From c5ad56a9e812917f62a6493b79d7621fd9efc4e2 Mon Sep 17 00:00:00 2001 From: liaozhaorun <1300336796@qq.com> Date: Thu, 5 Jun 2025 20:21:01 +0800 Subject: [PATCH] Rank2 --- .../factor/__pycache__/factor.cpython-313.pyc | Bin 167375 -> 167358 bytes main/train/Rank2_polars.ipynb | 2473 +++++++++++++++++ .../__pycache__/data_process.cpython-313.pyc | Bin 0 -> 12337 bytes .../__pycache__/indicators.cpython-313.pyc | Bin 0 -> 4384 bytes main/utils/data_process.py | 268 ++ main/utils/indicators.py | 130 + predictions_test.tsv | 1462 +++++----- 7 files changed, 3602 insertions(+), 731 deletions(-) create mode 100644 main/train/Rank2_polars.ipynb create mode 100644 main/utils/__pycache__/data_process.cpython-313.pyc create mode 100644 main/utils/__pycache__/indicators.cpython-313.pyc create mode 100644 main/utils/data_process.py create mode 100644 main/utils/indicators.py diff --git a/main/factor/__pycache__/factor.cpython-313.pyc b/main/factor/__pycache__/factor.cpython-313.pyc index 6d7b8cc5bee698118ad840f5966aa5000f0f4d73..f4d6cae2b74b4b3d2881527179b2b6761f881c96 100644 GIT binary patch delta 41 wcmX>Dkdh*;QMTwbt`g(dm)Xb{i&Z^J2omHP{ GXB+^n01q|* diff --git a/main/train/Rank2_polars.ipynb b/main/train/Rank2_polars.ipynb new file mode 100644 index 0000000..5481bf0 --- /dev/null +++ b/main/train/Rank2_polars.ipynb @@ -0,0 +1,2473 @@ +{ + "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", + "\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_24', '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", + "\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_24', '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 polars as pl\n", + "import numpy as np\n", + "# from tqdm import tqdm # Polars 通常处理速度快，可能不需要 tqdm，但如果需要可以保留\n", + "\n", + "def cs_mad_filter_polars(df: pl.DataFrame,\n", + " features: list[str],\n", + " k: float = 3.0,\n", + " scale_factor: float = 1.4826) -> pl.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行截面 MAD 去极值处理 (Polars 版本)。\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 (pl.DataFrame): 输入 Polars 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", + " Returns:\n", + " pl.DataFrame: 处理后的 Polars DataFrame (返回新 DataFrame，原 DataFrame 不变)。\n", + " \"\"\"\n", + " print(f\"开始截面 MAD 去极值处理 (k={k})...\")\n", + "\n", + " # 检查特征列是否存在\n", + " existing_features = [col for col in features if col in df.columns]\n", + " missing_features = [col for col in features if col not in df.columns]\n", + "\n", + " if missing_features:\n", + " print(f\"警告: DataFrame 中缺少以下特征列: {missing_features}。这些列将跳过去极值处理。\")\n", + "\n", + " if not existing_features:\n", + " print(\"没有找到需要处理的特征列。跳过去极值处理。\")\n", + " return df # 返回原始 DataFrame\n", + "\n", + " # 构建一个表达式列表，用于一次性处理所有特征列\n", + " expressions = []\n", + " for col in existing_features:\n", + " col_expr = pl.col(col)\n", + "\n", + " try:\n", + " # 计算截面中位数 (median) - 使用 over()\n", + " median_val = col_expr.median().over('trade_date')\n", + "\n", + " # 计算截面 MAD (Median Absolute Deviation from Median) - 使用 over()\n", + " mad_val = (col_expr - median_val).abs().median().over('trade_date')\n", + "\n", + " # 计算上下边界\n", + " # 确保 mad_val 不为 null 或 0，否则边界会是 NaN\n", + " lower_bound = median_val - k * scale_factor * mad_val\n", + " upper_bound = median_val + k * scale_factor * mad_val\n", + "\n", + " # 应用 Winsorization (裁剪值到边界内)\n", + " # 使用 when/then/otherwise 来确保边界值为 NaN 或 MAD 为 0 时不进行过滤，保留原始值\n", + " clipped_col_expr = pl.when(\n", + " lower_bound.is_not_null() &\n", + " upper_bound.is_not_null() &\n", + " mad_val.is_not_null() &\n", + " (mad_val != 0) # MAD 为 0 意味着所有值都相同，此时不应裁剪\n", + " ).then(col_expr.clip(lower_bound, upper_bound)).otherwise(col_expr).alias(col) # 保持列名不变\n", + "\n", + " expressions.append(clipped_col_expr)\n", + "\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。保留原始列。\")\n", + " expressions.append(col_expr) # 发生错误时保留原始列\n", + "\n", + " # 使用 with_columns 一次性应用所有表达式，创建新的 DataFrame\n", + " result_df = df.with_columns(expressions)\n", + "\n", + " print(\"截面 MAD 去极值处理完成。\")\n", + " return result_df\n", + "\n", + "\n", + "# --- 您的 cs_zscore_standardize_polars 函数 (确保其也是全 Polars 实现) ---\n", + "# （如果您的 cs_zscore_standardize_polars 也有类似问题，也需要按此模式修改）\n", + "def cs_zscore_standardize_polars(df: pl.DataFrame, features: list[str], epsilon: float = 1e-10) -> pl.DataFrame:\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + "\n", + " existing_features = [col for col in features if col in df.columns]\n", + " missing_features = [col for col in features if col not in df.columns]\n", + "\n", + " if missing_features:\n", + " print(f\"警告: DataFrame 中缺少以下特征列: {missing_features}。这些列将跳过标准化处理。\")\n", + "\n", + " if not existing_features:\n", + " print(\"没有找到需要标准化的特征列。跳过标准化处理。\")\n", + " return df\n", + "\n", + " expressions = []\n", + " for col in existing_features:\n", + " col_expr = pl.col(col)\n", + "\n", + " if not col_expr.dtype.is_numeric():\n", + " print(f\"警告: 列 '{col}' 不是数值类型 ({col_expr.dtype})，跳过此列的标准化处理。\")\n", + " expressions.append(col_expr)\n", + " continue\n", + "\n", + " mean_val = col_expr.mean().over('trade_date')\n", + " std_val = col_expr.std().over('trade_date')\n", + "\n", + " # 处理标准差为 0 或 NaN 的情况，防止 Inf/NaN 扩散\n", + " z_score_expr = pl.when(std_val.is_null() | (std_val == 0)).then(0) # 如果标准差为 null 或 0，设为 0 (或 np.nan，取决于业务逻辑).otherwise((col_expr - mean_val) / (std_val + epsilon)).alias(col)\n", + " expressions.append(z_score_expr)\n", + "\n", + " result_df = df.with_columns(expressions)\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + " return result_df\n", + "\n", + "\n", + "# --- 2. 行业市值中性化 ---\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", + "import polars as pl\n", + "import numpy as np # 用于 np.float64, np.nan 等\n", + "\n", + "def cs_zscore_standardize_polars(df: pl.DataFrame,\n", + " features: list,\n", + " epsilon: float = 1e-10) -> pl.DataFrame:\n", + " \"\"\"\n", + " 对指定特征列进行截面 Z-Score 标准化 (Polars 版本)。\n", + " 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon)\n", + "\n", + " Args:\n", + " df (pl.DataFrame): 输入 Polars 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", + " Returns:\n", + " pl.DataFrame: 处理后的 Polars DataFrame (返回新 DataFrame，原 DataFrame 不变)。\n", + " \"\"\"\n", + " print(\"开始截面 Z-Score 标准化...\")\n", + "\n", + " # 检查特征列是否存在\n", + " existing_features = [col for col in features if col in df.columns]\n", + " missing_features = [col for col in features if col not in df.columns]\n", + "\n", + " if missing_features:\n", + " print(f\"警告: DataFrame 中缺少以下特征列: {missing_features}。这些列将跳过标准化处理。\")\n", + "\n", + " if not existing_features:\n", + " print(\"没有找到需要标准化的特征列。跳过标准化处理。\")\n", + " return df # 返回原始 DataFrame\n", + "\n", + " # 构建一个表达式列表，用于一次性处理所有特征列\n", + " expressions = []\n", + " for col in existing_features:\n", + " col_expr = pl.col(col)\n", + "\n", + " # 确保列是数值类型，否则跳过\n", + " if not col_expr.dtype.is_numeric():\n", + " print(f\"警告: 列 '{col}' 不是数值类型 ({col_expr.dtype})，跳过此列的标准化处理。\")\n", + " expressions.append(col_expr) # 保留原始列\n", + " continue\n", + "\n", + " try:\n", + " # 计算截面均值和标准差\n", + " mean_val = col_expr.mean().over('trade_date')\n", + " std_val = col_expr.std().over('trade_date')\n", + "\n", + " # 计算 Z-Score\n", + " # 这里需要处理 std_val 可能是 0 的情况，即使有 epsilon，也可能因浮点精度问题导致问题\n", + " # 或当 trade_date 组内只有少数几个相同的值时 std_val 可能会是 NaN 或 0\n", + " # 使用 when/then/otherwise 来处理 std_val 为 0 或 NaN 的情况，防止 Inf/NaN 扩散\n", + " z_score_expr = pl.when(std_val.is_null() | (std_val == 0)).then(0).otherwise((col_expr - mean_val) / (std_val + epsilon)).alias(col)\n", + "\n", + " expressions.append(z_score_expr)\n", + "\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。保留原始列。\")\n", + " expressions.append(col_expr) # 发生错误时保留原始列\n", + "\n", + " # 使用 with_columns 一次性应用所有表达式，创建新的 DataFrame\n", + " result_df = df.with_columns(expressions)\n", + "\n", + " print(\"截面 Z-Score 标准化完成。\")\n", + " return result_df\n", + "\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": [ + "import pandas as pd\n", + "import polars as pl\n", + "import numpy as np\n", + "import gc\n", + "\n", + "# --- 配置参数 ---\n", + "days = 5\n", + "validation_days = 120 # 此处未使用，保留用于上下文\n", + "\n", + "\n", + "# --- 核心处理逻辑: Pandas 转 Polars，处理，再转回 Pandas ---\n", + "\n", + "# 1. Pandas DataFrame 转 Polars DataFrame\n", + "gc.collect() # 清理内存，为 Polars 腾出空间\n", + "df = pl.DataFrame(df)\n", + "\n", + "# 2. Polars 处理\n", + "# 确保数据已排序，对于 shift 操作至关重要\n", + "df = df.sort(['ts_code', 'trade_date'])\n", + "\n", + "# 计算 future_return\n", + "df = df.with_columns(\n", + " (\n", + " (pl.col('close').shift(-days).over('ts_code') / pl.col('close')) - 1\n", + " ).alias('future_return_1'),\n", + " (\n", + " (pl.col('close').shift(-2 * days).over('ts_code') / pl.col('close')) - 1\n", + " ).alias('future_return_2')\n", + ").with_columns(\n", + " (pl.col('future_return_1') + pl.col('future_return_2')).alias('future_return')\n", + ").drop(['future_return_1', 'future_return_2']) # 删除中间列\n", + "\n", + "# --- 修正：安全地计算 label ---\n", + "# 使用 pl.when().then().otherwise() 来处理 future_return 中的 NaN\n", + "# 如果 future_return 为空 (NaN)，则 label 也设为 None (Polars 中整数列的空值)\n", + "# 否则，计算 qcut 并转换为整数\n", + "df = df.with_columns(\n", + " pl.col(\"future_return\").qcut(50, allow_duplicates=True).over(\"trade_date\").alias(\"label\")\n", + ")\n", + "\n", + "# 根据 future_return 的全局分位数进行过滤\n", + "lower_bound_quantile = df['future_return'].quantile(0.001)\n", + "upper_bound_fixed = 0.6 # 固定上限值\n", + "\n", + "# 3. Polars DataFrame 转回 Pandas DataFrame\n", + "# df_final_pd = df.to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "29221dde", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "205\n" + ] + } + ], + "source": [ + "industry_df = pl.DataFrame(industry_df).sort('trade_date')\n", + "index_data = pl.DataFrame(index_data).sort('trade_date')\n", + "\n", + "feature_columns = (\n", + " df.head(10)\n", + " .join(industry_df, on=[\"cat_l2_code\", \"trade_date\"], how=\"left\")\n", + " .join(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", + " pl.col(feature_col).fill_null(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b76ea08a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (3, 3)\n", + "┌───────────┬─────────────────────┬─────────────┐\n", + "│ ts_code ┆ trade_date ┆ log_circ_mv │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ datetime[ns] ┆ f64 │\n", + "╞═══════════╪═════════════════════╪═════════════╡\n", + "│ 000001.SZ ┆ 2019-01-02 00:00:00 ┆ 16.574219 │\n", + "│ 000001.SZ ┆ 2019-01-03 00:00:00 ┆ 16.583965 │\n", + "│ 000001.SZ ┆ 2019-01-04 00:00:00 ┆ 16.633371 │\n", + "└───────────┴─────────────────────┴─────────────┘\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_24', '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 [00:00<00:00, 170.19it/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 [00:00<00:00, 767.01it/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 [00:26<00:00, 5.49it/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 [00:03<00:00, 37.17it/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_24', '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", + "148609\n", + "train_data最小日期: 2020-01-02\n", + "train_data最大日期: 2020-03-31\n", + "3591195\n", + "test_data最小日期: 2020-03-31\n", + "test_data最大日期: 2025-05-30\n", + "shape: (3, 3)\n", + "┌───────────┬─────────────────────┬─────────────┐\n", + "│ ts_code ┆ trade_date ┆ log_circ_mv │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ datetime[ns] ┆ f64 │\n", + "╞═══════════╪═════════════════════╪═════════════╡\n", + "│ 000001.SZ ┆ 2019-01-02 00:00:00 ┆ 16.574219 │\n", + "│ 000001.SZ ┆ 2019-01-03 00:00:00 ┆ 16.583965 │\n", + "│ 000001.SZ ┆ 2019-01-04 00:00:00 ┆ 16.633371 │\n", + "└───────────┴─────────────────────┴─────────────┘\n" + ] + } + ], + "source": [ + "from main.utils.data_process import * \n", + "\n", + "split_date = '2023-01-01'\n", + "split_date = pd.to_datetime('2020-03-31') # 将你的分割日期转换为 Pandas Timestamp\n", + "\n", + "# --- 训练数据处理 ---\n", + "train_data = df.filter(\n", + " (pl.col('trade_date') <= split_date) &\n", + " (pl.col('trade_date') >= pd.to_datetime('2020-01-01')) &\n", + " (pl.col('future_return') >= lower_bound_quantile) &\n", + " (pl.col('future_return') <= upper_bound_fixed)\n", + ")\n", + "\n", + "# --- 测试数据处理 ---\n", + "test_data = (\n", + " df.filter(\n", + " pl.col('trade_date') >= split_date\n", + " )\n", + ")\n", + "\n", + "print(df[['ts_code', 'trade_date', 'log_circ_mv']].head(3))\n", + "\n", + "numeric_columns = [\n", + " col for col, dtype in zip(df.columns, df.dtypes)\n", + " if dtype in [pl.Float64, pl.Float32, pl.Int64, pl.Int32]\n", + "]\n", + "numeric_columns = [col for col in numeric_columns if col in feature_columns]\n", + "print(feature_columns)\n", + "\n", + "train_data = train_data.with_columns([\n", + " pl.when(pl.col(col).is_infinite())\n", + " .then(np.nan)\n", + " .otherwise(pl.col(col))\n", + " .alias(col)\n", + " for col in numeric_columns\n", + "])\n", + "test_data = test_data.with_columns([\n", + " pl.when(pl.col(col).is_infinite())\n", + " .then(np.nan)\n", + " .otherwise(pl.col(col))\n", + " .alias(col)\n", + " for col in numeric_columns\n", + "])\n", + "\n", + "\n", + "train_data = train_data.drop_nulls(subset=[col for col in feature_columns if col in train_data.columns])\n", + "test_data = test_data.drop_nulls(subset=[col for col in feature_columns if col in test_data.columns])\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", + "train_data = train_data.to_pandas()\n", + "test_data = test_data.to_pandas()\n", + "gc.collect()\n", + "cs_mad_filter(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_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", + " # 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[target_column].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": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 9\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mtype\u001b[39m = \u001b[33m'\u001b[39m\u001b[33mlight\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 6\u001b[39m train_data[\u001b[33m'\u001b[39m\u001b[33mlabel2\u001b[39m\u001b[33m'\u001b[39m] = train_data.groupby(\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, group_keys=\u001b[38;5;28;01mFalse\u001b[39;00m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x.nsmallest(\u001b[32m1000\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtotal_mv\u001b[39m\u001b[33m'\u001b[39m))[\u001b[33m'\u001b[39m\u001b[33mfuture_return\u001b[39m\u001b[33m'\u001b[39m].transform(\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: pd.qcut(x, q=\u001b[32m50\u001b[39m, labels=\u001b[38;5;28;01mFalse\u001b[39;00m, duplicates=\u001b[33m'\u001b[39m\u001b[33mdrop\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 8\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m test_data[\u001b[33m'\u001b[39m\u001b[33mlabel2\u001b[39m\u001b[33m'\u001b[39m] = \u001b[43mtest_data\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtrade_date\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnsmallest\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtotal_mv\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mfuture_return\u001b[39m\u001b[33m'\u001b[39m].transform(\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: pd.qcut(x, q=\u001b[32m50\u001b[39m, labels=\u001b[38;5;28;01mFalse\u001b[39;00m, duplicates=\u001b[33m'\u001b[39m\u001b[33mdrop\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 11\u001b[39m )\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# light_params['feature_contri'] = feature_contri\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# print(f'feature_contri: {feature_contri}')\u001b[39;00m\n\u001b[32m 16\u001b[39m model, scaler, pca = train_model(train_data.groupby(\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, group_keys=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 17\u001b[39m .apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x.nsmallest(\u001b[32m1000\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtotal_mv\u001b[39m\u001b[33m'\u001b[39m))\n\u001b[32m 18\u001b[39m .merge(industry_df, on=[\u001b[33m'\u001b[39m\u001b[33mcat_l2_code\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m], how=\u001b[33m'\u001b[39m\u001b[33mleft\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 19\u001b[39m .merge(index_data, on=\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, how=\u001b[33m'\u001b[39m\u001b[33mleft\u001b[39m\u001b[33m'\u001b[39m), \n\u001b[32m 20\u001b[39m feature_columns, \u001b[38;5;28mtype\u001b[39m=\u001b[38;5;28mtype\u001b[39m, target_column=\u001b[33m'\u001b[39m\u001b[33mlabel2\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/groupby/groupby.py:1824\u001b[39m, in \u001b[36mGroupBy.apply\u001b[39m\u001b[34m(self, func, include_groups, *args, **kwargs)\u001b[39m\n\u001b[32m 1822\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[33m\"\u001b[39m\u001b[33mmode.chained_assignment\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 1823\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1824\u001b[39m result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_python_apply_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_selected_obj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1825\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 1826\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m.obj, Series)\n\u001b[32m 1827\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._selection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1828\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._selected_obj.shape != \u001b[38;5;28mself\u001b[39m._obj_with_exclusions.shape\n\u001b[32m 1829\u001b[39m ):\n\u001b[32m 1830\u001b[39m warnings.warn(\n\u001b[32m 1831\u001b[39m message=_apply_groupings_depr.format(\n\u001b[32m 1832\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).\u001b[34m__name__\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mapply\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 1835\u001b[39m stacklevel=find_stack_level(),\n\u001b[32m 1836\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/groupby/groupby.py:1885\u001b[39m, in \u001b[36mGroupBy._python_apply_general\u001b[39m\u001b[34m(self, f, data, not_indexed_same, is_transform, is_agg)\u001b[39m\n\u001b[32m 1850\u001b[39m \u001b[38;5;129m@final\u001b[39m\n\u001b[32m 1851\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_python_apply_general\u001b[39m(\n\u001b[32m 1852\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 1857\u001b[39m is_agg: \u001b[38;5;28mbool\u001b[39m = \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[32m 1858\u001b[39m ) -> NDFrameT:\n\u001b[32m 1859\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1860\u001b[39m \u001b[33;03m Apply function f in python space\u001b[39;00m\n\u001b[32m 1861\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 1883\u001b[39m \u001b[33;03m data after applying f\u001b[39;00m\n\u001b[32m 1884\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1885\u001b[39m values, mutated = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_grouper\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply_groupwise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m not_indexed_same \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 1887\u001b[39m not_indexed_same = mutated\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/groupby/ops.py:919\u001b[39m, in \u001b[36mBaseGrouper.apply_groupwise\u001b[39m\u001b[34m(self, f, data, axis)\u001b[39m\n\u001b[32m 917\u001b[39m \u001b[38;5;66;03m# group might be modified\u001b[39;00m\n\u001b[32m 918\u001b[39m group_axes = group.axes\n\u001b[32m--> \u001b[39m\u001b[32m919\u001b[39m res = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 920\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m mutated \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_indexed_like(res, group_axes, axis):\n\u001b[32m 921\u001b[39m mutated = \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 9\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(x)\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mtype\u001b[39m = \u001b[33m'\u001b[39m\u001b[33mlight\u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m 6\u001b[39m train_data[\u001b[33m'\u001b[39m\u001b[33mlabel2\u001b[39m\u001b[33m'\u001b[39m] = train_data.groupby(\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, group_keys=\u001b[38;5;28;01mFalse\u001b[39;00m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x.nsmallest(\u001b[32m1000\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtotal_mv\u001b[39m\u001b[33m'\u001b[39m))[\u001b[33m'\u001b[39m\u001b[33mfuture_return\u001b[39m\u001b[33m'\u001b[39m].transform(\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: pd.qcut(x, q=\u001b[32m50\u001b[39m, labels=\u001b[38;5;28;01mFalse\u001b[39;00m, duplicates=\u001b[33m'\u001b[39m\u001b[33mdrop\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 8\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m9\u001b[39m test_data[\u001b[33m'\u001b[39m\u001b[33mlabel2\u001b[39m\u001b[33m'\u001b[39m] = test_data.groupby(\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, group_keys=\u001b[38;5;28;01mFalse\u001b[39;00m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnsmallest\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtotal_mv\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m)[\u001b[33m'\u001b[39m\u001b[33mfuture_return\u001b[39m\u001b[33m'\u001b[39m].transform(\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: pd.qcut(x, q=\u001b[32m50\u001b[39m, labels=\u001b[38;5;28;01mFalse\u001b[39;00m, duplicates=\u001b[33m'\u001b[39m\u001b[33mdrop\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 11\u001b[39m )\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# feature_contri = [2 if feat.startswith('act_factor') or 'buy' in feat or 'sell' in feat else 1 for feat in feature_columns]\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# light_params['feature_contri'] = feature_contri\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# print(f'feature_contri: {feature_contri}')\u001b[39;00m\n\u001b[32m 16\u001b[39m model, scaler, pca = train_model(train_data.groupby(\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, group_keys=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 17\u001b[39m .apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x.nsmallest(\u001b[32m1000\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtotal_mv\u001b[39m\u001b[33m'\u001b[39m))\n\u001b[32m 18\u001b[39m .merge(industry_df, on=[\u001b[33m'\u001b[39m\u001b[33mcat_l2_code\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m], how=\u001b[33m'\u001b[39m\u001b[33mleft\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 19\u001b[39m .merge(index_data, on=\u001b[33m'\u001b[39m\u001b[33mtrade_date\u001b[39m\u001b[33m'\u001b[39m, how=\u001b[33m'\u001b[39m\u001b[33mleft\u001b[39m\u001b[33m'\u001b[39m), \n\u001b[32m 20\u001b[39m feature_columns, \u001b[38;5;28mtype\u001b[39m=\u001b[38;5;28mtype\u001b[39m, target_column=\u001b[33m'\u001b[39m\u001b[33mlabel2\u001b[39m\u001b[33m'\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/frame.py:7756\u001b[39m, in \u001b[36mDataFrame.nsmallest\u001b[39m\u001b[34m(self, n, columns, keep)\u001b[39m\n\u001b[32m 7646\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mnsmallest\u001b[39m(\n\u001b[32m 7647\u001b[39m \u001b[38;5;28mself\u001b[39m, n: \u001b[38;5;28mint\u001b[39m, columns: IndexLabel, keep: NsmallestNlargestKeep = \u001b[33m\"\u001b[39m\u001b[33mfirst\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 7648\u001b[39m ) -> DataFrame:\n\u001b[32m 7649\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 7650\u001b[39m \u001b[33;03m Return the first `n` rows ordered by `columns` in ascending order.\u001b[39;00m\n\u001b[32m 7651\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 7754\u001b[39m \u001b[33;03m Nauru 337000 182 NR\u001b[39;00m\n\u001b[32m 7755\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m7756\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mselectn\u001b[49m\u001b[43m.\u001b[49m\u001b[43mSelectNFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeep\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnsmallest\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/methods/selectn.py:61\u001b[39m, in \u001b[36mSelectN.nsmallest\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 59\u001b[39m \u001b[38;5;129m@final\u001b[39m\n\u001b[32m 60\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mnsmallest\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m---> \u001b[39m\u001b[32m61\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnsmallest\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/methods/selectn.py:218\u001b[39m, in \u001b[36mSelectNFrame.compute\u001b[39m\u001b[34m(self, method)\u001b[39m\n\u001b[32m 216\u001b[39m \u001b[38;5;66;03m# Below we save and reset the index in case index contains duplicates\u001b[39;00m\n\u001b[32m 217\u001b[39m original_index = frame.index\n\u001b[32m--> \u001b[39m\u001b[32m218\u001b[39m cur_frame = frame = \u001b[43mframe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreset_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 219\u001b[39m cur_n = n\n\u001b[32m 220\u001b[39m indexer = Index([], dtype=np.int64)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/frame.py:6417\u001b[39m, in \u001b[36mDataFrame.reset_index\u001b[39m\u001b[34m(self, level, drop, inplace, col_level, col_fill, allow_duplicates, names)\u001b[39m\n\u001b[32m 6415\u001b[39m new_obj = \u001b[38;5;28mself\u001b[39m\n\u001b[32m 6416\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m6417\u001b[39m new_obj = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 6418\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m allow_duplicates \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib.no_default:\n\u001b[32m 6419\u001b[39m allow_duplicates = validate_bool_kwarg(allow_duplicates, \u001b[33m\"\u001b[39m\u001b[33mallow_duplicates\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/generic.py:6811\u001b[39m, in \u001b[36mNDFrame.copy\u001b[39m\u001b[34m(self, deep)\u001b[39m\n\u001b[32m 6662\u001b[39m \u001b[38;5;129m@final\u001b[39m\n\u001b[32m 6663\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcopy\u001b[39m(\u001b[38;5;28mself\u001b[39m, deep: bool_t | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mTrue\u001b[39;00m) -> Self:\n\u001b[32m 6664\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 6665\u001b[39m \u001b[33;03m Make a copy of this object's indices and data.\u001b[39;00m\n\u001b[32m 6666\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 6809\u001b[39m \u001b[33;03m dtype: int64\u001b[39;00m\n\u001b[32m 6810\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m6811\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_mgr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdeep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6812\u001b[39m \u001b[38;5;28mself\u001b[39m._clear_item_cache()\n\u001b[32m 6813\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._constructor_from_mgr(data, axes=data.axes).__finalize__(\n\u001b[32m 6814\u001b[39m \u001b[38;5;28mself\u001b[39m, method=\u001b[33m\"\u001b[39m\u001b[33mcopy\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 6815\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/internals/managers.py:593\u001b[39m, in \u001b[36mBaseBlockManager.copy\u001b[39m\u001b[34m(self, deep)\u001b[39m\n\u001b[32m 590\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 591\u001b[39m new_axes = \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m.axes)\n\u001b[32m--> \u001b[39m\u001b[32m593\u001b[39m res = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcopy\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdeep\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdeep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 594\u001b[39m res.axes = new_axes\n\u001b[32m 596\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.ndim > \u001b[32m1\u001b[39m:\n\u001b[32m 597\u001b[39m \u001b[38;5;66;03m# Avoid needing to re-compute these\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/internals/managers.py:363\u001b[39m, in \u001b[36mBaseBlockManager.apply\u001b[39m\u001b[34m(self, f, align_keys, **kwargs)\u001b[39m\n\u001b[32m 361\u001b[39m applied = b.apply(f, **kwargs)\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m363\u001b[39m applied = \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 364\u001b[39m result_blocks = extend_blocks(applied, result_blocks)\n\u001b[32m 366\u001b[39m out = \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m).from_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m.axes)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/stock/lib/python3.13/site-packages/pandas/core/internals/blocks.py:796\u001b[39m, in \u001b[36mBlock.copy\u001b[39m\u001b[34m(self, deep)\u001b[39m\n\u001b[32m 794\u001b[39m refs: BlockValuesRefs | \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 795\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[32m--> \u001b[39m\u001b[32m796\u001b[39m values = \u001b[43mvalues\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 797\u001b[39m refs = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 798\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " + ] + } + ], + "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.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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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": null, + "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 +} diff --git a/main/utils/__pycache__/data_process.cpython-313.pyc b/main/utils/__pycache__/data_process.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8ba8266a1d48f5b418c7b1a41bd7ec36bffacb71 GIT binary patch literal 12337 zcmd5?eQ*;;mY>o0mn~Vc3>ae%m=6iCjX!{3z{FrajSaC-;>2bhA<0G{%g&5MLN=Q- zNdS?MU?*X1K6a5=vY6}vg56CFgpgcvwW->>I?al#oLP2{tF>hF*O@r%C3jU<_qs+ausug5boR0JfevU;byh#>xmUWBBKXU4aY1aXwG5EinQ 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cross_sectional_mean) / (cross_sectional_std + epsilon) + + Args: + df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。 + features (list): 需要处理的特征列名列表。 + epsilon (float): 防止除以零的小常数。 + + WARNING: 此函数会原地修改输入的 DataFrame 'df'。 + """ + print("开始截面 Z-Score 标准化...") + if not all(col in train_df.columns for col in features): + missing = [col for col in features if col not in train_df.columns] + print(f"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。") + return + + + for col in tqdm(features, desc="Standardizing"): + try: + # 使用 transform 计算截面均值和标准差 + mean = train_df[col].transform('mean') + std = train_df[col].transform('std') + + # 计算 Z-Score 并原地赋值 + train_df[col] = (train_df[col] - mean) / (std + epsilon) + test_df[col] = (test_df[col] - mean) / (std + epsilon) + + except KeyError: + 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。") + except Exception as e: + print(f"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。") + + print("截面 Z-Score 标准化完成。") + + +# --- 1. 中位数去极值 (MAD) --- + +def cs_mad_filter(df: pd.DataFrame, + features: list, + k: float = 3.0, + scale_factor: float = 1.4826): + """ + 对指定特征列进行截面 MAD 去极值处理 (原地修改)。 + + 方法: 对每日截面数据，计算 median 和 MAD， + 将超出 [median - k * scale * MAD, median + k * scale * MAD] 范围的值 + 替换为边界值 (Winsorization)。 + scale_factor=1.4826 使得 MAD 约等于正态分布的标准差。 + + Args: + df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。 + features (list): 需要处理的特征列名列表。 + k (float): MAD 的倍数，用于确定边界。默认为 3.0。 + scale_factor (float): MAD 的缩放因子。默认为 1.4826。 + + WARNING: 此函数会原地修改输入的 DataFrame 'df'。 + """ + print(f"开始截面 MAD 去极值处理 (k={k})...") + if not all(col in df.columns for col in features): + missing = [col for col in features if col not in df.columns] + print(f"错误: DataFrame 中缺少以下特征列: {missing}。跳过去极值处理。") + return + + grouped = df.groupby('trade_date') + + for col in tqdm(features, desc="MAD Filtering"): + try: + # 计算截面中位数 + median = grouped[col].transform('median') + # 计算截面 MAD (Median Absolute Deviation from Median) + mad = (df[col] - median).abs().groupby(df['trade_date']).transform('median') + + # 计算上下边界 + lower_bound = median - k * scale_factor * mad + upper_bound = median + k * scale_factor * mad + + # 原地应用 clip + df[col] = np.clip(df[col], lower_bound, upper_bound) + + except KeyError: + 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。") + except Exception as e: + print(f"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的 MAD 处理。") + + print("截面 MAD 去极值处理完成。") + + +# --- 2. 行业市值中性化 --- + +def cs_neutralize_industry_cap(df: pd.DataFrame, + features: list, + industry_col: str = 'cat_l2_code', + market_cap_col: str = 'circ_mv'): + """ + 对指定特征列进行截面行业和对数市值中性化 (原地修改)。 + 使用 OLS 回归: feature ~ 1 + log(market_cap) + C(industry) + 将回归残差写回原特征列。 + + Args: + df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date', features 列, + industry_col, market_cap_col。 + features (list): 需要处理的特征列名列表。 + industry_col (str): 行业分类列名。 + market_cap_col (str): 流通市值列名。 + + WARNING: 此函数会原地修改输入的 DataFrame 'df' 的 features 列。 + 计算量较大，可能耗时较长。 + 需要安装 statsmodels 库 (pip install statsmodels)。 + """ + print("开始截面行业市值中性化...") + required_cols = features + ['trade_date', industry_col, market_cap_col] + if not all(col in df.columns for col in required_cols): + missing = [col for col in required_cols if col not in df.columns] + print(f"错误: DataFrame 中缺少必需列: {missing}。无法进行中性化。") + return + + # 预处理：计算 log 市值，处理 industry code 可能的 NaN + log_cap_col = '_log_market_cap' + df[log_cap_col] = np.log1p(df[market_cap_col]) # log1p 处理 0 值 + # df[industry_col] = df[industry_col].cat.add_categories('UnknownIndustry') + # df[industry_col] = df[industry_col].fillna('UnknownIndustry') # 填充行业 NaN + # df[industry_col] = df[industry_col].astype('category') # 转为类别，ols 会自动处理 + + dates = df['trade_date'].unique() + all_residuals = [] # 用于收集所有日期的残差 + + for date in tqdm(dates, desc="Neutralizing"): + daily_data = df.loc[df['trade_date'] == date, features + [log_cap_col, industry_col]].copy() # 使用 .loc 获取副本 + + # 准备自变量 X (常数项 + log市值 + 行业哑变量) + X = daily_data[[log_cap_col]] + X = sm.add_constant(X, prepend=True) # 添加常数项 + # 创建行业哑变量 (drop_first=True 避免共线性) + industry_dummies = pd.get_dummies(daily_data[industry_col], prefix=industry_col, drop_first=True) + industry_dummies = industry_dummies.astype(int) + X = pd.concat([X, industry_dummies], axis=1) + + daily_residuals = daily_data[[col for col in features]].copy() # 创建用于存储残差的df + + for col in features: + Y = daily_data[col] + + # 处理 NaN 值，确保 X 和 Y 在相同位置有有效值 + valid_mask = Y.notna() & X.notna().all(axis=1) + if valid_mask.sum() < (X.shape[1] + 1): # 数据点不足以估计模型 + 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。") + daily_residuals[col] = np.nan + continue + + Y_valid = Y[valid_mask] + X_valid = X[valid_mask] + + # 执行 OLS 回归 + try: + model = sm.OLS(Y_valid.to_numpy(), X_valid.to_numpy()) + results = model.fit() + # 将残差填回对应位置 + daily_residuals.loc[valid_mask, col] = results.resid + daily_residuals.loc[~valid_mask, col] = np.nan # 原本无效的位置填充 NaN + except Exception as e: + print(f"警告: 日期 {date}, 特征 {col} 回归失败: {e}，填充 NaN。") + daily_residuals[col] = np.nan + break + + all_residuals.append(daily_residuals) + + # 合并所有日期的残差结果 + if all_residuals: + residuals_df = pd.concat(all_residuals) + # 将残差结果更新回原始 df (原地修改) + # 使用 update 比 merge 更适合基于索引的原地更新 + # 确保 residuals_df 的索引与 df 中对应部分一致 + df.update(residuals_df) + else: + print("没有有效的残差结果可以合并。") + + + # 清理临时列 + df.drop(columns=[log_cap_col], inplace=True) + print("截面行业市值中性化完成。") + + +# --- 3. Z-Score 标准化 --- + +def cs_zscore_standardize(df: pd.DataFrame, features: list, epsilon: float = 1e-10): + """ + 对指定特征列进行截面 Z-Score 标准化 (原地修改)。 + 方法: Z = (value - cross_sectional_mean) / (cross_sectional_std + epsilon) + + Args: + df (pd.DataFrame): 输入 DataFrame，需包含 'trade_date' 和 features 列。 + features (list): 需要处理的特征列名列表。 + epsilon (float): 防止除以零的小常数。 + + WARNING: 此函数会原地修改输入的 DataFrame 'df'。 + """ + print("开始截面 Z-Score 标准化...") + if not all(col in df.columns for col in features): + missing = [col for col in features if col not in df.columns] + print(f"错误: DataFrame 中缺少以下特征列: {missing}。跳过标准化处理。") + return + + grouped = df.groupby('trade_date') + + for col in tqdm(features, desc="Standardizing"): + try: + # 使用 transform 计算截面均值和标准差 + mean = grouped[col].transform('mean') + std = grouped[col].transform('std') + + # 计算 Z-Score 并原地赋值 + df[col] = (df[col] - mean) / (std + epsilon) + + except KeyError: + 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。") + except Exception as e: + print(f"警告: 处理列 '{col}' 时发生错误: {e}，跳过此列的标准化处理。") + + print("截面 Z-Score 标准化完成。") + +def fill_nan_with_daily_median(df: pd.DataFrame, feature_columns: list[str]) -> pd.DataFrame: + """ + 对指定特征列进行每日截面中位数填充缺失值 (NaN)。 + + 参数: + df (pd.DataFrame): 包含多日数据的DataFrame，需要包含 'trade_date' 和 feature_columns 中的列。 + feature_columns (list[str]): 需要进行缺失值填充的特征列名称列表。 + + 返回: + 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。 + """ + processed_df = df.copy() # 在副本上操作，保留原始数据 + + # 确保 trade_date 是 datetime 类型以便正确分组 + processed_df['trade_date'] = pd.to_datetime(processed_df['trade_date']) + + def _fill_daily_nan(group): + # group 是某一个交易日的 DataFrame + + # 遍历指定的特征列 + for feature_col in feature_columns: + # 检查列是否存在于当前分组中 + if feature_col in group.columns: + # 计算当日该特征的中位数 + median_val = group[feature_col].median() + + # 使用当日中位数填充该特征列的 NaN 值 + # inplace=True 会直接修改 group DataFrame + group[feature_col].fillna(median_val, inplace=True) + # else: + # print(f"Warning: Feature column '{feature_col}' not found in daily group for {group['trade_date'].iloc[0]}. Skipping.") + + return group + + # 按交易日期分组，并应用每日填充函数 + # group_keys=False 避免将分组键添加到结果索引中 + filled_df = processed_df.groupby('trade_date', group_keys=False).apply(_fill_daily_nan) + + return filled_df \ No newline at end of file diff --git a/main/utils/indicators.py b/main/utils/indicators.py new file mode 100644 index 0000000..dfe3c13 --- /dev/null +++ b/main/utils/indicators.py @@ -0,0 +1,130 @@ +import pandas as pd +import numpy as np + +def calculate_sharpe_sortino_labels_efficient( + df: pd.DataFrame, + N_days: int = 5, # 未来考察的天数，用于计算收益率序列 + risk_free_rate_annual: float = 0.02, # 年化无风险利率 (例如 0.02 表示 2%) + annualization_factor: float = np.sqrt(252), # 年化因子，日数据通常用sqrt(252) + min_periods_for_std: int = 2 # 计算标准差所需的最小有效数据点数 +) -> pd.DataFrame: + """ + 高效计算每只股票在每个交易日未来 N 日的年化夏普比率和索提诺比率，作为训练的Label。 + 函数会获取未来 N 天的每日收益率序列，并基于此序列计算夏普/索提诺。 + + Args: + df (pd.DataFrame): 输入 DataFrame，需包含 'ts_code', 'trade_date', 'close' 列。 + 'close' 列必须是股票收盘价。 + N_days (int): 未来考察的天数。例如，N_days=5 表示考察未来5个交易日。 + risk_free_rate_annual (float): 年化无风险利率。 + annualization_factor (float): 用于将日收益率转化为年化收益率的因子 (例如 np.sqrt(252))。 + min_periods_for_std (int): 计算标准差所需的最小有效数据点数。 + 如果未来N天有效数据少于此值，则 Label 为 NaN。 + + Returns: + pd.DataFrame: 原始DataFrame，新增 'sharpe_ratio_label' 和 'sortino_ratio_label' 列。 + 注意：数据末尾 N_days 行的 Label 将为 NaN。 + """ + df_copy = df + + # 确保数据已按股票和日期排序，这对于高效计算至关重要 + df_copy = df_copy.sort_values(by=['ts_code', 'trade_date']) + + # # 计算每日收益率 + # df_copy['pct_chg'] = df_copy.groupby('ts_code')['close'].pct_change() + + # 将年化无风险利率转换为每日无风险利率 + daily_risk_free_rate = risk_free_rate_annual / 252 # 假设每年252个交易日 + + def _calculate_metrics_for_window(returns_series): + """ + 辅助函数：计算单个滚动窗口内的夏普和索提诺比率。 + 这个函数将被 apply 调用于每个 (N_days) 收益率序列。 + """ + if len(returns_series.dropna()) < min_periods_for_std: + return np.nan, np.nan # 数据不足，返回NaN + + excess_returns = returns_series - daily_risk_free_rate + + # --- 夏普比率 --- + mean_excess_return = excess_returns.mean() + std_dev_returns = returns_series.std() # 夏普比率使用总收益率的标准差 + + sharpe = np.nan + if std_dev_returns != 0: + sharpe = (mean_excess_return / std_dev_returns) * annualization_factor + + # --- 索提诺比率 --- + downside_returns = returns_series[returns_series < daily_risk_free_rate] + sortino = np.nan + if not downside_returns.empty: + downside_deviation = np.sqrt(np.mean((downside_returns - daily_risk_free_rate)**2)) + if downside_deviation != 0: + sortino = (mean_excess_return / downside_deviation) * annualization_factor + + return sharpe, sortino + + # 使用 groupby().rolling() 结合 apply 来高效计算 + # 注意: 这里使用 shift(-N_days) 来获取未来 N_days 的窗口 + # 同时 rolling 窗口需要是 N_days 大小，并对 pct_chg 序列进行操作 + # 结果会被放置在窗口的末尾，但我们实际上需要它对应到窗口的起始位置 + # 因此，我们先计算，然后将其向上 shift N_days + 1 （或者根据 rolling 的行为调整） + + # 获取未来 N_days 的每日收益率序列 + # 为了避免在每一行中循环，我们构造一个辅助的DataFrame，其中包含未来N天的收益率 + # 这里使用一个更高效的思路：对每只股票，生成一个N天的收益率列表 + + # 使用 groupby().apply() 和 list comprehension 来构建未来N天的收益率列表 + # 为了获得未来N天的收益率，需要对pct_chg进行shift(-N_days) + # 然后再对每个N_days的窗口进行处理 + + # 方法一：先获取未来N天的收益率列，然后进行滑动窗口计算 （更推荐，效率高） + # 创建一个辅助列，表示未来 N 天的每日收益率列表 + # 注意：这里需要 N_days 长度的窗口，且数据点至少为 min_periods_for_std + + # 对每只股票进行分组，然后计算未来 N 天的夏普/索提诺 + # 这仍然涉及到对每个时间点获取未来N天的收益序列，Pandas 的 rolling 默认是向后看的 + # 为了实现向后看 N_days (即从当前日期 t 预测 t+1 到 t+N_days)，我们需要一些技巧 + + # 最直接且通常高效的方法是： + # 1. 计算日收益率。 + # 2. 对每个股票，对日收益率进行反向滚动，然后计算指标。 + # 或者更简单地，使用 shift(-N_days) 创建“未来的”日收益率列，然后进行滚动。 + + # 为了简化且保持效率，我们可以这样做： + # 1. 计算每个股票的每日收益率。 + # 2. 对于每个 (ts_code, trade_date) 行，我们将查看其未来的 N_days 每日收益率。 + # 这在 Pandas 中不直接通过 .rolling() 实现，因为 rolling 是“当前及之前”的窗口。 + # 我们可以用 groupby().apply() 配合自定义函数来模拟这个“未来窗口”的行为。 + + def _apply_metrics_per_stock(stock_df_group): + """ + 对单只股票的数据帧进行处理，计算其每个日期的未来 N 日夏普/索提诺比率。 + """ + sharpe_labels = [np.nan] * len(stock_df_group) + sortino_labels = [np.nan] * len(stock_df_group) + + # 遍历当前股票的每个日期，获取未来的 N_days 收益率序列 + for i in range(len(stock_df_group) - N_days): + # 获取未来 N_days 的每日收益率序列 + # 从当前日期 t 的下一天 (t+1) 开始，到 t+N_days + future_pct_chgs = stock_df_group['pct_chg'].iloc[i+1 : i+1+N_days].dropna() + + # 计算夏普和索提诺 + sharpe, sortino = _calculate_metrics_for_window(future_pct_chgs) + + # 赋值到对应日期 + sharpe_labels[i] = sharpe + sortino_labels[i] = sortino + + stock_df_group['sharpe_ratio_label'] = sharpe_labels + stock_df_group['sortino_ratio_label'] = sortino_labels + return stock_df_group + + # 对每个股票分组应用这个函数 + df_result = df_copy.groupby('ts_code', group_keys=False).apply(_apply_metrics_per_stock) + + # # 清理不再需要的中间列 + # df_result = df_result.drop(columns=['pct_chg']) + + return df_result diff --git a/predictions_test.tsv b/predictions_test.tsv index cdc0bd4..6ab0f48 100644 --- a/predictions_test.tsv +++ b/predictions_test.tsv @@ -1,51 +1,51 @@ trade_date,score,ts_code -2023-01-03,0.04264513757161395,002247.SZ +2023-01-03,0.04264513757161396,002247.SZ 2023-01-03,0.047473879793379845,002513.SZ -2023-01-03,0.05543341870580454,002629.SZ +2023-01-03,0.055433418705804545,002629.SZ 2023-01-03,0.061053204232209037,603030.SH 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