"""TRD13 — SMA200 regime + vol-target (long-flat).

HYPOTHESIS: Long when close > SMA200, flat otherwise.
Position sized by vol_target(20%, 30d). Pure regime-trend.

Small grid: SMA window {150, 200} x vol_target window {20, 30} days.
Only 2 param sets tested (4 total cells with BTC/ETH) to stay within budget.
Best config selected by min(BTC, ETH) full Sharpe.
"""

import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np

# --------------------------------------------------------------------------
# Signal factory
# --------------------------------------------------------------------------
def make_target(sma_win_bars: int, vol_win_days: int):
    """Returns a function df -> target_array using SMA regime + vol_target."""
    def target_fn(df):
        c = df["close"].values
        bpd = al.bars_per_day(df)

        # SMA computed causally (sma already uses rolling with min_periods=win)
        s200 = al.sma(c, sma_win_bars)

        # Direction: +1 when close > SMA, else 0 (long-flat)
        direction = np.where(c > s200, 1.0, 0.0)

        # Vol-targeted position
        vol_win = int(round(vol_win_days * bpd))
        pos = al.vol_target(direction, df, target_vol=0.20,
                            vol_win_days=vol_win_days, leverage_cap=2.0)

        # Mask NaN (during SMA warmup) -> flat
        pos = np.where(np.isnan(s200), 0.0, pos)
        return pos
    return target_fn


# --------------------------------------------------------------------------
# Grid: 2 configs × 2 TFs (1d, 12h)
# --------------------------------------------------------------------------
CONFIGS = [
    {"label": "SMA150_v20", "sma_days": 150, "vol_win": 20},
    {"label": "SMA200_v30", "sma_days": 200, "vol_win": 30},
]

TFS = ("1d", "12h")

reports = []
for cfg in CONFIGS:
    sma_days = cfg["sma_days"]
    vol_win = cfg["vol_win"]

    def make_fn(sd=sma_days, vw=vol_win):
        def target_fn(df):
            bpd = al.bars_per_day(df)
            sma_bars = int(round(sd * bpd))
            c = df["close"].values
            s = al.sma(c, sma_bars)
            direction = np.where(c > s, 1.0, 0.0)
            pos = al.vol_target(direction, df, target_vol=0.20,
                                vol_win_days=vw, leverage_cap=2.0)
            pos = np.where(np.isnan(s), 0.0, pos)
            return pos
        return target_fn

    name = f"TRD13_{cfg['label']}"
    rep = al.study_weights(name, make_fn(), tfs=TFS)
    reports.append((rep, cfg))

# --------------------------------------------------------------------------
# Pick best config by min(BTC_full_sharpe, ETH_full_sharpe) on best TF
# --------------------------------------------------------------------------
def best_score(rep):
    v = rep["verdict"]
    best_tf = v["best_tf"]
    # find the cell for best_tf
    for cell in rep["cells"]:
        if cell["tf"] == best_tf:
            btc_sh = cell["per_asset"]["BTC"]["full"]["sharpe"]
            eth_sh = cell["per_asset"]["ETH"]["full"]["sharpe"]
            return min(btc_sh, eth_sh)
    return -999.0

best_rep, best_cfg = max(reports, key=lambda x: best_score(x[0]))

print("\n" + "=" * 70)
print(f"BEST CONFIG: {best_cfg}")
print("=" * 70)
print(al.fmt(best_rep))
print("JSON:", al.as_json(best_rep))