"""CMB04 — Momentum + Low-Vol Filter
HYPOTHESIS: TSMOM long-flat taken only when realized vol is below its rolling median
(avoid high-vol whipsaw). Vol-target the rest.

Grid: 2 vol-filter windows (30d vs 60d rolling median lookback) x 2 TFs (1d, 12h) = 4 cells total.
Best config chosen by min(BTC,ETH) holdout Sharpe.
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np


def cmb04_target(df, vol_filter_days: int = 30):
    """
    TSMOM multi-horizon (1m/3m/6m) long-flat, gated by a low-vol filter:
    - Compute realized vol (30d) at each bar.
    - Compute rolling median of that vol over vol_filter_days.
    - Only take the TSMOM signal when realized_vol < rolling_median (low-vol regime).
    - In high-vol regime: go flat (0).
    - Vol-target the resulting direction.
    """
    c = df["close"].values.astype(float)
    bpd = al.bars_per_day(df)
    bpy = bpd * 365.25

    # --- TSMOM multi-horizon direction (1m, 3m, 6m) ---
    horizons = (30 * bpd, 90 * bpd, 180 * bpd)
    direction = np.zeros(len(c))
    for h in horizons:
        h = int(h)
        sig = np.full(len(c), np.nan)
        if h < len(c):
            sig[h:] = np.sign(c[h:] / c[:-h] - 1.0)
        direction += np.nan_to_num(sig, nan=0.0)
    # Majority vote -> long or flat
    direction = np.clip(np.sign(direction), 0.0, 1.0)  # long-flat only

    # --- Realized vol (30d causal) ---
    rv_win = max(2, 30 * bpd)
    r = al.simple_returns(c)
    rv = al.realized_vol(r, rv_win, bpy)

    # --- Rolling median of realized vol over vol_filter_days ---
    med_win = max(2, vol_filter_days * bpd)
    rv_median = (
        al._series_if_array(rv).rolling(med_win, min_periods=max(2, med_win // 2)).median().values
        if hasattr(al, "_series_if_array")
        else __import__("pandas").Series(rv).rolling(med_win, min_periods=max(2, med_win // 2)).median().values
    )

    # --- Gate: only enter when rv < median (low-vol regime) ---
    low_vol_gate = np.where(
        np.isfinite(rv) & np.isfinite(rv_median) & (rv < rv_median),
        1.0,
        0.0
    )
    gated_direction = direction * low_vol_gate

    # --- Vol-target the gated direction ---
    pos = al.vol_target(gated_direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
    return pos


def make_target_fn(vol_filter_days: int):
    def fn(df):
        return cmb04_target(df, vol_filter_days=vol_filter_days)
    return fn


if __name__ == "__main__":
    import pandas as pd

    best_rep = None
    best_hold = -9.0
    best_label = ""

    configs = [
        ("CMB04-vf30", 30),
        ("CMB04-vf60", 60),
    ]

    for label, vfd in configs:
        fn = make_target_fn(vfd)
        rep = al.study_weights(label, fn, tfs=("1d", "12h"))
        v = rep["verdict"]
        h = v.get("best_holdout_sharpe", -9)
        print(al.fmt(rep))
        print(f"  [grid] {label}: holdout={h:.3f}")
        if h > best_hold:
            best_hold = h
            best_rep = rep
            best_label = label

    print("\n=== BEST CONFIG ===", best_label)
    print(al.fmt(best_rep))
    print("JSON:", al.as_json(best_rep))