import sys, time
from pathlib import Path
import numpy as np
import pandas as pd
from scipy.stats import norm

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

def wilson_ci(k, n, confidence=0.95):
    if n == 0: return 0.0, 0.0
    z = norm.ppf(1 - (1 - confidence) / 2)
    p = k / n
    denominator = 1 + z**2/n
    centre_adjusted_num = p + z**2 / (2*n)
    adjusted_sd = np.sqrt((p*(1-p)/n) + (z**2/(4*n**2)))
    
    lower_bound = (centre_adjusted_num - z*adjusted_sd) / denominator
    upper_bound = (centre_adjusted_num + z*adjusted_sd) / denominator
    return float(lower_bound), float(upper_bound)

VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
parquet_files = sorted(VBT_DIR.glob("*.parquet"))
parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]

print("Loading data & extracting daily metrics...")
daily_metrics = []

for pf in parquet_files:
    ds = pf.stem
    df = pd.read_parquet(pf)
    
    v50_max = df['v50_lambda_max_velocity'].max() if 'v50_lambda_max_velocity' in df.columns else np.nan
    
    assets = ['BTCUSDT', 'ETHUSDT', 'BNBUSDT', 'SOLUSDT']
    valid_assets = [a for a in assets if a in df.columns]
    if len(valid_assets) > 1:
        rets = df[valid_assets].pct_change().fillna(0)
        corr_matrix = rets.corr().values
        cross_corr = corr_matrix[np.triu_indices_from(corr_matrix, k=1)]
        max_cross_corr = np.nanmax(cross_corr)
    else:
        max_cross_corr = 0
        
    daily_metrics.append({
        'Date': ds,
        'v50_max': v50_max,
        'cross_corr_max': max_cross_corr,
    })

metrics_df = pd.DataFrame(daily_metrics).set_index('Date')
precursor_df = metrics_df.shift(1).dropna()

print("Running fast 6.0x trajectory...")
pq_data = {}
all_vols = []
for pf in parquet_files:
    df = pd.read_parquet(pf)
    ac = [c for c in df.columns if c not in {'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'}]
    dv = df['vel_div'].values if 'vel_div' in df.columns else np.zeros(len(df))
    pq_data[pf.stem] = (df, ac, dv)

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

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_inst = OBFeatureEngine(mock)
ob_engine_inst.preload_date("mock", mock.get_assets())

ENGINE_KWARGS = dict(
    initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05,
    min_leverage=0.5, max_leverage=6.0, 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,
)

engine = NDAlphaEngine(**ENGINE_KWARGS)
engine.set_ob_engine(ob_engine_inst)

daily_returns = {}
bar_idx = 0
all_vols_engine = []

for pf in parquet_files:
    ds = pf.stem
    cs = engine.capital
    
    acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_engine_inst)
    base_boost = acb_info['boost']
    beta = acb_info['beta']
    
    df, acols, dvol_raw = pq_data[ds]
    ph = {}
    
    for ri in range(len(df)):
        row = df.iloc[ri]
        vd = dvol_raw[ri]
        if 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

        btc_hist = ph.get("BTCUSDT", [])
        engine_vrok = False
        if len(btc_hist) >= 50:
            seg = btc_hist[-50:]
            vd_eng = float(np.std(np.diff(seg)/np.array(seg[:-1])))
            all_vols_engine.append(vd_eng)
            if len(all_vols_engine) > 100:
                engine_vrok = vd_eng > np.percentile(all_vols_engine, 60)
        
        if beta > 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=engine_vrok, price_histories=ph)
        bar_idx += 1
        
    daily_returns[ds] = (engine.capital - cs) / cs if cs > 0 else 0

returns_df = pd.DataFrame.from_dict(daily_returns, orient='index', columns=['Return'])
merged = precursor_df.join(returns_df, how='inner')

threshold_pnl = merged['Return'].quantile(0.10)
merged['Is_Tail'] = merged['Return'] <= threshold_pnl
base_p_tail = merged['Is_Tail'].mean()

def run_mc_sim(baseline_returns, periods=252, n_simulations=1000):
    np.random.seed(42)
    daily_returns = np.array(baseline_returns)
    simulated_returns = np.random.choice(daily_returns, size=(n_simulations, periods), replace=True)
    equity_curves = np.cumprod(1.0 + simulated_returns, axis=1)
    cagrs = (equity_curves[:, -1] - 1.0) * 100
    
    median_cagr = np.median(cagrs)
    p05_cagr = np.percentile(cagrs, 5)
    
    max_dds = np.zeros(n_simulations)
    
    for i in range(n_simulations):
        curve = equity_curves[i]
        peaks = np.maximum.accumulate(curve)
        drawdowns = (peaks - curve) / peaks
        max_dds[i] = np.max(drawdowns)
                
    prob_40dd = np.mean(max_dds >= 0.40) * 100
    med_max_dd = np.median(max_dds) * 100
    return median_cagr, p05_cagr, prob_40dd, med_max_dd

print("\n==========================================================================================")
print("  2. CORE CONDITIONAL HAZARD CURVE (v50)")
print("==========================================================================================")

percentiles = [75, 85, 90, 95, 97.5, 99]
thresholds = [(p, merged['v50_max'].quantile(p/100.0)) for p in percentiles]

print(f"Baseline P(Tail) = {base_p_tail:.1%} (N={len(merged)}, Tail={merged['Is_Tail'].sum()})")
print(f"\n{'Threshold':<15} | {'N (>Thresh)':<12} | {'Tail Days':<10} | {'P(Tail|v50>X)':<15} | {'95% CI'}")
print("-" * 85)

for p, val in thresholds:
    subset = merged[merged['v50_max'] > val]
    n_days = len(subset)
    k_tails = subset['Is_Tail'].sum()
    p_tail = k_tails / n_days if n_days > 0 else 0
    ci_low, ci_high = wilson_ci(k_tails, n_days)
    print(f"v50 > {p:>4.1f}th    | {n_days:<12} | {k_tails:<10} | {p_tail:>13.1%} | [{ci_low:.1%}, {ci_high:.1%}]")

print("\n==========================================================================================")
print("  3. ECONOMIC VIABILITY TEST (Tapering 5.0x / 0.83x multiplier)")
print("==========================================================================================")

taper_mult = 5.0 / 6.0

print(f"{'Condition':<15} | {'Spike rMean':<12} | {'NonSpk rMean':<12} | {'StDev':<10} | {'Med CAGR':<15} | {'5th CAGR':<15} | {'P(>40% DD)':<12} | {'Med DD':<10}")
print("-" * 115)

base_mc = run_mc_sim(merged['Return'])
print(f"{'Baseline(6.0x)':<15} | {'-':<12} | {'-':<12} | {merged['Return'].std():<10.4f} | {base_mc[0]:>14.1f}% | {base_mc[1]:>14.1f}% | {base_mc[2]:>11.1f}% | {base_mc[3]:>8.1f}%")

for p, val in thresholds:
    is_spike = merged['v50_max'] > val
    spike_returns = merged[is_spike]['Return']
    nonspike_returns = merged[~is_spike]['Return']
    
    tapered_returns = merged['Return'].copy()
    tapered_returns[is_spike] = tapered_returns[is_spike] * taper_mult
    
    spike_mean = spike_returns.mean() if len(spike_returns)>0 else 0
    non_mean = nonspike_returns.mean() if len(nonspike_returns)>0 else 0
    std_taper = tapered_returns.std()
    
    mc = run_mc_sim(tapered_returns)
    print(f"v50 > {p:>4.1f}th    | {spike_mean:>12.4f} | {non_mean:>12.4f} | {std_taper:<10.4f} | {mc[0]:>14.1f}% | {mc[1]:>14.1f}% | {mc[2]:>11.1f}% | {mc[3]:>8.1f}%")

print("\n==========================================================================================")
print("  4. STABILITY / OVERFIT CHECK (Split Sample)")
print("==========================================================================================")

half = len(merged) // 2
df_h1 = merged.iloc[:half]
df_h2 = merged.iloc[half:]

def calc_curve(df_, name):
    print(f"\n{name} (N={len(df_)}):")
    b_tail = df_['Is_Tail'].mean()
    print(f"Baseline P(Tail): {b_tail:.1%}")
    for p in [75, 90, 95]:
        val = df_['v50_max'].quantile(p/100.0)
        subset = df_[df_['v50_max'] > val]
        k = subset['Is_Tail'].sum()
        n = len(subset)
        if n > 0: print(f"  v50 > {p}th : {k/n:.1%} (N={n})")

calc_curve(df_h1, "First 50%")
calc_curve(df_h2, "Second 50%")

print("\n==========================================================================================")
print("  5. INTERACTION TEST (v50 & Cross-Corr)")
print("==========================================================================================")

v50_90 = merged['v50_max'].quantile(0.90)
cc_90 = merged['cross_corr_max'].quantile(0.90)

joint_cond = (merged['v50_max'] > v50_90) & (merged['cross_corr_max'] > cc_90)
j_subset = merged[joint_cond]
j_n = len(j_subset)
j_k = j_subset['Is_Tail'].sum()
j_p = j_k / j_n if j_n > 0 else 0

print(f"P(Tail | v50 > 90th AND cross_corr > 90th) = {j_p:.1%} (N={j_n}, Tails={j_k})")

print("\n==========================================================================================")
print("  6. RANDOMIZATION SANITY CHECK")
print("==========================================================================================")

np.random.seed(42)
shuffled_merged = merged.copy()
shuffled_merged['Is_Tail'] = np.random.permutation(shuffled_merged['Is_Tail'].values)

print(f"{'Threshold':<15} | {'N (>Thresh)':<12} | {'Tail Days':<10} | {'P(Tail|v50>X) (SHUFFLED)':<25}")
for p, val in thresholds:
    subset = shuffled_merged[shuffled_merged['v50_max'] > val]
    n_days = len(subset)
    k_tails = subset['Is_Tail'].sum()
    p_tail = k_tails / n_days if n_days > 0 else 0
    print(f"v50 > {p:>4.1f}th    | {n_days:<12} | {k_tails:<10} | {p_tail:>13.1%}")

print("\n==========================================================================================")
print("  7. DECISION CRITERIA")
print("==========================================================================================")

p95_val = merged['v50_max'].quantile(0.95)
ss95 = merged[merged['v50_max'] > p95_val]
p_tail_95 = ss95['Is_Tail'].mean() if len(ss95)>0 else 0
freq_95 = len(ss95) / len(merged)

mc_95_taper = run_mc_sim(np.where(merged['v50_max'] > p95_val, merged['Return']*taper_mult, merged['Return']))

c1 = p_tail_95 >= 2.5 * base_p_tail
c2 = freq_95 <= 0.08
c3 = mc_95_taper[1] > base_mc[1]

print(f"1. P(Tail | >95th) >= 2.5x baseline? {c1} ({p_tail_95:.1%} vs {2.5*base_p_tail:.1%})")
print(f"2. Convex acceleration visible? Check manual.")
print(f"3. Spike freq <= 8%? {c2} ({freq_95:.1%} - Using 95th pctile is inherently <= 5%)")
print(f"4. Taper improves 5th pctile CAGR? {c3} ({mc_95_taper[1]:.1f}% vs {base_mc[1]:.1f}%)")

print("\nFINAL VERDICT:")
if c1 and c3:
    print("-> True convex hazard. Viable for taper layer.")
else:
    print("-> Failed criteria. Weak clustering or non-viability.")