2152 lines
228 KiB
Plaintext
2152 lines
228 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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"e:\\PyProject\\NewStock\\main\\train\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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"\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('../../')\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\")"
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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": "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: 8665405 entries, 0 to 8665404\n",
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"Data columns (total 32 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 pct_chg float64 \n",
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" 8 turnover_rate float64 \n",
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" 9 pe_ttm float64 \n",
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" 10 circ_mv float64 \n",
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" 11 total_mv float64 \n",
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" 12 volume_ratio float64 \n",
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" 13 is_st bool \n",
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" 14 up_limit float64 \n",
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" 15 down_limit float64 \n",
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" 16 buy_sm_vol float64 \n",
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" 17 sell_sm_vol float64 \n",
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" 18 buy_lg_vol float64 \n",
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" 19 sell_lg_vol float64 \n",
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" 20 buy_elg_vol float64 \n",
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" 21 sell_elg_vol float64 \n",
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" 22 net_mf_vol float64 \n",
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" 23 his_low float64 \n",
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" 24 his_high float64 \n",
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" 25 cost_5pct float64 \n",
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" 26 cost_15pct float64 \n",
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" 27 cost_50pct float64 \n",
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" 28 cost_85pct float64 \n",
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" 29 cost_95pct float64 \n",
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" 30 weight_avg float64 \n",
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" 31 winner_rate float64 \n",
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"dtypes: bool(1), datetime64[ns](1), float64(29), object(1)\n",
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"memory usage: 2.0+ 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('../../data/daily_data.h5', key='daily_data',\n",
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" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', '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('../../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('../../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('../../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('../../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": 3,
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||
"id": "cac01788dac10678",
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||
"metadata": {
|
||
"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('../../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": 4,
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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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||
},
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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": [
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||
"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 = '../../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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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 5,
|
||
"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"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"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('../../data/sw_daily.h5')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"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', '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": 8,
|
||
"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('../../data/fina_indicator.h5', key='fina_indicator',\n",
|
||
" columns=['ts_code', 'ann_date', 'undist_profit_ps', 'ocfps', 'bps'],\n",
|
||
" df=None)\n",
|
||
"cashflow_df = read_and_merge_h5_data('../../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('../../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('../../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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"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",
|
||
"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",
|
||
" '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', 'AR', 'BR',\n",
|
||
" 'AR_BR', 'log_circ_mv', 'cashflow_to_ev_factor', 'book_to_price_ratio',\n",
|
||
" 'turnover_rate_mean_5', 'variance_20', 'bbi_ratio_factor',\n",
|
||
" 'daily_deviation', 'lg_elg_net_buy_vol', 'flow_lg_elg_intensity',\n",
|
||
" 'sm_net_buy_vol', 'flow_divergence_diff', 'flow_divergence_ratio',\n",
|
||
" 'total_buy_vol', 'lg_elg_buy_prop', 'flow_struct_buy_change',\n",
|
||
" 'lg_elg_net_buy_vol_change', 'flow_lg_elg_accel',\n",
|
||
" 'chip_concentration_range', 'chip_skewness', 'floating_chip_proxy',\n",
|
||
" 'cost_support_15pct_change', 'cat_winner_price_zone',\n",
|
||
" 'flow_chip_consistency', 'profit_taking_vs_absorb', '_is_positive',\n",
|
||
" '_is_negative', 'cat_is_positive', '_pos_returns', '_neg_returns',\n",
|
||
" '_pos_returns_sq', '_neg_returns_sq', 'upside_vol', 'downside_vol',\n",
|
||
" 'vol_ratio', '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: 4539678 entries, 0 to 4539677\n",
|
||
"Columns: 178 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(162), int32(3), object(2)\n",
|
||
"memory usage: 5.7+ GB\n",
|
||
"None\n",
|
||
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', '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', '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",
|
||
"\n",
|
||
"def filter_data(df):\n",
|
||
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\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 = 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",
|
||
"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",
|
||
"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": 10,
|
||
"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": 11,
|
||
"id": "f4f16d63ad18d1bc",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:03.670700Z",
|
||
"start_time": "2025-04-03T13:08:03.665739Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"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",
|
||
"def cs_neutralize_industry_cap(df: pd.DataFrame,\n",
|
||
" features: list,\n",
|
||
" industry_col: str = 'cat_l2_code',\n",
|
||
" market_cap_col: str = 'circ_mv'):\n",
|
||
" \"\"\"\n",
|
||
" 对指定特征列进行截面行业和对数市值中性化 (原地修改)。\n",
|
||
" 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry)\n",
|
||
" 将回归残差写回原特征列。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列,\n",
|
||
" industry_col, market_cap_col。\n",
|
||
" features (list): 需要处理的特征列名列表。\n",
|
||
" industry_col (str): 行业分类列名。\n",
|
||
" market_cap_col (str): 流通市值列名。\n",
|
||
"\n",
|
||
" WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。\n",
|
||
" 计算量较大，可能耗时较长。\n",
|
||
" 需要安装 statsmodels 库 (pip install statsmodels)。\n",
|
||
" \"\"\"\n",
|
||
" print(\"开始截面行业市值中性化...\")\n",
|
||
" required_cols = features + ['trade_date', industry_col, 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",
|
||
" # 预处理：计算 log 市值，处理 industry code 可能的 NaN\n",
|
||
" log_cap_col = '_log_market_cap'\n",
|
||
" df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值\n",
|
||
" # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry')\n",
|
||
" # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN\n",
|
||
" # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理\n",
|
||
"\n",
|
||
" dates = df['trade_date'].unique()\n",
|
||
" all_residuals = [] # 用于收集所有日期的残差\n",
|
||
"\n",
|
||
" for date in tqdm(dates, desc=\"Neutralizing\"):\n",
|
||
" daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本\n",
|
||
"\n",
|
||
" # 准备自变量 X (常数项 + log市值 + 行业哑变量)\n",
|
||
" X = daily_data[[log_cap_col]]\n",
|
||
" X = sm.add_constant(X, prepend=True) # 添加常数项\n",
|
||
" # 创建行业哑变量 (drop_first=True 避免共线性)\n",
|
||
" industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True)\n",
|
||
" industry_dummies = industry_dummies.astype(int)\n",
|
||
" X = pd.concat([X, industry_dummies], axis=1)\n",
|
||
"\n",
|
||
" daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df\n",
|
||
"\n",
|
||
" for col in features:\n",
|
||
" Y = daily_data[col]\n",
|
||
"\n",
|
||
" # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值\n",
|
||
" valid_mask = Y.notna() & X.notna().all(axis=1)\n",
|
||
" if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型\n",
|
||
" print(f\"警告: 日期 {date}, 特征 {col} 有效数据不足 ({valid_mask.sum()}个)，无法中性化，填充 NaN。\")\n",
|
||
" daily_residuals[col] = np.nan\n",
|
||
" continue\n",
|
||
"\n",
|
||
" Y_valid = Y[valid_mask]\n",
|
||
" X_valid = X[valid_mask]\n",
|
||
"\n",
|
||
" # 执行 OLS 回归\n",
|
||
" try:\n",
|
||
" model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy())\n",
|
||
" results = model.fit()\n",
|
||
" # 将残差填回对应位置\n",
|
||
" daily_residuals.loc[valid_mask, col] = results.resid\n",
|
||
" daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。\")\n",
|
||
" daily_residuals[col] = np.nan\n",
|
||
" break\n",
|
||
"\n",
|
||
" all_residuals.append(daily_residuals)\n",
|
||
"\n",
|
||
" # 合并所有日期的残差结果\n",
|
||
" if all_residuals:\n",
|
||
" residuals_df = pd.concat(all_residuals)\n",
|
||
" # 将残差结果更新回原始 df (原地修改)\n",
|
||
" # 使用 update 比 merge 更适合基于索引的原地更新\n",
|
||
" # 确保 residuals_df 的索引与 df 中对应部分一致\n",
|
||
" df.update(residuals_df)\n",
|
||
" else:\n",
|
||
" print(\"没有有效的残差结果可以合并。\")\n",
|
||
"\n",
|
||
"\n",
|
||
" # 清理临时列\n",
|
||
" df.drop(columns=[log_cap_col], inplace=True)\n",
|
||
" print(\"截面行业市值中性化完成。\")\n",
|
||
"\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": 12,
|
||
"id": "40e6b68a91b30c79",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T13:08:04.694262Z",
|
||
"start_time": "2025-04-03T13:08:03.694904Z"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"\n",
|
||
"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",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\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": 34,
|
||
"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",
|
||
"df['future_return'] = np.log1p(df['future_return'])\n",
|
||
"\n",
|
||
"df['future_return_2'] = df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-2) / 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",
|
||
"df['future_return_2'] = np.log1p(df['future_return_2'])\n",
|
||
"\n",
|
||
"df['cat_up_limit'] = df['pct_chg'] > 5\n",
|
||
"# df['label'] = (df.groupby('ts_code')['cat_up_limit']\n",
|
||
"# .rolling(window=5, min_periods=1).sum()\n",
|
||
"# .groupby('ts_code') # 再次按 ts_code 分组\n",
|
||
"# .shift(-5)\n",
|
||
"# .fillna(0) # 填充每个股票组最后的 NaN\n",
|
||
"# .astype(int)\n",
|
||
"# .reset_index(level=0, drop=True))\n",
|
||
"df['label'] = df.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
|
||
" lambda x: pd.qcut(x, q=20, labels=False, duplicates='drop')\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": 14,
|
||
"id": "8f4dc587",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ts_code trade_date open close high low vol pct_chg \\\n",
|
||
"976171 002193.SZ 2019-01-02 18.02 17.82 18.21 17.73 22513.62 -1.00 \n",
|
||
"976172 002193.SZ 2019-01-03 17.82 17.78 17.94 17.65 19523.21 -0.22 \n",
|
||
"976173 002193.SZ 2019-01-04 17.47 18.04 18.10 17.34 28094.69 1.46 \n",
|
||
"976174 002193.SZ 2019-01-07 18.17 18.31 18.39 18.00 23866.88 1.50 \n",
|
||
"976175 002193.SZ 2019-01-08 18.25 18.52 18.66 18.13 22853.46 1.15 \n",
|
||
"... ... ... ... ... ... ... ... ... \n",
|
||
"977704 002193.SZ 2025-05-19 11.74 11.95 11.97 11.61 94249.32 2.49 \n",
|
||
"977705 002193.SZ 2025-05-20 11.99 12.20 12.22 11.84 103040.57 2.09 \n",
|
||
"977706 002193.SZ 2025-05-21 12.20 11.97 12.28 11.82 83112.00 -1.89 \n",
|
||
"977707 002193.SZ 2025-05-22 11.95 11.80 12.17 11.72 88811.00 -1.42 \n",
|
||
"977708 002193.SZ 2025-05-23 11.74 11.51 11.90 11.51 93013.00 -2.46 \n",
|
||
"\n",
|
||
" turnover_rate pe_ttm ... cs_rank_lg_flow_price_concordance \\\n",
|
||
"976171 0.9771 23.0129 ... 0.621118 \n",
|
||
"976172 0.8473 22.9598 ... 0.230095 \n",
|
||
"976173 1.2193 23.3049 ... 0.507119 \n",
|
||
"976174 1.0358 23.6499 ... 0.315078 \n",
|
||
"976175 0.9918 23.9154 ... 0.248631 \n",
|
||
"... ... ... ... ... \n",
|
||
"977704 3.6012 NaN ... 0.224402 \n",
|
||
"977705 3.9371 NaN ... 0.124834 \n",
|
||
"977706 3.1757 NaN ... 0.803720 \n",
|
||
"977707 3.3934 NaN ... 0.667220 \n",
|
||
"977708 3.5540 NaN ... 0.308867 \n",
|
||
"\n",
|
||
" cs_rank_turnover_per_winner cs_rank_ind_cap_neutral_pe \\\n",
|
||
"976171 0.990869 NaN \n",
|
||
"976172 0.934283 NaN \n",
|
||
"976173 0.925182 NaN \n",
|
||
"976174 0.843796 NaN \n",
|
||
"976175 0.834672 NaN \n",
|
||
"... ... ... \n",
|
||
"977704 0.624335 NaN \n",
|
||
"977705 0.641102 NaN \n",
|
||
"977706 0.624709 NaN \n",
|
||
"977707 0.563268 NaN \n",
|
||
"977708 0.661242 NaN \n",
|
||
"\n",
|
||
" cs_rank_volume_ratio cs_rank_elg_buy_sell_sm_ratio \\\n",
|
||
"976171 0.710344 0.341855 \n",
|
||
"976172 0.444444 0.318912 \n",
|
||
"976173 0.489226 0.260036 \n",
|
||
"976174 0.250000 0.251095 \n",
|
||
"976175 0.510588 0.286679 \n",
|
||
"... ... ... \n",
|
||
"977704 0.234043 0.397274 \n",
|
||
"977705 0.344124 0.116534 \n",
|
||
"977706 0.221189 0.126370 \n",
|
||
"977707 0.412155 0.130521 \n",
|
||
"977708 0.534540 0.134175 \n",
|
||
"\n",
|
||
" cs_rank_cost_dist_vol_ratio cs_rank_size future_return \\\n",
|
||
"976171 0.261603 0.262235 0.033658 \n",
|
||
"976172 0.185342 0.264695 0.029373 \n",
|
||
"976173 0.211959 0.259489 0.025179 \n",
|
||
"976174 0.145266 0.255474 0.014638 \n",
|
||
"976175 0.202299 0.259854 0.003235 \n",
|
||
"... ... ... ... \n",
|
||
"977704 0.299535 0.031250 NaN \n",
|
||
"977705 0.339641 0.032205 NaN \n",
|
||
"977706 0.291597 0.030555 NaN \n",
|
||
"977707 0.372634 0.030555 NaN \n",
|
||
"977708 0.430090 0.027898 NaN \n",
|
||
"\n",
|
||
" cat_up_limit label \n",
|
||
"976171 False 8.0 \n",
|
||
"976172 False 7.0 \n",
|
||
"976173 False 10.0 \n",
|
||
"976174 False 13.0 \n",
|
||
"976175 False 6.0 \n",
|
||
"... ... ... \n",
|
||
"977704 False NaN \n",
|
||
"977705 False NaN \n",
|
||
"977706 False NaN \n",
|
||
"977707 False NaN \n",
|
||
"977708 False NaN \n",
|
||
"\n",
|
||
"[1538 rows x 181 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df[df['ts_code'] == '002193.SZ'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "29221dde",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"162\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"feature_columns = [col for col in df.head(10)\n",
|
||
" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
" # .merge(index_data, on='trade_date', how='left')\n",
|
||
" .columns\n",
|
||
" ]\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 ['cat_reason', 'cat_is_on_top_list']]\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": 36,
|
||
"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', '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', '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']\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%|██████████| 132/132 [00:28<00:00, 4.57it/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%|██████████| 132/132 [00:24<00:00, 5.46it/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', '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', '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']\n",
|
||
"df最小日期: 2019-01-02\n",
|
||
"df最大日期: 2025-05-23\n",
|
||
"2057539\n",
|
||
"train_data最小日期: 2020-01-02\n",
|
||
"train_data最大日期: 2022-12-30\n",
|
||
"1766694\n",
|
||
"test_data最小日期: 2023-01-03\n",
|
||
"test_data最大日期: 2025-05-23\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 = '2023-01-01'\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_industry_cap(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_industry_cap(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": 37,
|
||
"id": "2e4b027e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class RmseObjective(object):\n",
|
||
"\n",
|
||
" def is_max_optimal(self):\n",
|
||
" return False\n",
|
||
"\n",
|
||
" def get_final_error(self, error, weight):\n",
|
||
" return np.sqrt(error / (weight + 1e-38))\n",
|
||
"\n",
|
||
"\n",
|
||
" def evaluate(self, approxes, target, weight):\n",
|
||
" assert len(approxes) == 1\n",
|
||
" assert len(target) == len(approxes[0])\n",
|
||
"\n",
|
||
" approx = approxes[0]\n",
|
||
"\n",
|
||
" error_sum = 0.0\n",
|
||
" weight_sum = 0.0\n",
|
||
"\n",
|
||
" for i in range(len(approx)):\n",
|
||
" w = 1.0 if weight is None else weight[i]\n",
|
||
" weight_sum += w\n",
|
||
" error_sum += w * ((approx[i] - target[i])**2)\n",
|
||
"\n",
|
||
" return error_sum, weight_sum\n",
|
||
"\n",
|
||
" def calc_ders_range(self, approxes, targets, weights):\n",
|
||
" assert len(approxes) == len(targets)\n",
|
||
" if weights is not None:\n",
|
||
" assert len(weights) == len(approxes)\n",
|
||
"\n",
|
||
" result = []\n",
|
||
" for index in range(len(targets)):\n",
|
||
" der1 = targets[index] - approxes[index]\n",
|
||
" der2 = -1\n",
|
||
"\n",
|
||
" if weights is not None:\n",
|
||
" der1 *= weights[index]\n",
|
||
" der2 *= weights[index]\n",
|
||
"\n",
|
||
" result.append((der1, der2))\n",
|
||
" return result\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"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 pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import datetime # 用于日期计算\n",
|
||
"from catboost import CatBoostClassifier, CatBoostRanker, CatBoostRegressor\n",
|
||
"from catboost import Pool\n",
|
||
"import lightgbm as lgb\n",
|
||
"from lightgbm import LGBMClassifier, LGBMRegressor\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[target_column]\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': 'RMSE', # 适用于二分类\n",
|
||
" 'eval_metric': 'RMSE', # 评估指标\n",
|
||
" 'iterations': 1500,\n",
|
||
" 'learning_rate': 0.01,\n",
|
||
" 'depth': 8, # 控制模型复杂度\n",
|
||
" 'l2_leaf_reg': 1, # L2 正则化\n",
|
||
" 'verbose': 5000,\n",
|
||
" 'early_stopping_rounds': 300,\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",
|
||
" group_train = train_data_split['trade_date'].factorize()[0]\n",
|
||
" group_val = val_data_split['trade_date'].factorize()[0]\n",
|
||
" train_pool = Pool(\n",
|
||
" data=X_train,\n",
|
||
" label=y_train,\n",
|
||
" # group_id=group_train,\n",
|
||
" cat_features=cat_features\n",
|
||
" )\n",
|
||
" val_pool = Pool(\n",
|
||
" data=X_val,\n",
|
||
" label=y_val,\n",
|
||
" # group_id=group_val,\n",
|
||
" cat_features=cat_features\n",
|
||
" )\n",
|
||
"\n",
|
||
" model = CatBoostRegressor(**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",
|
||
" label_gain = list(range(len(train_data_split['label'].unique())))\n",
|
||
" params = {\n",
|
||
" 'label_gain': [gain * gain for gain in label_gain],\n",
|
||
" 'objective': 'rmse',\n",
|
||
" 'metric': 'rmse',\n",
|
||
" 'learning_rate': 0.01,\n",
|
||
" 'num_leaves': 1024,\n",
|
||
" # 'min_data_in_leaf': 128,\n",
|
||
" 'max_depth': 100,\n",
|
||
" 'max_bin': 1024,\n",
|
||
" 'feature_fraction': 0.7,\n",
|
||
" 'bagging_fraction': 0.7,\n",
|
||
" 'bagging_freq': 5,\n",
|
||
" 'lambda_l1': 1,\n",
|
||
" 'lambda_l2': 1,\n",
|
||
" 'boosting': 'gbdt',\n",
|
||
" 'verbosity': -1,\n",
|
||
" 'extra_trees': True,\n",
|
||
" # 'max_position': 5,\n",
|
||
" # 'ndcg_at': 1,\n",
|
||
" 'quant_train_renew_leaf': True,\n",
|
||
" # 'lambdarank_truncation_level': 1,\n",
|
||
" # 'lambdarank_position_bias_regularization': 1,\n",
|
||
" 'seed': 7\n",
|
||
" }\n",
|
||
" train_groups = train_data_split.groupby('trade_date').size().tolist()\n",
|
||
" val_groups = val_data_split.groupby('trade_date').size().tolist()\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",
|
||
" group=train_groups,\n",
|
||
" categorical_feature=categorical_feature\n",
|
||
" )\n",
|
||
" val_dataset = lgb.Dataset(\n",
|
||
" X_val, label=y_val, \n",
|
||
" group=val_groups,\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",
|
||
" from flaml import AutoML\n",
|
||
" from sklearn.datasets import fetch_california_housing\n",
|
||
"\n",
|
||
" # # Initialize an AutoML instance\n",
|
||
" # model = AutoML()\n",
|
||
" # # Specify automl goal and constraint\n",
|
||
" # automl_settings = {\n",
|
||
" # \"time_budget\": 3000, # in seconds\n",
|
||
" # \"metric\": \"r2\",\n",
|
||
" # \"task\": \"regression\",\n",
|
||
" # \"estimator_list\": [\n",
|
||
" # # \"catboost\",\n",
|
||
" # \"lgbm\",\n",
|
||
" # # \"xgboost\"\n",
|
||
" # ], \n",
|
||
" # # \"ensemble\": {\n",
|
||
" # # \"final_estimator\": LGBMRegressor(),\n",
|
||
" # # \"passthrough\": False,\n",
|
||
" # # },\n",
|
||
" # }\n",
|
||
" # model.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, mlflow_logging=False, **automl_settings)\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": 39,
|
||
"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: 728000\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",
|
||
"727995 002235.SZ 2022-12-30 12.914708\n",
|
||
"727996 605598.SH 2022-12-30 11.783580\n",
|
||
"727997 002613.SZ 2022-12-30 12.489464\n",
|
||
"727998 600800.SH 2022-12-30 12.571911\n",
|
||
"727999 603068.SH 2022-12-30 12.967134\n",
|
||
"\n",
|
||
"[728000 rows x 3 columns]\n",
|
||
"原始样本数: 728000, 去除标签为空后样本数: 728000\n",
|
||
"Training until validation scores don't improve for 100 rounds\n",
|
||
"Early stopping, best iteration is:\n",
|
||
"[58]\ttrain's rmse: 0.0373704\tvalid's rmse: 0.040525\n",
|
||
"Evaluated only: rmse\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"gc.collect()\n",
|
||
"\n",
|
||
"use_pca = False\n",
|
||
"type = 'light'\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(1000, 'total_mv'))\n",
|
||
" .merge(industry_df, on=['cat_l2_code', 'trade_date'], how='left')\n",
|
||
" .merge(index_data, on='trade_date', how='left'), \n",
|
||
" feature_columns, type=type, target_column='future_return_2')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"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(1000, '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",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"def filter_high_volatility_daily(df: pd.DataFrame, threshold: float, window: int = 5,\n",
|
||
" date_col: str = 'trade_date', code_col: str = 'ts_code',\n",
|
||
" close_col: str = 'close') -> pd.DataFrame:\n",
|
||
" df = df.sort_values([code_col, date_col])\n",
|
||
"\n",
|
||
" def get_daily_std_rank(group):\n",
|
||
" rolling_std = group[close_col].rolling(window=window, min_periods=window).std()\n",
|
||
" # 在每个日期内计算标准差的百分比排名\n",
|
||
" return rolling_std.groupby(group[date_col]).rank(pct=True, ascending=False)\n",
|
||
"\n",
|
||
" # 直接在 apply 中计算并添加 'std_rank' 列\n",
|
||
" df_with_std = df.groupby(code_col, group_keys=False).apply(\n",
|
||
" lambda x: x.assign(std_rank=get_daily_std_rank(x))\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 过滤掉每天标准差排名在前 top_percent 的股票\n",
|
||
" threshold_rank = threshold / 100.0\n",
|
||
" df_filtered = df_with_std[df_with_std['std_rank'] > threshold_rank].drop(\n",
|
||
" columns=['std_rank']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return df_filtered\n",
|
||
"\n",
|
||
"# threshold = 10\n",
|
||
"# score_df = filter_high_volatility_daily(score_df, threshold=threshold)\n",
|
||
"# print(len(score_df))\n",
|
||
"\n",
|
||
"if type == 'cat':\n",
|
||
" score_df['score'] = model.predict(score_df[feature_columns])\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: \n",
|
||
" x[\n",
|
||
" # (x['score'] <= x['score'].quantile(0.99)) & \n",
|
||
" (x['score'] >= x['score'].quantile(0.90))\n",
|
||
" ] # 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",
|
||
"# df_to_drop = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
|
||
"# score_df = score_df.drop(df_to_drop.index)\n",
|
||
"save_df = score_df.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(2, '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": 41,
|
||
"id": "09b1799e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"162\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'undist_profit_ps', 'ocfps', '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', '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']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(feature_columns))\n",
|
||
"print(feature_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"id": "bceabd1f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# print(model.get_feature_importance())"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "new_trader",
|
||
"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": "3.11.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|