import sys, time
from pathlib import Path
import numpy as np
import pandas as pd

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

from nautilus_dolphin.nautilus.alpha_orchestrator import NDAlphaEngine
from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
from nautilus_dolphin.nautilus.ob_features import OBFeatureEngine
from nautilus_dolphin.nautilus.ob_provider import MockOBProvider

VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
META_COLS = {'timestamp', 'scan_number', 'v50_lambda_max_velocity', 'v150_lambda_max_velocity',
             'v300_lambda_max_velocity', 'v750_lambda_max_velocity', 'vel_div',
             'instability_50', 'instability_150'}

# Load parquet files correctly
parquet_files = sorted(VBT_DIR.glob("*.parquet"))
parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]

print("Loading data...")
all_vols = []
for pf in parquet_files[:2]:
    df = pd.read_parquet(pf)
    if 'BTCUSDT' not in df.columns: continue
    pr = df['BTCUSDT'].values
    for i in range(60, len(pr)):
        seg = pr[max(0,i-50):i]
        if len(seg)<10: continue
        v = float(np.std(np.diff(seg)/seg[:-1]))
        if v > 0: all_vols.append(v)
vol_p60 = float(np.percentile(all_vols, 60))

pq_data = {}
for pf in parquet_files:
    df = pd.read_parquet(pf)
    ac = [c for c in df.columns if c not in META_COLS]
    bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
    dv = np.full(len(df), np.nan)
    if bp is not None:
        for i in range(50, len(bp)):
            seg = bp[max(0,i-50):i]
            if len(seg)<10: continue
            dv[i] = float(np.std(np.diff(seg)/seg[:-1]))
    pq_data[pf.stem] = (df, ac, dv)

# Initialize systems
acb = AdaptiveCircuitBreaker()
acb.preload_w750([pf.stem for pf in parquet_files])

ENGINE_KWARGS = dict(
    initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05,
    min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0,
    fraction=0.20, fixed_tp_pct=0.0099, stop_pct=1.0, max_hold_bars=120,
    use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
    dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
    use_asset_selection=True, min_irp_alignment=0.45,
    use_sp_fees=True, use_sp_slippage=True,
    use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
    lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
)

def run_test(name, ob_engine=None):
    engine = NDAlphaEngine(**ENGINE_KWARGS)
    if ob_engine:
        engine.set_ob_engine(ob_engine)
        
    bar_idx = 0; peak_cap = engine.capital; max_dd = 0.0
    for pf in parquet_files:
        ds = pf.stem
        # ACB logic
        acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_engine)
        base_boost = acb_info['boost'] # This is now exactly (1.0 - cut)
        beta = acb_info['beta']
        
        df, acols, dvol = pq_data[ds]
        ph = {}
        for ri in range(len(df)):
            row = df.iloc[ri]; vd = row.get("vel_div")
            if vd is None or not np.isfinite(vd): bar_idx+=1; continue
            prices = {}
            for ac in acols:
                p = row[ac]
                if p and p > 0 and np.isfinite(p):
                    prices[ac] = float(p)
                    if ac not in ph: ph[ac] = []
                    ph[ac].append(float(p))
                    if len(ph[ac]) > 500: ph[ac] = ph[ac][-200:]
            if not prices: bar_idx+=1; continue
            
            vrok = False if ri < 100 else (np.isfinite(dvol[ri]) and dvol[ri] > vol_p60)
            
            # 3-scale meta-boost: ACB boost * (1 + beta * strength^3)
            if beta > 0 and base_boost > 1.0:
                ss = 0.0
                if vd < -0.02:
                    raw = (-0.02 - float(vd)) / (-0.02 - -0.05)
                    ss = min(1.0, max(0.0, raw)) ** 3.0
                engine.regime_size_mult = base_boost * (1.0 + beta * ss)
            else:
                engine.regime_size_mult = base_boost
                
            engine.process_bar(bar_idx=bar_idx, vel_div=float(vd), prices=prices, vol_regime_ok=vrok, price_histories=ph)
            
            # (no fraction reset needed)
            bar_idx += 1
            
        peak_cap = max(peak_cap, engine.capital)
        dd = (peak_cap - engine.capital) / peak_cap * 100
        max_dd = max(max_dd, dd)
        
    trades = engine.trade_history
    w = [t for t in trades if t.pnl_absolute > 0]
    l = [t for t in trades if t.pnl_absolute <= 0]
    gw = sum(t.pnl_absolute for t in w) if w else 0
    gl = abs(sum(t.pnl_absolute for t in l)) if l else 0
    
    roi = (engine.capital - 25000) / 25000 * 100
    pf_val = gw / gl if gl > 0 else 999
    wr = len(w) / len(trades) * 100 if trades else 0
    
    returns = [(t.pnl_absolute / (25000+sum(x.pnl_absolute for x in trades[:i]))) for i, t in enumerate(trades)]
    if not returns: returns = [0]
    
    mc_med_cagr, mc_tail_cagr, mc_p40, mc_med_dd = compute_mc_cagr(returns)

    print(f"\n{name}")
    print(f"  ROI:         {roi:+.2f}%")
    print(f"  MaxDD (Emp): {max_dd:.2f}%")
    print(f"  Trades:      {len(trades)}")
    print(f"  Win Rate:    {wr:.1f}%")
    print(f"  Prof Factor: {pf_val:.3f}")
    print(f"  MC Med CAGR: {mc_med_cagr:+.1f}%")
    print(f"  MC Med DD:   {mc_med_dd:.1f}%")
    print(f"  MC P(>40DD): {mc_p40:.1f}%")

# Helper for Monte Carlo
def compute_mc_cagr(returns, periods=252, n_simulations=1000):
    np.random.seed(42)
    dr = np.array(returns)
    if len(dr) == 0: return 0,0,0,0
    # Resample daily (simulated by block since returns are per trade, we approximate)
    sr = np.random.choice(dr, size=(n_simulations, periods), replace=True)
    eq = np.cumprod(1.0 + sr, axis=1)
    cagrs = (eq[:, -1] - 1.0) * 100
    med_cagr = np.median(cagrs)
    tail_cagr = np.percentile(cagrs, 5)
    
    max_dds = np.zeros(n_simulations)
    for i in range(n_simulations):
        curve = eq[i]
        peaks = np.maximum.accumulate(curve)
        dds = (peaks - curve) / peaks
        max_dds[i] = np.max(dds)
    p40 = np.mean(max_dds >= 0.40) * 100
    mdd = np.median(max_dds) * 100
    return med_cagr, tail_cagr, p40, mdd

# 1. Test Baseline
run_test("A. ACB v4 Baseline (No OB, Pure ACB)")

# 2. Test Full Production (ACB Cut + OB Engine Hard Skip)
mock = MockOBProvider(imbalance_bias=-0.09, depth_scale=1.0, 
                      assets=["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"],
                      imbalance_biases={"BNBUSDT": 0.20, "SOLUSDT": 0.20}) 
ob_engine = OBFeatureEngine(mock)
ob_engine.preload_date("mock", mock.get_assets())
run_test("B. Production Stack (ACB + OB Hard-Skip, 5.0x)", ob_engine)

from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine as ThrottledEngine
import json

def run_throttled_test(name, ob_engine=None):
    engine = ThrottledEngine(**ENGINE_KWARGS)
    if ob_engine:
        engine.set_ob_engine(ob_engine)
        
    bar_idx = 0; peak_cap = engine.capital; max_dd = 0.0
    for pf in parquet_files:
        ds = pf.stem
        acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_engine)
        base_boost = acb_info['boost']
        beta = acb_info['beta']
        
        eso_path = VBT_DIR / f"ESOTERIC_data_{ds}.json"
        eso = {}
        if eso_path.exists():
            with open(eso_path, 'r') as f:
                eso = json.load(f)
                
        hazard = 0.0
        dow = eso.get('day_of_week', -1)
        if dow == 1: hazard = 1.0
        elif dow in [0, 2]: hazard = 0.5
        if eso.get('moon_illumination', 0.5) <= 0.05: hazard = 1.0
        
        engine.set_esoteric_hazard_multiplier(hazard)
        # Enable 6.0x dynamic
        engine.set_mc_forewarner_status(True)
        
        df, acols, dvol = pq_data[ds]
        ph = {}
        for ri in range(len(df)):
            row = df.iloc[ri]; vd = row.get("vel_div")
            if vd is None or not np.isfinite(vd): bar_idx+=1; continue
            prices = {}
            for ac in acols:
                p = row[ac]
                if p and p > 0 and np.isfinite(p):
                    prices[ac] = float(p)
                    if ac not in ph: ph[ac] = []
                    ph[ac].append(float(p))
                    if len(ph[ac]) > 500: ph[ac] = ph[ac][-200:]
            if not prices: bar_idx+=1; continue
            
            vrok = False if ri < 100 else (np.isfinite(dvol[ri]) and dvol[ri] > vol_p60)
            
            if beta > 0 and base_boost > 1.0:
                ss = 0.0
                if vd < -0.02:
                    raw = (-0.02 - float(vd)) / (-0.02 - -0.05)
                    ss = min(1.0, max(0.0, raw)) ** 3.0
                engine.regime_size_mult = base_boost * (1.0 + beta * ss)
            else:
                engine.regime_size_mult = base_boost
                
            engine.process_bar(bar_idx=bar_idx, vel_div=float(vd), prices=prices, vol_regime_ok=vrok, price_histories=ph)
            bar_idx += 1
            
        peak_cap = max(peak_cap, engine.capital)
        dd = (peak_cap - engine.capital) / peak_cap * 100
        max_dd = max(max_dd, dd)
        
    trades = engine.trade_history
    w = [t for t in trades if t.pnl_absolute > 0]
    l = [t for t in trades if t.pnl_absolute <= 0]
    gw = sum(t.pnl_absolute for t in w) if w else 0
    gl = abs(sum(t.pnl_absolute for t in l)) if l else 0
    
    roi = (engine.capital - 25000) / 25000 * 100
    pf_val = gw / gl if gl > 0 else 999
    wr = len(w) / len(trades) * 100 if trades else 0
    
    returns = [(t.pnl_absolute / (25000+sum(x.pnl_absolute for x in trades[:i]))) for i, t in enumerate(trades)]
    if not returns: returns = [0]
    
    mc_med_cagr, mc_tail_cagr, mc_p40, mc_med_dd = compute_mc_cagr(returns)

    print(f"\n{name}")
    print(f"  ROI:         {roi:+.2f}%")
    print(f"  MaxDD (Emp): {max_dd:.2f}%")
    print(f"  Trades:      {len(trades)}")
    print(f"  Win Rate:    {wr:.1f}%")
    print(f"  Prof Factor: {pf_val:.3f}")
    print(f"  MC Med CAGR: {mc_med_cagr:+.1f}%")
    print(f"  MC Med DD:   {mc_med_dd:.1f}%")
    print(f"  MC P(>40DD): {mc_p40:.1f}%")

run_throttled_test("C. Ultimate Stack (Production OB + Dynamic 5-6x + EsoF Taper)", ob_engine)