"""MIC05 — Wide-range-bar follow-through.

HYPOTHESIS: After a wide-range bar (range > 2*ATR) closing strong (close near the
top 30% of the bar for longs, or bottom 30% for shorts), enter in the bar's direction
at close[i]; exit after k bars (or on TP/SL).

CAUSAL: ATR is computed up to bar i-1 (shifted), range and close strength computed
from bar i itself (known at close[i]). Entry fills at close[i].

Grid: k_bars in {3, 5, 7, 10} — only 1d, 2 assets, 4 param sets = 8 backtests total.
Best config selected by min-asset hold-out Sharpe.
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np

# ---------------------------------------------------------------------------
# Signal generator
# ---------------------------------------------------------------------------
def make_entries(df, k_bars: int = 5, atr_mult: float = 2.0, close_pct: float = 0.30):
    """Returns entries list len(df).

    Wide range bar: range > atr_mult * ATR(14) at bar i-1 (causal).
    Strong close long:  close >= low + (1 - close_pct) * range  (top 30%)
    Strong close short: close <= low + close_pct * range         (bottom 30%)
    """
    hi  = df["high"].values.astype(float)
    lo  = df["low"].values.astype(float)
    cl  = df["close"].values.astype(float)
    bar_range = hi - lo

    # ATR causal: shift by 1 so ATR at bar i uses data up to bar i-1
    atr_raw = al.atr(df, win=14)
    atr_shifted = np.roll(atr_raw, 1)
    atr_shifted[0] = atr_raw[0]

    entries = [None] * len(df)
    for i in range(1, len(df)):
        rng = bar_range[i]
        atr_i = atr_shifted[i]
        if atr_i <= 0 or not np.isfinite(atr_i):
            continue
        if rng < atr_mult * atr_i:
            continue   # not a wide-range bar
        close_rel = (cl[i] - lo[i]) / rng if rng > 0 else 0.5
        if close_rel >= (1.0 - close_pct):
            # Strong bullish wide bar -> long follow-through
            entries[i] = {"dir": 1, "tp": None, "sl": None, "max_bars": k_bars}
        elif close_rel <= close_pct:
            # Strong bearish wide bar -> short follow-through
            entries[i] = {"dir": -1, "tp": None, "sl": None, "max_bars": k_bars}
    return entries


# ---------------------------------------------------------------------------
# Grid search over k_bars
# ---------------------------------------------------------------------------
K_BARS_GRID = [3, 5, 7, 10]

best_rep = None
best_hold = -999

for k in K_BARS_GRID:
    rep = al.study_signals(
        f"MIC05-k{k}",
        lambda df, _k=k: make_entries(df, k_bars=_k),
        tfs=("1d",),
    )
    min_hold = rep["verdict"].get("best_holdout_sharpe", -999)
    print(f"k={k:2d}: grade={rep['verdict']['grade']} "
          f"full={rep['verdict'].get('best_full_sharpe', 'N/A')} "
          f"hold={min_hold}")
    if min_hold > best_hold:
        best_hold = min_hold
        best_rep = rep

# Rename best rep with canonical ID
best_rep["name"] = "MIC05"
print("\n--- BEST CONFIG ---")
print(al.fmt(best_rep))
print("JSON:", al.as_json(best_rep))