2025-06-01 15:59:29 +08:00
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import numpy as np
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import talib
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import pandas as pd
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def get_technical_factor(df):
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# 按股票和日期排序
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df = df.sort_values(by=['ts_code', 'trade_date'])
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grouped = df.groupby('ts_code', group_keys=False)
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df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)
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df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)
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# 因子 1：短期成交量变化率
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df['volume_change_rate'] = (
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grouped['vol'].rolling(window=2).mean() /
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grouped['vol'].rolling(window=5).mean() - 1
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).reset_index(level=0, drop=True) # 确保索引对齐
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# 因子 2：成交量突破信号
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max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐
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df['cat_volume_breakout'] = (df['vol'] > max_volume)
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# 因子 3：换手率均线偏离度
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mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)
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std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)
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df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover
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# 因子 4：换手率激增信号
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df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)
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# 因子 5：量比均值
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df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)
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# 因子 6：量比突破信号
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max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)
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df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)
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# 因子 7：成交量与换手率的综合动量因子
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alpha = 0.5
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df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']
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# 因子 8：量价共振因子
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df['price_change_rate'] = grouped['close'].pct_change()
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df['resonance_factor'] = df['volume_ratio'] * df['price_change_rate']
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# 计算 up 和 down
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df['log_close'] = np.log(df['close'])
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df['vol_spike'] = grouped.apply(
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lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)
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)
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df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']
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df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()
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df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']
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df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']
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# 计算 ATR
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df['atr_14'] = grouped.apply(
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lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),
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index=x.index)
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)
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df['atr_6'] = grouped.apply(
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lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),
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index=x.index)
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)
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# 计算 OBV 及其均线
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df['obv'] = grouped.apply(
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lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)
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)
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df['maobv_6'] = grouped.apply(
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lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)
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)
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df['obv-maobv_6'] = df['obv'] - df['maobv_6']
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# 计算 RSI
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df['rsi_3'] = grouped.apply(
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lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)
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)
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df['rsi_6'] = grouped.apply(
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lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)
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)
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df['rsi_9'] = grouped.apply(
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lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)
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)
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# 计算 return_10 和 return_20
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df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)
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df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)
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df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)
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# df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)
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# 计算标准差指标
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df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())
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df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())
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df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())
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df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())
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df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())
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# 计算比值指标
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df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']
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df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']
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# 计算标准差差值
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df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']
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return df
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def get_act_factor(df, cat=True):
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# 按股票和日期排序
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df = df.sort_values(by=['ts_code', 'trade_date'])
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grouped = df.groupby('ts_code', group_keys=False)
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# 计算 EMA 指标
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df['_ema_5'] = grouped['close'].apply(
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lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)
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)
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df['_ema_13'] = grouped['close'].apply(
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lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)
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)
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df['_ema_20'] = grouped['close'].apply(
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lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)
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)
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df['_ema_60'] = grouped['close'].apply(
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lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)
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)
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# 计算 act_factor1, act_factor2, act_factor3, act_factor4
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df['act_factor1'] = grouped['_ema_5'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
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)
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df['act_factor2'] = grouped['_ema_13'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
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)
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df['act_factor3'] = grouped['_ema_20'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
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)
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df['act_factor4'] = grouped['_ema_60'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
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)
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if cat:
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df['cat_af1'] = df['act_factor1'] > 0
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df['cat_af2'] = df['act_factor2'] > df['act_factor1']
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df['cat_af3'] = df['act_factor3'] > df['act_factor2']
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df['cat_af4'] = df['act_factor4'] > df['act_factor3']
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# 计算 act_factor5 和 act_factor6
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df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']
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df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(
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df['act_factor1'] ** 2 + df['act_factor2'] ** 2)
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# 根据 trade_date 截面计算排名
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df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
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df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
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df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
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return df
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def get_money_flow_factor(df):
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# 计算资金流相关因子（字段名称见 tushare 数据说明）
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df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']
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df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']
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df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']
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df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']
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df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']
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df['log(circ_mv)'] = np.log(df['circ_mv'])
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return df
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def get_alpha_factor(df):
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df = df.sort_values(by=['ts_code', 'trade_date'])
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grouped = df.groupby('ts_code')
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# alpha_022: 当前 close 与 5 日前 close 差值
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# df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))
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def rolling_covariance(x, y, window):
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return x.rolling(window).cov(y)
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def delta(series, period):
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return series.diff(period)
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def rank(series):
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return series.rank(pct=True)
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def stddev(series, window):
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return series.rolling(window).std()
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# 计算改进后的 Alpha 22 因子
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window_high_volume = 5
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window_close_stddev = 20
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period_delta = 5
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df['cov'] = rolling_covariance(df['high'], df['volume'], window_high_volume)
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df['delta_cov'] = delta(df['cov'], period_delta)
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df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))
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df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
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# alpha_003: (close - open) / (high - low)
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df['alpha_003'] = np.where(df['high'] != df['low'],
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(df['close'] - df['open']) / (df['high'] - df['low']),
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0)
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# alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名
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df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)
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df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
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# alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名
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df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
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df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
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return df
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def get_limit_factor(df):
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# 按股票和日期排序
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df = df.sort_values(by=['ts_code', 'trade_date'])
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# 分组处理
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grouped = df.groupby('ts_code', group_keys=False)
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# 1. 今日是否涨停/跌停
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df['cat_up_limit'] = (df['close'] == df['up_limit']).astype(int) # 是否涨停（1表示涨停，0表示未涨停）
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df['cat_down_limit'] = (df['close'] == df['down_limit']).astype(int) # 是否跌停（1表示跌停，0表示未跌停）
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# 2. 最近涨跌停次数（过去20个交易日）
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df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
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drop=True)
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df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
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drop=True)
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# 3. 最近连续涨跌停天数
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def calculate_consecutive_limits(series):
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"""
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计算连续涨停/跌停天数。
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"""
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consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
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consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
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return consecutive_up, consecutive_down
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# 连续涨停天数
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df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
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lambda x: calculate_consecutive_limits(x)[0]
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).reset_index(level=0, drop=True)
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# 连续跌停天数
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# df['consecutive_down_limit'] = grouped['cat_down_limit'].apply(
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# lambda x: calculate_consecutive_limits(x)[1]
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# ).reset_index(level=0, drop=True)
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return df
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def get_cyp_perf_factor(df):
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# 预处理：按股票代码和时间排序
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df = df.sort_values(by=['ts_code', 'trade_date'])
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# 按股票代码分组处理
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grouped = df.groupby('ts_code', group_keys=False)
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df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])
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df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])
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df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])
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df['lock_factor'] = df['turnover_rate'] * (
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1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))
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df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
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df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
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def rolling_corr(group):
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roc_close = group['close'].pct_change()
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roc_weight = group['weight_avg'].pct_change()
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return roc_close.rolling(10).corr(roc_weight)
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df['price_cost_divergence'] = grouped.apply(rolling_corr)
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def calc_atr(group):
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high, low, close = group['high'], group['low'], group['close']
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tr = np.maximum(high - low,
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np.maximum(abs(high - close.shift()),
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abs(low - close.shift())))
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return tr.rolling(14).mean()
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df['atr_14'] = grouped.apply(calc_atr)
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df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']
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# 12. 小盘股筹码集中度
|
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|
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
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# 16. 筹码稳定性指数 (20日波动率)
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|
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
|
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|
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
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# 17. 成本区间突破标记
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|
df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())
|
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|
|
# 18. 黄金筹码共振 (复合事件)
|
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|
|
df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &
|
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|
|
|
|
(df['volume_ratio'] > 1.5) &
|
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|
|
|
|
(df['winner_rate'] > 0.7))
|
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|
|
|
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|
|
|
|
# 20. 筹码-流动性风险
|
|
|
|
|
|
df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (
|
|
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|
|
|
1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))
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|
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|
|
df.drop(columns=['weight_std20'], inplace=True, errors='ignore')
|
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|
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|
|
return df
|
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|
|
def get_mv_factors(df):
|
|
|
|
|
|
"""
|
|
|
|
|
|
计算多个因子并生成最终的综合因子。
|
|
|
|
|
|
|
|
|
|
|
|
参数:
|
|
|
|
|
|
df (pd.DataFrame): 包含 ts_code, trade_date, turnover_rate, pe_ttm, pb, ps, circ_mv, volume_ratio, vol 等列的数据框。
|
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|
|
|
|
|
|
|
|
|
|
返回:
|
|
|
|
|
|
pd.DataFrame: 包含新增因子和最终综合因子的数据框。
|
|
|
|
|
|
"""
|
|
|
|
|
|
# 按 ts_code 和 trade_date 排序
|
|
|
|
|
|
df = df.sort_values(by=['ts_code', 'trade_date'])
|
|
|
|
|
|
|
|
|
|
|
|
# 按 ts_code 分组
|
|
|
|
|
|
grouped = df.groupby('ts_code', group_keys=False)
|
|
|
|
|
|
|
|
|
|
|
|
# 1. 市值流动比因子
|
|
|
|
|
|
df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
# 2. 市值调整成交量因子
|
|
|
|
|
|
df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
# 3. 市值加权换手率因子
|
|
|
|
|
|
df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])
|
|
|
|
|
|
|
|
|
|
|
|
# 4. 非线性市值成交量因子
|
|
|
|
|
|
df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
# 5. 市值量比因子
|
|
|
|
|
|
df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
# 6. 市值动量因子
|
|
|
|
|
|
df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
# 7. 市值波动率因子
|
|
|
|
|
|
df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)
|
|
|
|
|
|
df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 8. 市值成长性因子
|
|
|
|
|
|
df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)
|
|
|
|
|
|
df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)
|
|
|
|
|
|
|
|
|
|
|
|
# # 标准化因子
|
|
|
|
|
|
# factor_columns = [
|
|
|
|
|
|
# 'mv_turnover_ratio', 'mv_adjusted_volume', 'mv_weighted_turnover',
|
|
|
|
|
|
# 'nonlinear_mv_volume', 'mv_volume_ratio', 'mv_momentum',
|
|
|
|
|
|
# 'mv_volatility', 'mv_growth'
|
|
|
|
|
|
# ]
|
|
|
|
|
|
# scaler = StandardScaler()
|
|
|
|
|
|
# df[factor_columns] = scaler.fit_transform(df[factor_columns])
|
|
|
|
|
|
#
|
|
|
|
|
|
# # 加权合成因子
|
|
|
|
|
|
# weights = [0.2, 0.15, 0.15, 0.1, 0.1, 0.1, 0.1, 0.1] # 各因子权重
|
|
|
|
|
|
# df['final_combined_factor'] = df[factor_columns].dot(weights)
|
|
|
|
|
|
|
|
|
|
|
|
return df
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
import talib
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_rolling_factor(df):
|
|
|
|
|
|
old_columns = df.columns.tolist()[:]
|
|
|
|
|
|
# 按股票和日期排序
|
|
|
|
|
|
df = df.sort_values(by=['ts_code', 'trade_date'])
|
|
|
|
|
|
grouped = df.groupby('ts_code', group_keys=False)
|
|
|
|
|
|
|
|
|
|
|
|
df["gap_next_open"] = (df["open"].shift(-1) - df["close"]) / df["close"]
|
|
|
|
|
|
|
|
|
|
|
|
df['return_skew'] = grouped['pct_chg'].rolling(window=5).skew().reset_index(0, drop=True)
|
|
|
|
|
|
df['return_kurtosis'] = grouped['pct_chg'].rolling(window=5).kurt().reset_index(0, drop=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 因子 1：短期成交量变化率
|
|
|
|
|
|
df['volume_change_rate'] = (
|
|
|
|
|
|
grouped['vol'].rolling(window=2).mean() /
|
|
|
|
|
|
grouped['vol'].rolling(window=10).mean() - 1
|
|
|
|
|
|
).reset_index(level=0, drop=True) # 确保索引对齐
|
|
|
|
|
|
|
|
|
|
|
|
# 因子 2：成交量突破信号
|
|
|
|
|
|
max_volume = grouped['vol'].rolling(window=5).max().reset_index(level=0, drop=True) # 确保索引对齐
|
|
|
|
|
|
df['cat_volume_breakout'] = (df['vol'] > max_volume)
|
|
|
|
|
|
|
|
|
|
|
|
# 因子 3：换手率均线偏离度
|
|
|
|
|
|
mean_turnover = grouped['turnover_rate'].rolling(window=3).mean().reset_index(level=0, drop=True)
|
|
|
|
|
|
std_turnover = grouped['turnover_rate'].rolling(window=3).std().reset_index(level=0, drop=True)
|
|
|
|
|
|
df['turnover_deviation'] = (df['turnover_rate'] - mean_turnover) / std_turnover
|
|
|
|
|
|
|
|
|
|
|
|
# 因子 4：换手率激增信号
|
|
|
|
|
|
df['cat_turnover_spike'] = (df['turnover_rate'] > mean_turnover + 2 * std_turnover)
|
|
|
|
|
|
|
|
|
|
|
|
# 因子 5：量比均值
|
|
|
|
|
|
df['avg_volume_ratio'] = grouped['volume_ratio'].rolling(window=3).mean().reset_index(level=0, drop=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 因子 6：量比突破信号
|
|
|
|
|
|
max_volume_ratio = grouped['volume_ratio'].rolling(window=5).max().reset_index(level=0, drop=True)
|
|
|
|
|
|
df['cat_volume_ratio_breakout'] = (df['volume_ratio'] > max_volume_ratio)
|
|
|
|
|
|
|
|
|
|
|
|
df['vol_spike'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()
|
|
|
|
|
|
|
|
|
|
|
|
# 计算 ATR
|
|
|
|
|
|
df['atr_14'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=14),
|
|
|
|
|
|
index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['atr_6'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.ATR(x['high'].values, x['low'].values, x['close'].values, timeperiod=6),
|
|
|
|
|
|
index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 计算 OBV 及其均线
|
|
|
|
|
|
df['obv'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['maobv_6'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.SMA(x['obv'].values, timeperiod=6), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
df['rsi_3'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=3), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['rsi_6'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=6), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['rsi_9'] = grouped.apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.RSI(x['close'].values, timeperiod=9), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 计算 return_10 和 return_20
|
|
|
|
|
|
df['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)
|
|
|
|
|
|
df['return_10'] = grouped['close'].apply(lambda x: x / x.shift(10) - 1)
|
|
|
|
|
|
df['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)
|
|
|
|
|
|
|
|
|
|
|
|
# df['avg_close_5'] = grouped['close'].apply(lambda x: x.rolling(window=5).mean() / x)
|
|
|
|
|
|
|
|
|
|
|
|
# 计算标准差指标
|
|
|
|
|
|
df['std_return_5'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=5).std())
|
|
|
|
|
|
df['std_return_15'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=15).std())
|
|
|
|
|
|
df['std_return_25'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=25).std())
|
|
|
|
|
|
df['std_return_90'] = grouped['close'].apply(lambda x: x.pct_change().rolling(window=90).std())
|
|
|
|
|
|
df['std_return_90_2'] = grouped['close'].apply(lambda x: x.shift(10).pct_change().rolling(window=90).std())
|
|
|
|
|
|
|
|
|
|
|
|
# 计算 EMA 指标
|
|
|
|
|
|
df['_ema_5'] = grouped['close'].apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=5), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['_ema_13'] = grouped['close'].apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=13), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['_ema_20'] = grouped['close'].apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=20), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
|
|
df['_ema_60'] = grouped['close'].apply(
|
|
|
|
|
|
lambda x: pd.Series(talib.EMA(x.values, timeperiod=60), index=x.index)
|
|
|
|
|
|
)
|
|
|
|
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# 计算 act_factor1, act_factor2, act_factor3, act_factor4
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df['act_factor1'] = grouped['_ema_5'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
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)
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df['act_factor2'] = grouped['_ema_13'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
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)
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df['act_factor3'] = grouped['_ema_20'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
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)
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df['act_factor4'] = grouped['_ema_60'].apply(
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lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
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)
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# 根据 trade_date 截面计算排名
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df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
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df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
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df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
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df['log(circ_mv)'] = np.log(df['circ_mv'])
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def rolling_covariance(x, y, window):
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return x.rolling(window).cov(y)
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def delta(series, period):
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return series.diff(period)
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def rank(series):
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return series.rank(pct=True)
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def stddev(series, window):
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return series.rolling(window).std()
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window_high_volume = 5
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window_close_stddev = 20
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period_delta = 5
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df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)
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df['delta_cov'] = delta(df['cov'], period_delta)
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df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))
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df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
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df['alpha_003'] = np.where(df['high'] != df['low'],
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(df['close'] - df['open']) / (df['high'] - df['low']),
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0)
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df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)
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df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
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df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
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|
df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
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df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）
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df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）
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df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
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|
drop=True)
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|
df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
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|
drop=True)
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# 3. 最近连续涨跌停天数
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|
|
def calculate_consecutive_limits(series):
|
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|
|
|
"""
|
|
|
|
|
|
计算连续涨停/跌停天数。
|
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|
|
|
|
"""
|
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|
|
consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
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|
|
consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
|
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|
|
return consecutive_up, consecutive_down
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|
# 连续涨停天数
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|
|
df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
|
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|
|
|
lambda x: calculate_consecutive_limits(x)[0]
|
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|
|
).reset_index(level=0, drop=True)
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df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
|
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|
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|
|
df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
|
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|
|
def rolling_corr(group):
|
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|
|
|
roc_close = group['close'].pct_change()
|
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|
|
|
|
roc_weight = group['weight_avg'].pct_change()
|
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|
|
|
return roc_close.rolling(10).corr(roc_weight)
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|
|
df['price_cost_divergence'] = grouped.apply(rolling_corr)
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|
|
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
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|
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|
|
|
|
|
# 16. 筹码稳定性指数 (20日波动率)
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|
|
|
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
|
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|
|
|
|
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
|
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|
|
|
|
|
|
|
|
|
# 17. 成本区间突破标记
|
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|
|
|
|
df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())
|
|
|
|
|
|
|
|
|
|
|
|
# 20. 筹码-流动性风险
|
|
|
|
|
|
df['liquidity_risk'] = (df['cost_95pct'] - df['cost_5pct']) * (
|
|
|
|
|
|
1 / grouped['vol'].transform(lambda x: x.rolling(10).mean()))
|
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|
|
|
|
|
|
|
|
|
|
# 7. 市值波动率因子
|
|
|
|
|
|
df['turnover_std'] = grouped['turnover_rate'].rolling(window=20).std().reset_index(level=0, drop=True)
|
|
|
|
|
|
df['mv_volatility'] = grouped.apply(lambda x: x['turnover_std'] / x['circ_mv']).reset_index(level=0, drop=True)
|
|
|
|
|
|
|
|
|
|
|
|
# 8. 市值成长性因子
|
|
|
|
|
|
df['volume_growth'] = grouped['vol'].pct_change(periods=20).reset_index(level=0, drop=True)
|
|
|
|
|
|
df['mv_growth'] = grouped.apply(lambda x: x['volume_growth'] / x['circ_mv']).reset_index(level=0, drop=True)
|
|
|
|
|
|
|
|
|
|
|
|
df.drop(columns=['weight_std20'], inplace=True, errors='ignore')
|
|
|
|
|
|
new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]
|
|
|
|
|
|
|
|
|
|
|
|
return df, new_columns
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_simple_factor(df):
|
|
|
|
|
|
old_columns = df.columns.tolist()[:]
|
|
|
|
|
|
df = df.sort_values(by=['ts_code', 'trade_date'])
|
|
|
|
|
|
|
|
|
|
|
|
alpha = 0.5
|
|
|
|
|
|
df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']
|
|
|
|
|
|
df['resonance_factor'] = df['volume_ratio'] * df['pct_chg']
|
|
|
|
|
|
df['log_close'] = np.log(df['close'])
|
|
|
|
|
|
|
|
|
|
|
|
df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']
|
|
|
|
|
|
|
|
|
|
|
|
df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']
|
|
|
|
|
|
df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']
|
|
|
|
|
|
|
|
|
|
|
|
df['obv-maobv_6'] = df['obv'] - df['maobv_6']
|
|
|
|
|
|
|
|
|
|
|
|
# 计算比值指标
|
|
|
|
|
|
df['std_return_5 / std_return_90'] = df['std_return_5'] / df['std_return_90']
|
|
|
|
|
|
df['std_return_5 / std_return_25'] = df['std_return_5'] / df['std_return_25']
|
|
|
|
|
|
|
|
|
|
|
|
# 计算标准差差值
|
|
|
|
|
|
df['std_return_90 - std_return_90_2'] = df['std_return_90'] - df['std_return_90_2']
|
|
|
|
|
|
|
|
|
|
|
|
df['cat_af1'] = df['act_factor1'] > 0
|
|
|
|
|
|
df['cat_af2'] = df['act_factor2'] > df['act_factor1']
|
|
|
|
|
|
df['cat_af3'] = df['act_factor3'] > df['act_factor2']
|
|
|
|
|
|
df['cat_af4'] = df['act_factor4'] > df['act_factor3']
|
|
|
|
|
|
|
|
|
|
|
|
# 计算 act_factor5 和 act_factor6
|
|
|
|
|
|
df['act_factor5'] = df['act_factor1'] + df['act_factor2'] + df['act_factor3'] + df['act_factor4']
|
|
|
|
|
|
df['act_factor6'] = (df['act_factor1'] - df['act_factor2']) / np.sqrt(
|
|
|
|
|
|
df['act_factor1'] ** 2 + df['act_factor2'] ** 2)
|
|
|
|
|
|
|
|
|
|
|
|
df['active_buy_volume_large'] = df['buy_lg_vol'] / df['net_mf_vol']
|
|
|
|
|
|
df['active_buy_volume_big'] = df['buy_elg_vol'] / df['net_mf_vol']
|
|
|
|
|
|
df['active_buy_volume_small'] = df['buy_sm_vol'] / df['net_mf_vol']
|
|
|
|
|
|
|
|
|
|
|
|
df['buy_lg_vol_minus_sell_lg_vol'] = (df['buy_lg_vol'] - df['sell_lg_vol']) / df['net_mf_vol']
|
|
|
|
|
|
df['buy_elg_vol_minus_sell_elg_vol'] = (df['buy_elg_vol'] - df['sell_elg_vol']) / df['net_mf_vol']
|
|
|
|
|
|
|
|
|
|
|
|
df['log(circ_mv)'] = np.log(df['circ_mv'])
|
|
|
|
|
|
|
|
|
|
|
|
df['ctrl_strength'] = (df['cost_85pct'] - df['cost_15pct']) / (df['his_high'] - df['his_low'])
|
|
|
|
|
|
|
|
|
|
|
|
df['low_cost_dev'] = (df['close'] - df['cost_5pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
|
|
|
|
|
|
|
|
|
|
|
df['asymmetry'] = (df['cost_95pct'] - df['cost_50pct']) / (df['cost_50pct'] - df['cost_5pct'])
|
|
|
|
|
|
|
|
|
|
|
|
df['lock_factor'] = df['turnover_rate'] * (
|
|
|
|
|
|
1 - (df['cost_95pct'] - df['cost_5pct']) / (df['his_high'] - df['his_low']))
|
|
|
|
|
|
|
|
|
|
|
|
df['cat_vol_break'] = (df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2)
|
|
|
|
|
|
|
|
|
|
|
|
df['cost_atr_adj'] = (df['cost_95pct'] - df['cost_5pct']) / df['atr_14']
|
|
|
|
|
|
|
|
|
|
|
|
# 12. 小盘股筹码集中度
|
|
|
|
|
|
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
|
|
|
|
|
|
|
|
|
|
|
|
df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &
|
|
|
|
|
|
(df['volume_ratio'] > 1.5) &
|
|
|
|
|
|
(df['winner_rate'] > 0.7))
|
|
|
|
|
|
|
|
|
|
|
|
df['mv_turnover_ratio'] = df['turnover_rate'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
df['mv_adjusted_volume'] = df['vol'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
df['mv_weighted_turnover'] = df['turnover_rate'] * (1 / df['circ_mv'])
|
|
|
|
|
|
|
|
|
|
|
|
df['nonlinear_mv_volume'] = df['vol'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
df['mv_volume_ratio'] = df['volume_ratio'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
df['mv_momentum'] = df['turnover_rate'] * df['volume_ratio'] / df['circ_mv']
|
|
|
|
|
|
|
|
|
|
|
|
drop_columns = [col for col in df.columns if col.startswith('_')]
|
|
|
|
|
|
df.drop(columns=drop_columns, inplace=True, errors='ignore')
|
|
|
|
|
|
|
|
|
|
|
|
new_columns = [col for col in df.columns.tolist()[:] if col not in old_columns]
|
|
|
|
|
|
return df, new_columns
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def calculate_indicators(df):
|
|
|
|
|
|
"""
|
|
|
|
|
|
计算四个指标：当日涨跌幅、5日移动平均、RSI、MACD。
|
|
|
|
|
|
"""
|
|
|
|
|
|
df = df.sort_values('trade_date')
|
|
|
|
|
|
df['daily_return'] = (df['close'] - df['pre_close']) / df['pre_close'] * 100
|
|
|
|
|
|
# df['5_day_ma'] = df['close'].rolling(window=5).mean()
|
|
|
|
|
|
delta = df['close'].diff()
|
|
|
|
|
|
gain = delta.where(delta > 0, 0)
|
|
|
|
|
|
loss = -delta.where(delta < 0, 0)
|
|
|
|
|
|
avg_gain = gain.rolling(window=14).mean()
|
|
|
|
|
|
avg_loss = loss.rolling(window=14).mean()
|
|
|
|
|
|
rs = avg_gain / avg_loss
|
|
|
|
|
|
df['RSI'] = 100 - (100 / (1 + rs))
|
|
|
|
|
|
|
|
|
|
|
|
# 计算MACD
|
|
|
|
|
|
ema12 = df['close'].ewm(span=12, adjust=False).mean()
|
|
|
|
|
|
ema26 = df['close'].ewm(span=26, adjust=False).mean()
|
|
|
|
|
|
df['MACD'] = ema12 - ema26
|
|
|
|
|
|
df['Signal_line'] = df['MACD'].ewm(span=9, adjust=False).mean()
|
|
|
|
|
|
df['MACD_hist'] = df['MACD'] - df['Signal_line']
|
|
|
|
|
|
|
|
|
|
|
|
# 4. 情绪因子1：市场上涨比例（Up Ratio）
|
|
|
|
|
|
df['up_ratio'] = df['daily_return'].apply(lambda x: 1 if x > 0 else 0)
|
|
|
|
|
|
df['up_ratio_20d'] = df['up_ratio'].rolling(window=20).mean() # 过去20天上涨比例
|
|
|
|
|
|
|
|
|
|
|
|
# 5. 情绪因子2：成交量变化率（Volume Change Rate）
|
|
|
|
|
|
df['volume_mean'] = df['vol'].rolling(window=20).mean() # 过去20天的平均成交量
|
|
|
|
|
|
df['volume_change_rate'] = (df['vol'] - df['volume_mean']) / df['volume_mean'] * 100 # 成交量变化率
|
|
|
|
|
|
|
|
|
|
|
|
# 6. 情绪因子3：波动率（Volatility）
|
|
|
|
|
|
df['volatility'] = df['daily_return'].rolling(window=20).std() # 过去20天的日收益率标准差
|
|
|
|
|
|
|
|
|
|
|
|
# 7. 情绪因子4：成交额变化率（Amount Change Rate）
|
|
|
|
|
|
df['amount_mean'] = df['amount'].rolling(window=20).mean() # 过去20天的平均成交额
|
|
|
|
|
|
df['amount_change_rate'] = (df['amount'] - df['amount_mean']) / df['amount_mean'] * 100 # 成交额变化率
|
|
|
|
|
|
|
|
|
|
|
|
return df
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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def generate_index_indicators(h5_filename):
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df = pd.read_hdf(h5_filename, key='index_data')
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df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')
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df = df.sort_values('trade_date')
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# 计算每个ts_code的相关指标
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df_indicators = []
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for ts_code in df['ts_code'].unique():
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df_index = df[df['ts_code'] == ts_code].copy()
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df_index = calculate_indicators(df_index)
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df_indicators.append(df_index)
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# 合并所有指数的结果
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df_all_indicators = pd.concat(df_indicators, ignore_index=True)
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# 保留trade_date列，并将同一天的数据按ts_code合并成一行
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df_final = df_all_indicators.pivot_table(
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index='trade_date',
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columns='ts_code',
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values=['daily_return', 'RSI', 'MACD', 'Signal_line',
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'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',
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'amount_change_rate', 'amount_mean'],
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aggfunc='last'
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)
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df_final.columns = [f"{col[1]}_{col[0]}" for col in df_final.columns]
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df_final = df_final.reset_index()
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return df_final
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def read_industry_data(h5_filename):
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# 读取 H5 文件中所有的行业数据
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industry_data = pd.read_hdf(h5_filename, key='sw_daily', columns=[
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'ts_code', 'trade_date', 'open', 'close', 'high', 'low', 'pe', 'pb', 'vol'
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]) # 假设 H5 文件的键是 'industry_data'
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industry_data = industry_data.sort_values(by=['ts_code', 'trade_date'])
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industry_data = industry_data.reindex()
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industry_data['trade_date'] = pd.to_datetime(industry_data['trade_date'], format='%Y%m%d')
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grouped = industry_data.groupby('ts_code', group_keys=False)
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industry_data['obv'] = grouped.apply(
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lambda x: pd.Series(talib.OBV(x['close'].values, x['vol'].values), index=x.index)
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)
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industry_data['return_5'] = grouped['close'].apply(lambda x: x / x.shift(5) - 1)
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industry_data['return_20'] = grouped['close'].apply(lambda x: x / x.shift(20) - 1)
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industry_data = get_act_factor(industry_data, cat=False)
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industry_data = industry_data.sort_values(by=['trade_date', 'ts_code'])
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# # 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
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# factor_columns = ['obv', 'return_5', 'return_20', 'act_factor1', 'act_factor2', 'act_factor3', 'act_factor4'] # 因子列
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#
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# for factor in factor_columns:
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# if factor in industry_data.columns:
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# # 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
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# industry_data[f'{factor}_deviation'] = industry_data.groupby('trade_date')[factor].transform(
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# lambda x: x - x.mean())
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industry_data['return_5_percentile'] = industry_data.groupby('trade_date')['return_5'].transform(
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lambda x: x.rank(pct=True))
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industry_data['return_20_percentile'] = industry_data.groupby('trade_date')['return_20'].transform(
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lambda x: x.rank(pct=True))
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industry_data = industry_data.drop(columns=['open', 'close', 'high', 'low', 'pe', 'pb', 'vol'])
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industry_data = industry_data.rename(
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columns={col: f'industry_{col}' for col in industry_data.columns if col not in ['ts_code', 'trade_date']})
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industry_data = industry_data.rename(columns={'ts_code': 'cat_l2_code'})
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return industry_data
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