import sys
from pathlib import Path
import json
import numpy as np
import time

HCM_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin'))
sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin' / 'dvae'))

# We'll monkeypatch run_backtest to add progress bars
import exp_shared

def replicate():
    print("="*60)
    print("EXACT REPLICATION OF D_LIQ_GOLD PERFECT MAKER (181.81%)")
    print("="*60)
    exp_shared.ensure_jit()
    
    perfect_kwargs = {
        'seed': 42,
        'sp_maker_entry_rate': 1.0,
        'sp_maker_exit_rate': 1.0,
        'use_sp_fees': True,
        'use_sp_slippage': False,
    }

    print("Running D_liq (8x/9x) configuration in PERFECT MAKER mode...")
    # Using the monkeypatched version that prints daily progress to avoid 'locked up' appearance
    from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
    
    # Manually running loop here for transparency
    from exp_shared import load_data, ENGINE_KWARGS, MC_BASE_CFG
    from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
    import pandas as pd
    import gc

    d = load_data()
    kw = ENGINE_KWARGS.copy()
    kw.update(perfect_kwargs)
    
    acb = AdaptiveCircuitBreaker()
    acb.preload_w750(d['date_strings'])
    
    eng = create_d_liq_engine(**kw)
    eng.set_ob_engine(d['ob_eng'])
    eng.set_acb(acb)
    
    daily_caps, daily_pnls = [], []
    total_days = len(d['parquet_files'])
    
    t_start = time.time()
    for i, pf in enumerate(d['parquet_files']):
        ds = pf.stem
        # Print progress every 5 days or first/last
        if i == 0 or i == total_days-1 or (i+1) % 10 == 0:
            elapsed = time.time() - t_start
            print(f"  Day {i+1}/{total_days}: {ds} | Cap: ${eng.capital:,.2f} | Trades: {len(eng.trade_history)} | Elapsed: {elapsed:.0f}s")
        
        df = pd.read_parquet(pf)
        acols = [c for c in df.columns if c not in exp_shared.META_COLS]
        
        bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
        dvol = np.full(len(df), np.nan)
        if bp is not None:
            diffs = np.zeros(len(bp), dtype=np.float64)
            diffs[1:] = np.diff(bp) / bp[:-1]
            for j in range(50, len(bp)):
                dvol[j] = np.std(diffs[j-50:j])

        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)
        
        del df
        gc.collect()

    tr = eng.trade_history
    n = len(tr)
    roi = (eng.capital - 25000.0) / 25000.0 * 100.0
    
    print("\nREPLICATION RESULTS:")
    print(f"  ROI:    {roi:.2f}% (Target: 181.81%)")
    print(f"  Trades: {n} (Target: 2155)")
    print(f"  Final Cap: ${eng.capital:,.2f}")
    
    if abs(roi - 181.81) < 0.2:
        print("\nGOLD CERTIFICATION: SUCCESS ✓")
    else:
        print(f"\nGOLD CERTIFICATION: FAILED ✗ (Diff: {roi-181.81:+.2f}pp)")

if __name__ == "__main__":
    replicate()