"""
负向指标 (5维) — calculate_negative_features(df)
注意: #53 trend_consistency_20d 与 #39 同名不同义，更新字典时会覆盖 #39
"""
import numpy as np
import pandas as pd


def calculate_negative_features(ctx) -> pd.DataFrame:
    """计算负向指标 (5维)"""
    kline = ctx.kline.copy()
    if kline.empty:
        return pd.DataFrame()

    kline = kline.sort_values(['stock_code', 'trade_date'])
    candidates = ctx.candidates

    features = {}
    grouped = kline.groupby('stock_code')

    for code, group in grouped:
        if code not in candidates:
            continue
        g = group.tail(120)
        if len(g) < 20:
            continue

        closes = g['close'].values
        opens = g['open'].values
        highs = g['high'].values
        lows = g['low'].values
        volumes = g['volume'].values

        feat = {}

        # 53. trend_consistency_20d (负向版本): |mean(return>0) - 0.5| × 100
        # 衡量偏离均衡的程度，越接近50%越弱
        rets_20 = np.diff(closes[-21:]) / np.where(closes[-21:-1] > 0, closes[-21:-1], 1)
        feat['trend_consistency_20d'] = abs(np.mean(rets_20 > 0) - 0.5) * 100

        # 54. max_consecutive_direction_20d: 最大连续同向天数
        rets_sign = np.sign(np.diff(closes[-21:]))
        max_consec = 0
        curr_consec = 0
        curr_sign = 0
        for s in rets_sign:
            if s != 0 and s == curr_sign:
                curr_consec += 1
            elif s != 0:
                curr_sign = s
                curr_consec = 1
            else:
                curr_consec = 0
            max_consec = max(max_consec, curr_consec)
        feat['max_consecutive_direction_20d'] = max_consec

        # 55. gap_risk_20d: mean(|open_t - close_{t-1}|/close_{t-1} > 0.02) × 100
        gap_count = 0
        n = 0
        for i in range(max(0, len(g) - 20), len(g)):
            if i > 0 and closes[i - 1] > 0:
                gap_pct = abs(opens[i] - closes[i - 1]) / closes[i - 1]
                if gap_pct > 0.02:
                    gap_count += 1
                n += 1
        feat['gap_risk_20d'] = gap_count / n * 100 if n > 0 else 0

        # 56. liquidity_drying_up_20d: min(vol_20d)/mean(vol_60d)
        vol_20 = volumes[-20:]
        vol_60 = volumes[-60:] if len(volumes) >= 60 else volumes
        feat['liquidity_drying_up_20d'] = (
            np.min(vol_20) / np.mean(vol_60) if np.mean(vol_60) > 0 else 1
        )

        # 57. price_stagnation_20d: (max(high_20d)-min(low_20d))/close × 100
        h20 = np.max(highs[-20:])
        l20 = np.min(lows[-20:])
        feat['price_stagnation_20d'] = (
            (h20 - l20) / closes[-1] * 100 if closes[-1] > 0 else 0
        )

        features[code] = feat

    return pd.DataFrame.from_dict(features, orient='index')