|
||
" print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的 MAD 处理。\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。\")\n",
|
||
"\n",
|
||
" print(\"截面 MAD 去极值处理完成。\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# --- 2. 行业市值中性化 ---\n",
|
||
"\n",
|
||
"from tqdm import tqdm\n",
|
||
"\n",
|
||
"def cs_neutralize_market_cap_numpy(df: pd.DataFrame,\n",
|
||
" features: list,\n",
|
||
" market_cap_col: str = 'circ_mv'):\n",
|
||
" \"\"\"\n",
|
||
" 对 DataFrame 中的指定特征进行截面市值中性化 (NumPy 优化)。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 包含数据的 DataFrame，需要有 'trade_date' 和 market_cap_col 列。\n",
|
||
" features (list): 需要进行市值中性化的特征列名列表。\n",
|
||
" market_cap_col (str): 包含市值数据的列名，默认为 'circ_mv'。\n",
|
||
" \"\"\"\n",
|
||
" print(\"开始截面市值中性化 (NumPy 优化)...\")\n",
|
||
" required_cols = features + ['trade_date', market_cap_col]\n",
|
||
" if not all(col in df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in df.columns]\n",
|
||
" print(f\"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" df_copy = df\n",
|
||
" log_cap_col = '_log_market_cap'\n",
|
||
" df_copy[log_cap_col] = np.log1p(df_copy[market_cap_col])\n",
|
||
"\n",
|
||
" # 创建一个 DataFrame 来存储所有日期的残差结果\n",
|
||
" residuals_container = pd.DataFrame(index=df_copy.index, columns=features, dtype=float)\n",
|
||
"\n",
|
||
" for date, group_df in tqdm(df_copy.groupby('trade_date'), desc=\"Neutralizing by Date (NumPy)\"):\n",
|
||
" # 准备 X 矩阵 (自变量)：常数项和对数市值\n",
|
||
" X_daily = np.concatenate([np.ones((len(group_df), 1)), group_df[[log_cap_col]].values], axis=1)\n",
|
||
"\n",
|
||
" for feature_col in features:\n",
|
||
" Y_daily = group_df[feature_col].values\n",
|
||
"\n",
|
||
" # 处理 NaN：只对有效数据对进行回归\n",
|
||
" valid_mask_y = ~np.isnan(Y_daily)\n",
|
||
" valid_mask_x = ~np.isnan(X_daily).any(axis=1)\n",
|
||
" valid_mask = valid_mask_y & valid_mask_x\n",
|
||
"\n",
|
||
" current_feature_indices = group_df.index[valid_mask]\n",
|
||
"\n",
|
||
" if np.sum(valid_mask) < X_daily.shape[1] + 1:\n",
|
||
" # 有效数据不足，此特征在此日期保持 NaN\n",
|
||
" continue\n",
|
||
"\n",
|
||
" Y_valid = Y_daily[valid_mask]\n",
|
||
" X_valid = X_daily[valid_mask, :]\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # 使用 np.linalg.lstsq 进行 OLS 计算\n",
|
||
" beta, sum_sq_resid, rank, s = np.linalg.lstsq(X_valid, Y_valid, rcond=None)\n",
|
||
"\n",
|
||
" # 计算预测值 Y_hat = X_valid @ beta\n",
|
||
" Y_hat_valid = X_valid @ beta\n",
|
||
"\n",
|
||
" # 计算残差 residuals = Y_valid - Y_hat_valid\n",
|
||
" residuals_valid = Y_valid - Y_hat_valid\n",
|
||
"\n",
|
||
" # 将计算得到的残差放回 residuals_container\n",
|
||
" residuals_container.loc[current_feature_indices, feature_col] = residuals_valid\n",
|
||
"\n",
|
||
" except np.linalg.LinAlgError:\n",
|
||
" pass\n",
|
||
" except Exception as e:\n",
|
||
" pass\n",
|
||
"\n",
|
||
" # 将所有计算得到的残差更新回原始的 df (原地修改)\n",
|
||
" for feature_col in features:\n",
|
||
" df[feature_col] = residuals_container[feature_col]\n",
|
||
"\n",
|
||
" # 清理临时列\n",
|
||
" df.drop(columns=[log_cap_col], inplace=True, errors='ignore')\n",
|
||
" print(\"截面市值中性化完成 (NumPy 优化)。\")\n",
|
||
"\n",
|
||
"# --- 3. Z-Score 标准化 ---\n",
|
||
"\n",
|
||
"def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10):\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行截面 Z-Score 标准化 (原地修改)。\n",
|
||
" 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。\n",
|
||
" features (list): 需要处理的特征列名列表。\n",
|
||
" epsilon (float): 防止除以零的小常数。\n",
|
||
"\n",
|
||
" WARNING: 此函数会原地修改输入的 DataFrame 'df'。\n",
|
||
" \"\"\"\n",
|
||
" print(\"开始截面 Z-Score 标准化...\")\n",
|
||
" if not all(col in df.columns for col in features):\n",
|
||
" missing = [col for col in features if col not in df.columns]\n",
|
||
" print(f\"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" grouped = df.groupby('trade_date')\n",
|
||
"\n",
|
||
" for col in tqdm(features, desc=\"Standardizing\"):\n",
|
||
" try:\n",
|
||
" # 使用 transform 计算截面均值和标准差\n",
|
||
" mean = grouped[col].transform('mean')\n",
|
||
" std = grouped[col].transform('std')\n",
|
||
"\n",
|
||
" # 计算 Z-Score 并原地赋值\n",
|
||
" df[col] = (df[col] - mean) / (std + epsilon)\n",
|
||
"\n",
|
||
" except KeyError:\n",
|
||
" print(f\"警告: 列 '{col}' 可能不存在或在分组中出错，跳过此列的标准化处理。\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。\")\n",
|
||
"\n",
|
||
" print(\"截面 Z-Score 标准化完成。\")\n",
|
||
"\n",
|
||
"def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行每日截面中位数填充缺失值 (NaN)。\n",
|
||
"\n",
|
||
" 参数:\n",
|
||
" df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。\n",
|
||
" feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。\n",
|
||
"\n",
|
||
" 返回:\n",
|
||
" pd.DataFrame: 包含缺失值填充后特征列的DataFrame。在输入DataFrame的副本上操作。\n",
|
||
" \"\"\"\n",
|
||
" processed_df = df.copy() # 在副本上操作，保留原始数据\n",
|
||
"\n",
|
||
" # 确保 trade_date 是 datetime 类型以便正确分组\n",
|
||
" processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date'])\n",
|
||
"\n",
|
||
" def _fill_daily_nan(group):\n",
|
||
" # group 是某一个交易日的 DataFrame\n",
|
||
"\n",
|
||
" # 遍历指定的特征列\n",
|
||
" for feature_col in feature_columns:\n",
|
||
" # 检查列是否存在于当前分组中\n",
|
||
" if feature_col in group.columns:\n",
|
||
" # 计算当日该特征的中位数\n",
|
||
" median_val = group[feature_col].median()\n",
|
||
"\n",
|
||
" # 使用当日中位数填充该特征列的 NaN 值\n",
|
||
" # inplace=True 会直接修改 group DataFrame\n",
|
||
" group[feature_col].fillna(median_val, inplace=True)\n",
|
||
" # else:\n",
|
||
" # print(f\"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.\")\n",
|
||
"\n",
|
||
" return group\n",
|
||
"\n",
|
||
" # 按交易日期分组，并应用每日填充函数\n",
|
||
" # group_keys=False 避免将分组键添加到结果索引中\n",
|
||
" filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan)\n",
|
||
"\n",
|
||
" return filled_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "40e6b68a91b30c79",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:04.694262Z",
|
||
"start_time": "2025-04-03T13:08:03.694904Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def remove_outliers_label_percentile(label: pd.Series, lower_percentile: float = 0.01, upper_percentile: float = 0.99,\n",
|
||
" log=True):\n",
|
||
" if not (0 <= lower_percentile < upper_percentile <= 1):\n",
|
||
" raise ValueError(\"Percentile values must satisfy 0 <= lower_percentile < upper_percentile <= 1.\")\n",
|
||
"\n",
|
||
" # Calculate lower and upper bounds based on percentiles\n",
|
||
" lower_bound = label.quantile(lower_percentile)\n",
|
||
" upper_bound = label.quantile(upper_percentile)\n",
|
||
"\n",
|
||
" # Filter out values outside the bounds\n",
|
||
" filtered_label = label[(label >= lower_bound) & (label <= upper_bound)]\n",
|
||
"\n",
|
||
" # Print the number of removed outliers\n",
|
||
" if log:\n",
|
||
" print(f\"Removed {len(label) - len(filtered_label)} outliers.\")\n",
|
||
" return filtered_label\n",
|
||
"\n",
|
||
"\n",
|
||
"def calculate_risk_adjusted_target(df, days=5):\n",
|
||
" df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
" df['future_close'] = df.groupby('ts_code')['close'].shift(-days)\n",
|
||
" df['future_open'] = df.groupby('ts_code')['open'].shift(-1)\n",
|
||
" df['future_return'] = (df['future_close'] - df['future_open']) / df['future_open']\n",
|
||
"\n",
|
||
" df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(\n",
|
||
" level=0, drop=True)\n",
|
||
" sharpe_ratio = df['future_return'] * df['future_volatility']\n",
|
||
" sharpe_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
|
||
"\n",
|
||
" return sharpe_ratio\n",
|
||
"\n",
|
||
"\n",
|
||
"def calculate_score(df, days=5, lambda_param=1.0):\n",
|
||
" def calculate_max_drawdown(prices):\n",
|
||
" peak = prices.iloc[0] # 初始化峰值\n",
|
||
" max_drawdown = 0 # 初始化最大回撤\n",
|
||
"\n",
|
||
" for price in prices:\n",
|
||
" if price > peak:\n",
|
||
" peak = price # 更新峰值\n",
|
||
" else:\n",
|
||
" drawdown = (peak - price) / peak # 计算当前回撤\n",
|
||
" max_drawdown = max(max_drawdown, drawdown) # 更新最大回撤\n",
|
||
"\n",
|
||
" return max_drawdown\n",
|
||
"\n",
|
||
" def compute_stock_score(stock_df):\n",
|
||
" stock_df = stock_df.sort_values(by=['trade_date'])\n",
|
||
" future_return = stock_df['future_return']\n",
|
||
" # 使用已有的 pct_chg 字段计算波动率\n",
|
||
" volatility = stock_df['pct_chg'].rolling(days).std().shift(-days)\n",
|
||
" max_drawdown = stock_df['close'].rolling(days).apply(calculate_max_drawdown, raw=False).shift(-days)\n",
|
||
" score = future_return - lambda_param * max_drawdown\n",
|
||
" return score\n",
|
||
"\n",
|
||
" # # 确保 DataFrame 按照股票代码和交易日期排序\n",
|
||
" # df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"\n",
|
||
" # 对每个股票分别计算 score\n",
|
||
" df['score'] = df.groupby('ts_code').apply(compute_stock_score).reset_index(level=0, drop=True)\n",
|
||
"\n",
|
||
" return df['score']\n",
|
||
"\n",
|
||
"\n",
|
||
"def remove_highly_correlated_features(df, feature_columns, threshold=0.9):\n",
|
||
" numeric_features = df[feature_columns].select_dtypes(include=[np.number]).columns.tolist()\n",
|
||
" if not numeric_features:\n",
|
||
" raise ValueError(\"No numeric features found in the provided data.\")\n",
|
||
"\n",
|
||
" corr_matrix = df[numeric_features].corr().abs()\n",
|
||
" upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
|
||
" to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
|
||
" remaining_features = [col for col in feature_columns if col not in to_drop\n",
|
||
" or 'act' in col or 'af' in col]\n",
|
||
" return remaining_features\n",
|
||
"\n",
|
||
"\n",
|
||
"def cross_sectional_standardization(df, features):\n",
|
||
" df_sorted = df.sort_values(by='trade_date') # 按时间排序\n",
|
||
" df_standardized = df_sorted.copy()\n",
|
||
"\n",
|
||
" for date in df_sorted['trade_date'].unique():\n",
|
||
" # 获取当前时间点的数据\n",
|
||
" current_data = df_standardized[df_standardized['trade_date'] == date]\n",
|
||
"\n",
|
||
" # 只对指定特征进行标准化\n",
|
||
" scaler = StandardScaler()\n",
|
||
" standardized_values = scaler.fit_transform(current_data[features])\n",
|
||
"\n",
|
||
" # 将标准化结果重新赋值回去\n",
|
||
" df_standardized.loc[df_standardized['trade_date'] == date, features] = standardized_values\n",
|
||
"\n",
|
||
" return df_standardized\n",
|
||
"\n",
|
||
"\n",
|
||
"def neutralize_manual_revised(df: pd.DataFrame, features: list, industry_col: str, mkt_cap_col: str) -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 手动实现简单回归以提升速度，通过构建 Series 确保索引对齐。\n",
|
||
" 对特征在行业内部进行市值中性化。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df: 输入的 DataFrame，包含特征、行业分类和市值列。\n",
|
||
" features: 需要进行中性化的特征列名列表。\n",
|
||
" industry_col: 行业分类列的列名。\n",
|
||
" mkt_cap_col: 市值列的列名。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" 中性化后的 DataFrame。\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" df[mkt_cap_col] = pd.to_numeric(df[mkt_cap_col], errors='coerce')\n",
|
||
" df_cleaned = df.dropna(subset=[mkt_cap_col]).copy()\n",
|
||
" df_cleaned = df_cleaned[df_cleaned[mkt_cap_col] > 0].copy()\n",
|
||
"\n",
|
||
" if df_cleaned.empty:\n",
|
||
" print(\"警告: 清理市值异常值后 DataFrame 为空。\")\n",
|
||
" return df # 返回原始或空df，取决于清理前的状态\n",
|
||
"\n",
|
||
" processed_df = df\n",
|
||
"\n",
|
||
" for col in features:\n",
|
||
" if col not in df_cleaned.columns:\n",
|
||
" print(f\"警告: 特征列 '{col}' 不存在于清理后的 DataFrame 中，已跳过。\")\n",
|
||
" # 对于原始 df 中该列不存在的，在结果 df 中也保持原样（可能全是NaN）\n",
|
||
" processed_df[col] = df[col] if col in df.columns else np.nan\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # 跳过对控制变量本身进行中性化\n",
|
||
" if col == mkt_cap_col or col == industry_col:\n",
|
||
" print(f\"警告: 特征列 '{col}' 是控制变量或内部使用的列，跳过中性化。\")\n",
|
||
" # 在结果 df 中也保持原样\n",
|
||
" processed_df[col] = df[col] if col in df.columns else np.nan\n",
|
||
" continue\n",
|
||
"\n",
|
||
" residual_series = pd.Series(index=df_cleaned.index, dtype=float)\n",
|
||
"\n",
|
||
" # 在分组前处理特征列的 NaN，只对有因子值的行进行回归计算\n",
|
||
" df_subset_factor = df_cleaned.dropna(subset=[col]).copy()\n",
|
||
"\n",
|
||
" if not df_subset_factor.empty:\n",
|
||
" for industry, group in df_subset_factor.groupby(industry_col):\n",
|
||
" x = group[mkt_cap_col] # 市值对数\n",
|
||
" y = group[col] # 因子值\n",
|
||
"\n",
|
||
" # 确保有足够的数据点 (>1) 且市值对数有方差 (>0) 进行回归计算\n",
|
||
" # 检查 np.var > 一个很小的正数，避免浮点数误差导致的零方差判断问题\n",
|
||
" if len(group) > 1 and np.var(x) > 1e-9:\n",
|
||
" try:\n",
|
||
" beta = np.cov(y, x)[0, 1] / np.var(x)\n",
|
||
" alpha = np.mean(y) - beta * np.mean(x)\n",
|
||
"\n",
|
||
" # 计算残差\n",
|
||
" resid = y - (alpha + beta * x)\n",
|
||
"\n",
|
||
" # 将计算出的残差存储到 residual_series 中，通过索引自动对齐\n",
|
||
" residual_series.loc[resid.index] = resid\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" # 捕获可能的计算异常，例如np.cov或np.var因为极端数据报错\n",
|
||
" print(f\"警告: 在行业 {industry} 计算回归时发生错误: {e}。该组残差将设为原始值或 NaN。\")\n",
|
||
" # 此时该组的残差会保持 residual_series 初始化时的 NaN 或后续处理\n",
|
||
" # 也可以选择保留原始值：residual_series.loc[group.index] = group[col]\n",
|
||
"\n",
|
||
" else:\n",
|
||
" residual_series.loc[group.index] = group[col] # 保留原始因子值\n",
|
||
" processed_df.loc[residual_series.index, col] = residual_series\n",
|
||
"\n",
|
||
"\n",
|
||
" else:\n",
|
||
" processed_df[col] = np.nan # 或 df[col] if col in df.columns else np.nan\n",
|
||
"\n",
|
||
" return processed_df\n",
|
||
"\n",
|
||
"\n",
|
||
"import gc\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"\n",
|
||
"def mad_filter(df, features, n=3):\n",
|
||
" for col in features:\n",
|
||
" median = df[col].median()\n",
|
||
" mad = np.median(np.abs(df[col] - median))\n",
|
||
" upper = median + n * mad\n",
|
||
" lower = median - n * mad\n",
|
||
" df[col] = np.clip(df[col], lower, upper) # 截断极值\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def percentile_filter(df, features, lower_percentile=0.01, upper_percentile=0.99):\n",
|
||
" for col in features:\n",
|
||
" # 按日期分组计算上下百分位数\n",
|
||
" lower_bound = df.groupby('trade_date')[col].transform(\n",
|
||
" lambda x: x.quantile(lower_percentile)\n",
|
||
" )\n",
|
||
" upper_bound = df.groupby('trade_date')[col].transform(\n",
|
||
" lambda x: x.quantile(upper_percentile)\n",
|
||
" )\n",
|
||
" # 截断超出范围的值\n",
|
||
" df[col] = np.clip(df[col], lower_bound, upper_bound)\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"from scipy.stats import iqr\n",
|
||
"\n",
|
||
"\n",
|
||
"def iqr_filter(df, features):\n",
|
||
" for col in features:\n",
|
||
" df[col] = df.groupby('trade_date')[col].transform(\n",
|
||
" lambda x: (x - x.median()) / iqr(x) if iqr(x) != 0 else x\n",
|
||
" )\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def quantile_filter(df, features, lower_quantile=0.01, upper_quantile=0.99, window=60):\n",
|
||
" df = df.copy()\n",
|
||
" for col in features:\n",
|
||
" # 计算 rolling 统计量，需要按日期进行 groupby\n",
|
||
" rolling_lower = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(lower_quantile))\n",
|
||
" rolling_upper = df.groupby('trade_date')[col].transform(lambda x: x.rolling(window=min(len(x), window)).quantile(upper_quantile))\n",
|
||
"\n",
|
||
" # 对数据进行裁剪\n",
|
||
" df[col] = np.clip(df[col], rolling_lower, rolling_upper)\n",
|
||
" \n",
|
||
" return df\n",
|
||
"\n",
|
||
"def select_top_features_by_rankic(df: pd.DataFrame, feature_columns: list, n: int, target_column: str = 'future_return') -> list:\n",
|
||
" \"\"\"\n",
|
||
" 计算给定特征与目标列的 RankIC，并返回 RankIC 绝对值最高的 n 个特征。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df: 包含特征列和目标列的 Pandas DataFrame。\n",
|
||
" feature_columns: 包含所有待评估特征列名的列表。\n",
|
||
" n: 希望选取的 RankIC 绝对值最高的特征数量。\n",
|
||
" target_column: 目标列的名称，用于计算 RankIC。默认为 'future_return'。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" 包含 RankIC 绝对值最高的 n 个特征列名的列表。\n",
|
||
" \"\"\"\n",
|
||
" numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
" numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
" if target_column not in df.columns:\n",
|
||
" raise ValueError(f\"目标列 '{target_column}' 不存在于 DataFrame 中。\")\n",
|
||
"\n",
|
||
" rankic_scores = {}\n",
|
||
" for feature in numeric_columns:\n",
|
||
" if feature not in df.columns:\n",
|
||
" print(f\"警告: 特征列 '{feature}' 不存在于 DataFrame 中，已跳过。\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # 计算特征与目标列的 RankIC (斯皮尔曼相关系数)\n",
|
||
" # dropna() 是为了处理缺失值，确保相关性计算不失败\n",
|
||
" valid_data = df[[feature, target_column]].dropna()\n",
|
||
" if len(valid_data) > 1: # 确保有足够的数据点进行相关性计算\n",
|
||
" # 计算斯皮尔曼相关性\n",
|
||
" correlation = valid_data[feature].corr(valid_data[target_column], method='spearman')\n",
|
||
" rankic_scores[feature] = abs(correlation) # 使用绝对值来衡量相关性强度\n",
|
||
" else:\n",
|
||
" rankic_scores[feature] = 0 # 数据不足，RankIC设为0或跳过\n",
|
||
"\n",
|
||
" # 将 RankIC 分数转换为 Series 便于排序\n",
|
||
" rankic_series = pd.Series(rankic_scores)\n",
|
||
"\n",
|
||
" # 按 RankIC 绝对值降序排序，选取前 n 个特征\n",
|
||
" # handle case where n might be larger than available features\n",
|
||
" n_actual = min(n, len(rankic_series))\n",
|
||
" top_features = rankic_series.sort_values(ascending=False).head(n_actual).index.tolist()\n",
|
||
" top_features = [col for col in feature_columns if col in top_features or col not in numeric_columns]\n",
|
||
" return top_features\n",
|
||
"\n",
|
||
"def create_deviation_within_dates(df, feature_columns):\n",
|
||
" groupby_col = 'cat_l2_code' # 使用 trade_date 进行分组\n",
|
||
" new_columns = {}\n",
|
||
" ret_feature_columns = feature_columns[:]\n",
|
||
"\n",
|
||
" # 自动选择所有数值型特征\n",
|
||
" num_features = [col for col in feature_columns if 'cat' not in col and 'index' not in col]\n",
|
||
"\n",
|
||
" # num_features = ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'cat_vol_spike', 'obv', 'maobv_6', 'return_5', 'return_10', 'return_20', 'std_return_5', 'std_return_15', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'act_factor5', 'act_factor6', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'alpha_022', 'alpha_003', 'alpha_007', 'alpha_013']\n",
|
||
" num_features = [col for col in num_features if 'cat' not in col and 'industry' not in col]\n",
|
||
" num_features = [col for col in num_features if 'limit' not in col]\n",
|
||
" num_features = [col for col in num_features if 'cyq' not in col]\n",
|
||
"\n",
|
||
" # 遍历所有数值型特征\n",
|
||
" for feature in num_features:\n",
|
||
" if feature == 'trade_date': # 不需要对 'trade_date' 计算偏差\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # grouped_mean = df.groupby(['trade_date'])[feature].transform('mean')\n",
|
||
" # deviation_col_name = f'deviation_mean_{feature}'\n",
|
||
" # new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
|
||
" # ret_feature_columns.append(deviation_col_name)\n",
|
||
"\n",
|
||
" grouped_mean = df.groupby(['trade_date', groupby_col])[feature].transform('mean')\n",
|
||
" deviation_col_name = f'deviation_mean_{feature}'\n",
|
||
" new_columns[deviation_col_name] = df[feature] - grouped_mean\n",
|
||
" ret_feature_columns.append(deviation_col_name)\n",
|
||
"\n",
|
||
" # 将新计算的偏差特征与原始 DataFrame 合并\n",
|
||
" df = pd.concat([df, pd.DataFrame(new_columns)], axis=1)\n",
|
||
"\n",
|
||
" # for feature in ['obv', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4']:\n",
|
||
" # df[f'deviation_industry_{feature}'] = df[feature] - df[f'industry_{feature}']\n",
|
||
"\n",
|
||
" return df, ret_feature_columns\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "47c12bb34062ae7a",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T14:57:50.841165Z",
|
||
"start_time": "2025-04-03T14:49:25.889057Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"days = 5\n",
|
||
"validation_days = 120\n",
|
||
"\n",
|
||
"import gc\n",
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"df = df.sort_values(by=['ts_code', 'trade_date'])\n",
|
||
"df['future_return'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1)\n",
|
||
"# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n",
|
||
"# df.groupby('ts_code')['open'].shift(-1)\n",
|
||
"\n",
|
||
"df['cat_up_limit'] = df['pct_chg'] > 5\n",
|
||
"df['label'] = df.groupby('ts_code')['cat_up_limit'].rolling(window=5, min_periods=1).max().groupby('ts_code').shift(-5).fillna(0).astype(int).reset_index(level=0, drop=True)\n",
|
||
"\n",
|
||
"filter_index = df['future_return'].between(df['future_return'].quantile(0.01), df['future_return'].quantile(0.99))\n",
|
||
"\n",
|
||
"# for col in [col for col in df.columns]:\n",
|
||
"# train_data[col] = train_data[col].astype('str')\n",
|
||
"# test_data[col] = test_data[col].astype('str')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "29221dde",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"191\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"feature_columns = [col for col in df.head(10).merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left').merge(index_data, on='trade_date', how='left').columns]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in ['trade_date',\n",
|
||
" 'ts_code',\n",
|
||
" 'label']]\n",
|
||
"feature_columns = [col for col in feature_columns if 'future' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'label' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'score' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'gen' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'is_st' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'pe_ttm' not in col]\n",
|
||
"# feature_columns = [col for col in feature_columns if 'volatility' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'circ_mv' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if 'code' not in col]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
|
||
"feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
||
"# feature_columns = [col for col in feature_columns if col not in ['ts_code', 'trade_date', 'vol_std_5', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_007', 'consecutive_up_limit', 'mv_volatility', 'volume_growth', 'mv_growth', 'arbr']]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in ['intraday_lg_flow_corr_20', \n",
|
||
" 'cap_neutral_cost_metric', \n",
|
||
" 'hurst_net_mf_vol_60', \n",
|
||
" 'complex_factor_deap_1', \n",
|
||
" 'lg_buy_consolidation_20',\n",
|
||
" 'cs_rank_ind_cap_neutral_pe',\n",
|
||
" 'cs_rank_opening_gap',\n",
|
||
" 'cs_rank_ind_adj_lg_flow']]\n",
|
||
"feature_columns = [col for col in feature_columns if col not in ['roa', 'roe']]\n",
|
||
"print(len(feature_columns))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "03ee5daf",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# df = fill_nan_with_daily_median(df, feature_columns)\n",
|
||
"for feature_col in [col for col in feature_columns if col in df.columns]:\n",
|
||
" # median_val = df[feature_col].median()\n",
|
||
" df[feature_col].fillna(0, inplace=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "b76ea08a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 000001.SZ 2019-01-02 16.574219\n",
|
||
"1 000001.SZ 2019-01-03 16.583965\n",
|
||
"2 000001.SZ 2019-01-04 16.633371\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"去除极值\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 100%|██████████| 131/131 [00:23<00:00, 5.61it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 100%|██████████| 131/131 [00:03<00:00, 34.28it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 0it [00:00, ?it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 0it [00:00, ?it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"feature_columns: ['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"df最小日期: 2019-01-02\n",
|
||
"df最大日期: 2025-10-10\n",
|
||
"3509747\n",
|
||
"train_data最小日期: 2020-01-02\n",
|
||
"train_data最大日期: 2024-12-31\n",
|
||
"559901\n",
|
||
"test_data最小日期: 2025-01-02\n",
|
||
"test_data最大日期: 2025-10-10\n",
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 000001.SZ 2019-01-02 16.574219\n",
|
||
"1 000001.SZ 2019-01-03 16.583965\n",
|
||
"2 000001.SZ 2019-01-04 16.633371\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"split_date = '2025-01-01'\n",
|
||
"train_data = df[filter_index & (df['trade_date'] <= split_date) & (df['trade_date'] >= '2020-01-01')]\n",
|
||
"test_data = df[(df['trade_date'] >= split_date)]\n",
|
||
"\n",
|
||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n",
|
||
"\n",
|
||
"industry_df = industry_df.sort_values(by=['trade_date'])\n",
|
||
"index_data = index_data.sort_values(by=['trade_date'])\n",
|
||
"\n",
|
||
"# train_data = train_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"# train_data = train_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"# test_data = test_data.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"# test_data = test_data.merge(index_data, on='trade_date', how='left')\n",
|
||
"\n",
|
||
"train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n",
|
||
"\n",
|
||
"# feature_columns_new = feature_columns[:]\n",
|
||
"# train_data, _ = create_deviation_within_dates(train_data, [col for col in feature_columns if col in train_data.columns])\n",
|
||
"# test_data, _ = create_deviation_within_dates(test_data, [col for col in feature_columns if col in train_data.columns])\n",
|
||
"\n",
|
||
"# feature_columns = [\n",
|
||
"# 'undist_profit_ps', \n",
|
||
"# 'AR_BR',\n",
|
||
"# 'pe_ttm',\n",
|
||
"# 'alpha_22_improved', \n",
|
||
"# 'alpha_003', \n",
|
||
"# 'alpha_007', \n",
|
||
"# 'alpha_013', \n",
|
||
"# 'cat_up_limit', \n",
|
||
"# 'cat_down_limit', \n",
|
||
"# 'up_limit_count_10d', \n",
|
||
"# 'down_limit_count_10d', \n",
|
||
"# 'consecutive_up_limit', \n",
|
||
"# 'vol_break', \n",
|
||
"# 'weight_roc5', \n",
|
||
"# 'price_cost_divergence', \n",
|
||
"# 'smallcap_concentration', \n",
|
||
"# 'cost_stability', \n",
|
||
"# 'high_cost_break_days', \n",
|
||
"# 'liquidity_risk', \n",
|
||
"# 'turnover_std', \n",
|
||
"# 'mv_volatility', \n",
|
||
"# 'volume_growth', \n",
|
||
"# 'mv_growth', \n",
|
||
"# 'lg_flow_mom_corr_20_60', \n",
|
||
"# 'lg_flow_accel', \n",
|
||
"# 'profit_pressure', \n",
|
||
"# 'underwater_resistance', \n",
|
||
"# 'cost_conc_std_20', \n",
|
||
"# 'profit_decay_20', \n",
|
||
"# 'vol_amp_loss_20', \n",
|
||
"# 'vol_drop_profit_cnt_5', \n",
|
||
"# 'lg_flow_vol_interact_20', \n",
|
||
"# 'cost_break_confirm_cnt_5', \n",
|
||
"# 'atr_norm_channel_pos_14', \n",
|
||
"# 'turnover_diff_skew_20', \n",
|
||
"# 'lg_sm_flow_diverge_20', \n",
|
||
"# 'pullback_strong_20_20', \n",
|
||
"# 'vol_wgt_hist_pos_20', \n",
|
||
"# 'vol_adj_roc_20',\n",
|
||
"# 'cashflow_to_ev_factor',\n",
|
||
"# 'ocfps',\n",
|
||
"# 'book_to_price_ratio',\n",
|
||
"# 'turnover_rate_mean_5',\n",
|
||
"# 'variance_20',\n",
|
||
"# 'bbi_ratio_factor'\n",
|
||
"# ]\n",
|
||
"# feature_columns = [col for col in feature_columns if col in train_data.columns]\n",
|
||
"# feature_columns = [col for col in feature_columns if not col.startswith('_')]\n",
|
||
"\n",
|
||
"numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
"numeric_columns = [col for col in numeric_columns if col in feature_columns]\n",
|
||
"# feature_columns = select_top_features_by_rankic(df, numeric_columns, n=10)\n",
|
||
"print(feature_columns)\n",
|
||
"\n",
|
||
"# train_data = fill_nan_with_daily_median(train_data, feature_columns)\n",
|
||
"# test_data = fill_nan_with_daily_median(test_data, feature_columns)\n",
|
||
"\n",
|
||
"train_data = train_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
|
||
"train_data = train_data.dropna(subset=['label'])\n",
|
||
"train_data = train_data.reset_index(drop=True)\n",
|
||
"# print(test_data.tail())\n",
|
||
"test_data = test_data.dropna(subset=[col for col in feature_columns if col in train_data.columns])\n",
|
||
"# test_data = test_data.dropna(subset=['label'])\n",
|
||
"test_data = test_data.reset_index(drop=True)\n",
|
||
"\n",
|
||
"transform_feature_columns = feature_columns\n",
|
||
"transform_feature_columns = [col for col in transform_feature_columns if col in feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
|
||
"# transform_feature_columns.remove('undist_profit_ps')\n",
|
||
"print('去除极值')\n",
|
||
"cs_mad_filter(train_data, transform_feature_columns)\n",
|
||
"# print('中性化')\n",
|
||
"# cs_neutralize_market_cap_numpy(train_data, transform_feature_columns)\n",
|
||
"# print('标准化')\n",
|
||
"# cs_zscore_standardize(train_data, transform_feature_columns)\n",
|
||
"\n",
|
||
"cs_mad_filter(test_data, transform_feature_columns)\n",
|
||
"# cs_neutralize_market_cap_numpy(test_data, transform_feature_columns)\n",
|
||
"# cs_zscore_standardize(test_data, transform_feature_columns)\n",
|
||
"\n",
|
||
"mad_filter_feature_columns = [col for col in feature_columns if col not in transform_feature_columns and not col.startswith('cat') and col in train_data.columns]\n",
|
||
"cs_mad_filter(train_data, mad_filter_feature_columns)\n",
|
||
"cs_mad_filter(test_data, mad_filter_feature_columns)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f'feature_columns: {feature_columns}')\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"df最小日期: {df['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"df最大日期: {df['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(train_data))\n",
|
||
"print(f\"train_data最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"train_data最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"print(len(test_data))\n",
|
||
"print(f\"test_data最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
|
||
"print(f\"test_data最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
|
||
"\n",
|
||
"cat_columns = [col for col in feature_columns if col.startswith('cat')]\n",
|
||
"for col in cat_columns:\n",
|
||
" train_data[col] = train_data[col].astype('category')\n",
|
||
" test_data[col] = test_data[col].astype('category')\n",
|
||
"\n",
|
||
"print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "3ff2d1c5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"import matplotlib.pyplot as plt # 保持 matplotlib 导入，尽管LightGBM的绘图功能已移除\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"import datetime # 用于日期计算\n",
|
||
"from catboost import CatBoostClassifier\n",
|
||
"from catboost import Pool\n",
|
||
"import lightgbm as lgb\n",
|
||
"\n",
|
||
"def train_model(train_data_df, feature_columns,\n",
|
||
" print_info=True, # 调整参数名，更通用\n",
|
||
" validation_days=180, use_pca=False, split_date=None,\n",
|
||
" target_column='label', type='light'): # 增加目标列参数\n",
|
||
"\n",
|
||
" print('train data size: ', len(train_data_df))\n",
|
||
" print(train_data_df[['ts_code', 'trade_date', 'log_circ_mv']])\n",
|
||
" # 确保数据按时间排序\n",
|
||
" train_data_df = train_data_df.sort_values(by='trade_date')\n",
|
||
"\n",
|
||
" # 识别数值型特征列\n",
|
||
" numeric_feature_columns = train_data_df[feature_columns].select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
|
||
"\n",
|
||
" # 去除标签为空的样本\n",
|
||
" initial_len = len(train_data_df)\n",
|
||
" train_data_df = train_data_df.dropna(subset=[target_column])\n",
|
||
"\n",
|
||
" if print_info:\n",
|
||
" print(f'原始样本数: {initial_len}, 去除标签为空后样本数: {len(train_data_df)}')\n",
|
||
"\n",
|
||
" # 提取特征和标签，只取数值型特征用于线性回归\n",
|
||
" \n",
|
||
" if split_date is None:\n",
|
||
" all_dates = train_data_df['trade_date'].unique() # 获取所有唯一的 trade_date\n",
|
||
" split_date = all_dates[-validation_days] # 划分点为倒数第 validation_days 天\n",
|
||
" train_data_split = train_data_df[train_data_df['trade_date'] < split_date] # 训练集\n",
|
||
" val_data_split = train_data_df[train_data_df['trade_date'] >= split_date] # 验证集\n",
|
||
" \n",
|
||
" X_train = train_data_split[feature_columns]\n",
|
||
" y_train = train_data_split[target_column]\n",
|
||
" \n",
|
||
" X_val = val_data_split[feature_columns]\n",
|
||
" y_val = val_data_split['label']\n",
|
||
"\n",
|
||
"\n",
|
||
" # # 标准化数值特征 (使用 StandardScaler 对训练集fit并transform, 对验证集只transform)\n",
|
||
" scaler = StandardScaler()\n",
|
||
" # X_train = scaler.fit_transform(X_train)\n",
|
||
"\n",
|
||
" # 训练线性回归模型\n",
|
||
" # model = LogisticRegression(random_state=42)\n",
|
||
" \n",
|
||
" # # 使用处理后的特征和样本权重进行训练\n",
|
||
" # model.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"\n",
|
||
" if type == 'cat':\n",
|
||
" params = {\n",
|
||
" 'loss_function': 'Logloss', # 适用于二分类\n",
|
||
" 'eval_metric': 'Logloss', # 评估指标\n",
|
||
" 'iterations': 1500,\n",
|
||
" 'learning_rate': 0.01,\n",
|
||
" 'depth': 10, # 控制模型复杂度\n",
|
||
" 'l2_leaf_reg': 50, # L2 正则化\n",
|
||
" 'verbose': 5000,\n",
|
||
" 'early_stopping_rounds': 300,\n",
|
||
" # 'od_type': 'Iter', # Overfitting detector type\n",
|
||
" # 'od_wait': 300, # Number of iterations to wait after the bes\n",
|
||
" 'one_hot_max_size': 50,\n",
|
||
" 'class_weights': [0.6, 1.2],\n",
|
||
" 'task_type': 'GPU',\n",
|
||
" 'has_time': True,\n",
|
||
" 'random_seed': 7\n",
|
||
" }\n",
|
||
" cat_features = [i for i, col in enumerate(feature_columns) if col.startswith('cat')]\n",
|
||
" train_pool = Pool(data=X_train, label=y_train, cat_features=cat_features)\n",
|
||
" val_pool = Pool(data=X_val, label=y_val, cat_features=cat_features)\n",
|
||
"\n",
|
||
"\n",
|
||
" model = CatBoostClassifier(**params)\n",
|
||
" model.fit(train_pool,\n",
|
||
" eval_set=val_pool, \n",
|
||
" plot=True, \n",
|
||
" use_best_model=True\n",
|
||
" )\n",
|
||
" elif type == 'light':\n",
|
||
" params = {\n",
|
||
" 'objective': 'binary',\n",
|
||
" 'metric': 'average_precision',\n",
|
||
" 'learning_rate': 0.01,\n",
|
||
" 'is_unbalance': True,\n",
|
||
" 'num_leaves': 2048,\n",
|
||
" 'min_data_in_leaf': 1024,\n",
|
||
" 'max_depth': 32,\n",
|
||
" 'max_bin': 1024,\n",
|
||
" 'feature_fraction': 0.5,\n",
|
||
" 'bagging_fraction': 0.5,\n",
|
||
" 'bagging_freq': 1,\n",
|
||
" 'lambda_l1': 50,\n",
|
||
" 'lambda_l2': 50,\n",
|
||
" 'verbosity': -1,\n",
|
||
" 'num_threads' : 8\n",
|
||
" }\n",
|
||
" categorical_feature = [col for col in feature_columns if 'cat' in col]\n",
|
||
" train_dataset = lgb.Dataset(\n",
|
||
" X_train, label=y_train,\n",
|
||
" categorical_feature=categorical_feature\n",
|
||
" )\n",
|
||
" val_dataset = lgb.Dataset(\n",
|
||
" X_val, label=y_val,\n",
|
||
" categorical_feature=categorical_feature\n",
|
||
" )\n",
|
||
"\n",
|
||
" evals = {}\n",
|
||
" callbacks = [lgb.log_evaluation(period=1000),\n",
|
||
" lgb.callback.record_evaluation(evals),\n",
|
||
" lgb.early_stopping(100, first_metric_only=True)\n",
|
||
" ]\n",
|
||
" # 训练模型\n",
|
||
" model = lgb.train(\n",
|
||
" params, train_dataset, num_boost_round=1000,\n",
|
||
" valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
|
||
" callbacks=callbacks\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 打印特征重要性（如果需要）\n",
|
||
" if True:\n",
|
||
" lgb.plot_metric(evals)\n",
|
||
" lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
" return model, scaler, None # 返回训练好的模型、scaler 和 pca 对象"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "c6eb5cd4-e714-420a-ac48-39af3e11ee81",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T15:03:18.426481Z",
|
||
"start_time": "2025-04-03T15:02:19.926352Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"train data size: 60600\n",
|
||
" ts_code trade_date log_circ_mv\n",
|
||
"0 600306.SH 2020-01-02 11.552040\n",
|
||
"1 603269.SH 2020-01-02 11.324801\n",
|
||
"2 002633.SZ 2020-01-02 11.759023\n",
|
||
"3 603991.SH 2020-01-02 11.181150\n",
|
||
"4 000691.SZ 2020-01-02 11.677910\n",
|
||
"... ... ... ...\n",
|
||
"60595 002211.SZ 2024-12-31 11.938823\n",
|
||
"60596 002058.SZ 2024-12-31 11.941882\n",
|
||
"60597 600883.SH 2024-12-31 11.948840\n",
|
||
"60598 002072.SZ 2024-12-31 11.589595\n",
|
||
"60599 600149.SH 2024-12-31 11.951859\n",
|
||
"\n",
|
||
"[60600 rows x 3 columns]\n",
|
||
"原始样本数: 60600, 去除标签为空后样本数: 60600\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "30b53365fe7b4643b79cadcb7280cf28",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0:\tlearn: 0.6885716\ttest: 0.6919121\tbest: 0.6919121 (0)\ttotal: 823ms\tremaining: 20m 34s\n",
|
||
"bestTest = 0.667731918\n",
|
||
"bestIteration = 74\n",
|
||
"Shrink model to first 75 iterations.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"use_pca = False\n",
|
||
"type = 'cat'\n",
|
||
"# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\n",
|
||
"# light_params['feature_contri'] = feature_contri\n",
|
||
"# print(f'feature_contri: {feature_contri}')\n",
|
||
"model, scaler, pca = train_model(train_data\n",
|
||
" .dropna(subset=['label']).groupby('trade_date', group_keys=False)\n",
|
||
" .apply(lambda x: x.nsmallest(50, 'total_mv'))\n",
|
||
" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
" .merge(index_data, on='trade_date', how='left'), feature_columns, type=type)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "5d1522a7538db91b",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T15:04:39.656944Z",
|
||
"start_time": "2025-04-03T15:04:39.298483Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"score_df = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(300, 'total_mv'))\n",
|
||
"# score_df = fill_nan_with_daily_median(score_df, ['pe_ttm'])\n",
|
||
"# score_df = score_df[score_df['pe_ttm'] > 0]\n",
|
||
"score_df = score_df.merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
"score_df = score_df.merge(index_data, on='trade_date', how='left')\n",
|
||
"# score_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(50, 'total_mv')).reset_index()\n",
|
||
"numeric_columns = score_df.select_dtypes(include=['float64', 'int64']).columns\n",
|
||
"numeric_columns = [col for col in feature_columns if col in numeric_columns]\n",
|
||
"# score_df.loc[:, numeric_columns] = scaler.transform(score_df[numeric_columns])\n",
|
||
"# score_df = cross_sectional_standardization(score_df, numeric_columns)\n",
|
||
"\n",
|
||
"if type == 'cat':\n",
|
||
" score_df['score'] = model.predict_proba(score_df[feature_columns])[:, 1]\n",
|
||
"elif type == 'light':\n",
|
||
" score_df['score'] = model.predict(score_df[feature_columns])\n",
|
||
"score_df['score_ranks'] = score_df.groupby('trade_date')['score'].rank(ascending=True)\n",
|
||
"\n",
|
||
"score_df = score_df.groupby('trade_date', group_keys=False).apply(\n",
|
||
" lambda x: x[x['score'] >= x['score'].quantile(0.90)] # 计算90%分位数作为阈值，筛选分数>=阈值的行\n",
|
||
").reset_index(drop=True) # drop=True 避免添加旧索引列\n",
|
||
"# save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1, 'score')).reset_index()\n",
|
||
"save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(2, 'total_mv')).reset_index()\n",
|
||
"save_df = save_df.sort_values(['trade_date', 'score'])\n",
|
||
"save_df[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "c1c40917",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"current_date = datetime.datetime.now()\n",
|
||
"\n",
|
||
"# 2. 格式化日期为字符串，例如 '2025-07-06'\n",
|
||
"# 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",
|
||
"\n",
|
||
"# model.save_model(model_filename)\n",
|
||
"# print(f\"模型已保存到: {model_filename}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "09b1799e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"191\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', 'AR', 'BR', 'AR_BR', 'cashflow_to_ev_factor', 'book_to_price_ratio', 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor', 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity', 'sm_net_buy_vol', 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change', 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel', 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy', 'cost_support_15pct_change', 'cat_winner_price_zone', 'flow_chip_consistency', 'profit_taking_vs_absorb', 'cat_is_positive', 'upside_vol', 'downside_vol', 'vol_ratio', 'return_skew', 'return_kurtosis', 'volume_change_rate', 'cat_volume_breakout', 'turnover_deviation', 'cat_turnover_spike', 'avg_volume_ratio', 'cat_volume_ratio_breakout', 'vol_spike', 'vol_std_5', 'atr_14', 'atr_6', 'obv', 'maobv_6', 'rsi_3', 'return_5', 'return_20', 'std_return_5', 'std_return_90', 'std_return_90_2', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4', 'rank_act_factor1', 'rank_act_factor2', 'rank_act_factor3', 'cov', 'delta_cov', 'alpha_22_improved', 'alpha_003', 'alpha_007', 'alpha_013', 'vol_break', 'weight_roc5', 'smallcap_concentration', 'cost_stability', 'high_cost_break_days', 'liquidity_risk', 'turnover_std', 'mv_volatility', 'volume_growth', 'mv_growth', 'momentum_factor', 'resonance_factor', 'log_close', 'cat_vol_spike', 'up', 'down', 'obv_maobv_6', 'std_return_5_over_std_return_90', 'std_return_90_minus_std_return_90_2', 'cat_af2', 'cat_af3', 'cat_af4', 'act_factor5', 'act_factor6', 'active_buy_volume_large', 'active_buy_volume_big', 'active_buy_volume_small', 'buy_lg_vol_minus_sell_lg_vol', 'buy_elg_vol_minus_sell_elg_vol', 'ctrl_strength', 'low_cost_dev', 'asymmetry', 'lock_factor', 'cat_vol_break', 'cost_atr_adj', 'cat_golden_resonance', 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover', 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum', 'lg_flow_mom_corr_20_60', 'lg_flow_accel', 'profit_pressure', 'underwater_resistance', 'cost_conc_std_20', 'profit_decay_20', 'vol_amp_loss_20', 'vol_drop_profit_cnt_5', 'lg_flow_vol_interact_20', 'cost_break_confirm_cnt_5', 'atr_norm_channel_pos_14', 'turnover_diff_skew_20', 'lg_sm_flow_diverge_20', 'pullback_strong_20_20', 'vol_wgt_hist_pos_20', 'vol_adj_roc_20', 'cs_rank_net_lg_flow_val', 'cs_rank_elg_buy_ratio', 'cs_rank_rel_profit_margin', 'cs_rank_cost_breadth', 'cs_rank_dist_to_upper_cost', 'cs_rank_winner_rate', 'cs_rank_intraday_range', 'cs_rank_close_pos_in_range', 'cs_rank_pos_in_hist_range', 'cs_rank_vol_x_profit_margin', 'cs_rank_lg_flow_price_concordance', 'cs_rank_turnover_per_winner', 'cs_rank_volume_ratio', 'cs_rank_elg_buy_sell_sm_ratio', 'cs_rank_cost_dist_vol_ratio', 'cs_rank_size', 'cat_up_limit', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"[]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(feature_columns))\n",
|
||
"print(feature_columns)\n",
|
||
"print([col for col in feature_columns if 'total_mv' in col])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "e53b209a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"15540 3509747\n",
|
||
" ts_code trade_date turnover_rate\n",
|
||
"0 000001.SZ 2025-01-02 0.9377\n",
|
||
"1 000001.SZ 2025-01-03 0.5950\n",
|
||
"2 000001.SZ 2025-01-06 0.5594\n",
|
||
"3 000001.SZ 2025-01-07 0.3854\n",
|
||
"4 000001.SZ 2025-01-08 0.5475\n",
|
||
"... ... ... ...\n",
|
||
"559896 605599.SH 2025-09-26 0.3434\n",
|
||
"559897 605599.SH 2025-09-29 0.3943\n",
|
||
"559898 605599.SH 2025-09-30 0.4982\n",
|
||
"559899 605599.SH 2025-10-09 1.0319\n",
|
||
"559900 605599.SH 2025-10-10 0.8859\n",
|
||
"\n",
|
||
"[559901 rows x 3 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(train_data[train_data['pct_chg'] > 7]), len(train_data))\n",
|
||
"print(test_data[['ts_code', 'trade_date', 'turnover_rate']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "364e821a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"def calculate_binary_classification_metrics(df: pd.DataFrame, score_col: str, label_col: str, future_return_col: str = None, total_mv_col: str = None, n_mv_bins: int = 10, threshold: float = 0.5):\n",
|
||
" \"\"\"\n",
|
||
" 计算二分类模型的评估指标，可选择计算 score 和 future_return 的相关性，\n",
|
||
" 并可选择计算 score 在按总市值 (total_mv) 分为 n 份后的每个分组上的预测性能（ROC AUC）。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df: 包含 score (预测概率或置信度), label (真实二分类标签), 可选的 future_return 和 total_mv 的 Pandas DataFrame。\n",
|
||
" score_col: 包含模型预测 score 的列名。\n",
|
||
" label_col: 包含真实二分类标签 (0 或 1) 的列名。\n",
|
||
" future_return_col: (可选) 包含未来收益率的列名，用于计算相关性。\n",
|
||
" total_mv_col: (可选) 包含总市值的列名，用于按市值分 n 份分析预测性能。\n",
|
||
" n_mv_bins: (可选) 将总市值分为多少份，默认为 5。\n",
|
||
" threshold: 将 score 转换为预测类别的阈值，默认为 0.5。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" 一个包含以下评估指标的字典：\n",
|
||
" - accuracy: 准确率\n",
|
||
" - precision: 精确率\n",
|
||
" - recall: 召回率\n",
|
||
" - f1: F1 分数\n",
|
||
" - roc_auc: ROC AUC 值\n",
|
||
" - fpr: ROC 曲线的假正率 (False Positive Rate)\n",
|
||
" - tpr: ROC 曲线的真正率 (True Positive Rate)\n",
|
||
" - thresholds: ROC 曲线的阈值\n",
|
||
" - score_return_correlation: (如果 future_return_col 提供) score 和 future_return 的皮尔逊相关系数\n",
|
||
" - mv_roc_auc: (如果 total_mv_col 提供) 一个字典，包含按总市值分为 n 份后的每个市值分组对应的 ROC AUC 值\n",
|
||
" \"\"\"\n",
|
||
" y_true = df[label_col].values\n",
|
||
" y_score = df[score_col].values\n",
|
||
" y_pred = (y_score >= threshold).astype(int)\n",
|
||
"\n",
|
||
" metrics = {}\n",
|
||
" metrics['accuracy'] = accuracy_score(y_true, y_pred)\n",
|
||
" metrics['precision'] = precision_score(y_true, y_pred)\n",
|
||
" metrics['recall'] = recall_score(y_true, y_pred)\n",
|
||
" metrics['f1'] = f1_score(y_true, y_pred)\n",
|
||
" metrics['roc_auc'] = roc_auc_score(y_true, y_score)\n",
|
||
" metrics['fpr'], metrics['tpr'], metrics['thresholds'] = roc_curve(y_true, y_score)\n",
|
||
"\n",
|
||
" if future_return_col in df.columns:\n",
|
||
" metrics['score_return_correlation'] = df[score_col].corr(df[future_return_col])\n",
|
||
"\n",
|
||
" if total_mv_col in df.columns and n_mv_bins > 1:\n",
|
||
" metrics['mv_roc_auc'] = {}\n",
|
||
" df['mv_quantile'] = pd.cut(df[total_mv_col], bins=n_mv_bins, labels=False, duplicates='drop')\n",
|
||
" for i in range(df['mv_quantile'].nunique()):\n",
|
||
" mv_group = df[df['mv_quantile'] == i]\n",
|
||
" if len(mv_group) > 0 and len(np.unique(mv_group[label_col])) > 1 and len(np.unique(mv_group[score_col])) > 1:\n",
|
||
" roc_auc_mv = roc_auc_score(mv_group[label_col], mv_group[score_col])\n",
|
||
" lower_bound = df[total_mv_col][df['mv_quantile'] == i].min()\n",
|
||
" upper_bound = df[total_mv_col][df['mv_quantile'] == i].max()\n",
|
||
" metrics['mv_roc_auc'][f'{lower_bound:.0e}-{upper_bound:.0e}'] = roc_auc_mv\n",
|
||
" else:\n",
|
||
" lower_bound = df[total_mv_col][df['mv_quantile'] == i].min()\n",
|
||
" upper_bound = df[total_mv_col][df['mv_quantile'] == i].max()\n",
|
||
" metrics['mv_roc_auc'][f'{lower_bound:.0e}-{upper_bound:.0e}'] = np.nan\n",
|
||
" print(f'{lower_bound:.0e}-{upper_bound:.0e}')\n",
|
||
" df.drop(columns=['mv_quantile'], inplace=True)\n",
|
||
"\n",
|
||
" return metrics\n",
|
||
"\n",
|
||
"def plot_roc_curve(metrics: dict):\n",
|
||
" plt.figure(figsize=(8, 6))\n",
|
||
" plt.plot(metrics['fpr'], metrics['tpr'], color='darkorange', lw=2, label=f'ROC curve (AUC = {metrics[\"roc_auc\"]:.2f})')\n",
|
||
" plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
|
||
" plt.xlabel('False Positive Rate')\n",
|
||
" plt.ylabel('True Positive Rate')\n",
|
||
" plt.title('Receiver Operating Characteristic (ROC)')\n",
|
||
" plt.legend(loc=\"lower right\")\n",
|
||
" plt.grid(True)\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "1f6e6336",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"7e+04-9e+04\n",
|
||
"9e+04-1e+05\n",
|
||
"1e+05-1e+05\n",
|
||
"1e+05-2e+05\n",
|
||
"2e+05-2e+05\n",
|
||
"2e+05-2e+05\n",
|
||
"2e+05-2e+05\n",
|
||
"2e+05-3e+05\n",
|
||
"3e+05-3e+05\n",
|
||
"3e+05-3e+05\n",
|
||
"二分类评估指标:\n",
|
||
"accuracy: 0.6690\n",
|
||
"precision: 0.0000\n",
|
||
"recall: 0.0000\n",
|
||
"f1: 0.0000\n",
|
||
"roc_auc: 0.6421\n",
|
||
"fpr: (array of length 2338)\n",
|
||
"tpr: (array of length 2338)\n",
|
||
"thresholds: (array of length 2338)\n",
|
||
"score_return_correlation: 0.0225\n",
|
||
"mv_roc_auc: {'7e+04-9e+04': np.float64(0.6513157894736842), '9e+04-1e+05': np.float64(0.5768921095008052), '1e+05-1e+05': np.float64(0.6421728904950381), '1e+05-2e+05': np.float64(0.6613848565392216), '2e+05-2e+05': np.float64(0.6637681784060829), '2e+05-3e+05': np.float64(0.6137690379795642), '3e+05-3e+05': np.float64(0.5940703428773416)}\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"for sd in [\n",
|
||
" # save_df1, \n",
|
||
" # save_df2, \n",
|
||
" score_df\n",
|
||
" ]:\n",
|
||
" # 计算二分类指标\n",
|
||
" evaluation_metrics = calculate_binary_classification_metrics(sd, score_col='score', label_col='label', threshold=0.6,\n",
|
||
" future_return_col='future_return', total_mv_col='total_mv')\n",
|
||
"\n",
|
||
" # 打印指标\n",
|
||
" print(\"二分类评估指标:\")\n",
|
||
" for metric, value in evaluation_metrics.items():\n",
|
||
" if isinstance(value, (float, int)):\n",
|
||
" print(f\"{metric}: {value:.4f}\")\n",
|
||
" elif isinstance(value, (list, tuple, np.ndarray)):\n",
|
||
" print(f\"{metric}: (array of length {len(value)})\")\n",
|
||
" else:\n",
|
||
" print(f\"{metric}: {value}\")\n",
|
||
"\n",
|
||
" # 绘制 ROC 曲线\n",
|
||
" plot_roc_curve(evaluation_metrics)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "7e9023cc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def analyze_factors(\n",
|
||
" df: pd.DataFrame,\n",
|
||
" feature_columns: list[str],\n",
|
||
" target_column: str = 'target', # 假设目标列默认为 'target'\n",
|
||
" trade_date_col: str = 'trade_date', # 假设日期列默认为 'trade_date'\n",
|
||
" mcap_col: str = 'total_mv', # 新增: 市值列名称\n",
|
||
" mcap_bins: int = 5 # 新增: 市值分位数的数量 (例如 5 表示五分位数)\n",
|
||
") -> pd.DataFrame:\n",
|
||
" \"\"\"\n",
|
||
" 分析DataFrame中指定特征列的各种指标，包括基本统计、相关性、日间IC、ICIR以及在不同市值分位数上的IC。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 包含日期、目标列、特征列和市值列的DataFrame。\n",
|
||
" 需要包含 trade_date_col, target_column, feature_columns 和 mcap_col 中的所有列。\n",
|
||
" feature_columns (list[str]): 需要分析的特征列名称列表。\n",
|
||
" target_column (str): 目标变量列的名称。\n",
|
||
" trade_date_col (str): 交易日期列的名称。\n",
|
||
" mcap_col (str): 市值列的名称。\n",
|
||
" mcap_bins (int): 市值分位数的数量 (例如 5 表示五分位数)。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" pd.DataFrame: 包含各个因子分析指标的汇总DataFrame。\n",
|
||
" 同时打印因子在不同市值分位数上的平均IC表格。\n",
|
||
" 如果输入数据或列有问题，可能返回空或包含NaN的DataFrame。\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" # --- 数据校验 ---\n",
|
||
" required_cols = [trade_date_col, target_column, mcap_col] + feature_columns\n",
|
||
" if not all(col in df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in df.columns]\n",
|
||
" print(f\"错误: 输入DataFrame缺少必需的列: {missing}\")\n",
|
||
" return pd.DataFrame() # 返回空DataFrame\n",
|
||
"\n",
|
||
" # 确保日期列是 datetime 类型\n",
|
||
" df = df.copy() # 在副本上操作\n",
|
||
" df[trade_date_col] = pd.to_datetime(df[trade_date_col], errors='coerce')\n",
|
||
" df.dropna(subset=[trade_date_col], inplace=True) # 移除日期转换失败的行\n",
|
||
"\n",
|
||
" # 过滤掉那些在 feature_columns, target_column, mcap_col 上有 NaN 的行，以确保后续计算是在完整数据上\n",
|
||
" # 直接在 df 副本上进行清洗\n",
|
||
" initial_rows_before_clean = len(df)\n",
|
||
" df.dropna(subset=feature_columns + [target_column, mcap_col], inplace=True)\n",
|
||
" rows_dropped_clean = initial_rows_before_clean - len(df)\n",
|
||
" if rows_dropped_clean > 0:\n",
|
||
" print(f\"警告: 移除了 {rows_dropped_clean} 行，因为其特征、目标或市值列存在空值。\")\n",
|
||
"\n",
|
||
" if df.empty:\n",
|
||
" print(\"错误: 清理缺失值后数据为空，无法进行因子分析。\")\n",
|
||
" return pd.DataFrame() # 返回空DataFrame\n",
|
||
"\n",
|
||
"\n",
|
||
" print(f\"开始分析 {len(feature_columns)} 个因子指标...\")\n",
|
||
"\n",
|
||
" # --- 1. 基本因子统计量 ---\n",
|
||
" basic_stats = df[feature_columns].describe().T\n",
|
||
"\n",
|
||
" print(\"\\n--- 基本因子统计量 ---\")\n",
|
||
" print(basic_stats)\n",
|
||
"\n",
|
||
" # --- 2. 因子与目标变量的整体相关性 ---\n",
|
||
" overall_correlation = {}\n",
|
||
" for feature in feature_columns:\n",
|
||
" # 在清理后的 df 上计算相关性\n",
|
||
" if df[[feature, target_column]].dropna().shape[0] > 1: # 确保至少有两个有效数据点\n",
|
||
" overall_correlation[feature] = {\n",
|
||
" 'Pearson_Correlation_with_Target': df[feature].corr(df[target_column], method='pearson'),\n",
|
||
" 'Spearman_Correlation_with_Target': df[feature].corr(df[target_column], method='spearman')\n",
|
||
" }\n",
|
||
" else:\n",
|
||
" overall_correlation[feature] = {\n",
|
||
" 'Pearson_Correlation_with_Target': np.nan,\n",
|
||
" 'Spearman_Correlation_with_Target': np.nan\n",
|
||
" }\n",
|
||
" overall_corr_df = pd.DataFrame.from_dict(overall_correlation, orient='index')\n",
|
||
"\n",
|
||
" print(\"\\n--- 因子与目标变量的整体相关性 ---\")\n",
|
||
" print(overall_corr_df)\n",
|
||
"\n",
|
||
" # --- 3. 因子之间的相关性矩阵 ---\n",
|
||
" # 在清理后的 df 上计算相关性\n",
|
||
" factor_correlation_matrix = df[feature_columns].corr(method='spearman') # 改回 Spearman\n",
|
||
"\n",
|
||
" print(\"\\n--- 因子之间的相关性矩阵 (Spearman) ---\") # 修正打印信息\n",
|
||
" print(factor_correlation_matrix)\n",
|
||
"\n",
|
||
" # --- 4. 日间 IC 和 ICIR ---\n",
|
||
" print(\"\\n--- 计算日间 IC (Spearman 相关性) 和 ICIR ---\")\n",
|
||
"\n",
|
||
" # 直接在清理后的 df 上计算每日 IC\n",
|
||
" if df.empty: # 理论上上面已经检查过，这里再检查一次更安全\n",
|
||
" daily_ic_series = pd.Series(dtype=float) # 空 Series\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n",
|
||
" }, index=feature_columns)\n",
|
||
" else:\n",
|
||
" daily_ic_series = df.groupby(trade_date_col).apply(\n",
|
||
" lambda day_group: {\n",
|
||
" feature: day_group[feature].corr(day_group[target_column], method='spearman')\n",
|
||
" for feature in feature_columns if day_group.shape[0] > 1 # 确保每日数据点多于1才能计算相关性\n",
|
||
" }\n",
|
||
" ).apply(pd.Series) # 将字典结果转换为 DataFrame\n",
|
||
"\n",
|
||
" # 计算 IC 的统计量\n",
|
||
" if not daily_ic_series.empty:\n",
|
||
" ic_mean = daily_ic_series.mean()\n",
|
||
" ic_std = daily_ic_series.std()\n",
|
||
" # 避免除以零\n",
|
||
" ic_ir = ic_mean / ic_std.replace(0, np.nan) # 使用 replace 0 为 NaN\n",
|
||
"\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': ic_mean,\n",
|
||
" 'Std_Dev_IC': ic_std,\n",
|
||
" 'ICIR': ic_ir\n",
|
||
" })\n",
|
||
" print(\"\\n--- 日间 IC 和 ICIR (Spearman) ---\")\n",
|
||
" print(ic_stats)\n",
|
||
" else:\n",
|
||
" ic_stats = pd.DataFrame({\n",
|
||
" 'Mean_IC (Spearman)': np.nan, 'Std_Dev_IC': np.nan, 'ICIR': np.nan\n",
|
||
" }, index=feature_columns)\n",
|
||
"\n",
|
||
"\n",
|
||
" # --- 5. 因子在不同市值分位数上的平均 IC ---\n",
|
||
" print(f\"\\n--- 计算因子在 {mcap_bins} 个市值分位数上的平均 IC (Spearman) ---\")\n",
|
||
"\n",
|
||
" # 在清理后的 df 上计算每日市值分位数，直接添加到 df 中\n",
|
||
" # 使用 transform() 和 qcut() 在每个日期分组内计算分位数\n",
|
||
" # labels=False 返回整数 0 to mcap_bins-1\n",
|
||
" # duplicates='drop' 处理在某些日期股票数量少于 bins 导致分位数边缘重复的情况，会返回 NaN\n",
|
||
" # 添加一个临时列来存储分位数\n",
|
||
" mcap_bin_col_name = f'_mcap_bin_{mcap_bins}'\n",
|
||
" df[mcap_bin_col_name] = df.groupby(trade_date_col)[mcap_col].transform(\n",
|
||
" lambda x: pd.qcut(x, q=mcap_bins, labels=False, duplicates='drop') if len(x) >= mcap_bins else np.nan # 确保股票数量足够进行分位数划分\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 过滤掉无法划分分位数 (NaN) 的行，进行分位数 IC 计算\n",
|
||
" # 创建一个临时 DataFrame df_binned_analysis\n",
|
||
" df_binned_analysis = df.dropna(subset=[mcap_bin_col_name]).copy()\n",
|
||
"\n",
|
||
" if df_binned_analysis.empty:\n",
|
||
" print(\"错误: 划分市值分位数后数据为空，无法计算分位数上的 IC。\")\n",
|
||
" avg_ic_by_bin = pd.DataFrame(index=range(mcap_bins), columns=feature_columns) # Placeholder\n",
|
||
" else:\n",
|
||
" # 按日期和市值分位数分组，计算每个分组内的因子与目标变量的截面相关性 (分位数IC)\n",
|
||
" binned_ic_by_day = df_binned_analysis.groupby([trade_date_col, mcap_bin_col_name]).apply(\n",
|
||
" lambda group: {\n",
|
||
" feature: group[feature].corr(group[target_column], method='spearman')\n",
|
||
" for feature in feature_columns if group.shape[0] > 1 # 确保分位数组内数据点多于1\n",
|
||
" }\n",
|
||
" ).apply(pd.Series) # 将嵌套结果转为 DataFrame\n",
|
||
"\n",
|
||
" # 对每个分位数组的每日 IC 求平均\n",
|
||
" # unstack(level=mcap_bin_col_name) 将 mcap_bin 作为列\n",
|
||
" # mean(axis=0) 对日期索引求平均\n",
|
||
" avg_ic_by_bin = binned_ic_by_day.unstack(level=mcap_bin_col_name).mean(axis=0).unstack()\n",
|
||
"\n",
|
||
" # 重命名索引和列，使表格更清晰\n",
|
||
" if not avg_ic_by_bin.empty:\n",
|
||
" # Index name will be the original column name used for grouping ('_mcap_bin_X')\n",
|
||
" # Rename the index name explicitly\n",
|
||
" avg_ic_by_bin.index.name = 'MarketCap_Bin'\n",
|
||
" avg_ic_by_bin.columns.name = 'Feature'\n",
|
||
" # 可以根据需要对分位数 bin 索引进行排序 (虽然 pd.qcut labels=False usually sorts)\n",
|
||
" avg_ic_by_bin = avg_ic_by_bin.sort_index()\n",
|
||
"\n",
|
||
" print(avg_ic_by_bin)\n",
|
||
"\n",
|
||
"\n",
|
||
" # --- 6. 汇总所有指标 ---\n",
|
||
" # 将基本统计、整体相关性、IC/ICIR 合并到一个 DataFrame\n",
|
||
" # 注意：合并时需要根据索引进行对齐 (因子名称)\n",
|
||
" summary_df = basic_stats\n",
|
||
" summary_df = summary_df.merge(overall_corr_df, left_index=True, right_index=True, how='left')\n",
|
||
" summary_df = summary_df.merge(ic_stats, left_index=True, right_index=True, how='left')\n",
|
||
"\n",
|
||
" # print(\"\\n--- 因子分析汇总报告 ---\")\n",
|
||
" # print(summary_df)\n",
|
||
"\n",
|
||
" # --- 清理临时列 'mcap_bin' ---\n",
|
||
" # 修正：在函数结束时从我们一直在操作的 df 副本中删除临时列\n",
|
||
" if mcap_bin_col_name in df.columns:\n",
|
||
" df.drop(columns=[mcap_bin_col_name], inplace=True)\n",
|
||
"\n",
|
||
"\n",
|
||
" return summary_df # 主要返回汇总报告，分位数IC单独打印\n",
|
||
"\n",
|
||
"# # 运行分析函数\n",
|
||
"# factor_analysis_report = analyze_factors(test_data.copy(), feature_columns, 'future_return')\n",
|
||
"\n",
|
||
"# print(\"\\n--- 最终汇总报告 DataFrame ---\")\n",
|
||
"# print(factor_analysis_report)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "a0000d75",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始分析 'score' 在 'circ_mv' 和 'future_return' 下的表现...\n",
|
||
"准备数据，处理 NaN 值...\n",
|
||
"原始数据 5550 行，移除 NaN 后剩余 5297 行用于分析。\n",
|
||
"对 'circ_mv' 和 'future_return' 进行 100 分位数分箱...\n",
|
||
"按二维分箱分组计算 Spearman Rank IC...\n",
|
||
"整理结果用于绘图...\n",
|
||
"circ_mv_bin 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 \\\n",
|
||
"future_return_bin ... \n",
|
||
"0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"... .. .. .. .. .. .. .. .. .. .. ... .. .. .. \n",
|
||
"95 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"96 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"97 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"98 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN \n",
|
||
"\n",
|
||
"circ_mv_bin 93 94 95 96 97 98 99 \n",
|
||
"future_return_bin \n",
|
||
"0 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"1 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"2 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"3 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"4 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"... .. .. .. .. .. .. .. \n",
|
||
"95 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"96 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"97 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"98 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"99 NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"\n",
|
||
"[100 rows x 100 columns]\n",
|
||
"生成热力图...\n",
|
||
"分析完成。\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from scipy.stats import spearmanr\n",
|
||
"from tqdm import tqdm # 用于显示进度条 (可选)\n",
|
||
"\n",
|
||
"# 设置 Matplotlib/Seaborn 样式 (可选)\n",
|
||
"sns.set_theme(style=\"whitegrid\")\n",
|
||
"plt.rcParams['font.sans-serif'] = ['SimHei'] # 或者其他支持中文的字体\n",
|
||
"plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题\n",
|
||
"\n",
|
||
"def analyze_score_performance_2d(score_df: pd.DataFrame,\n",
|
||
" score_col: str = 'score',\n",
|
||
" label_col: str = 'label',\n",
|
||
" condition1_col: str = 'circ_mv',\n",
|
||
" condition2_col: str = 'future_return',\n",
|
||
" n_bins: int = 100,\n",
|
||
" min_samples_per_bin: int = 30): # 每个格子最少样本数\n",
|
||
" \"\"\"\n",
|
||
" 分析 score 在两个条件下 (如市值、未来收益) 的二维分箱表现。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" score_df (pd.DataFrame): 包含分数、标签和条件列的 DataFrame。\n",
|
||
" score_col (str): 预测分数所在的列名。\n",
|
||
" label_col (str): 目标标签所在的列名 (应为数值或可排序类别)。\n",
|
||
" condition1_col (str): 第一个条件列名 (例如 'circ_mv')。\n",
|
||
" condition2_col (str): 第二个条件列名 (例如 'future_return')。\n",
|
||
" n_bins (int): 每个条件划分的箱数 (分位数数量)。\n",
|
||
" min_samples_per_bin (int): 计算指标所需的最小样本数，小于此数目的格子结果将被屏蔽。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" tuple: 包含 (performance_pivot, count_pivot, fig)\n",
|
||
" performance_pivot: 以二维分箱为索引/列的 Spearman 相关系数矩阵。\n",
|
||
" count_pivot: 每个二维分箱的样本数量矩阵。\n",
|
||
" fig: 生成的热力图 Matplotlib Figure 对象。\n",
|
||
" \"\"\"\n",
|
||
" print(f\"开始分析 '{score_col}' 在 '{condition1_col}' 和 '{condition2_col}' 下的表现...\")\n",
|
||
"\n",
|
||
" required_cols = [score_col, label_col, condition1_col, condition2_col]\n",
|
||
" if not all(col in score_df.columns for col in required_cols):\n",
|
||
" missing = [col for col in required_cols if col not in score_df.columns]\n",
|
||
" raise ValueError(f\"输入 DataFrame 缺少必需列: {missing}\")\n",
|
||
"\n",
|
||
" # --- 1. 数据准备和清洗 ---\n",
|
||
" print(\"准备数据，处理 NaN 值...\")\n",
|
||
" # 只保留需要的列，并移除包含 NaN 的行，避免影响分箱和计算\n",
|
||
" analysis_df = score_df[required_cols].dropna().copy()\n",
|
||
" n_original = len(score_df)\n",
|
||
" n_after_drop = len(analysis_df)\n",
|
||
" print(f\"原始数据 {n_original} 行，移除 NaN 后剩余 {n_after_drop} 行用于分析。\")\n",
|
||
"\n",
|
||
" if n_after_drop < min_samples_per_bin * n_bins: # 检查数据量是否过少\n",
|
||
" print(f\"警告: 清理 NaN 后数据量 ({n_after_drop}) 可能不足以支持 {n_bins}x{n_bins} 的精细分箱分析。\")\n",
|
||
" if n_after_drop < min_samples_per_bin:\n",
|
||
" print(\"错误: 有效数据过少，无法进行分析。\")\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 2. 二维分箱 ---\n",
|
||
" print(f\"对 '{condition1_col}' 和 '{condition2_col}' 进行 {n_bins} 分位数分箱...\")\n",
|
||
" bin1_col = f'{condition1_col}_bin'\n",
|
||
" bin2_col = f'{condition2_col}_bin'\n",
|
||
"\n",
|
||
" try:\n",
|
||
" # 使用 qcut 进行分位数分箱，labels=False 返回 0 到 n_bins-1 的整数标签\n",
|
||
" # duplicates='drop' 会丢弃导致边界不唯一的重复值所在的箱子，可能导致某些箱号缺失\n",
|
||
" # 对于可视化，这通常可以接受，但如果需要严格的等分，需先 rank\n",
|
||
" analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col], q=n_bins, labels=False, duplicates='drop')\n",
|
||
" analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col], q=n_bins, labels=False, duplicates='drop')\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"错误: 分箱失败，请检查数据分布或减少 n_bins。错误信息: {e}\")\n",
|
||
" # 可以尝试先 rank 再 qcut\n",
|
||
" # analysis_df[bin1_col] = pd.qcut(analysis_df[condition1_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n",
|
||
" # analysis_df[bin2_col] = pd.qcut(analysis_df[condition2_col].rank(method='first'), q=n_bins, labels=False, duplicates='raise')\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 3. 分组计算表现指标 (Spearman Rank IC) ---\n",
|
||
" print(\"按二维分箱分组计算 Spearman Rank IC...\")\n",
|
||
"\n",
|
||
" def safe_spearmanr(x, y):\n",
|
||
" \"\"\"安全计算 Spearman 相关性，处理数据量过少的情况\"\"\"\n",
|
||
" if len(x) < max(2, min_samples_per_bin): # 要求至少有 min_samples_per_bin 个点才计算\n",
|
||
" return np.nan\n",
|
||
" corr, p_value = spearmanr(x, y)\n",
|
||
" return corr if not np.isnan(corr) else np.nan # 确保返回 NaN 而不是 None 或其他\n",
|
||
"\n",
|
||
" # 按两个分箱列分组\n",
|
||
" grouped = analysis_df.groupby([bin1_col, bin2_col])\n",
|
||
"\n",
|
||
" # 计算每个格子的 Spearman 相关系数\n",
|
||
" # apply 可能较慢，但计算相关性通常需要 apply\n",
|
||
" performance_series = grouped.apply(lambda sub: safe_spearmanr(sub[score_col], sub[label_col]))\n",
|
||
"\n",
|
||
" # 计算每个格子的样本数量\n",
|
||
" count_series = grouped.size()\n",
|
||
"\n",
|
||
" # --- 4. 结果整理成 Pivot Table (用于绘图) ---\n",
|
||
" print(\"整理结果用于绘图...\")\n",
|
||
" try:\n",
|
||
" # 将 performance_series 转换成二维矩阵\n",
|
||
" # index 为 condition1_bin, columns 为 condition2_bin\n",
|
||
" performance_pivot = performance_series.unstack(level=0) # level=0 对应第一个 groupby key (bin1_col)\n",
|
||
" count_pivot = count_series.unstack(level=0)\n",
|
||
"\n",
|
||
" # 可选：按列和索引排序，确保顺序正确\n",
|
||
" performance_pivot = performance_pivot.sort_index(axis=0).sort_index(axis=1)\n",
|
||
" count_pivot = count_pivot.sort_index(axis=0).sort_index(axis=1)\n",
|
||
" \n",
|
||
" print(performance_pivot)\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"错误: 无法将结果转换为二维矩阵，可能因为分箱不均匀或数据问题: {e}\")\n",
|
||
" return None, None, None\n",
|
||
"\n",
|
||
" # --- 5. 可视化：绘制热力图 ---\n",
|
||
" print(\"生成热力图...\")\n",
|
||
" fig, ax = plt.subplots(figsize=(16, 12)) # 调整图像大小\n",
|
||
"\n",
|
||
" # 使用 count_pivot 创建一个 mask，屏蔽掉样本量过小的格子\n",
|
||
" mask = count_pivot < min_samples_per_bin\n",
|
||
"\n",
|
||
" # 绘制热力图\n",
|
||
" sns.heatmap(performance_pivot,\n",
|
||
" annot=False, # 100x100 个格子加注释会太密集\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"viridis\", # 选择颜色映射, 'viridis', 'coolwarm', 'RdYlGn' 等都不错\n",
|
||
" linewidths=.5,\n",
|
||
" linecolor='lightgray',\n",
|
||
" # mask=mask, # 应用 mask\n",
|
||
" ax=ax,\n",
|
||
" cbar_kws={'label': f'Spearman Rank IC ({score_col} vs {label_col})'}) # 颜色条标签\n",
|
||
"\n",
|
||
" # 设置标题和轴标签\n",
|
||
" ax.set_title(f'{score_col} 表现分析 (Rank IC vs {label_col})\\n基于 {condition1_col} 和 {condition2_col} {n_bins}x{n_bins} 分箱', fontsize=16)\n",
|
||
" ax.set_xlabel(f'{condition1_col} 分位数 (0 -> 高)', fontsize=12)\n",
|
||
" ax.set_ylabel(f'{condition2_col} 分位数 (0 -> 高)', fontsize=12)\n",
|
||
"\n",
|
||
" # 可选：调整刻度标签，避免显示所有 100 个刻度\n",
|
||
" if n_bins > 20:\n",
|
||
" tick_interval = n_bins // 10 # 大约显示 10 个刻度\n",
|
||
" ax.set_xticks(np.arange(0, n_bins, tick_interval) + 0.5)\n",
|
||
" ax.set_yticks(np.arange(0, n_bins, tick_interval) + 0.5)\n",
|
||
" ax.set_xticklabels(np.arange(0, n_bins, tick_interval))\n",
|
||
" ax.set_yticklabels(np.arange(0, n_bins, tick_interval))\n",
|
||
"\n",
|
||
" plt.xticks(rotation=45, ha='right')\n",
|
||
" plt.yticks(rotation=0)\n",
|
||
" plt.tight_layout() # 调整布局\n",
|
||
"\n",
|
||
" print(\"分析完成。\")\n",
|
||
" return performance_pivot, count_pivot, fig\n",
|
||
"\n",
|
||
"# --- 如何使用 ---\n",
|
||
"# 假设你的包含预测结果和所需列的 DataFrame 是 final_predictions_df\n",
|
||
"# 确保它包含 'score', 'label', 'circ_mv', 'future_return'\n",
|
||
"\n",
|
||
"# # 示例调用 (你需要有实际的 score_df)\n",
|
||
"try:\n",
|
||
" # 确保数据类型正确\n",
|
||
" cols_to_numeric = ['score', 'label', 'circ_mv', 'future_return']\n",
|
||
" for col in cols_to_numeric:\n",
|
||
" if col in score_df.columns:\n",
|
||
" score_df[col] = pd.to_numeric(score_df[col], errors='coerce')\n",
|
||
"\n",
|
||
" # 调用分析函数\n",
|
||
" performance_matrix, count_matrix, heatmap_figure = analyze_score_performance_2d(\n",
|
||
" score_df,\n",
|
||
" n_bins=100, # 你要求的100分箱\n",
|
||
" min_samples_per_bin=50 # 每个格子至少需要50个样本才显示IC，可以调整\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 显示图像\n",
|
||
" if heatmap_figure:\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # 可以查看具体的 performance_matrix 和 count_matrix\n",
|
||
" # print(\"\\nPerformance Matrix (Spearman IC):\")\n",
|
||
" # print(performance_matrix)\n",
|
||
" # print(\"\\nCount Matrix:\")\n",
|
||
" # print(count_matrix)\n",
|
||
"\n",
|
||
"except ValueError as ve:\n",
|
||
" print(f\"数据错误: {ve}\")\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"发生未知错误: {e}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "a436dba4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Empty DataFrame\n",
|
||
"Columns: [ts_code, trade_date, is_st]\n",
|
||
"Index: []\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df[(df['ts_code'] == '600242.SH') & (df['trade_date'] >= '2023-06-01')][['ts_code', 'trade_date', 'is_st']])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "stock",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|