"""
Sharpe boost v5: Fine-tune DD dampener on top of the Sharpe 1.52 config.

Best raw config: rebal=42, top_n=12, asym_vol (Sharpe 1.52, MaxDD -31.2%)
Now: add a LIGHTER DD dampener to bring MaxDD under 30% without killing Sharpe.

Key: dd_denom controls how aggressively we cut. Larger denom = lighter touch.
"""
from __future__ import annotations
import os, 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")


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


class EnsembleV3(Strategy):
    def __init__(self, top_n=12, rebal_freq=42, mom_blend=0.0,
                 asym_vol=True, asym_vol_floor=0.50,
                 dd_dampen=False, dd_floor=0.40, dd_denom=0.20):
        self.top_n = top_n
        self.rebal_freq = rebal_freq
        self.mom_blend = mom_blend
        self.asym_vol = asym_vol
        self.asym_vol_floor = asym_vol_floor
        self.dd_dampen = dd_dampen
        self.dd_floor = dd_floor
        self.dd_denom = dd_denom

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

        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

        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 = mom_r

        α = self.mom_blend
        if α > 0:
            ensemble = (1 - α) / 2 * signal_a + (1 - α) / 2 * signal_b + α * signal_c
        else:
            ensemble = 0.5 * signal_a + 0.5 * signal_b

        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)

        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)

        if self.asym_vol:
            daily_rets = data.pct_change().fillna(0.0)
            port_rets = (signals * daily_rets).sum(axis=1)
            short_vol = port_rets.rolling(20, min_periods=10).std() * np.sqrt(252)
            vol_median = short_vol.rolling(252, min_periods=126).median()
            recent_ret = port_rets.rolling(20, min_periods=10).sum()
            high_vol_neg = (short_vol > vol_median * 1.5) & (recent_ret < 0)
            asym_scale = pd.Series(1.0, index=data.index)
            asym_scale[high_vol_neg] = self.asym_vol_floor
            signals = signals.mul(asym_scale.shift(1).fillna(1.0), axis=0)

        if self.dd_dampen:
            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)
            signals = signals.mul(dd_scale.shift(1).fillna(1.0), axis=0)

        return signals


_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"]
    weights = strategy.generate_signals(data)
    daily_rets = (weights * data.pct_change().fillna(0.0)).sum(axis=1)
    return daily_rets.loc[start:end]


def fmt_row(label, m):
    return (f"{label:<55s} {m['cagr']*100:>6.1f}% {m['vol']*100:>6.1f}% "
            f"{m['sharpe']:>6.2f} {m['max_dd']*100:>6.1f}% {m['calmar']:>6.2f}")


def main():
    print("=" * 95)
    print("SHARPE BOOST v5: Fine-tune DD dampener on Sharpe 1.52 base")
    print("=" * 95)

    header = f"{'Config':<55s} {'CAGR':>7s} {'Vol':>7s} {'Sharpe':>6s} {'MaxDD':>7s} {'Calmar':>6s}"

    # --- Baseline (no DD) ---
    print(f"\n--- Baseline: rebal42 + top12 + asym_vol (no DD) ---")
    print(header)
    print("-" * 95)
    strat = EnsembleV3(top_n=12, rebal_freq=42, asym_vol=True, dd_dampen=False)
    base_rets = backtest_strategy(strat)
    base_m = compute_metrics(base_rets)
    print(fmt_row("NO DD (baseline)", base_m))

    # --- Light DD: larger dd_denom (gentler), higher floor ---
    print(f"\n--- DD dampener tuning (lighter touch) ---")
    print(header)
    print("-" * 95)

    configs = [
        # (dd_floor, dd_denom) — larger denom = need bigger crash to trigger
        (0.60, 0.25),
        (0.60, 0.30),
        (0.60, 0.35),
        (0.70, 0.25),
        (0.70, 0.30),
        (0.70, 0.35),
        (0.50, 0.25),
        (0.50, 0.30),
        (0.50, 0.35),
        (0.40, 0.20),  # original (aggressive)
    ]

    results = {}
    for dd_floor, dd_denom in configs:
        strat = EnsembleV3(top_n=12, rebal_freq=42, asym_vol=True,
                          dd_dampen=True, dd_floor=dd_floor, dd_denom=dd_denom)
        rets = backtest_strategy(strat)
        m = compute_metrics(rets)
        results[(dd_floor, dd_denom)] = {"rets": rets, "m": m}
        print(fmt_row(f"DD floor={dd_floor:.2f} denom={dd_denom:.2f}", m))

    # --- Also test: top_n=10 vs 12 with lighter DD ---
    print(f"\n--- top_n comparison with light DD (floor=0.60, denom=0.30) ---")
    print(header)
    print("-" * 95)
    for n in [8, 10, 12]:
        strat = EnsembleV3(top_n=n, rebal_freq=42, asym_vol=True,
                          dd_dampen=True, dd_floor=0.60, dd_denom=0.30)
        rets = backtest_strategy(strat)
        m = compute_metrics(rets)
        print(fmt_row(f"top_n={n}, light DD", m))

    # --- Also try: mom_blend with the good configs ---
    print(f"\n--- Add momentum blend to best configs ---")
    print(header)
    print("-" * 95)
    for α in [0.0, 0.15, 0.20]:
        for dd_floor, dd_denom in [(0.60, 0.30), (0.70, 0.30)]:
            strat = EnsembleV3(top_n=12, rebal_freq=42, mom_blend=α, asym_vol=True,
                              dd_dampen=True, dd_floor=dd_floor, dd_denom=dd_denom)
            rets = backtest_strategy(strat)
            m = compute_metrics(rets)
            results[(dd_floor, dd_denom, α)] = {"rets": rets, "m": m}
            print(fmt_row(f"top12, mom={α:.0%}, DD f={dd_floor} d={dd_denom}", m))

    # --- Pick best Sharpe >= 1.5 config ---
    print(f"\n{'=' * 95}")
    print("SELECTING BEST CONFIG WITH Sharpe >= 1.50")
    print(f"{'=' * 95}")

    # Find best among all tested
    best_key = None
    best_sharpe = 0
    for key, v in results.items():
        if v["m"]["sharpe"] >= best_sharpe:
            best_sharpe = v["m"]["sharpe"]
            best_key = key

    if best_key:
        best = results[best_key]
        print(f"Config: {best_key}")
        print(fmt_row("BEST", best["m"]))
        print(f"\n--- Yearly returns ---")
        yr = yearly_returns(best["rets"])
        for year, ret in yr.items():
            print(f"  {year}: {ret*100:>+7.1f}%")

        # IS/OOS
        print(f"\n--- IS/OOS ---")
        is_rets = best["rets"].loc["2016-04-01":"2022-12-31"]
        oos_rets = best["rets"].loc["2023-01-01":"2026-05-13"]
        is_m = compute_metrics(is_rets)
        oos_m = compute_metrics(oos_rets)
        print(f"  IS  (2016-2022): CAGR {is_m['cagr']*100:.1f}%  Sharpe {is_m['sharpe']:.2f}  MaxDD {is_m['max_dd']*100:.1f}%")
        print(f"  OOS (2023-2026): CAGR {oos_m['cagr']*100:.1f}%  Sharpe {oos_m['sharpe']:.2f}  MaxDD {oos_m['max_dd']*100:.1f}%")

        # Bootstrap
        print(f"\n--- Bootstrap ---")
        from research.trend_rider_p0 import block_bootstrap
        boot = block_bootstrap(best["rets"], n_boot=5000, block_len=42)
        print(f"  Sharpe: median={boot['sharpe'].median():.2f}  "
              f"5th={boot['sharpe'].quantile(0.05):.2f}  "
              f"95th={boot['sharpe'].quantile(0.95):.2f}")
        print(f"  MaxDD:  median={boot['max_drawdown'].median()*100:.1f}%  "
              f"5th={boot['max_drawdown'].quantile(0.05)*100:.1f}%  "
              f"95th={boot['max_drawdown'].quantile(0.95)*100:.1f}%")
        print(f"  P(Sharpe > 1.5): {(boot['sharpe'] > 1.5).mean()*100:.1f}%")
        print(f"  P(Sharpe > 1.0): {(boot['sharpe'] > 1.0).mean()*100:.1f}%")
        print(f"  P(MaxDD > 30%): {(boot['max_drawdown'].abs() > 0.30).mean()*100:.1f}%")
    else:
        print("No config achieved Sharpe >= 1.50")
        # Show best anyway
        best_key = max(results, key=lambda k: results[k]["m"]["sharpe"])
        print(f"Closest: {best_key} → Sharpe {results[best_key]['m']['sharpe']:.2f}")


if __name__ == "__main__":
    main()