import sys, time, datetime, zoneinfo, json, math
from pathlib import Path
import numpy as np
import pandas as pd

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

from astropy.time import Time
import astropy.coordinates as coord
import astropy.units as u
from astropy.coordinates import solar_system_ephemeris, get_body, EarthLocation

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

class MarketIndicators:
    def __init__(self):
        self.regions = [
            {'name': 'Americas', 'tz': 'America/New_York', 'pop': 1000, 'liq_weight': 0.35},
            {'name': 'EMEA', 'tz': 'Europe/London', 'pop': 2200, 'liq_weight': 0.30},
            {'name': 'South_Asia', 'tz': 'Asia/Kolkata', 'pop': 1400, 'liq_weight': 0.05},
            {'name': 'East_Asia', 'tz': 'Asia/Shanghai', 'pop': 1600, 'liq_weight': 0.20},
            {'name': 'Oceania_SEA', 'tz': 'Asia/Singapore', 'pop': 800, 'liq_weight': 0.10}
        ]
        self.cycle_length_days = 1460
        self.last_halving = datetime.datetime(2024, 4, 20, tzinfo=datetime.timezone.utc)

    def get_calendar_items(self, now):
        return {
            'year': now.year,
            'month': now.month,
            'day_of_month': now.day,
            'hour': now.hour,
            'minute': now.minute,
            'day_of_week': now.weekday(),
            'week_of_year': now.isocalendar().week
        }

    def get_market_cycle_position(self, now_utc):
        days_since_halving = (now_utc - self.last_halving).days
        position = (days_since_halving % self.cycle_length_days) / self.cycle_length_days
        return position

    def get_moon_phase(self, now_utc):
        t = Time(now_utc)
        with solar_system_ephemeris.set('builtin'):
            moon = get_body('moon', t)
            sun = get_body('sun', t)
            elongation = sun.separation(moon)
            phase_angle = np.arctan2(sun.distance * np.sin(elongation),
                                     moon.distance - sun.distance * np.cos(elongation))
            illumination = (1 + np.cos(phase_angle)) / 2.0
            
            phase_name = "WAXING"
            if illumination < 0.03: phase_name = "NEW_MOON"
            elif illumination > 0.97: phase_name = "FULL_MOON"
            elif illumination < 0.5: phase_name = "WAXING_CRESCENT" if moon.dec.deg > sun.dec.deg else "WANING_CRESCENT"
            else: phase_name = "WAXING_GIBBOUS" if moon.dec.deg > sun.dec.deg else "WANING_GIBBOUS"

        return {'illumination': float(illumination), 'phase_name': phase_name}

    def is_mercury_retrograde(self, now_utc):
        t = Time(now_utc)
        is_retro = False
        try:
            with solar_system_ephemeris.set('builtin'):
                loc = EarthLocation.of_site('greenwich')
                merc_now = get_body('mercury', t, loc)
                merc_later = get_body('mercury', t + 1 * u.day, loc)
                
                lon_now = merc_now.geometrictrueecliptic.lon.deg
                lon_later = merc_later.geometrictrueecliptic.lon.deg
                
                diff = (lon_later - lon_now) % 360
                is_retro = diff > 180 
        except Exception as e:
            pass
        return is_retro

    def get_indicators(self, now_utc):
        # We drop hour-specific ones since we evaluate at a fixed time daily
        moon_data = self.get_moon_phase(now_utc)
        calendar = self.get_calendar_items(now_utc)
        return {
            'timestamp': now_utc.isoformat(),
            'day_of_week': calendar['day_of_week'],
            'week_of_year': calendar['week_of_year'],
            'market_cycle_position': round(self.get_market_cycle_position(now_utc), 4),
            'moon_illumination': moon_data['illumination'],
            'moon_phase_name': moon_data['phase_name'],
            'mercury_retrograde': int(self.is_mercury_retrograde(now_utc)),
        }

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 precursor AND ESOTERIC metrics...")

daily_metrics = []
mi = MarketIndicators()

for pf in parquet_files:
    ds = pf.stem
    df = pd.read_parquet(pf)
    
    # 1. Structural Precursors
    vd = df['vel_div'].fillna(0).values
    vol_accel = np.diff(vd, prepend=vd[0])
    daily_vol_accel_max = np.max(np.abs(vol_accel))
    
    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)]
        mean_cross_corr = np.nanmean(cross_corr)
        max_cross_corr = np.nanmax(cross_corr)
    else:
        mean_cross_corr = 0; max_cross_corr = 0
        
    entropy_max = df['instability_50'].max() if 'instability_50' in df.columns else 0
    v750_max = df['v750_lambda_max_velocity'].max() if 'v750_lambda_max_velocity' in df.columns else 0
    v50_max = df['v50_lambda_max_velocity'].max() if 'v50_lambda_max_velocity' in df.columns else 0
    
    # 2. Esoteric Factors
    y, m, d = map(int, ds.split('-'))
    dt_utc = datetime.datetime(y, m, d, 12, 0, 0, tzinfo=datetime.timezone.utc)
    eso = mi.get_indicators(dt_utc)
    
    # Optionally save to disk as requested
    eso_path = VBT_DIR / f"ESOTERIC_data_{ds}.json"
    if not eso_path.exists():
        with open(eso_path, 'w') as f:
            json.dump(eso, f, indent=2)

    daily_metrics.append({
        'Date': ds,
        'vol_accel_max': daily_vol_accel_max,
        'cross_corr_mean': mean_cross_corr,
        'cross_corr_max': max_cross_corr,
        'entropy_max': entropy_max,
        'v750_max': v750_max,
        'v50_max': v50_max,
        'day_of_week': eso['day_of_week'],
        'week_of_year': eso['week_of_year'],
        'market_cycle_position': eso['market_cycle_position'],
        'moon_illumination': eso['moon_illumination'],
        'mercury_retrograde': eso['mercury_retrograde']
    })

metrics_df = pd.DataFrame(daily_metrics).set_index('Date')
precursor_df = metrics_df.shift(1).dropna() # Shift 1 day to ensure it's a T-1 precursor

print("Running fast 6.0x trajectory to isolate daily PnL...")

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)
    
    if 'BTCUSDT' in df.columns:
        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))

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()

print(f"\n==================================================================================================")
print(f"  CONDITIONAL PROBABILITY ANALYSIS: P(Tail | Precursor Spike)")
print(f"  Baseline P(Tail) = {base_p_tail:.1%}")
print(f"==================================================================================================")
print(f"{'Feature':<25} | {'P(Tail|X > 75th)':<20} | {'P(Tail|X > 90th)':<20} | {'P(Tail|X > 95th)':<20}")
print("-" * 98)

features = ['vol_accel_max', 'v750_max', 'v50_max', 'entropy_max', 'cross_corr_max']

for f in features:
    p75 = merged[f].quantile(0.75)
    p90 = merged[f].quantile(0.90)
    p95 = merged[f].quantile(0.95)
    
    cond_75 = merged[merged[f] > p75]['Is_Tail'].mean() if len(merged[merged[f]>p75]) > 0 else 0
    cond_90 = merged[merged[f] > p90]['Is_Tail'].mean() if len(merged[merged[f]>p90]) > 0 else 0
    cond_95 = merged[merged[f] > p95]['Is_Tail'].mean() if len(merged[merged[f]>p95]) > 0 else 0
    
    print(f"{f:<25} | {cond_75:>19.1%} | {cond_90:>19.1%} | {cond_95:>19.1%}")

print(f"\n==================================================================================================")
print(f"  ESOTERIC FACTORS CORRELATION WITH EXTREME LEFT TAILS")
print(f"==================================================================================================")

esoteric_features = ['day_of_week', 'moon_illumination', 'market_cycle_position', 'mercury_retrograde']
for f in esoteric_features:
    # See if Esoteric factors map to higher chance of tail events
    if f == 'day_of_week':
        print("\nDay of Week P(Tail):")
        for d in range(7):
            subset = merged[merged[f] == d]
            if len(subset) > 0:
                print(f"  Day {d}: {subset['Is_Tail'].mean():.1%} (N={len(subset)})")
    elif f == 'mercury_retrograde':
        print("\nMercury Retrograde P(Tail):")
        for d in [0, 1]:
            subset = merged[merged[f] == d]
            if len(subset) > 0:
                print(f"  Retrograde={d}: {subset['Is_Tail'].mean():.1%} (N={len(subset)})")
    else:
        # Continuous values
        p75 = merged[f].quantile(0.75)
        cond_75 = merged[merged[f] > p75]['Is_Tail'].mean() if len(merged[merged[f]>p75]) > 0 else 0
        p25 = merged[f].quantile(0.25)
        cond_25 = merged[merged[f] < p25]['Is_Tail'].mean() if len(merged[merged[f]<p25]) > 0 else 0
        print(f"\n{f}:")
        print(f"  P(Tail | Top 25% {f}): {cond_75:.1%}")
        print(f"  P(Tail | Bot 25% {f}): {cond_25:.1%}")