"""Quick test: D_LIQ with and without set_esoteric_hazard_multiplier(0.0)
to confirm that's the root cause of 181.81% → 145.84% regression.
"""
import sys, time, math
sys.stdout.reconfigure(encoding='utf-8', errors='replace')
from pathlib import Path
import numpy as np

_HERE = Path(__file__).resolve().parent
sys.path.insert(0, str(_HERE.parent))

from exp_shared import ensure_jit, ENGINE_KWARGS, load_data, load_forewarner, MC_BASE_CFG
from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker


def _run(eng, d, fw, call_esof):
    acb = AdaptiveCircuitBreaker()
    acb.preload_w750(d['date_strings'])
    eng.set_ob_engine(d['ob_eng'])
    eng.set_acb(acb)
    if fw is not None:
        eng.set_mc_forewarner(fw, MC_BASE_CFG)
    if call_esof:
        eng.set_esoteric_hazard_multiplier(0.0)

    # Print leverage state BEFORE trading
    print(f"  base_max_leverage = {eng.base_max_leverage}")
    print(f"  abs_max_leverage  = {eng.abs_max_leverage}")
    print(f"  bet_sizer.max_lev = {eng.bet_sizer.max_leverage}")

    t0 = time.time()
    daily_caps, daily_pnls = [], []
    for pf_file in d['parquet_files']:
        ds = pf_file.stem
        df, acols, dvol = d['pq_data'][ds]
        cap_before = eng.capital
        vol_ok = np.where(np.isfinite(dvol), dvol > d['vol_p60'], False)
        eng.process_day(ds, df, acols, vol_regime_ok=vol_ok)
        daily_caps.append(eng.capital)
        daily_pnls.append(eng.capital - cap_before)

    tr  = eng.trade_history
    n   = len(tr)
    roi = (eng.capital - 25000.0) / 25000.0 * 100.0
    peak, max_dd = 25000.0, 0.0
    for cap in daily_caps:
        peak = max(peak, cap)
        max_dd = max(max_dd, (peak - cap) / peak * 100.0)

    def _abs(t):
        return t.pnl_absolute if hasattr(t, 'pnl_absolute') else t.pnl_pct * 250.0
    wins   = [t for t in tr if _abs(t) > 0]
    losses = [t for t in tr if _abs(t) <= 0]
    pf = sum(_abs(t) for t in wins) / max(abs(sum(_abs(t) for t in losses)), 1e-9)

    lev_vals = [t.leverage for t in tr if hasattr(t, 'leverage') and t.leverage > 0]
    avg_lev  = float(np.mean(lev_vals)) if lev_vals else 0.0
    liq_stops = getattr(eng, 'liquidation_stops', 0)

    print(f"  ROI={roi:+.2f}%  T={n}  DD={max_dd:.2f}%  PF={pf:.4f}  avg_lev={avg_lev:.2f}x  liq_stops={liq_stops}  ({time.time()-t0:.0f}s)")
    return roi, n, max_dd, avg_lev


def main():
    ensure_jit()
    d  = load_data()
    fw = load_forewarner()

    print("\n=== WITH set_esoteric_hazard_multiplier(0.0) ===")
    eng_a = create_d_liq_engine(**ENGINE_KWARGS)
    roi_a, n_a, dd_a, lev_a = _run(eng_a, d, fw, call_esof=True)

    print("\n=== WITHOUT set_esoteric_hazard_multiplier(0.0) ===")
    eng_b = create_d_liq_engine(**ENGINE_KWARGS)
    roi_b, n_b, dd_b, lev_b = _run(eng_b, d, fw, call_esof=False)

    print(f"\nGOLD TARGET: ROI=181.81%, T=2155, DD=17.65%, avg_lev=4.09x")
    print(f"WITH stomp:    ROI={roi_a:+.2f}%  T={n_a}  DD={dd_a:.2f}%  avg_lev={lev_a:.2f}x")
    print(f"WITHOUT stomp: ROI={roi_b:+.2f}%  T={n_b}  DD={dd_b:.2f}%  avg_lev={lev_b:.2f}x")


if __name__ == '__main__':
    main()