"""DD curve analysis — identify where 32.35% drawdown peaks occur.""" import sys, time import numpy as np import pandas as pd from pathlib import Path sys.path.insert(0, str(Path(__file__).parent)) from nautilus_dolphin.nautilus.ob_features import OBFeatureEngine from nautilus_dolphin.nautilus.ob_provider import MockOBProvider from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker from mc.mc_ml import DolphinForewarner VBT_DIR = Path(r'C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache') META_COLS = {'vel_div','timestamp','bar_index','date'} VD_THRESH, VD_EXTREME, CONVEXITY = -0.02, -0.05, 3.0 ENGINE_KWARGS = dict( initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05, fraction=0.20, min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0, 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, 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, ) MC_BASE_CFG = { 'trial_id': 0, 'vel_div_threshold': -0.020, 'vel_div_extreme': -0.050, 'use_direction_confirm': True, 'dc_lookback_bars': 7, 'dc_min_magnitude_bps': 0.75, 'dc_skip_contradicts': True, 'dc_leverage_boost': 1.00, 'dc_leverage_reduce': 0.50, 'vd_trend_lookback': 10, 'min_leverage': 0.50, 'max_leverage': 5.00, 'leverage_convexity': 3.00, 'fraction': 0.20, 'use_alpha_layers': True, 'use_dynamic_leverage': True, 'fixed_tp_pct': 0.0099, 'stop_pct': 1.00, 'max_hold_bars': 120, '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': 5.00, 'ob_confirm_rate': 0.40, 'ob_imbalance_bias': -0.09, 'ob_depth_scale': 1.00, 'use_asset_selection': True, 'min_irp_alignment': 0.45, 'lookback': 100, 'acb_beta_high': 0.80, 'acb_beta_low': 0.20, 'acb_w750_threshold_pct': 60, } forewarner = DolphinForewarner(models_dir=str(Path('mc_results/models'))) parquet_files = sorted([p for p in VBT_DIR.glob('*.parquet') if 'catalog' not in str(p)]) acb = AdaptiveCircuitBreaker() acb.preload_w750([pf.stem for pf in parquet_files]) # Vol baseline 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) def strength_cubic(vel_div): if vel_div >= VD_THRESH: return 0.0 raw = (VD_THRESH - vel_div) / (VD_THRESH - VD_EXTREME) return min(1.0, max(0.0, raw)) ** CONVEXITY OB_ASSETS = ['BTCUSDT','ETHUSDT','BNBUSDT','SOLUSDT'] _mock_ob = MockOBProvider( imbalance_bias=-0.09, depth_scale=1.0, assets=OB_ASSETS, imbalance_biases={'BTCUSDT': -0.086, 'ETHUSDT': -0.092, 'BNBUSDT': +0.05, 'SOLUSDT': +0.05}, ) ob_eng = OBFeatureEngine(_mock_ob) ob_eng.preload_date('mock', OB_ASSETS) engine = NDAlphaEngine(**ENGINE_KWARGS) engine.set_ob_engine(ob_eng) engine.set_esoteric_hazard_multiplier(0.0) bar_idx = 0; ph = {}; dstats = [] for pf in parquet_files: ds = pf.stem cs = engine.capital engine.regime_direction = -1 engine.regime_dd_halt = False acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_eng) base_boost = acb_info['boost'] beta = acb_info['beta'] eff_max_lev = float(ENGINE_KWARGS['max_leverage']) * base_boost mc_cfg = dict(MC_BASE_CFG); mc_cfg['max_leverage'] = eff_max_lev mc_report = forewarner.assess_config_dict(mc_cfg) mc_red = mc_report.catastrophic_probability > 0.25 or mc_report.envelope_score < -1.0 mc_orange = (not mc_red) and (mc_report.envelope_score < 0 or mc_report.catastrophic_probability > 0.10) mc_size_scale = 0.5 if mc_orange else 1.0 if mc_red: engine.regime_dd_halt = True df, acols, dvol = pq_data[ds] day_idx_before = len(engine.trade_history) bid = 0 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; bid += 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; bid += 1; continue vrok = False if bid < 100 else (np.isfinite(dvol[ri]) and dvol[ri] > vol_p60) if beta > 0: ss = strength_cubic(float(vd)) engine.regime_size_mult = base_boost * (1.0 + beta * ss) * mc_size_scale else: engine.regime_size_mult = base_boost * mc_size_scale engine.process_bar(bar_idx=bar_idx, vel_div=float(vd), prices=prices, vol_regime_ok=vrok, price_histories=ph) bar_idx += 1; bid += 1 day_trades = engine.trade_history[day_idx_before:] dw = [t for t in day_trades if t.pnl_absolute > 0] dl = [t for t in day_trades if t.pnl_absolute <= 0] avg_loss = float(np.mean([t.pnl_pct for t in dl]) * 100) if dl else 0.0 avg_win = float(np.mean([t.pnl_pct for t in dw]) * 100) if dw else 0.0 dstats.append({ 'date': ds, 'pnl': engine.capital - cs, 'cap': engine.capital, 'boost': base_boost, 'beta': beta, 'eff_lev': eff_max_lev, 'trades': len(day_trades), 'wins': len(dw), 'losses': len(dl), 'avg_win': avg_win, 'avg_loss': avg_loss, }) # Build DD curve peak = 25000.0 dd_curve = [] for s in dstats: peak = max(peak, s['cap']) dd = (peak - s['cap']) / peak * 100 dd_curve.append(dd) max_dd_idx = int(np.argmax(dd_curve)) print('\n=== PER-DATE EQUITY + DD CURVE ===') print(f' {"Date":<12} {"Capital":>10} {"Daily P&L":>10} {"DD%":>7} {"Boost":>7} {"eLev":>6} {"T":>4} {"W/L":>7} {"AvgW%":>7} {"AvgL%":>7}') for i, (s, dd) in enumerate(zip(dstats, dd_curve)): marker = ' <<< DD PEAK' if i == max_dd_idx else '' # Show all days with DD > 2% or large pnl swings if dd > 2.0 or abs(s['pnl']) > 500 or marker: wl = f"{s['wins']}/{s['losses']}" print(f' {s["date"]:<12} {s["cap"]:>10,.0f} {s["pnl"]:>+10,.0f} {dd:>7.2f}% ' f'{s["boost"]:>7.2f}x {s["eff_lev"]:>6.2f}x {s["trades"]:>4} {wl:>7} ' f'{s["avg_win"]:>+7.3f} {s["avg_loss"]:>+7.3f}{marker}') print(f'\n Peak DD: {dd_curve[max_dd_idx]:.2f}% on {dstats[max_dd_idx]["date"]}') print(f' Final capital: ${engine.capital:,.2f} ROI: {(engine.capital-25000)/25000*100:+.2f}%') print('\nWorst 10 daily P&L:') worst = sorted(dstats, key=lambda x: x['pnl'])[:10] for s in worst: print(f' {s["date"]}: P&L={s["pnl"]:+,.0f} boost={s["boost"]:.2f}x eLev={s["eff_lev"]:.2f}x T={s["trades"]} W/L={s["wins"]}/{s["losses"]} AvgL={s["avg_loss"]:+.3f}%')