2435 lines
263 KiB
Plaintext
2435 lines
263 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "79a7758178bafdd3",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:46:06.987506Z",
|
||
"start_time": "2025-04-03T12:46:06.259551Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/mnt/d/PyProject/NewStock\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%load_ext autoreload\n",
|
||
"%autoreload 2\n",
|
||
"\n",
|
||
"import gc\n",
|
||
"import os\n",
|
||
"import sys\n",
|
||
"sys.path.append('/mnt/d/PyProject/NewStock/')\n",
|
||
"print(os.getcwd())\n",
|
||
"import pandas as pd\n",
|
||
"from main.factor.factor import get_rolling_factor, get_simple_factor\n",
|
||
"from main.utils.factor import read_industry_data\n",
|
||
"from main.utils.factor_processor import calculate_score\n",
|
||
"from main.utils.utils import read_and_merge_h5_data, merge_with_industry_data\n",
|
||
"\n",
|
||
"import warnings\n",
|
||
"\n",
|
||
"warnings.filterwarnings(\"ignore\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "a79cafb06a7e0e43",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:00.212859Z",
|
||
"start_time": "2025-04-03T12:46:06.998047Z"
|
||
},
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"daily data\n",
|
||
"daily basic\n",
|
||
"inner merge on ['ts_code', 'trade_date']\n",
|
||
"stk limit\n",
|
||
"left merge on ['ts_code', 'trade_date']\n",
|
||
"money flow\n",
|
||
"left merge on ['ts_code', 'trade_date']\n",
|
||
"cyq perf\n",
|
||
"left merge on ['ts_code', 'trade_date']\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 8692146 entries, 0 to 8692145\n",
|
||
"Data columns (total 33 columns):\n",
|
||
" # Column Dtype \n",
|
||
"--- ------ ----- \n",
|
||
" 0 ts_code object \n",
|
||
" 1 trade_date datetime64[ns]\n",
|
||
" 2 open float64 \n",
|
||
" 3 close float64 \n",
|
||
" 4 high float64 \n",
|
||
" 5 low float64 \n",
|
||
" 6 vol float64 \n",
|
||
" 7 pct_chg float64 \n",
|
||
" 8 amount float64 \n",
|
||
" 9 turnover_rate float64 \n",
|
||
" 10 pe_ttm float64 \n",
|
||
" 11 circ_mv float64 \n",
|
||
" 12 total_mv float64 \n",
|
||
" 13 volume_ratio float64 \n",
|
||
" 14 is_st bool \n",
|
||
" 15 up_limit float64 \n",
|
||
" 16 down_limit float64 \n",
|
||
" 17 buy_sm_vol float64 \n",
|
||
" 18 sell_sm_vol float64 \n",
|
||
" 19 buy_lg_vol float64 \n",
|
||
" 20 sell_lg_vol float64 \n",
|
||
" 21 buy_elg_vol float64 \n",
|
||
" 22 sell_elg_vol float64 \n",
|
||
" 23 net_mf_vol float64 \n",
|
||
" 24 his_low float64 \n",
|
||
" 25 his_high float64 \n",
|
||
" 26 cost_5pct float64 \n",
|
||
" 27 cost_15pct float64 \n",
|
||
" 28 cost_50pct float64 \n",
|
||
" 29 cost_85pct float64 \n",
|
||
" 30 cost_95pct float64 \n",
|
||
" 31 weight_avg float64 \n",
|
||
" 32 winner_rate float64 \n",
|
||
"dtypes: bool(1), datetime64[ns](1), float64(30), object(1)\n",
|
||
"memory usage: 2.1+ GB\n",
|
||
"None\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from main.utils.utils import read_and_merge_h5_data\n",
|
||
"\n",
|
||
"print('daily data')\n",
|
||
"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_data.h5', key='daily_data',\n",
|
||
" columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'amount'],\n",
|
||
" df=None)\n",
|
||
"\n",
|
||
"print('daily basic')\n",
|
||
"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/daily_basic.h5', key='daily_basic',\n",
|
||
" columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'total_mv', 'volume_ratio',\n",
|
||
" 'is_st'], df=df, join='inner')\n",
|
||
"\n",
|
||
"print('stk limit')\n",
|
||
"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_limit.h5', key='stk_limit',\n",
|
||
" columns=['ts_code', 'trade_date', 'pre_close', 'up_limit', 'down_limit'],\n",
|
||
" df=df)\n",
|
||
"print('money flow')\n",
|
||
"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/money_flow.h5', key='money_flow',\n",
|
||
" columns=['ts_code', 'trade_date', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol',\n",
|
||
" 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol'],\n",
|
||
" df=df)\n",
|
||
"print('cyq perf')\n",
|
||
"df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/cyq_perf.h5', key='cyq_perf',\n",
|
||
" columns=['ts_code', 'trade_date', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct',\n",
|
||
" 'cost_50pct',\n",
|
||
" 'cost_85pct', 'cost_95pct', 'weight_avg', 'winner_rate'],\n",
|
||
" df=df)\n",
|
||
"print(df.info())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "cac01788dac10678",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.527104Z",
|
||
"start_time": "2025-04-03T12:47:00.488715Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"industry\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print('industry')\n",
|
||
"industry_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/industry_data.h5', key='industry_data',\n",
|
||
" columns=['ts_code', 'l2_code', 'in_date'],\n",
|
||
" df=None, on=['ts_code'], join='left')\n",
|
||
"\n",
|
||
"\n",
|
||
"def merge_with_industry_data(df, industry_df):\n",
|
||
" # 确保日期字段是 datetime 类型\n",
|
||
" df['trade_date'] = pd.to_datetime(df['trade_date'])\n",
|
||
" industry_df['in_date'] = pd.to_datetime(industry_df['in_date'])\n",
|
||
"\n",
|
||
" # 对 industry_df 按 ts_code 和 in_date 排序\n",
|
||
" industry_df_sorted = industry_df.sort_values(['in_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # 对原始 df 按 ts_code 和 trade_date 排序\n",
|
||
" df_sorted = df.sort_values(['trade_date', 'ts_code'])\n",
|
||
"\n",
|
||
" # 使用 merge_asof 进行向后合并\n",
|
||
" merged = pd.merge_asof(\n",
|
||
" df_sorted,\n",
|
||
" industry_df_sorted,\n",
|
||
" by='ts_code', # 按 ts_code 分组\n",
|
||
" left_on='trade_date',\n",
|
||
" right_on='in_date',\n",
|
||
" direction='backward'\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 获取每个 ts_code 的最早 in_date 记录\n",
|
||
" min_in_date_per_ts = (industry_df_sorted\n",
|
||
" .groupby('ts_code')\n",
|
||
" .first()\n",
|
||
" .reset_index()[['ts_code', 'l2_code']])\n",
|
||
"\n",
|
||
" # 填充未匹配到的记录（trade_date 早于所有 in_date 的情况）\n",
|
||
" merged['l2_code'] = merged['l2_code'].fillna(\n",
|
||
" merged['ts_code'].map(min_in_date_per_ts.set_index('ts_code')['l2_code'])\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 保留需要的列并重置索引\n",
|
||
" result = merged.reset_index(drop=True)\n",
|
||
" return result\n",
|
||
"\n",
|
||
"\n",
|
||
"# 使用示例\n",
|
||
"df = merge_with_industry_data(df, industry_df)\n",
|
||
"# print(mdf[mdf['ts_code'] == '600751.SH'][['ts_code', 'trade_date', 'l2_code']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "c4e9e1d31da6dba6",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-03T12:47:10.719252Z",
|
||
"start_time": "2025-04-03T12:47:10.541247Z"
|
||
},
|
||
"jupyter": {
|
||
"source_hidden": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from main.factor.factor import *\n",
|
||
"\n",
|
||
"def calculate_indicators(df):\n",
|
||
" \"\"\"\n",
|
||
" 计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。\n",
|
||
" \"\"\"\n",
|
||
" df = df.sort_values('trade_date')\n",
|
||
" df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
|
||
" # df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
|
||
" delta = df['close'].diff()\n",
|
||
" gain = delta.where(delta > 0, 0)\n",
|
||
" loss = -delta.where(delta < 0, 0)\n",
|
||
" avg_gain = gain.rolling(window=14).mean()\n",
|
||
" avg_loss = loss.rolling(window=14).mean()\n",
|
||
" rs = avg_gain / avg_loss\n",
|
||
" df['RSI'] = 100 - (100 / (1 + rs))\n",
|
||
"\n",
|
||
" # 计算MACD\n",
|
||
" ema12 = df['close'].ewm(span=12, adjust=False).mean()\n",
|
||
" ema26 = df['close'].ewm(span=26, adjust=False).mean()\n",
|
||
" df['MACD'] = ema12 - ema26\n",
|
||
" df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
|
||
" df['MACD_hist'] = df['MACD'] - df['Signal_line']\n",
|
||
"\n",
|
||
" # 4. 情绪因子1：市场上涨比例（Up Ratio）\n",
|
||
" df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)\n",
|
||
" df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例\n",
|
||
"\n",
|
||
" # 5. 情绪因子2：成交量变化率（Volume Change Rate）\n",
|
||
" df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量\n",
|
||
" df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率\n",
|
||
"\n",
|
||
" # 6. 情绪因子3：波动率（Volatility）\n",
|
||
" df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差\n",
|
||
"\n",
|
||
" # 7. 情绪因子4：成交额变化率（Amount Change Rate）\n",
|
||
" df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额\n",
|
||
" df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率\n",
|
||
"\n",
|
||
" # df = sentiment_panic_greed_index(df)\n",
|
||
" # df = sentiment_market_breadth_proxy(df)\n",
|
||
" # df = sentiment_reversal_indicator(df)\n",
|
||
"\n",
|
||
" return df\n",
|
||
"\n",
|
||
"\n",
|
||
"def generate_index_indicators(h5_filename):\n",
|
||
" df = pd.read_hdf(h5_filename, key='index_data')\n",
|
||
" df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
|
||
" df = df.sort_values('trade_date')\n",
|
||
"\n",
|
||
" # 计算每个ts_code的相关指标\n",
|
||
" df_indicators = []\n",
|
||
" for ts_code in df['ts_code'].unique():\n",
|
||
" df_index = df[df['ts_code'] == ts_code].copy()\n",
|
||
" df_index = calculate_indicators(df_index)\n",
|
||
" df_indicators.append(df_index)\n",
|
||
"\n",
|
||
" # 合并所有指数的结果\n",
|
||
" df_all_indicators = pd.concat(df_indicators, ignore_index=True)\n",
|
||
"\n",
|
||
" # 保留trade_date列，并将同一天的数据按ts_code合并成一行\n",
|
||
" df_final = df_all_indicators.pivot_table(\n",
|
||
" index='trade_date',\n",
|
||
" columns='ts_code',\n",
|
||
" values=['daily_return', \n",
|
||
" 'RSI', 'MACD', 'Signal_line', 'MACD_hist', \n",
|
||
" # 'sentiment_panic_greed_index',\n",
|
||
" 'up_ratio_20d', 'volume_change_rate', 'volatility',\n",
|
||
" 'amount_change_rate', 'amount_mean'],\n",
|
||
" aggfunc='last'\n",
|
||
" )\n",
|
||
"\n",
|
||
" df_final.columns = [f\"{col[1]}_{col[0]}\" for col in df_final.columns]\n",
|
||
" df_final = df_final.reset_index()\n",
|
||
"\n",
|
||
" return df_final\n",
|
||
"\n",
|
||
"\n",
|
||
"# 使用函数\n",
|
||
"h5_filename = '/mnt/d/PyProject/NewStock/data/index_data.h5'\n",
|
||
"index_data = generate_index_indicators(h5_filename)\n",
|
||
"index_data = index_data.dropna()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"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"
|
||
]
|
||
},
|
||
{
|
||
"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('/mnt/d/PyProject/NewStock/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', 'amount', 'circ_mv', 'total_mv', 'is_st', 'up_limit', 'down_limit', 'buy_sm_vol', 'sell_sm_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'his_low', 'his_high', 'cost_5pct', 'cost_15pct', 'cost_50pct', 'cost_85pct', 'cost_95pct', 'weight_avg', 'in_date']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"origin_columns = df.columns.tolist()\n",
|
||
"origin_columns = [col for col in origin_columns if\n",
|
||
" col not in ['turnover_rate', 'pe_ttm', 'volume_ratio', 'vol', 'pct_chg', 'l2_code', 'winner_rate']]\n",
|
||
"origin_columns = [col for col in origin_columns if col not in index_data.columns]\n",
|
||
"origin_columns = [col for col in origin_columns if 'cyq' not in col]\n",
|
||
"print(origin_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 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('/mnt/d/PyProject/NewStock/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('/mnt/d/PyProject/NewStock/data/cashflow.h5', key='cashflow',\n",
|
||
" columns=['ts_code', 'ann_date', 'n_cashflow_act'],\n",
|
||
" df=None)\n",
|
||
"balancesheet_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/balancesheet.h5', key='balancesheet',\n",
|
||
" columns=['ts_code', 'ann_date', 'money_cap', 'total_liab'],\n",
|
||
" df=None)\n",
|
||
"top_list_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/top_list.h5', key='top_list',\n",
|
||
" columns=['ts_code', 'trade_date', 'reason'],\n",
|
||
" df=None)\n",
|
||
"\n",
|
||
"top_list_df = top_list_df.sort_values(by='trade_date', ascending=False).drop_duplicates(subset=['ts_code', 'trade_date'], keep='first').sort_values(by='trade_date')\n",
|
||
"\n",
|
||
"stk_holdertrade_df = read_and_merge_h5_data('/mnt/d/PyProject/NewStock/data/stk_holdertrade.h5', key='stk_holdertrade',\n",
|
||
" columns=['ts_code', 'ann_date', 'in_de', 'change_ratio'],\n",
|
||
" df=None)"
|
||
]
|
||
},
|
||
{
|
||
"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": [
|
||
"开始计算股东增减持因子...\n",
|
||
"警告: 'in_de' 列中存在未映射的值，可能导致 _direction 列出现NaN。\n",
|
||
"股东增减持因子计算完成。\n",
|
||
"Calculating cat_senti_mom_vol_spike...\n",
|
||
"Finished cat_senti_mom_vol_spike.\n",
|
||
"Calculating cat_senti_pre_breakout...\n",
|
||
"Calculating atr_10 as it's missing...\n",
|
||
"Calculating atr_40 as it's missing...\n",
|
||
"Finished cat_senti_pre_breakout.\n",
|
||
"计算因子 ts_turnover_rate_acceleration_5_20\n",
|
||
"计算因子 ts_vol_sustain_10_30\n",
|
||
"计算因子 cs_amount_outlier_10\n",
|
||
"计算因子 ts_ff_to_total_turnover_ratio\n",
|
||
"计算因子 ts_price_volume_trend_coherence_5_20\n",
|
||
"计算因子 ts_ff_turnover_rate_surge_10\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"开始计算因子: AR, BR (原地修改)...\n",
|
||
"因子 AR, BR 计算成功。\n",
|
||
"因子 AR, BR 计算流程结束。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"使用 'ann_date' 作为财务数据生效日期。\n",
|
||
"警告: 从 financial_data_subset 中移除了 366 行，因为其 'ts_code' 或 'ann_date' 列存在空值。\n",
|
||
"计算 BBI...\n",
|
||
"--- 计算日级别偏离度 (使用 pct_chg) ---\n",
|
||
"--- 计算日级别动量基准 (使用 pct_chg) ---\n",
|
||
"日级别动量基准计算完成 (使用 pct_chg)。\n",
|
||
"日级别偏离度计算完成 (使用 pct_chg)。\n",
|
||
"--- 计算日级别行业偏离度 (使用 pct_chg 和行业基准) ---\n",
|
||
"--- 计算日级别行业动量基准 (使用 pct_chg 和 cat_l2_code) ---\n",
|
||
"错误: 计算日级别行业动量基准需要以下列: ['pct_chg', 'cat_l2_code', 'trade_date', 'ts_code']。\n",
|
||
"错误: 计算日级别行业偏离度需要以下列: ['pct_chg', 'daily_industry_positive_benchmark', 'daily_industry_negative_benchmark']。请先运行 daily_industry_momentum_benchmark(df)。\n",
|
||
"Index(['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol',\n",
|
||
" 'pct_chg', 'amount', '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', 'holder_net_change_sum_10d',\n",
|
||
" 'holder_increase_days_10d', 'holder_decrease_days_10d',\n",
|
||
" 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d',\n",
|
||
" 'holder_direction_score_10d', 'cat_senti_mom_vol_spike',\n",
|
||
" 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20',\n",
|
||
" 'ts_vol_sustain_10_30', 'cs_amount_outlier_10',\n",
|
||
" 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20',\n",
|
||
" 'ts_ff_turnover_rate_surge_10', '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 senti_strong_inflow...\n",
|
||
"Finished senti_strong_inflow.\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: 4554725 entries, 0 to 4554724\n",
|
||
"Columns: 194 entries, ts_code to cs_rank_size\n",
|
||
"dtypes: bool(10), datetime64[ns](1), float64(173), int64(6), object(4)\n",
|
||
"memory usage: 6.3+ GB\n",
|
||
"None\n",
|
||
"['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol', 'pct_chg', 'amount', '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', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'holder_direction_score_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', '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', 'senti_strong_inflow', '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": [
|
||
"import numpy as np\n",
|
||
"from main.factor.factor import *\n",
|
||
"from main.factor.money_factor import * \n",
|
||
"\n",
|
||
"\n",
|
||
"def filter_data(df):\n",
|
||
" # df = df.groupby('trade_date').apply(lambda x: x.nlargest(1000, 'act_factor1'))\n",
|
||
" df = df[~df[\"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",
|
||
"\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",
|
||
"df = holder_trade_factors(df, stk_holdertrade_df)\n",
|
||
"\n",
|
||
"df = cat_senti_mom_vol_spike(\n",
|
||
" df,\n",
|
||
" return_period=3,\n",
|
||
" return_threshold=0.03, # 近3日涨幅超3%\n",
|
||
" volume_ratio_threshold=1.3,\n",
|
||
" current_pct_chg_min=0.0, # 当日必须收红\n",
|
||
" current_pct_chg_max=0.05,\n",
|
||
") # 当日涨幅不宜过大\n",
|
||
"\n",
|
||
"df = cat_senti_pre_breakout(\n",
|
||
" df,\n",
|
||
" atr_short_N=10,\n",
|
||
" atr_long_M=40,\n",
|
||
" vol_atrophy_N=10,\n",
|
||
" vol_atrophy_M=40,\n",
|
||
" price_stab_N=5,\n",
|
||
" price_stab_threshold=0.06,\n",
|
||
" current_pct_chg_min_signal=0.002,\n",
|
||
" current_pct_chg_max_signal=0.05,\n",
|
||
" volume_ratio_signal_threshold=1.1,\n",
|
||
")\n",
|
||
"\n",
|
||
"df = ts_turnover_rate_acceleration_5_20(df)\n",
|
||
"df = ts_vol_sustain_10_30(df)\n",
|
||
"# df = cs_turnover_rate_relative_strength_20(df)\n",
|
||
"df = cs_amount_outlier_10(df)\n",
|
||
"df = ts_ff_to_total_turnover_ratio(df)\n",
|
||
"df = ts_price_volume_trend_coherence_5_20(df)\n",
|
||
"# df = ts_turnover_rate_trend_strength_5(df)\n",
|
||
"df = ts_ff_turnover_rate_surge_10(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 = calculate_strong_inflow_signal(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": 13,
|
||
"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', group_keys=False)['close'].apply(lambda x: x.shift(-days) / x - 1) + df.groupby('ts_code', group_keys=False)['close'].apply(lambda x: x.shift(-2 * days) / x - 1)\n",
|
||
"\n",
|
||
"# df['future_return'] = (df.groupby('ts_code')['close'].shift(-days) - df.groupby('ts_code')['open'].shift(-1)) / \\\n",
|
||
"# df.groupby('ts_code')['open'].shift(-1)\n",
|
||
"\n",
|
||
"# df['cat_up_limit'] = df['pct_chg'] > 5\n",
|
||
"# df['label'] = (df.groupby('ts_code')['cat_up_limit']\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=50, 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",
|
||
"filter_index = df['future_return'].between(df['future_return'].quantile(0.001), 0.6)\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": "29221dde",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"205\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": 15,
|
||
"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": 16,
|
||
"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', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', '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', 'senti_strong_inflow', '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', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"去除极值\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 100%|██████████| 144/144 [00:07<00:00, 19.10it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"标准化\n",
|
||
"开始截面 Z-Score 标准化...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Standardizing: 100%|██████████| 144/144 [00:02<00:00, 58.47it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 Z-Score 标准化完成。\n",
|
||
"开始截面 MAD 去极值处理 (k=3.0)...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MAD Filtering: 100%|██████████| 144/144 [00:05<00:00, 25.72it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 MAD 去极值处理完成。\n",
|
||
"开始截面 Z-Score 标准化...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Standardizing: 100%|██████████| 144/144 [00:01<00:00, 81.71it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"截面 Z-Score 标准化完成。\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', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', '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', 'senti_strong_inflow', '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', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n",
|
||
"df最小日期: 2019-01-02\n",
|
||
"df最大日期: 2025-05-30\n",
|
||
"1091062\n",
|
||
"train_data最小日期: 2020-01-02\n",
|
||
"train_data最大日期: 2022-12-30\n",
|
||
"869968\n",
|
||
"test_data最小日期: 2023-01-03\n",
|
||
"test_data最大日期: 2025-05-30\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')].groupby(\n",
|
||
" 'trade_date', group_keys=False).apply(lambda x: x.nsmallest(1500, 'total_mv'))\n",
|
||
"test_data = df[(df['trade_date'] >= split_date)].groupby(\n",
|
||
" 'trade_date', group_keys=False).apply(lambda x: x.nsmallest(1500, 'total_mv'))\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",
|
||
"# train_data['label'] = train_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
|
||
"# lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n",
|
||
"# )\n",
|
||
"# test_data['label'] = test_data.groupby('trade_date', group_keys=False)['future_return'].transform(\n",
|
||
"# lambda x: pd.qcut(x, q=100, labels=False, duplicates='drop')\n",
|
||
"# )\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": 17,
|
||
"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 LGBMRanker, 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",
|
||
" train_data_split = train_data_split.sort_values('trade_date')\n",
|
||
" val_data_split = val_data_split.sort_values('trade_date')\n",
|
||
"\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': 'QueryRMSE', # 适用于二分类\n",
|
||
" 'eval_metric': 'NDCG', # 评估指标\n",
|
||
" 'iterations': 1500,\n",
|
||
" 'learning_rate': 0.03,\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",
|
||
"\n",
|
||
" model = CatBoostRanker(**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",
|
||
" \n",
|
||
" params = {\n",
|
||
" 'label_gain': [gain * gain for gain in label_gain],\n",
|
||
" 'objective': 'lambdarank',\n",
|
||
" 'metric': 'ndcg',\n",
|
||
" 'learning_rate': 0.01,\n",
|
||
" # 'num_leaves': 1024,\n",
|
||
" # 'min_data_in_leaf': 256,\n",
|
||
" # 'max_depth': 10,\n",
|
||
" # 'max_bin': 1024,\n",
|
||
" 'feature_fraction': 0.5,\n",
|
||
" 'bagging_fraction': 0.5,\n",
|
||
" 'bagging_freq': 5,\n",
|
||
" # 'lambda_l1': 1,\n",
|
||
" 'lambda_l2': 50,\n",
|
||
" 'boosting': 'gbdt',\n",
|
||
" 'verbosity': -1,\n",
|
||
" 'extra_trees': True,\n",
|
||
" # 'max_position': 5,\n",
|
||
" 'ndcg_at': '5',\n",
|
||
" 'quant_train_renew_leaf': True,\n",
|
||
" 'lambdarank_truncation_level': 10,\n",
|
||
" # 'lambdarank_position_bias_regularization': 1,\n",
|
||
" 'seed': 7\n",
|
||
" }\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",
|
||
" # params['feature_contri'] = feature_contri\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(300, first_metric_only=False)\n",
|
||
" ]\n",
|
||
" # 训练模型\n",
|
||
" model = lgb.train(\n",
|
||
" params, train_dataset, num_boost_round=1000,\n",
|
||
" valid_sets=[train_dataset, val_dataset], valid_names=['train', 'valid'],\n",
|
||
" callbacks=callbacks\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 打印特征重要性（如果需要）\n",
|
||
" if True:\n",
|
||
" lgb.plot_metric(evals)\n",
|
||
" lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # 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\": 600, # in seconds\n",
|
||
" # \"metric\": \"ndcg@1\",\n",
|
||
" # \"task\": \"rank\",\n",
|
||
" # \"estimator_list\": [\n",
|
||
" # \"catboost\",\n",
|
||
" # \"lgbm\",\n",
|
||
" # \"xgboost\"\n",
|
||
" # ], \n",
|
||
" # \"ensemble\": {\n",
|
||
" # \"final_estimator\": LGBMRanker(),\n",
|
||
" # \"passthrough\": False,\n",
|
||
" # },\n",
|
||
" # }\n",
|
||
" # model.fit(X_train=X_train, y_train=y_train, groups=train_groups,\n",
|
||
" # X_val=X_val, y_val=y_val,groups_val=val_groups,\n",
|
||
" # mlflow_logging=False, **automl_settings)\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": 18,
|
||
"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 -1.625251\n",
|
||
"1 603269.SH 2020-01-02 -2.029142\n",
|
||
"2 002633.SZ 2020-01-02 -1.257360\n",
|
||
"3 603991.SH 2020-01-02 -2.284466\n",
|
||
"4 000691.SZ 2020-01-02 -1.401531\n",
|
||
"... ... ... ...\n",
|
||
"727995 002578.SZ 2022-12-30 0.648406\n",
|
||
"727996 002766.SZ 2022-12-30 0.571533\n",
|
||
"727997 600495.SH 2022-12-30 0.802632\n",
|
||
"727998 002119.SZ 2022-12-30 0.751421\n",
|
||
"727999 000151.SZ 2022-12-30 0.371616\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 300 rounds\n",
|
||
"Early stopping, best iteration is:\n",
|
||
"[22]\ttrain's ndcg@5: 0.428245\tvalid's ndcg@5: 0.432426\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",
|
||
"\n",
|
||
"train_data['label2'] = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))['future_return'].transform(\n",
|
||
" lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
|
||
")\n",
|
||
"test_data['label2'] = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nsmallest(1000, 'total_mv'))['future_return'].transform(\n",
|
||
" lambda x: pd.qcut(x, q=50, labels=False, duplicates='drop')\n",
|
||
")\n",
|
||
"\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='label2')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"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",
|
||
"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(5, '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": 20,
|
||
"id": "fed2d6c3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2023-01-03 00:00:00\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(test_data['trade_date'].min())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "1f3c1331",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"成功连接到 Redis 服务器: 140.143.91.66:6389，数据库 0\n",
|
||
"DataFrame 已使用 Pickle 序列化并写入 Redis，键为 'save_df'\n",
|
||
"从 Redis 读取到的 Pickle 序列化数据 (前 20 字节):\n",
|
||
"b'\\x80\\x04\\x95\\xbf\\x04\\x01\\x00\\x00\\x00\\x00\\x00\\x8c\\x11pandas.'\n",
|
||
"\n",
|
||
"从 Redis 加载的 DataFrame (使用 Pickle):\n",
|
||
" index ts_code trade_date open close high low vol \\\n",
|
||
"4 36 002247.SZ 2023-01-03 16.15 16.80 16.87 16.09 0.514578 \n",
|
||
"3 26 002513.SZ 2023-01-03 15.41 15.77 15.95 15.41 -0.499029 \n",
|
||
"2 14 002629.SZ 2023-01-03 15.17 15.54 15.58 15.13 0.631716 \n",
|
||
"1 5 603030.SH 2023-01-03 8.08 8.32 8.32 8.05 -0.033641 \n",
|
||
"0 3 000691.SZ 2023-01-03 13.87 14.24 14.36 13.63 -0.030740 \n",
|
||
"... ... ... ... ... ... ... ... ... \n",
|
||
"2904 58031 002524.SZ 2025-05-30 19.49 19.40 20.27 19.31 0.455783 \n",
|
||
"2903 58034 002084.SZ 2025-05-30 11.88 11.68 11.88 11.58 0.610122 \n",
|
||
"2902 58029 600159.SH 2025-05-30 13.66 13.55 13.86 13.50 -0.170606 \n",
|
||
"2901 58035 002775.SZ 2025-05-30 17.84 17.55 17.84 17.41 -0.705858 \n",
|
||
"2900 58019 600408.SH 2025-05-30 8.64 8.51 8.64 8.43 0.684604 \n",
|
||
"\n",
|
||
" pct_chg amount ... 000905.SH_up_ratio_20d \\\n",
|
||
"4 0.715898 31834.487 ... 0.3 \n",
|
||
"3 0.121763 25452.447 ... 0.3 \n",
|
||
"2 0.299045 55379.071 ... 0.3 \n",
|
||
"1 0.241548 22271.706 ... 0.3 \n",
|
||
"0 0.279880 38602.205 ... 0.3 \n",
|
||
"... ... ... ... ... \n",
|
||
"2904 0.552905 76113.818 ... 0.6 \n",
|
||
"2903 -0.070682 67595.352 ... 0.6 \n",
|
||
"2902 0.005989 28573.069 ... 0.6 \n",
|
||
"2901 0.241111 19025.635 ... 0.6 \n",
|
||
"2900 0.788027 40827.689 ... 0.6 \n",
|
||
"\n",
|
||
" 399006.SZ_up_ratio_20d 000852.SH_volatility 000905.SH_volatility \\\n",
|
||
"4 0.40 1.036997 0.828596 \n",
|
||
"3 0.40 1.036997 0.828596 \n",
|
||
"2 0.40 1.036997 0.828596 \n",
|
||
"1 0.40 1.036997 0.828596 \n",
|
||
"0 0.40 1.036997 0.828596 \n",
|
||
"... ... ... ... \n",
|
||
"2904 0.45 1.089861 0.850444 \n",
|
||
"2903 0.45 1.089861 0.850444 \n",
|
||
"2902 0.45 1.089861 0.850444 \n",
|
||
"2901 0.45 1.089861 0.850444 \n",
|
||
"2900 0.45 1.089861 0.850444 \n",
|
||
"\n",
|
||
" 399006.SZ_volatility 000852.SH_volume_change_rate \\\n",
|
||
"4 0.935322 5.203088 \n",
|
||
"3 0.935322 5.203088 \n",
|
||
"2 0.935322 5.203088 \n",
|
||
"1 0.935322 5.203088 \n",
|
||
"0 0.935322 5.203088 \n",
|
||
"... ... ... \n",
|
||
"2904 1.195355 -2.039466 \n",
|
||
"2903 1.195355 -2.039466 \n",
|
||
"2902 1.195355 -2.039466 \n",
|
||
"2901 1.195355 -2.039466 \n",
|
||
"2900 1.195355 -2.039466 \n",
|
||
"\n",
|
||
" 000905.SH_volume_change_rate 399006.SZ_volume_change_rate score \\\n",
|
||
"4 -0.750721 8.827360 0.042645 \n",
|
||
"3 -0.750721 8.827360 0.047474 \n",
|
||
"2 -0.750721 8.827360 0.055433 \n",
|
||
"1 -0.750721 8.827360 0.061053 \n",
|
||
"0 -0.750721 8.827360 0.068349 \n",
|
||
"... ... ... ... \n",
|
||
"2904 -12.002493 5.078672 0.064251 \n",
|
||
"2903 -12.002493 5.078672 0.064517 \n",
|
||
"2902 -12.002493 5.078672 0.065030 \n",
|
||
"2901 -12.002493 5.078672 0.068848 \n",
|
||
"2900 -12.002493 5.078672 0.074780 \n",
|
||
"\n",
|
||
" score_ranks \n",
|
||
"4 996.0 \n",
|
||
"3 997.0 \n",
|
||
"2 998.0 \n",
|
||
"1 999.0 \n",
|
||
"0 1000.0 \n",
|
||
"... ... \n",
|
||
"2904 996.0 \n",
|
||
"2903 997.0 \n",
|
||
"2902 998.0 \n",
|
||
"2901 999.0 \n",
|
||
"2900 1000.0 \n",
|
||
"\n",
|
||
"[2905 rows x 248 columns]\n",
|
||
"\n",
|
||
"验证成功：原始 DataFrame 和从 Redis 加载的 DataFrame 一致。\n",
|
||
"\n",
|
||
"清理了 Redis 中键 'save_df' 的数据。\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import redis\n",
|
||
"import pickle\n",
|
||
"\n",
|
||
"redis_host = '140.143.91.66'\n",
|
||
"redis_port = 6389\n",
|
||
"redis_db = 0\n",
|
||
"redis_key = 'save_df'\n",
|
||
"\n",
|
||
"try:\n",
|
||
" # 1. 连接到 Redis 服务器\n",
|
||
" r = redis.Redis(host=redis_host, port=redis_port, db=redis_db, password='Redis520102')\n",
|
||
" r.ping()\n",
|
||
" print(f\"\\n成功连接到 Redis 服务器: {redis_host}:{redis_port}，数据库 {redis_db}\")\n",
|
||
"\n",
|
||
" # 2. 将 DataFrame 写入 Redis (使用 Pickle 序列化)\n",
|
||
" df_serialized = pickle.dumps(save_df)\n",
|
||
" r.set(redis_key, df_serialized)\n",
|
||
" print(f\"DataFrame 已使用 Pickle 序列化并写入 Redis，键为 '{redis_key}'\")\n",
|
||
"\n",
|
||
" # 3. 从 Redis 读取数据 (获取 Pickle 序列化的字节流)\n",
|
||
" retrieved_serialized = r.get(redis_key)\n",
|
||
"\n",
|
||
" if retrieved_serialized:\n",
|
||
" print(f\"从 Redis 读取到的 Pickle 序列化数据 (前 20 字节):\")\n",
|
||
" print(retrieved_serialized[:20])\n",
|
||
"\n",
|
||
" # 4. 使用 Pickle 反序列化回 Pandas DataFrame\n",
|
||
" loaded_df = pickle.loads(retrieved_serialized)\n",
|
||
" print(\"\\n从 Redis 加载的 DataFrame (使用 Pickle):\")\n",
|
||
" print(loaded_df)\n",
|
||
"\n",
|
||
" # 5. 验证原始 DataFrame 和加载的 DataFrame 是否一致\n",
|
||
" if save_df.equals(loaded_df):\n",
|
||
" print(\"\\n验证成功：原始 DataFrame 和从 Redis 加载的 DataFrame 一致。\")\n",
|
||
" else:\n",
|
||
" print(\"\\n验证失败：原始 DataFrame 和从 Redis 加载的 DataFrame 不一致！\")\n",
|
||
"\n",
|
||
" else:\n",
|
||
" print(f\"错误：无法从 Redis 获取键 '{redis_key}' 的值。\")\n",
|
||
"\n",
|
||
" # 6. 清理测试数据 (可选)\n",
|
||
" r.delete(redis_key)\n",
|
||
" print(f\"\\n清理了 Redis 中键 '{redis_key}' 的数据。\")\n",
|
||
"\n",
|
||
"except redis.exceptions.ConnectionError as e:\n",
|
||
" print(f\"无法连接到 Redis 服务器: {e}\")\n",
|
||
" print(\"请确保您的 Redis 服务器已启动并且主机和端口配置正确。\")\n",
|
||
"except redis.exceptions.TimeoutError as e:\n",
|
||
" print(f\"连接 Redis 服务器超时: {e}\")\n",
|
||
" print(\"请检查您的网络连接和 Redis 服务器状态。\")\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"测试 Redis 时发生未知错误: {e}\")\n",
|
||
" print(f\"测试 Redis 时发生未知错误: {e}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "09b1799e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"205\n",
|
||
"['vol', 'pct_chg', 'turnover_rate', 'volume_ratio', 'winner_rate', 'holder_net_change_sum_10d', 'holder_increase_days_10d', 'holder_decrease_days_10d', 'holder_any_increase_flag_10d', 'holder_any_decrease_flag_10d', 'cat_senti_mom_vol_spike', 'cat_senti_pre_breakout', 'ts_turnover_rate_acceleration_5_20', 'ts_vol_sustain_10_30', 'cs_amount_outlier_10', 'ts_ff_to_total_turnover_ratio', 'ts_price_volume_trend_coherence_5_20', 'ts_ff_turnover_rate_surge_10', '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', 'senti_strong_inflow', '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', 'industry_obv', 'industry_return_5', 'industry_return_20', 'industry__ema_5', 'industry__ema_13', 'industry__ema_20', 'industry__ema_60', 'industry_act_factor1', 'industry_act_factor2', 'industry_act_factor3', 'industry_act_factor4', 'industry_act_factor5', 'industry_act_factor6', 'industry_rank_act_factor1', 'industry_rank_act_factor2', 'industry_rank_act_factor3', 'industry_return_5_percentile', 'industry_return_20_percentile', '000852.SH_MACD', '000905.SH_MACD', '399006.SZ_MACD', '000852.SH_MACD_hist', '000905.SH_MACD_hist', '399006.SZ_MACD_hist', '000852.SH_RSI', '000905.SH_RSI', '399006.SZ_RSI', '000852.SH_Signal_line', '000905.SH_Signal_line', '399006.SZ_Signal_line', '000852.SH_amount_change_rate', '000905.SH_amount_change_rate', '399006.SZ_amount_change_rate', '000852.SH_amount_mean', '000905.SH_amount_mean', '399006.SZ_amount_mean', '000852.SH_daily_return', '000905.SH_daily_return', '399006.SZ_daily_return', '000852.SH_up_ratio_20d', '000905.SH_up_ratio_20d', '399006.SZ_up_ratio_20d', '000852.SH_volatility', '000905.SH_volatility', '399006.SZ_volatility', '000852.SH_volume_change_rate', '000905.SH_volume_change_rate', '399006.SZ_volume_change_rate']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(len(feature_columns))\n",
|
||
"print(feature_columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "bceabd1f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\n",
|
||
"\n",
|
||
"NDCG 结果\n",
|
||
"{'ndcg@1': np.float64(0.4489795918367347), 'ndcg@3': np.float64(0.40668217598446815), 'ndcg@5': np.float64(0.45584495629735)}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"def calculate_ndcg(df: pd.DataFrame, score_col: str, label_col: str, group_id: str = 'trade_date', k_values: list = [1, 3, 5, 10]):\n",
|
||
" \"\"\"\n",
|
||
" 计算 DataFrame 中 score 列和 label 列的 NDCG 值。\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" df (pd.DataFrame): 包含 score (排序学习预测分数) 和 label (相关性标签) 的 DataFrame。\n",
|
||
" 假设每个需要排序的组（例如，每天的股票）在 DataFrame 中是连续的。\n",
|
||
" score_col (str): 包含模型预测分数的列名。\n",
|
||
" label_col (str): 包含相关性标签的列名。标签值越高表示相关性越高。\n",
|
||
" k_values (list): 一个整数列表，表示计算 NDCG 的 top-k 值。\n",
|
||
" 例如，[1, 3, 5] 将计算 NDCG@1, NDCG@3 和 NDCG@5。\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" dict: 一个字典，包含每个 k 值对应的平均 NDCG 值。\n",
|
||
" 例如: {'ndcg@1': 0.85, 'ndcg@3': 0.78, 'ndcg@5': 0.72, 'ndcg@10': 0.65}\n",
|
||
" \"\"\"\n",
|
||
" ndcg_scores = {f'ndcg@{k}': [] for k in k_values}\n",
|
||
"\n",
|
||
" def dcg_at_k(r, k):\n",
|
||
" r = np.asarray(r)[:k] if len(r) > 0 else np.zeros(k)\n",
|
||
" return np.sum(r / np.log2(np.arange(2, r.size + 2)))\n",
|
||
"\n",
|
||
" def ndcg_at_k(r, k):\n",
|
||
" dcg_max = dcg_at_k(sorted(r, reverse=True), k)\n",
|
||
" if not dcg_max:\n",
|
||
" return 0.\n",
|
||
" return dcg_at_k(r, k) / dcg_max\n",
|
||
"\n",
|
||
" # 假设 DataFrame 已经按照需要排序的组（例如，'trade_date'）进行了分组，\n",
|
||
" # 并且每个组内的顺序不重要，我们只需要计算每个组的 NDCG。\n",
|
||
" # 如果需要按特定组计算 NDCG，请先对 DataFrame 进行分组。\n",
|
||
" if group_id not in df.columns:\n",
|
||
" print(\"警告: DataFrame 中没有 'group_id' 列。假设整个 DataFrame 是一个需要排序的组。\")\n",
|
||
" group_df = df.sort_values(by=score_col, ascending=False)\n",
|
||
" relevant_labels = group_df[label_col].values\n",
|
||
" for k in k_values:\n",
|
||
" ndcg_scores[f'ndcg@{k}'].append(ndcg_at_k(relevant_labels, k))\n",
|
||
" else:\n",
|
||
" for _, group_df in df.groupby(group_id):\n",
|
||
" group_df_sorted = group_df.sort_values(by=score_col, ascending=False)\n",
|
||
" relevant_labels = group_df_sorted[label_col].values\n",
|
||
" for k in k_values:\n",
|
||
" ndcg_scores[f'ndcg@{k}'].append(ndcg_at_k(relevant_labels, k))\n",
|
||
"\n",
|
||
" avg_ndcg = {k: np.mean(v) if v else np.nan for k, v in ndcg_scores.items()}\n",
|
||
" return avg_ndcg\n",
|
||
"\n",
|
||
"\n",
|
||
"ndcg_results_single_group = calculate_ndcg(score_df, score_col='score', label_col='label', k_values=[1, 3, 5], group_id=None)\n",
|
||
"print(\"\\nNDCG 结果\")\n",
|
||
"print(ndcg_results_single_group)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "44f64679",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" ts_code trade_date open close high low vol pct_chg \\\n",
|
||
"1632028 002652.SZ 2019-01-02 19.59 19.64 19.89 19.28 20196.79 1.03 \n",
|
||
"1632029 002652.SZ 2019-01-03 19.74 19.44 19.84 19.33 15731.99 -1.02 \n",
|
||
"1632030 002652.SZ 2019-01-04 19.33 19.94 19.99 19.08 21099.93 2.57 \n",
|
||
"1632031 002652.SZ 2019-01-07 20.04 21.95 21.95 20.04 83534.19 10.08 \n",
|
||
"1632032 002652.SZ 2019-01-08 23.21 21.65 23.87 21.65 149377.97 -1.37 \n",
|
||
"... ... ... ... ... ... ... ... ... \n",
|
||
"1633576 002652.SZ 2025-05-26 14.75 14.85 15.11 14.55 99560.80 1.02 \n",
|
||
"1633577 002652.SZ 2025-05-27 14.90 15.00 15.11 14.70 101184.00 1.01 \n",
|
||
"1633578 002652.SZ 2025-05-28 15.11 14.85 15.16 14.80 75859.20 -1.00 \n",
|
||
"1633579 002652.SZ 2025-05-29 15.00 15.36 15.36 14.85 126044.40 3.43 \n",
|
||
"1633580 002652.SZ 2025-05-30 15.36 15.11 15.41 14.95 107732.00 -1.63 \n",
|
||
"\n",
|
||
" amount turnover_rate ... cs_rank_vol_x_profit_margin \\\n",
|
||
"1632028 7867.047 0.3964 ... 0.608839 \n",
|
||
"1632029 6121.460 0.3088 ... 0.586710 \n",
|
||
"1632030 8245.083 0.4141 ... 0.682847 \n",
|
||
"1632031 35514.117 1.6394 ... 0.987591 \n",
|
||
"1632032 67160.354 2.9317 ... 0.765693 \n",
|
||
"... ... ... ... ... \n",
|
||
"1633576 29428.560 1.9443 ... 0.652159 \n",
|
||
"1633577 30112.801 1.9760 ... 0.657694 \n",
|
||
"1633578 22507.876 1.4814 ... 0.664673 \n",
|
||
"1633579 38068.857 2.4615 ... 0.921236 \n",
|
||
"1633580 32385.927 2.1039 ... 0.702990 \n",
|
||
"\n",
|
||
" cs_rank_lg_flow_price_concordance cs_rank_turnover_per_winner \\\n",
|
||
"1632028 0.203142 0.864865 \n",
|
||
"1632029 0.156684 0.763417 \n",
|
||
"1632030 0.184009 0.660949 \n",
|
||
"1632031 0.734940 0.700000 \n",
|
||
"1632032 0.874042 0.914234 \n",
|
||
"... ... ... \n",
|
||
"1633576 0.122259 0.394684 \n",
|
||
"1633577 0.092722 0.414756 \n",
|
||
"1633578 0.684945 0.323363 \n",
|
||
"1633579 0.295779 0.390828 \n",
|
||
"1633580 0.705316 0.419934 \n",
|
||
"\n",
|
||
" cs_rank_ind_cap_neutral_pe cs_rank_volume_ratio \\\n",
|
||
"1632028 NaN 0.646930 \n",
|
||
"1632029 NaN 0.251279 \n",
|
||
"1632030 NaN 0.311724 \n",
|
||
"1632031 NaN 0.988313 \n",
|
||
"1632032 NaN 0.990142 \n",
|
||
"... ... ... \n",
|
||
"1633576 NaN 0.400997 \n",
|
||
"1633577 NaN 0.450150 \n",
|
||
"1633578 NaN 0.199236 \n",
|
||
"1633579 NaN 0.640744 \n",
|
||
"1633580 NaN 0.537542 \n",
|
||
"\n",
|
||
" cs_rank_elg_buy_sell_sm_ratio cs_rank_cost_dist_vol_ratio \\\n",
|
||
"1632028 0.341855 0.678941 \n",
|
||
"1632029 0.318912 0.402916 \n",
|
||
"1632030 0.260036 0.460713 \n",
|
||
"1632031 0.796350 0.988501 \n",
|
||
"1632032 0.598905 0.991571 \n",
|
||
"... ... ... \n",
|
||
"1633576 0.153987 0.620930 \n",
|
||
"1633577 0.156198 0.643403 \n",
|
||
"1633578 0.153373 0.484546 \n",
|
||
"1633579 0.623795 0.764374 \n",
|
||
"1633580 0.133056 0.703987 \n",
|
||
"\n",
|
||
" cs_rank_size future_return label \n",
|
||
"1632028 0.258948 0.158859 40.0 \n",
|
||
"1632029 0.258123 0.136831 37.0 \n",
|
||
"1632030 0.257664 0.106319 39.0 \n",
|
||
"1632031 0.290146 -0.072893 4.0 \n",
|
||
"1632032 0.282482 -0.057737 5.0 \n",
|
||
"... ... ... ... \n",
|
||
"1633576 0.032226 NaN NaN \n",
|
||
"1633577 0.032901 NaN NaN \n",
|
||
"1633578 0.032237 NaN NaN \n",
|
||
"1633579 0.034231 NaN NaN \n",
|
||
"1633580 0.033887 NaN NaN \n",
|
||
"\n",
|
||
"[1553 rows x 196 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df[df['ts_code'] == '002652.SZ'])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "stock",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|