"""Mini 5Y test - first 10 dates only"""
import sys, time
sys.stdout.reconfigure(encoding='utf-8', errors='replace')
print('MINI 5Y BACKTEST (first 10 dates only)')
print('='*50)

t0 = time.time()
from pathlib import Path
import numpy as np
import pandas as pd
from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker

VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines")
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'}

parquet_files = sorted(VBT_DIR.glob("*.parquet"))[:10]  # Only 10 files
print(f'Processing {len(parquet_files)} files...')

# ACB
acb = AdaptiveCircuitBreaker()
date_strings = [pf.stem for pf in parquet_files]
acb.preload_w750(date_strings)
print(f'ACB w750 threshold: {acb._w750_threshold:.6f}')

# Engine
engine = NDAlphaEngine(
    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=0.0095, 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,
    sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.50,
    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.set_acb(acb)
engine.set_esoteric_hazard_multiplier(0.0)

# Process
for i, pf in enumerate(parquet_files):
    ds = pf.stem
    df = pd.read_parquet(pf)
    acols = [c for c in df.columns if c not in META_COLS]
    
    # Compute vol regime
    bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
    dvol = np.full(len(df), np.nan)
    if bp is not None:
        for j in range(50, len(bp)):
            seg = bp[max(0,j-50):j]
            if len(seg)<10: continue
            dvol[j] = float(np.std(np.diff(seg)/seg[:-1]))
    vol_p60 = 0.001  # Fixed for mini test
    vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60, False)
    
    stats = engine.process_day(ds, df, acols, vol_regime_ok=vol_ok)
    print(f"  [{i+1}] {ds}: trades={stats['trades']} P&L=${stats['pnl']:+.0f} cap=${engine.capital:,.0f}")

print('='*50)
print(f'DONE in {time.time()-t0:.1f}s')
print(f'Final capital: ${engine.capital:,.2f}')
print(f'Total trades: {len(engine.trade_history)}')