"""TRD02 — EMA Cross Long-Short Strategy.

HYPOTHESIS: Long when EMA(fast) > EMA(slow), SHORT when fast < slow.
Compared to TRD01 (long-flat), this uses the full directional signal (+1/-1).
Grid: (fast, slow) in {(10,50), (20,100), (50,200)}.
Vol-targeted position (target_vol=20%, leverage cap 2x).

Key question: does shorting add alpha vs long-flat in crypto (strong upward drift)?
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np

# Grid: (fast, slow) pairs — 3 param sets, tested on 2 TFs = 6 total backtests max
GRID = [
    (10, 50),
    (20, 100),
    (50, 200),
]

def make_target(fast: int, slow: int):
    """Returns a target_fn for the given EMA fast/slow parameters.
    Signal is decided with data <= close[i] (causal EMA), vol-targeted.
    Long (+1) when fast > slow, SHORT (-1) when fast < slow.
    """
    def target_fn(df):
        c = df["close"].values.astype(float)
        e_fast = al.ema(c, fast)
        e_slow = al.ema(c, slow)
        # Direction: +1 when fast > slow, -1 otherwise (long-SHORT, not long-flat)
        direction = np.where(e_fast > e_slow, 1.0, -1.0)
        # Warmup: NaN-out until slow EMA has enough data (approx 3x slow period)
        warmup = slow * 3
        direction[:warmup] = 0.0
        # Vol-target the position
        tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
        return tgt
    return target_fn


def main():
    best_rep = None
    best_score = -9999.0
    best_params = None

    for (fast, slow) in GRID:
        name = f"TRD02_ema{fast}_{slow}"
        print(f"\n=== Testing {name} ===")
        rep = al.study_weights(
            name,
            make_target(fast, slow),
            tfs=("1d", "12h"),
        )
        verdict = rep["verdict"]
        score = verdict.get("best_holdout_sharpe", -9999.0) or -9999.0
        print(al.fmt(rep))
        print("JSON:", al.as_json(rep))

        if score > best_score:
            best_score = score
            best_rep = rep
            best_params = (fast, slow)

    print(f"\n\n=== BEST CONFIG: EMA({best_params[0]},{best_params[1]}) ===")
    print(al.fmt(best_rep))
    print("JSON:", al.as_json(best_rep))


if __name__ == "__main__":
    main()