"""MIC08 — OBV Trend
Hypothesis: On-balance-volume trend: long when OBV above its EMA (volume confirms price).
Long-flat. Continuous weights via al.study_weights.

Grid: obv_ema_period in (20, 50) x tfs (1d, 12h) = 4 total backtests.
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np


def compute_obv(df) -> np.ndarray:
    """Compute On-Balance-Volume causally."""
    close = df["close"].values
    volume = df["volume"].values
    n = len(close)
    obv = np.zeros(n)
    for i in range(1, n):
        if close[i] > close[i - 1]:
            obv[i] = obv[i - 1] + volume[i]
        elif close[i] < close[i - 1]:
            obv[i] = obv[i - 1] - volume[i]
        else:
            obv[i] = obv[i - 1]
    return obv


def make_target(ema_period: int):
    def target(df) -> np.ndarray:
        obv = compute_obv(df)
        obv_ema = al.ema(obv, ema_period)
        # Long when OBV > its EMA, flat otherwise
        signal = np.where(obv > obv_ema, 1.0, 0.0)
        # Use vol-targeting to size the position
        sized = al.vol_target(signal, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
        return sized
    return target


# Grid search: 2 EMA periods x 2 timeframes = 4 total backtests
results = []
for ema_p in (20, 50):
    rep = al.study_weights(
        f"MIC08-OBV-EMA{ema_p}",
        make_target(ema_p),
        tfs=("1d", "12h"),
    )
    results.append((rep["verdict"].get("best_holdout_sharpe", -9), ema_p, rep))

# Pick best by hold-out Sharpe
results.sort(key=lambda x: x[0], reverse=True)
best_holdout, best_ema, best_rep = results[0]

print(f"\n=== Best config: EMA period={best_ema} ===")
print(al.fmt(best_rep))
print("JSON:", al.as_json(best_rep))