"""
Enhanced Recovery Momentum Strategy.

Improvements over base RecoveryMomentumStrategy:
1. Inverse-volatility weighting (allocate more to lower-vol winners → better risk-adjusted)
2. Monthly rebalancing (controls turnover)
3. Momentum filter gate: only pick stocks with positive 6-month momentum
   (avoids "dead cat bounces" — recovery without underlying trend)
4. Volatility regime scaling: reduce exposure when market vol is elevated
"""

import numpy as np
import pandas as pd
from strategies.base import Strategy


class EnhancedRecoveryMomentumStrategy(Strategy):
    """
    Recovery + momentum with inverse-vol weighting and regime awareness.
    """

    def __init__(
        self,
        recovery_window: int = 63,
        mom_lookback: int = 252,
        mom_skip: int = 21,
        intermediate_mom: int = 126,
        vol_window: int = 60,
        rebal_freq: int = 21,
        top_n: int = 20,
        regime_scale: bool = True,
    ):
        self.recovery_window = recovery_window
        self.mom_lookback = mom_lookback
        self.mom_skip = mom_skip
        self.intermediate_mom = intermediate_mom
        self.vol_window = vol_window
        self.rebal_freq = rebal_freq
        self.top_n = top_n
        self.regime_scale = regime_scale

    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
        # Factor 1: Recovery — price / rolling min
        recovery = data / data.rolling(self.recovery_window, min_periods=self.recovery_window).min() - 1

        # Factor 2: 12-1 month momentum
        momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip)

        # Factor 3: Intermediate momentum (6m) as a filter gate
        # Only consider stocks with positive intermediate trend
        intermediate = data.shift(self.mom_skip).pct_change(self.intermediate_mom - self.mom_skip)
        trend_gate = intermediate > 0

        # Cross-sectional percentile ranks
        rec_rank = recovery.rank(axis=1, pct=True, na_option="keep")
        mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")

        # Composite score (50/50), gated by intermediate trend
        composite = 0.5 * rec_rank + 0.5 * mom_rank
        composite = composite.where(trend_gate, np.nan)

        # Select top_n
        rank = composite.rank(axis=1, ascending=False, na_option="bottom")
        n_valid = composite.notna().sum(axis=1)
        enough = n_valid >= self.top_n
        top_mask = (rank <= self.top_n) & enough.values.reshape(-1, 1)

        # Inverse-vol weighting among selected stocks
        returns = data.pct_change()
        vol = returns.rolling(self.vol_window, min_periods=30).std().replace(0, np.nan)
        inv_vol = (1.0 / vol).where(top_mask, 0.0)

        row_sums = inv_vol.sum(axis=1).replace(0, np.nan)
        signals = inv_vol.div(row_sums, axis=0).fillna(0.0)

        # Regime scaling: reduce exposure when market vol is high
        if self.regime_scale:
            market_vol = vol.mean(axis=1)
            vol_median = market_vol.rolling(252, min_periods=126).median()
            vol_ratio = (vol_median / market_vol).clip(0.5, 1.2)
            signals = signals.mul(vol_ratio, axis=0)
            # Re-normalize: cap at 1.0
            row_totals = signals.sum(axis=1)
            overflow = row_totals > 1.0
            signals.loc[overflow] = signals.loc[overflow].div(
                row_totals[overflow], axis=0
            )

        # Monthly rebalance: keep only rebal-day signals, forward-fill
        warmup = max(self.mom_lookback, self.recovery_window, self.vol_window + 252)
        rebal_mask = pd.Series(False, index=data.index)
        rebal_indices = list(range(warmup, len(data), self.rebal_freq))
        rebal_mask.iloc[rebal_indices] = True

        signals[~rebal_mask] = np.nan
        signals = signals.ffill().fillna(0.0)
        signals.iloc[:warmup] = 0.0

        return signals.shift(1).fillna(0.0)