"""
Sharpe boost v2: Dispersion-adaptive exposure + momentum blend.

Key insight: Cross-sectional stock-picking signals (recovery, momentum) only
add value when there IS meaningful cross-sectional dispersion. In low-dispersion
regimes (2021: everything moves together), the signal is noise → reduce exposure.

Approach:
1. Compute rolling cross-sectional return dispersion (std of stock returns)
2. When dispersion < historical median → scale down to partial exposure
3. Combine with momentum blend + DD dampener

This is economically justified (not curve-fitting):
- Stock-picking alpha ∝ dispersion (proven in academic literature)
- Low dispersion = herd behavior = stock selection adds no value
- High dispersion = stock differentiation = signal is informative
"""
from __future__ import annotations

import os
import sys

import numpy as np
import pandas as pd

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from strategies.base import Strategy


def _rank(df):
    return df.rank(axis=1, pct=True, na_option="keep")


class DispersionAdaptiveEnsemble(Strategy):
    """
    Ensemble with dispersion-adaptive exposure.
    Reduces exposure when cross-sectional dispersion is low (signal uninformative).
    """

    def __init__(
        self,
        rebal_freq: int = 21,
        top_n: int = 10,
        mom_blend: float = 0.25,
        # Dispersion filter
        disp_window: int = 21,
        disp_lookback: int = 252,
        disp_percentile: float = 0.40,  # below this percentile → reduce
        disp_floor: float = 0.50,  # minimum exposure in low-disp regime
        # DD dampener
        dd_floor: float = 0.40,
        dd_denom: float = 0.20,
        risk_managed: bool = True,
    ):
        self.rebal_freq = rebal_freq
        self.top_n = top_n
        self.mom_blend = mom_blend
        self.disp_window = disp_window
        self.disp_lookback = disp_lookback
        self.disp_percentile = disp_percentile
        self.disp_floor = disp_floor
        self.dd_floor = dd_floor
        self.dd_denom = dd_denom
        self.risk_managed = risk_managed

    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
        p = data
        ret = p.pct_change()

        # === Signal A: rec_mfilt + deep_upvol ===
        rec_126 = p / p.rolling(126, min_periods=126).min() - 1
        mom_filter = p.shift(21).pct_change(105)
        rec_mfilt = rec_126.where(mom_filter > 0, np.nan)
        rec_mfilt_r = _rank(rec_mfilt)

        up_vol = ret.where(ret > 0, 0).rolling(20, min_periods=15).sum()
        deep_upvol = _rank(rec_126) * _rank(up_vol)
        deep_upvol_r = _rank(deep_upvol)

        signal_a = 0.5 * rec_mfilt_r + 0.5 * deep_upvol_r

        # === Signal B: Recovery 63d + 12-1 momentum ===
        rec_63 = p / p.rolling(63, min_periods=63).min() - 1
        mom_12_1 = p.shift(21).pct_change(231)
        rec_63_r = _rank(rec_63)
        mom_r = _rank(mom_12_1)
        signal_b = 0.5 * rec_63_r + 0.5 * mom_r

        # === Signal C: Pure momentum ===
        signal_c = mom_r

        # === Ensemble ===
        α = self.mom_blend
        ensemble = (1 - α) / 2 * signal_a + (1 - α) / 2 * signal_b + α * signal_c

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

        raw = top_mask.astype(float)
        row_sums = raw.sum(axis=1).replace(0, np.nan)
        signals = raw.div(row_sums, axis=0).fillna(0.0)

        # === Monthly rebalance ===
        warmup = 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
        signals = signals.shift(1).fillna(0.0)  # PIT

        # === Dispersion-adaptive exposure ===
        # Cross-sectional dispersion: std of stock returns each day
        cs_disp = ret.std(axis=1)
        # Rolling mean of dispersion
        disp_smooth = cs_disp.rolling(self.disp_window, min_periods=10).mean()
        # Historical percentile rank
        disp_pctile = disp_smooth.rolling(
            self.disp_lookback, min_periods=126
        ).rank(pct=True)

        # Scale: 1.0 when dispersion is high, floor when low
        # Linear interpolation between floor and 1.0
        disp_scale = self.disp_floor + (1.0 - self.disp_floor) * (
            (disp_pctile - 0.0) / (self.disp_percentile)
        ).clip(0.0, 1.0)
        # PIT: use yesterday's dispersion estimate
        disp_scale_lagged = disp_scale.shift(1).fillna(1.0)

        signals = signals.mul(disp_scale_lagged, axis=0)

        # === Market DD dampener ===
        if self.risk_managed:
            daily_rets = data.pct_change().fillna(0.0)
            mkt_rets = daily_rets.mean(axis=1)
            mkt_eq = (1 + mkt_rets).cumprod()
            mkt_dd = mkt_eq / mkt_eq.cummax() - 1
            dd_scale = (1.0 + mkt_dd / self.dd_denom).clip(
                lower=self.dd_floor, upper=1.0
            )
            dd_scale_lagged = dd_scale.shift(1).fillna(1.0)
            signals = signals.mul(dd_scale_lagged, axis=0)

        return signals


# ---------------------------------------------------------------------------
# Evaluation
# ---------------------------------------------------------------------------

def compute_metrics(daily_rets: pd.Series) -> dict:
    eq = (1 + daily_rets).cumprod()
    n_years = len(daily_rets) / 252.0
    cagr = eq.iloc[-1] ** (1.0 / n_years) - 1.0
    vol = daily_rets.std() * np.sqrt(252)
    sharpe = daily_rets.mean() / daily_rets.std() * np.sqrt(252) if daily_rets.std() > 0 else 0
    running_max = eq.cummax()
    dd = eq / running_max - 1
    max_dd = dd.min()
    calmar = cagr / abs(max_dd) if max_dd != 0 else 0
    return {"cagr": cagr, "vol": vol, "sharpe": sharpe, "max_dd": max_dd, "calmar": calmar}


def yearly_returns(daily_rets: pd.Series) -> pd.Series:
    eq = (1 + daily_rets).cumprod()
    yearly = eq.resample("YE").last().pct_change()
    yearly.iloc[0] = eq.resample("YE").last().iloc[0] - 1
    yearly.index = yearly.index.year
    return yearly


_DATA_CACHE = {}


def backtest_strategy(strategy, start="2016-04-01", end="2026-05-13"):
    import data_manager
    if "data" not in _DATA_CACHE:
        from universe import get_sp500
        tickers = get_sp500()
        data_manager.update("us", tickers)
        _DATA_CACHE["data"] = data_manager.load("us")
    data = _DATA_CACHE["data"]
    if data is None:
        raise RuntimeError("No data loaded")
    weights = strategy.generate_signals(data)
    daily_rets = (weights * data.pct_change().fillna(0.0)).sum(axis=1)
    return daily_rets.loc[start:end]


def main():
    print("=" * 80)
    print("SHARPE BOOST v2: Dispersion-Adaptive Exposure")
    print("=" * 80)

    # --- Test 1: Dispersion filter only (no DD dampener) ---
    print("\n--- Dispersion filter sweep (risk_managed=False) ---")
    print(f"{'disp_pct':>8s} {'floor':>6s} {'CAGR':>7s} {'Vol':>7s} {'Sharpe':>7s} {'MaxDD':>7s} {'Calmar':>7s}")
    print("-" * 60)

    configs = [
        (0.30, 0.40),
        (0.30, 0.50),
        (0.40, 0.40),
        (0.40, 0.50),
        (0.40, 0.60),
        (0.50, 0.40),
        (0.50, 0.50),
        (0.50, 0.60),
    ]

    for dp, df in configs:
        strat = DispersionAdaptiveEnsemble(
            top_n=10, mom_blend=0.25, disp_percentile=dp,
            disp_floor=df, risk_managed=False
        )
        rets = backtest_strategy(strat)
        m = compute_metrics(rets)
        print(f"{dp:>8.2f} {df:>6.2f} {m['cagr']*100:>6.1f}% {m['vol']*100:>6.1f}% "
              f"{m['sharpe']:>7.2f} {m['max_dd']*100:>6.1f}% {m['calmar']:>7.2f}")

    # --- Test 2: Dispersion filter + DD dampener ---
    print("\n--- Dispersion filter + DD dampener (risk_managed=True) ---")
    print(f"{'disp_pct':>8s} {'floor':>6s} {'CAGR':>7s} {'Vol':>7s} {'Sharpe':>7s} {'MaxDD':>7s} {'Calmar':>7s}")
    print("-" * 60)

    for dp, df in configs:
        strat = DispersionAdaptiveEnsemble(
            top_n=10, mom_blend=0.25, disp_percentile=dp,
            disp_floor=df, risk_managed=True
        )
        rets = backtest_strategy(strat)
        m = compute_metrics(rets)
        print(f"{dp:>8.2f} {df:>6.2f} {m['cagr']*100:>6.1f}% {m['vol']*100:>6.1f}% "
              f"{m['sharpe']:>7.2f} {m['max_dd']*100:>6.1f}% {m['calmar']:>7.2f}")

    # --- Test 3: Best dispersion config — yearly breakdown ---
    print(f"\n{'=' * 80}")
    print("BEST CONFIG: disp_pct=0.40, floor=0.50, risk_managed=True")
    print(f"{'=' * 80}")

    best_strat = DispersionAdaptiveEnsemble(
        top_n=10, mom_blend=0.25, disp_percentile=0.40,
        disp_floor=0.50, risk_managed=True
    )
    best_rets = backtest_strategy(best_strat)
    best_m = compute_metrics(best_rets)
    print(f"CAGR: {best_m['cagr']*100:.1f}%  Vol: {best_m['vol']*100:.1f}%  "
          f"Sharpe: {best_m['sharpe']:.2f}  MaxDD: {best_m['max_dd']*100:.1f}%  "
          f"Calmar: {best_m['calmar']:.2f}")

    print("\n--- Yearly returns ---")
    yr = yearly_returns(best_rets)
    for year, ret in yr.items():
        print(f"  {year}: {ret*100:>+7.1f}%")

    # --- Test 4: No filter baseline for comparison ---
    print(f"\n--- Baseline (no dispersion filter, no DD) ---")
    baseline = DispersionAdaptiveEnsemble(
        top_n=10, mom_blend=0.25, disp_percentile=0.0,
        disp_floor=1.0, risk_managed=False
    )
    base_rets = backtest_strategy(baseline)
    base_m = compute_metrics(base_rets)
    print(f"CAGR: {base_m['cagr']*100:.1f}%  Vol: {base_m['vol']*100:.1f}%  "
          f"Sharpe: {base_m['sharpe']:.2f}  MaxDD: {base_m['max_dd']*100:.1f}%")

    # --- Test 5: Dispersion diagnostics for 2021 ---
    print(f"\n{'=' * 80}")
    print("DISPERSION DIAGNOSTIC: Is 2021 actually low dispersion?")
    print(f"{'=' * 80}")

    import data_manager
    data = _DATA_CACHE["data"]
    ret = data.pct_change()
    cs_disp = ret.std(axis=1)
    disp_smooth = cs_disp.rolling(21, min_periods=10).mean()

    for year in range(2017, 2027):
        yr_disp = disp_smooth.loc[f"{year}"]
        if len(yr_disp) > 0:
            print(f"  {year}: avg disp = {yr_disp.mean()*100:.2f}%  "
                  f"median = {yr_disp.median()*100:.2f}%")


if __name__ == "__main__":
    main()