NewStock/main/train/DoubleQuntile.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "79a7758178bafdd3",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2025-02-16T08:24:31.295363Z",
     "start_time": "2025-02-16T08:24:31.248141Z"
    }
   },
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:26:37.921393Z",
     "start_time": "2025-02-16T08:26:37.822365Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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",
    "    # 计算当日涨跌幅\n",
    "    df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100\n",
    "\n",
    "    # 计算5日移动平均\n",
    "    df['5_day_ma'] = df['close'].rolling(window=5).mean()\n",
    "\n",
    "    # 计算RSI（14日）\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",
    "    return df\n",
    "\n",
    "def generate_index_indicators(h5_filename):\n",
    "    \"\"\"\n",
    "    从H5文件中读取指数行情数据，计算相关指标，返回结果。\n",
    "\n",
    "    参数:\n",
    "        h5_filename (str): 存储指数行情数据的H5文件路径\n",
    "\n",
    "    返回:\n",
    "        DataFrame: 包含计算结果的DataFrame，每行代表一天，包含所有指数的指标\n",
    "    \"\"\"\n",
    "    # 读取指数行情数据\n",
    "    df = pd.read_hdf(h5_filename, key='index_data')\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",
    "        # 获取某个指数的日线数据\n",
    "        df_index = df[df['ts_code'] == ts_code].copy()\n",
    "\n",
    "        # 计算相关指标\n",
    "        df_index = calculate_indicators(df_index)\n",
    "\n",
    "        # 将结果添加到df_indicators列表\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', '5_day_ma', 'RSI', 'MACD', 'Signal_line', 'MACD_hist'],\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",
    "    df_final['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')\n",
    "\n",
    "    return df_final\n",
    "\n",
    "\n",
    "# 使用函数\n",
    "h5_filename = '../../data/index_data.h5'\n",
    "index_data = generate_index_indicators(h5_filename)\n",
    "index_data = index_data.dropna()\n",
    "# 打印结果\n",
    "print(index_data.head())\n"
   ],
   "id": "b216cc5529d07cad",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date  000852.SH_5_day_ma  000905.SH_5_day_ma  399006.SZ_5_day_ma  \\\n",
      "1314 2019-09-06           4108.5648           4064.9986           1013.1790   \n",
      "1315 2019-09-05           4132.3992           4081.7042           1031.9632   \n",
      "1316 2019-09-04           4164.2720           4107.3572           1048.1040   \n",
      "1317 2019-09-03           4204.0682           4141.2734           1071.9208   \n",
      "1318 2019-09-02           4232.8298           4163.0654           1090.8244   \n",
      "\n",
      "      000852.SH_MACD  000905.SH_MACD  399006.SZ_MACD  000852.SH_MACD_hist  \\\n",
      "1314       20.380810       34.702099       15.173396            -2.837620   \n",
      "1315       28.830595       41.792560       21.448935             4.902761   \n",
      "1316       32.263643       44.346834       26.714792             9.561498   \n",
      "1317       39.294963       49.978947       32.917542            18.983194   \n",
      "1318       44.192628       52.871877       37.373758            28.626657   \n",
      "\n",
      "      000905.SH_MACD_hist  399006.SZ_MACD_hist  000852.SH_RSI  000905.SH_RSI  \\\n",
      "1314             1.270353            -9.734351      39.526629      45.136516   \n",
      "1315             8.678401            -5.892400      49.265364      53.813061   \n",
      "1316            13.402276            -2.099643      53.026120      56.084097   \n",
      "1317            22.384958             3.578197      66.615288      68.521794   \n",
      "1318            30.874127             8.928962      68.827733      70.274913   \n",
      "\n",
      "      399006.SZ_RSI  000852.SH_Signal_line  000905.SH_Signal_line  \\\n",
      "1314      38.536512              23.218429              33.431746   \n",
      "1315      47.245649              23.927834              33.114158   \n",
      "1316      51.408682              22.702144              30.944558   \n",
      "1317      63.606060              20.311770              27.593989   \n",
      "1318      69.525065              15.565971              21.997750   \n",
      "\n",
      "      399006.SZ_Signal_line  000852.SH_daily_return  000905.SH_daily_return  \\\n",
      "1314              24.907747               -2.133006               -1.914426   \n",
      "1315              27.341335                1.676448                1.436414   \n",
      "1316              28.814435               -0.741755               -0.725607   \n",
      "1317              29.339346                0.839908                0.864581   \n",
      "1318              28.444796               -0.331791               -0.402711   \n",
      "\n",
      "      399006.SZ_daily_return  \n",
      "1314               -2.676700  \n",
      "1315                2.454294  \n",
      "1316                0.127868  \n",
      "1317                2.933411  \n",
      "1318                4.066145  \n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "a79cafb06a7e0e43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:25:13.872207Z",
     "start_time": "2025-02-16T08:24:31.906361Z"
    }
   },
   "source": [
    "from code.utils.utils import read_and_merge_h5_data\n",
    "\n",
    "print('daily data')\n",
    "df = read_and_merge_h5_data('../../data/daily_data.h5', key='daily_data',\n",
    "                            columns=['ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'vol'],\n",
    "                            df=None)\n",
    "\n",
    "print('daily basic')\n",
    "df = read_and_merge_h5_data('../../data/daily_basic.h5', key='daily_basic',\n",
    "                            columns=['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'circ_mv', 'volume_ratio',\n",
    "                                     'is_st'], df=df, join='inner')\n",
    "\n",
    "print('stk limit')\n",
    "df = read_and_merge_h5_data('../../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('../../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)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "daily data\n",
      "daily basic\n",
      "stk limit\n",
      "money flow\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:26:43.228123Z",
     "start_time": "2025-02-16T08:26:41.895312Z"
    }
   },
   "cell_type": "code",
   "source": "df = df.merge(index_data, on='trade_date', how='left')",
   "id": "7357147395bda969",
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "c4e9e1d31da6dba6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:26:43.275563Z",
     "start_time": "2025-02-16T08:26:43.228123Z"
    }
   },
   "source": [
    "origin_columns = df.columns.tolist()\n",
    "origin_columns = [col for col in origin_columns if col not in ['turnover_rate', 'pe_ttm', 'volume_ratio']]"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "a735bc02ceb4d872",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2025-02-16T08:26:43.388955Z",
     "start_time": "2025-02-16T08:26:43.310081Z"
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   },
   "source": [
    "import numpy as np\n",
    "import talib\n",
    "\n",
    "def get_technical_factor(df):\n",
    "    # 按股票和日期排序\n",
    "    df = df.sort_values(by=['ts_code', 'trade_date'])\n",
    "    grouped = df.groupby('ts_code', group_keys=False)\n",
    "\n",
    "    # 计算 up 和 down\n",
    "    df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']\n",
    "    df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']\n",
    "\n",
    "    # 计算 ATR\n",
    "    df['atr_14'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14), index=x.index)\n",
    "    )\n",
    "    df['atr_6'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6), index=x.index)\n",
    "    )\n",
    "\n",
    "    # 计算 OBV 及其均线\n",
    "    df['obv'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)\n",
    "    )\n",
    "    df['maobv_6'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)\n",
    "    )\n",
    "    df['obv-maobv_6'] = df['obv'] - df['maobv_6']\n",
    "\n",
    "    # 计算 RSI\n",
    "    df['rsi_3'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)\n",
    "    )\n",
    "    df['rsi_6'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)\n",
    "    )\n",
    "    df['rsi_9'] = grouped.apply(\n",
    "        lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)\n",
    "    )\n",
    "\n",
    "    # 计算 return_10 和 return_20\n",
    "    df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)\n",
    "    df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)\n",
    "\n",
    "    # 计算 avg_close_5\n",
    "    df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)\n",
    "\n",
    "    # 计算标准差指标\n",
    "    df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())\n",
    "    df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())\n",
    "    df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())\n",
    "    df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())\n",
    "    df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())\n",
    "\n",
    "    # 计算比值指标\n",
    "    df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']\n",
    "    df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']\n",
    "\n",
    "    # 计算标准差差值\n",
    "    df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_act_factor(df):\n",
    "    # 按股票和日期排序\n",
    "    df = df.sort_values(by=['ts_code', 'trade_date'])\n",
    "    grouped = df.groupby('ts_code', group_keys=False)\n",
    "    # 计算 EMA 指标\n",
    "    df['ema_5'] = grouped['close'].apply(\n",
    "        lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)\n",
    "    )\n",
    "    df['ema_13'] = grouped['close'].apply(\n",
    "        lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)\n",
    "    )\n",
    "    df['ema_20'] = grouped['close'].apply(\n",
    "        lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)\n",
    "    )\n",
    "    df['ema_60'] = grouped['close'].apply(\n",
    "        lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)\n",
    "    )\n",
    "\n",
    "    # 计算 act_factor1, act_factor2, act_factor3, act_factor4\n",
    "    df['act_factor1'] = grouped['ema_5'].apply(\n",
    "        lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50\n",
    "    )\n",
    "    df['act_factor2'] = grouped['ema_13'].apply(\n",
    "        lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40\n",
    "    )\n",
    "    df['act_factor3'] = grouped['ema_20'].apply(\n",
    "        lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21\n",
    "    )\n",
    "    df['act_factor4'] = grouped['ema_60'].apply(\n",
    "        lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10\n",
    "    )\n",
    "\n",
    "    df['cat_af1'] = df['act_factor1'] > 0\n",
    "    df['cat_af2'] = df['act_factor2'] > df['act_factor1']\n",
    "    df['cat_af3'] = df['act_factor3'] > df['act_factor2']\n",
    "    df['cat_af4'] = df['act_factor4'] > df['act_factor3']\n",
    "\n",
    "    # 计算 act_factor5 和 act_factor6\n",
    "    df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']\n",
    "    df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(df['act_factor1']**2 + df['act_factor2']**2)\n",
    "\n",
    "    # 根据 trade_date 截面计算排名\n",
    "    df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)\n",
    "    df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)\n",
    "    df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_money_flow_factor(df):\n",
    "    # 计算资金流相关因子（字段名称见 tushare 数据说明）\n",
    "    df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']\n",
    "    df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']\n",
    "    df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']\n",
    "\n",
    "    df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']\n",
    "    df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']\n",
    "\n",
    "    df['log(circ_mv)'] = np.log(df['circ_mv'])\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_alpha_factor(df):\n",
    "    df = df.sort_values(by=['ts_code', 'trade_date'])\n",
    "    grouped = df.groupby('ts_code')\n",
    "\n",
    "    # alpha_022: 当前 close 与 5 日前 close 差值\n",
    "    df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))\n",
    "\n",
    "    # alpha_003: (close - open) / (high - low)\n",
    "    df['alpha_003'] = np.where(df['high'] != df['low'],\n",
    "                               (df['close'] - df['open']) / (df['high'] - df['low']),\n",
    "                               0)\n",
    "\n",
    "    # alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名\n",
    "    df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)\n",
    "    df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)\n",
    "\n",
    "    # alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名\n",
    "    df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())\n",
    "    df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)\n",
    "\n",
    "    return df\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "dbe2fd8021b9417f",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-02-16T08:27:52.356590Z",
     "start_time": "2025-02-16T08:26:43.420269Z"
    }
   },
   "source": [
    "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.reset_index(drop=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "df = filter_data(df)\n",
    "df = get_technical_factor(df)\n",
    "df = get_act_factor(df)\n",
    "df = get_money_flow_factor(df)\n",
    "df = get_alpha_factor(df)\n",
    "print(df.info())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5453316 entries, 1962 to 5453315\n",
      "Data columns (total 87 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   turnover_rate                    float64       \n",
      " 8   pe_ttm                           float64       \n",
      " 9   circ_mv                          float64       \n",
      " 10  volume_ratio                     float64       \n",
      " 11  is_st                            bool          \n",
      " 12  up_limit                         float64       \n",
      " 13  down_limit                       float64       \n",
      " 14  buy_sm_vol                       float64       \n",
      " 15  sell_sm_vol                      float64       \n",
      " 16  buy_lg_vol                       float64       \n",
      " 17  sell_lg_vol                      float64       \n",
      " 18  buy_elg_vol                      float64       \n",
      " 19  sell_elg_vol                     float64       \n",
      " 20  net_mf_vol                       float64       \n",
      " 21  000852.SH_5_day_ma               float64       \n",
      " 22  000905.SH_5_day_ma               float64       \n",
      " 23  399006.SZ_5_day_ma               float64       \n",
      " 24  000852.SH_MACD                   float64       \n",
      " 25  000905.SH_MACD                   float64       \n",
      " 26  399006.SZ_MACD                   float64       \n",
      " 27  000852.SH_MACD_hist              float64       \n",
      " 28  000905.SH_MACD_hist              float64       \n",
      " 29  399006.SZ_MACD_hist              float64       \n",
      " 30  000852.SH_RSI                    float64       \n",
      " 31  000905.SH_RSI                    float64       \n",
      " 32  399006.SZ_RSI                    float64       \n",
      " 33  000852.SH_Signal_line            float64       \n",
      " 34  000905.SH_Signal_line            float64       \n",
      " 35  399006.SZ_Signal_line            float64       \n",
      " 36  000852.SH_daily_return           float64       \n",
      " 37  000905.SH_daily_return           float64       \n",
      " 38  399006.SZ_daily_return           float64       \n",
      " 39  up                               float64       \n",
      " 40  down                             float64       \n",
      " 41  atr_14                           float64       \n",
      " 42  atr_6                            float64       \n",
      " 43  obv                              float64       \n",
      " 44  maobv_6                          float64       \n",
      " 45  obv-maobv_6                      float64       \n",
      " 46  rsi_3                            float64       \n",
      " 47  rsi_6                            float64       \n",
      " 48  rsi_9                            float64       \n",
      " 49  return_10                        float64       \n",
      " 50  return_20                        float64       \n",
      " 51  avg_close_5                      float64       \n",
      " 52  std_return_5                     float64       \n",
      " 53  std_return_15                    float64       \n",
      " 54  std_return_25                    float64       \n",
      " 55  std_return_90                    float64       \n",
      " 56  std_return_90_2                  float64       \n",
      " 57  std_return_5 / std_return_90     float64       \n",
      " 58  std_return_5 / std_return_25     float64       \n",
      " 59  std_return_90 - std_return_90_2  float64       \n",
      " 60  ema_5                            float64       \n",
      " 61  ema_13                           float64       \n",
      " 62  ema_20                           float64       \n",
      " 63  ema_60                           float64       \n",
      " 64  act_factor1                      float64       \n",
      " 65  act_factor2                      float64       \n",
      " 66  act_factor3                      float64       \n",
      " 67  act_factor4                      float64       \n",
      " 68  cat_af1                          bool          \n",
      " 69  cat_af2                          bool          \n",
      " 70  cat_af3                          bool          \n",
      " 71  cat_af4                          bool          \n",
      " 72  act_factor5                      float64       \n",
      " 73  act_factor6                      float64       \n",
      " 74  rank_act_factor1                 float64       \n",
      " 75  rank_act_factor2                 float64       \n",
      " 76  rank_act_factor3                 float64       \n",
      " 77  active_buy_volume_large          float64       \n",
      " 78  active_buy_volume_big            float64       \n",
      " 79  active_buy_volume_small          float64       \n",
      " 80  buy_lg_vol_minus_sell_lg_vol     float64       \n",
      " 81  buy_elg_vol_minus_sell_elg_vol   float64       \n",
      " 82  log(circ_mv)                     float64       \n",
      " 83  alpha_022                        float64       \n",
      " 84  alpha_003                        float64       \n",
      " 85  alpha_007                        float64       \n",
      " 86  alpha_013                        float64       \n",
      "dtypes: bool(5), datetime64[ns](1), float64(80), object(1)\n",
      "memory usage: 3.4+ GB\n",
      "None\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:27:52.874223Z",
     "start_time": "2025-02-16T08:27:52.717759Z"
    }
   },
   "cell_type": "code",
   "source": [
    "feature_columns = [col for col in df.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 'score' not in col]\n",
    "feature_columns = [col for col in feature_columns if col not in origin_columns]\n",
    "print(feature_columns)"
   ],
   "id": "2132103543a77819",
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "5f3d9aece75318cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.569134Z",
     "start_time": "2025-02-16T08:42:37.956365Z"
    }
   },
   "source": [
    "def get_qcuts(series, quantiles):\n",
    "    q = pd.qcut(series, q=quantiles, labels=False, duplicates='drop')\n",
    "    return q[-1]  # 返回窗口最后一个元素的分位数标签\n",
    "\n",
    "\n",
    "window = 5\n",
    "quantiles = 20\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_return'] = (df['future_close'] - df['close']) / df['close']\n",
    "\n",
    "    df['future_volatility'] = df.groupby('ts_code')['future_return'].rolling(days, min_periods=1).std().reset_index(level=0, drop=True)\n",
    "    df['sharpe_ratio'] = df['future_return'] * df['future_volatility']\n",
    "    df['sharpe_ratio'].replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "\n",
    "    return df['sharpe_ratio']\n",
    "\n",
    "def get_label(df):\n",
    "    # labels = df['future_af13'] - df['act_factor1']\n",
    "    labels = df['future_close5']\n",
    "    # labels = df['future_af11']\n",
    "    # labels = df['ema_5'].shift(-1) - df['close']\n",
    "    # df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\n",
    "    # df['label'] = calculate_risk_adjusted_target(df, days=5)\n",
    "    # lower_percentile = df['label'].quantile(0.01)  # 1%分位数\n",
    "    # upper_percentile = df['label'].quantile(0.99)  # 99%分位数\n",
    "    # labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\n",
    "    # labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\n",
    "    return labels\n",
    "\n",
    "df['label'] = get_label(df)\n",
    "train_data = df[df['trade_date'] <= '2023-01-01']\n",
    "test_data = df[df['trade_date'] >= '2023-01-01']\n",
    "\n",
    "train_data = train_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n",
    "test_data = test_data.groupby('trade_date', group_keys=False).apply(lambda x: x.nlargest(1000, 'return_20'))\n",
    "\n",
    "# train_data = get_future_data(train_data)\n",
    "\n",
    "# df = df[['ts_code', 'trade_date', 'open', 'close']]\n",
    "\n",
    "train_data, test_data = train_data.replace([np.inf, -np.inf], np.nan), test_data.replace([np.inf, -np.inf], np.nan)\n",
    "train_data = train_data.dropna(subset=feature_columns)\n",
    "train_data = train_data.reset_index(drop=True)\n",
    "train_data['label'] = get_label(train_data)\n",
    "\n",
    "test_data = test_data.dropna(subset=feature_columns)\n",
    "test_data = test_data.reset_index(drop=True)\n",
    "test_data['label'] = get_label(test_data)\n",
    "\n",
    "print(len(train_data))\n",
    "print(f\"最小日期: {train_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
    "print(f\"最大日期: {train_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n",
    "print(len(test_data))\n",
    "print(f\"最小日期: {test_data['trade_date'].min().strftime('%Y-%m-%d')}\")\n",
    "print(f\"最大日期: {test_data['trade_date'].max().strftime('%Y-%m-%d')}\")\n"
   ],
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[1], line 34\u001B[0m\n\u001B[0;32m     24\u001B[0m     \u001B[38;5;66;03m# labels = df['future_af11']\u001B[39;00m\n\u001B[0;32m     25\u001B[0m     \u001B[38;5;66;03m# labels = df['ema_5'].shift(-1) - df['close']\u001B[39;00m\n\u001B[0;32m     26\u001B[0m     \u001B[38;5;66;03m# df['label'] = (df['future_af11'] - df['act_factor1']) / df['act_factor1']\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     30\u001B[0m     \u001B[38;5;66;03m# labels = df['label'].clip(lower=lower_percentile, upper=upper_percentile)\u001B[39;00m\n\u001B[0;32m     31\u001B[0m     \u001B[38;5;66;03m# labels = calculate_risk_adjusted_return(df, days=3, history_days=3, method='ratio')\u001B[39;00m\n\u001B[0;32m     32\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m labels\n\u001B[1;32m---> 34\u001B[0m df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m get_label(\u001B[43mdf\u001B[49m)\n\u001B[0;32m     35\u001B[0m train_data \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2023-01-01\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m     36\u001B[0m test_data \u001B[38;5;241m=\u001B[39m df[df[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtrade_date\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m2023-01-01\u001B[39m\u001B[38;5;124m'\u001B[39m]\n",
      "\u001B[1;31mNameError\u001B[0m: name 'df' is not defined"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "8f134d435f71e9e2",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.569134800Z",
     "start_time": "2025-02-16T08:28:25.333768Z"
    }
   },
   "source": [
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import optuna\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import os\n",
    "import json\n",
    "import pickle\n",
    "import hashlib\n",
    "\n",
    "def train_light_model(train_data_df, test_data_df, params, feature_columns, callbacks, evals,\n",
    "                      print_feature_importance=True, num_boost_round=100,\n",
    "                      use_optuna=False):\n",
    "\n",
    "    X_train = train_data_df[feature_columns]\n",
    "    y_train = train_data_df['label']\n",
    "\n",
    "    X_val = test_data_df[feature_columns]\n",
    "    y_val = test_data_df['label']\n",
    "\n",
    "    categorical_feature = [col for col in feature_columns if 'cat' in col]\n",
    "    train_data = lgb.Dataset(X_train, label=y_train, categorical_feature=categorical_feature)\n",
    "    val_data = lgb.Dataset(X_val, label=y_val, categorical_feature=categorical_feature)\n",
    "    model = lgb.train(\n",
    "        params, train_data, num_boost_round=num_boost_round,\n",
    "        valid_sets=[train_data, val_data], valid_names=['train', 'valid'],\n",
    "        callbacks=callbacks\n",
    "    )\n",
    "\n",
    "    if print_feature_importance:\n",
    "        lgb.plot_metric(evals)\n",
    "        # lgb.plot_tree(model, figsize=(20, 8))\n",
    "        lgb.plot_importance(model, importance_type='split', max_num_features=20)\n",
    "        plt.show()\n",
    "    return model\n",
    "\n",
    "\n",
    "from catboost import CatBoostRegressor\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def train_catboost(df, feature_columns, params=None):\n",
    "    \"\"\"\n",
    "    训练 CatBoost 排序模型\n",
    "    - df: 包含因子、date、instrument 和 label 的 DataFrame\n",
    "    - num_boost_round: 训练的轮数\n",
    "    - print_feature_importance: 是否打印特征重要性\n",
    "    - plot: 是否绘制特征重要性图\n",
    "    - split_date: 用于划分训练集和验证集的日期（比如 '2020-01-01'）\n",
    "\n",
    "    返回训练好的模型\n",
    "    \"\"\"\n",
    "    df_sorted = df.sort_values(by=['trade_date', 'label'], ascending=[True, False])\n",
    "\n",
    "    df_sorted = df_sorted.sort_values(by='trade_date')\n",
    "    unique_dates = df_sorted['trade_date'].unique()\n",
    "    val_date_count = int(len(unique_dates) * 0.1)\n",
    "    val_dates = unique_dates[-val_date_count:]\n",
    "    val_indices = df_sorted[df_sorted['trade_date'].isin(val_dates)].index\n",
    "    train_indices = df_sorted[~df_sorted['trade_date'].isin(val_dates)].index\n",
    "\n",
    "    # 获取训练集和验证集的样本\n",
    "    train_df = df_sorted.iloc[train_indices].sort_values(by=['trade_date', 'label'], ascending=[True, False])\n",
    "    val_df = df_sorted.iloc[val_indices].sort_values(by=['trade_date', 'label'], ascending=[True, False])\n",
    "\n",
    "    X_train = train_df[feature_columns]\n",
    "    y_train = train_df['label']\n",
    "\n",
    "    X_val = val_df[feature_columns]\n",
    "    y_val = val_df['label']\n",
    "\n",
    "    model = CatBoostRegressor(**params)\n",
    "    model.fit(X_train,\n",
    "              y_train,\n",
    "              eval_set=(X_val, y_val))\n",
    "\n",
    "    return model"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "beeb098799ecfa6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.569134800Z",
     "start_time": "2025-02-16T08:29:41.943281Z"
    }
   },
   "source": [
    "print('train data size: ', len(train_data))\n",
    "\n",
    "light_params9 = {\n",
    "    # 'objective': 'regression',\n",
    "    # 'metric': 'l2',\n",
    "    'objective': 'quantile',  # 分位回归\n",
    "    'metric': 'quantile',  # 使用 quantile 作为评估指标\n",
    "    'alpha': 0.75,  # 90% 分位数\n",
    "    'learning_rate': 0.05,\n",
    "    'is_unbalance': True,\n",
    "    'num_leaves': 2048,\n",
    "    'min_data_in_leaf': 256,\n",
    "    'max_depth': 32,\n",
    "    'max_bin': 1024,\n",
    "    # 'feature_fraction': 0.7,\n",
    "    # 'bagging_fraction': 0.7,\n",
    "    # 'bagging_freq': 5,\n",
    "    # 'lambda_l1': 10,\n",
    "    # 'lambda_l2': 10,\n",
    "    'verbosity': -1,\n",
    "    # 'device': 'gpu'\n",
    "}\n",
    "evals = {}\n",
    "light_model9 = train_light_model(train_data, test_data, light_params9, feature_columns,\n",
    "                                 [lgb.log_evaluation(period=500),\n",
    "                                  lgb.callback.record_evaluation(evals),\n",
    "                                  lgb.early_stopping(50, first_metric_only=True)\n",
    "                                  ], evals,\n",
    "                                 num_boost_round=10000, use_optuna=False,\n",
    "                                 print_feature_importance=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data size:  1170833\n",
      "Training until validation scores don't improve for 50 rounds\n",
      "Early stopping, best iteration is:\n",
      "[147]\ttrain's quantile: 0.00152696\tvalid's quantile: 0.00164099\n",
      "Evaluated only: quantile\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "10bdf199fcff6b48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.569134800Z",
     "start_time": "2025-02-16T08:30:40.002290Z"
    }
   },
   "source": [
    "light_params1 = {\n",
    "    # 'objective': 'regression',\n",
    "    # 'metric': 'l2',\n",
    "    'objective': 'quantile',  # 分位回归\n",
    "    'metric': 'quantile',  # 使用 quantile 作为评估指标\n",
    "    'alpha': 0.25,  # 90% 分位数\n",
    "    'learning_rate': 0.05,\n",
    "    'is_unbalance': True,\n",
    "    'num_leaves': 2048,\n",
    "    'min_data_in_leaf': 256,\n",
    "    'max_depth': 32,\n",
    "    'max_bin': 1024,\n",
    "    # 'feature_fraction': 0.7,\n",
    "    # 'bagging_fraction': 0.7,\n",
    "    # 'bagging_freq': 5,\n",
    "    # 'lambda_l1': 10,\n",
    "    # 'lambda_l2': 10,\n",
    "    'verbosity': -1,\n",
    "    # 'device': 'gpu'\n",
    "}\n",
    "evals = {}\n",
    "light_model1 = train_light_model(train_data, test_data, light_params1, feature_columns,\n",
    "                                 [lgb.log_evaluation(period=500),\n",
    "                                  lgb.callback.record_evaluation(evals),\n",
    "                                  lgb.early_stopping(50, first_metric_only=True)\n",
    "                                  ], evals,\n",
    "                                 num_boost_round=10000, use_optuna=False,\n",
    "                                 print_feature_importance=True)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds\n",
      "Early stopping, best iteration is:\n",
      "[82]\ttrain's quantile: 0.00160062\tvalid's quantile: 0.00150239\n",
      "Evaluated only: quantile\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "5bb96ca8492e74d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.584794600Z",
     "start_time": "2025-02-16T08:31:20.861518Z"
    }
   },
   "source": [
    "score_df = train_data\n",
    "score_df['score'] = light_model9.predict(score_df[feature_columns]) + light_model1.predict(score_df[feature_columns])\n",
    "# train_data['score'] = catboost_model.predict(train_data[feature_columns])\n",
    "# score_df = score_df[score_df['score'] > 0]\n",
    "predictions_train = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
    "predictions_train[['trade_date', 'score', 'ts_code']].to_csv('predictions_train.tsv', index=False)\n"
   ],
   "outputs": [],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "id": "5d1522a7538db91b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.584794600Z",
     "start_time": "2025-02-16T08:31:26.697459Z"
    }
   },
   "source": [
    "score_df = test_data\n",
    "score_df['score'] = light_model9.predict(score_df[feature_columns]) / light_model1.predict(score_df[feature_columns])\n",
    "# test_data['score'] = catboost_model.predict(test_data[feature_columns])\n",
    "# score_df = score_df[score_df['score'] > 0]\n",
    "predictions_test = score_df.loc[score_df.groupby('trade_date')['score'].idxmax()]\n",
    "predictions_test[['trade_date', 'score', 'ts_code']].to_csv('predictions_test.tsv', index=False)"
   ],
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "b427ce41-9739-4e9e-bea8-5f2551fec5d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T08:42:38.584794600Z",
     "start_time": "2025-02-16T08:31:28.801289Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}