"""TRD12 — Triple-MA alignment (SMA10 > SMA50 > SMA200).
Long only when all three SMAs are in full bullish alignment; flat otherwise.
No look-ahead: SMA values at i use close[0..i], position held during bar i+1.
"""
import sys
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
import altlib as al
import numpy as np


def triple_ma_weights(df, short=10, mid=50, long=200, use_vol_target=True):
    """Return position array: +1 when SMA_short > SMA_mid > SMA_long, else 0."""
    c = df["close"].values
    s = al.sma(c, short)
    m = al.sma(c, mid)
    l = al.sma(c, long)

    # Bullish alignment: short > mid > long
    bullish = (s > m) & (m > l)

    # Direction: +1 or 0 (long-only)
    direction = np.where(bullish, 1.0, 0.0)

    # Replace NaN regions (first `long` bars) with 0
    direction = np.where(np.isnan(s) | np.isnan(m) | np.isnan(l), 0.0, direction)

    if use_vol_target:
        return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
    else:
        return direction


# Run study on 1d and 12h timeframes (Triple-MA needs long history, so >=12h)
# We try two configurations: with and without vol-targeting
# That's 2 configs x 2 TFs = 4 total backtests (within the <=6 limit)

print("=" * 60)
print("TRD12 — Triple-MA alignment (SMA10 > SMA50 > SMA200)")
print("=" * 60)

# Config 1: with vol-targeting
rep_vt = al.study_weights(
    "TRD12-VT",
    lambda df: triple_ma_weights(df, use_vol_target=True),
    tfs=("1d", "12h"),
)
print("\n--- Vol-targeted ---")
print(al.fmt(rep_vt))
print("JSON:", al.as_json(rep_vt))

# Config 2: raw (no vol-targeting, simple long/flat)
rep_raw = al.study_weights(
    "TRD12-RAW",
    lambda df: triple_ma_weights(df, use_vol_target=False),
    tfs=("1d", "12h"),
)
print("\n--- Raw (no vol-target) ---")
print(al.fmt(rep_raw))
print("JSON:", al.as_json(rep_raw))
