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RollingRankCopy赚钱-sharp-1.43

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liaozhaorun
2025-04-28 11:01:04 +08:00
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import numpy as np
import talib
import pandas as pd
def get_technical_factor(df):
# 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['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=5).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)
# 因子 7：成交量与换手率的综合动量因子
alpha = 0.5
df['momentum_factor'] = df['volume_change_rate'] + alpha * df['turnover_deviation']
# 因子 8：量价共振因子
df['price_change_rate'] = grouped['close'].pct_change()
df['resonance_factor'] = df['volume_ratio'] * df['price_change_rate']
# 计算 up 和 down
df['log_close'] = np.log(df['close'])
df['vol_spike'] = grouped.apply(
lambda x: pd.Series(x['vol'].rolling(20).mean(), index=x.index)
)
df['cat_vol_spike'] = df['vol'] > 2 * df['vol_spike']
df['vol_std_5'] = df['vol'].pct_change().rolling(5).std()
df['up'] = (df['high'] - df[['close', 'open']].max(axis=1)) / df['close']
df['down'] = (df[['close', 'open']].min(axis=1) - df['low']) / df['close']
# 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['obv-maobv_6'] = df['obv'] - df['maobv_6']
# 计算 RSI
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']
return df
def get_act_factor(df, cat=True):
# 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)
# 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)
)
# 计算 act_factor1, act_factor2, act_factor3, act_factor4
df['act_factor1'] = grouped['_ema_5'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
)
df['act_factor2'] = grouped['_ema_13'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
)
df['act_factor3'] = grouped['_ema_20'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
)
df['act_factor4'] = grouped['_ema_60'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
)
if cat:
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)
# 根据 trade_date 截面计算排名
df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
return df
def get_money_flow_factor(df):
# 计算资金流相关因子（字段名称见 tushare 数据说明）
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'])
return df
def get_alpha_factor(df):
df = df.sort_values(by=['ts_code', 'trade_date'])
grouped = df.groupby('ts_code')
# alpha_022: 当前 close 与 5 日前 close 差值
# df['alpha_022'] = grouped['close'].transform(lambda x: x - x.shift(5))
def rolling_covariance(x, y, window):
return x.rolling(window).cov(y)
def delta(series, period):
return series.diff(period)
def rank(series):
return series.rank(pct=True)
def stddev(series, window):
return series.rolling(window).std()
# 计算改进后的 Alpha 22 因子
window_high_volume = 5
window_close_stddev = 20
period_delta = 5
df['cov'] = rolling_covariance(df['high'], df['volume'], window_high_volume)
df['delta_cov'] = delta(df['cov'], period_delta)
df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))
df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
# alpha_003: (close - open) / (high - low)
df['alpha_003'] = np.where(df['high'] != df['low'],
(df['close'] - df['open']) / (df['high'] - df['low']),
0)
# alpha_007: 计算过去5日 close 与 vol 的相关性，并按 trade_date 排名
df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)
df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
# alpha_013: 计算过去5日 close 之和 - 20日 close 之和，并按 trade_date 排名
df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
return df
def get_limit_factor(df):
# 按股票和日期排序
df = df.sort_values(by=['ts_code', 'trade_date'])
# 分组处理
grouped = df.groupby('ts_code', group_keys=False)
# 1. 今日是否涨停/跌停
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）
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）
# 2. 最近涨跌停次数（过去20个交易日）
df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
drop=True)
df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
drop=True)
# 3. 最近连续涨跌停天数
def calculate_consecutive_limits(series):
"""
计算连续涨停/跌停天数。
"""
consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
return consecutive_up, consecutive_down
# 连续涨停天数
df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
lambda x: calculate_consecutive_limits(x)[0]
).reset_index(level=0, drop=True)
# 连续跌停天数
# df['consecutive_down_limit'] = grouped['cat_down_limit'].apply(
# lambda x: calculate_consecutive_limits(x)[1]
# ).reset_index(level=0, drop=True)
return df
def get_cyp_perf_factor(df):
# 预处理：按股票代码和时间排序
df = df.sort_values(by=['ts_code', 'trade_date'])
# 按股票代码分组处理
grouped = df.groupby('ts_code', group_keys=False)
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['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
def rolling_corr(group):
roc_close = group['close'].pct_change()
roc_weight = group['weight_avg'].pct_change()
return roc_close.rolling(10).corr(roc_weight)
df['price_cost_divergence'] = grouped.apply(rolling_corr)
def calc_atr(group):
high, low, close = group['high'], group['low'], group['close']
tr = np.maximum(high - low,
np.maximum(abs(high - close.shift()),
abs(low - close.shift())))
return tr.rolling(14).mean()
df['atr_14'] = grouped.apply(calc_atr)
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'])
# 16. 筹码稳定性指数 (20日波动率)
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
# 17. 成本区间突破标记
df['high_cost_break_days'] = grouped.apply(lambda g: g['close'].gt(g['cost_95pct']).rolling(5).sum())
# 18. 黄金筹码共振 (复合事件)
df['cat_golden_resonance'] = ((df['close'] > df['weight_avg']) &
(df['volume_ratio'] > 1.5) &
(df['winner_rate'] > 0.7))
# 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()))
df.drop(columns=['weight_std20'], inplace=True, errors='ignore')
return df
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。
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)
)
# 计算 act_factor1, act_factor2, act_factor3, act_factor4
df['act_factor1'] = grouped['_ema_5'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 50
)
df['act_factor2'] = grouped['_ema_13'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 40
)
df['act_factor3'] = grouped['_ema_20'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 21
)
df['act_factor4'] = grouped['_ema_60'].apply(
lambda x: np.arctan((x / x.shift(1) - 1) * 100) * 57.3 / 10
)
# 根据 trade_date 截面计算排名
df['rank_act_factor1'] = df.groupby('trade_date', group_keys=False)['act_factor1'].rank(ascending=False, pct=True)
df['rank_act_factor2'] = df.groupby('trade_date', group_keys=False)['act_factor2'].rank(ascending=False, pct=True)
df['rank_act_factor3'] = df.groupby('trade_date', group_keys=False)['act_factor3'].rank(ascending=False, pct=True)
df['log(circ_mv)'] = np.log(df['circ_mv'])
def rolling_covariance(x, y, window):
return x.rolling(window).cov(y)
def delta(series, period):
return series.diff(period)
def rank(series):
return series.rank(pct=True)
def stddev(series, window):
return series.rolling(window).std()
window_high_volume = 5
window_close_stddev = 20
period_delta = 5
df['cov'] = rolling_covariance(df['high'], df['vol'], window_high_volume)
df['delta_cov'] = delta(df['cov'], period_delta)
df['_rank_stddev'] = rank(stddev(df['close'], window_close_stddev))
df['alpha_22_improved'] = -1 * df['delta_cov'] * df['_rank_stddev']
df['alpha_003'] = np.where(df['high'] != df['low'],
(df['close'] - df['open']) / (df['high'] - df['low']),
0)
df['alpha_007'] = grouped.apply(lambda x: x['close'].rolling(5).corr(x['vol'])).reset_index(level=0, drop=True)
df['alpha_007'] = df.groupby('trade_date', group_keys=False)['alpha_007'].rank(ascending=True, pct=True)
df['alpha_013'] = grouped['close'].transform(lambda x: x.rolling(5).sum() - x.rolling(20).sum())
df['alpha_013'] = df.groupby('trade_date', group_keys=False)['alpha_013'].rank(ascending=True, pct=True)
df['cat_up_limit'] = (df['close'] == df['up_limit']) # 是否涨停（1表示涨停，0表示未涨停）
df['cat_down_limit'] = (df['close'] == df['down_limit']) # 是否跌停（1表示跌停，0表示未跌停）
df['up_limit_count_10d'] = grouped['cat_up_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
drop=True)
df['down_limit_count_10d'] = grouped['cat_down_limit'].rolling(window=10, min_periods=1).sum().reset_index(level=0,
drop=True)
# 3. 最近连续涨跌停天数
def calculate_consecutive_limits(series):
"""
计算连续涨停/跌停天数。
"""
consecutive_up = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
consecutive_down = series * (series.groupby((series != series.shift()).cumsum()).cumcount() + 1)
return consecutive_up, consecutive_down
# 连续涨停天数
df['consecutive_up_limit'] = grouped['cat_up_limit'].apply(
lambda x: calculate_consecutive_limits(x)[0]
).reset_index(level=0, drop=True)
df['vol_break'] = np.where((df['close'] > df['cost_85pct']) & (df['volume_ratio'] > 2), 1, 0)
df['weight_roc5'] = grouped['weight_avg'].apply(lambda x: x.pct_change(5))
def rolling_corr(group):
roc_close = group['close'].pct_change()
roc_weight = group['weight_avg'].pct_change()
return roc_close.rolling(10).corr(roc_weight)
df['price_cost_divergence'] = grouped.apply(rolling_corr)
df['smallcap_concentration'] = (1 / df['circ_mv']) * (df['cost_85pct'] - df['cost_15pct'])
# 16. 筹码稳定性指数 (20日波动率)
df['weight_std20'] = grouped['weight_avg'].apply(lambda x: x.rolling(20).std())
df['cost_stability'] = df['weight_std20'] / grouped['weight_avg'].transform(lambda x: x.rolling(20).mean())
# 17. 成本区间突破标记
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()))
# 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
def generate_index_indicators(h5_filename):
df = pd.read_hdf(h5_filename, key='index_data')
df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')
df = df.sort_values('trade_date')
# 计算每个ts_code的相关指标
df_indicators = []
for ts_code in df['ts_code'].unique():
df_index = df[df['ts_code'] == ts_code].copy()
df_index = calculate_indicators(df_index)
df_indicators.append(df_index)
# 合并所有指数的结果
df_all_indicators = pd.concat(df_indicators, ignore_index=True)
# 保留trade_date列，并将同一天的数据按ts_code合并成一行
df_final = df_all_indicators.pivot_table(
index='trade_date',
columns='ts_code',
values=['daily_return', 'RSI', 'MACD', 'Signal_line',
'MACD_hist', 'up_ratio_20d', 'volume_change_rate', 'volatility',
'amount_change_rate', 'amount_mean'],
aggfunc='last'
)
df_final.columns = [f"{col[1]}_{col[0]}" for col in df_final.columns]
df_final = df_final.reset_index()
return df_final

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import numpy as np
import pandas as pd
def read_and_merge_h5_data(h5_filename, key, columns, df=None, join='left', on=['ts_code', 'trade_date'], prefix=None):
processed_columns = []
for col in columns:
if col.startswith('_'):
processed_columns.append(col[1:]) # 去掉下划线
else:
processed_columns.append(col)
# 从 HDF5 文件读取数据，选择需要的列
data = pd.read_hdf(h5_filename, key=key, columns=processed_columns)
# 修改列名，如果列名以前有 _，加上 _
for col in data.columns:
if col not in columns: # 只有不在 columns 中的列才需要加下划线
new_col = f'_{col}'
data.rename(columns={col: new_col}, inplace=True)
if prefix is not None:
for col in data.columns:
if col not in ['ts_code', 'trade_date']: # 只有不在 columns 中的列才需要加下划线
new_col = f'{prefix}_{col}'
data.rename(columns={col: new_col}, inplace=True)
# 如果传入的 df 不为空，则进行合并
if df is not None and not df.empty:
print(f'{join} merge on {on}')
if 'trade_date' in on:
# 确保两个 DataFrame 都有 ts_code 和 trade_date 列
df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d')
data['trade_date'] = pd.to_datetime(data['trade_date'], format='%Y%m%d')
# 根据 ts_code 和 trade_date 合并
merged_df = pd.merge(df, data, on=on, how=join)
else:
# 如果 df 为空，则直接返回读取的数据
merged_df = data
return merged_df
def calculate_risk_adjusted_return(df, days=1, method='ratio', lambda_=0.5, eps=1e-8):
"""
计算单只股票的风险调整收益。
参数：
- df: DataFrame，包含 'ts_code' 和 'close' 列，按日期排序（假设 'trade_date' 已排序）。
- days: 预测未来多少天的收益，默认1天。
- method: 'ratio'（收益/波动率） 或 'difference'（收益 - λ * 波动率）。
- lambda_: 风险惩罚系数，仅当 method='difference' 时有效。
- eps: 防止除零的小常数。
返回：
- df：添加 'risk_adj_return' 列的 DataFrame，表示风险调整后的收益。
"""
# 确保数据按 ts_code 和 trade_date 排序
df = df.sort_values(by=['ts_code', 'trade_date'])
# 计算未来的对数收益率
df['future_return'] = np.log(df.groupby('ts_code')['close'].shift(-days) / df['close'])
# 计算历史收益（对数收益率）
df['historical_return'] = np.log(df.groupby('ts_code')['close'].shift(1) / df['close'])
# 计算波动率（历史收益的标准差）
df['volatility'] = df.groupby('ts_code')['historical_return'].rolling(window=days).std().reset_index(level=0,
drop=True)
# 根据选择的 method 计算风险调整收益
if method == 'ratio':
# 收益/波动率（防止除零）
df['risk_adj_return'] = df['future_return'] / (df['volatility'] + eps)
elif method == 'difference':
# 收益 - λ * 波动率
df['risk_adj_return'] = df['future_return'] - lambda_ * df['volatility']
else:
raise ValueError("Invalid method. Use 'ratio' or 'difference'.")
return df
# import polars as pl
#
# def read_and_merge_h5_data_polars(h5_filename, key, columns, df=None, join='left', on=['ts_code', 'trade_date']):
# processed_columns = []
# for col in columns:
# if col.startswith('_'):
# processed_columns.append(col[1:]) # 去掉下划线
# else:
# processed_columns.append(col)
#
# # 从 HDF5 文件读取数据，选择需要的列
# pd_df = pd.read_hdf(h5_filename, key=key, columns=processed_columns)
#
# # 将 Pandas DataFrame 转换为 Polars DataFrame
# data = pl.from_pandas(pd_df)
#
# # 修改列名，如果列名以前有 _，加上 _
# data = data.rename({col: f'_{col}' for col in data.columns if col not in columns})
#
# # 如果传入的 df 不为空，则进行合并
# if df is not None and not df.is_empty():
# print(f'{join} merge on {on}')
#
# # 确保两个 DataFrame 都有 ts_code 和 trade_date 列
# # df = df.with_columns(pl.col('trade_date').str.strptime(pl.Datetime, format='%Y%m%d'))
# # data = data.with_columns(pl.col('trade_date').str.strptime(pl.Datetime, format='%Y%m%d'))
#
# # 根据 ts_code 和 trade_date 合并
# merged_df = df.join(data, on=on, how=join)
# else:
# # 如果 df 为空，则直接返回读取的数据
# merged_df = data
#
# return merged_df