148 lines
5.2 KiB
Python
148 lines
5.2 KiB
Python
"""
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情绪弹性特征 (4维) — calculate_relaxed_emotion_features(df, market_regime)
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180 日窗口，筛选 advance_ratio < 0.20 恐慌日 + advance_ratio > 0.55 贪婪日。
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"""
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import numpy as np
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import pandas as pd
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from core.scoring.config import PANIC_ADVANCE_RATIO, GREED_ADVANCE_RATIO, WINDOW_180D
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def calculate_relaxed_emotion_features(ctx) -> pd.DataFrame:
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"""计算情绪弹性特征 (4维)"""
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kline = ctx.kline.copy()
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mkt = ctx.market_regime.copy() if ctx.market_regime is not None else pd.DataFrame()
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if kline.empty or mkt.empty:
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return pd.DataFrame()
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# 筛选恐慌日和贪婪日
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panic_dates = set()
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greed_dates = set()
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for _, row in mkt.iterrows():
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td = row['trade_date']
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ar = row.get('advance_ratio', 0.5)
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if ar < PANIC_ADVANCE_RATIO:
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panic_dates.add(td.date() if hasattr(td, 'date') else td)
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if ar > GREED_ADVANCE_RATIO:
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greed_dates.add(td.date() if hasattr(td, 'date') else td)
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kline = kline.sort_values(['stock_code', 'trade_date'])
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kline['td'] = (kline['trade_date'].dt.date
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if hasattr(kline['trade_date'], 'dt')
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else pd.to_datetime(kline['trade_date']).dt.date)
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candidates = ctx.candidates
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# 计算全市场平均振幅 (用于恐慌/贪婪 ratio)
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kline['amp'] = (kline['high'] - kline['low']) / np.where(
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kline['open'] > 0, kline['open'], 1
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)
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market_amp = kline.groupby('td')['amp'].mean().to_dict()
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features = {}
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grouped = kline.groupby('stock_code')
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for code, group in grouped:
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if code not in candidates:
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continue
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if len(group) < 20:
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continue
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g = group.sort_values('trade_date').tail(WINDOW_180D)
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closes = g['close'].values
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opens = g['open'].values
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highs = g['high'].values
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lows = g['low'].values
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dates = g['td'].values
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amps = (highs - lows) / np.where(opens > 0, opens, 1)
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feat = {}
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# 恐慌日分析
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panic_indices = [i for i, d in enumerate(dates) if d in panic_dates]
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if panic_indices:
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panic_amp_ratios = []
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panic_drop_ratios = []
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panic_rebounds = []
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for pi in panic_indices:
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td = dates[pi]
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mkt_amp_val = market_amp.get(td, amps[pi])
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if mkt_amp_val > 0:
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panic_amp_ratios.append(amps[pi] / mkt_amp_val)
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# 恐慌日跌幅
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if pi > 0 and closes[pi - 1] > 0:
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drop = (closes[pi] - closes[pi - 1]) / closes[pi - 1]
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# 全市场跌幅: 用 advance_ratio 估算
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ar = _get_advance_ratio(mkt, td)
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market_drop = -0.02 if ar < PANIC_ADVANCE_RATIO else -0.005
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if market_drop != 0:
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panic_drop_ratios.append(drop / market_drop)
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# 恐慌次日反弹
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if pi + 1 < len(g) and closes[pi] > 0:
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panic_rebounds.append(highs[pi + 1] / closes[pi] - 1)
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# 64. relaxed_panic_amplitude_ratio
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feat['relaxed_panic_amplitude_ratio'] = (
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np.median(panic_amp_ratios) if panic_amp_ratios else 1.0
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)
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# 65. relaxed_panic_rebound_strength
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feat['relaxed_panic_rebound_strength'] = (
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np.median(panic_rebounds) if panic_rebounds else 0.0
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)
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else:
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feat['relaxed_panic_amplitude_ratio'] = 1.0
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feat['relaxed_panic_rebound_strength'] = 0.0
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# 贪婪日分析
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greed_indices = [i for i, d in enumerate(dates) if d in greed_dates]
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if greed_indices:
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greed_gains = []
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greed_amp_ratios = []
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for gi in greed_indices:
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td = dates[gi]
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if gi > 0 and closes[gi - 1] > 0:
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gain = (closes[gi] - closes[gi - 1]) / closes[gi - 1]
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# 全市场收益
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ar = _get_advance_ratio(mkt, td)
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market_gain = 0.01 if ar > GREED_ADVANCE_RATIO else 0.003
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if market_gain > 0:
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greed_gains.append(gain / market_gain)
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mkt_amp_val = market_amp.get(td, amps[gi])
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if mkt_amp_val > 0:
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greed_amp_ratios.append(amps[gi] / mkt_amp_val)
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# 66. relaxed_greed_relative_gain
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feat['relaxed_greed_relative_gain'] = (
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np.median(greed_gains) if greed_gains else 0.0
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)
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# 67. relaxed_greed_amplitude_ratio
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feat['relaxed_greed_amplitude_ratio'] = (
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np.median(greed_amp_ratios) if greed_amp_ratios else 1.0
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)
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else:
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feat['relaxed_greed_relative_gain'] = 0.0
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feat['relaxed_greed_amplitude_ratio'] = 1.0
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features[code] = feat
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return pd.DataFrame.from_dict(features, orient='index')
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def _get_advance_ratio(mkt_df, trade_date):
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"""获取指定日期的 advance_ratio"""
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if mkt_df is None or mkt_df.empty:
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return 0.5
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td = trade_date
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match = mkt_df[mkt_df['trade_date'] == td]
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if not match.empty:
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return match.iloc[0].get('advance_ratio', 0.5)
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return 0.5
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