"""GO1 / GO2 OOD Dress Rehearsal - Baseline run on all available parquet data. Run: python run_go1_ood.py python run_go1_ood.py --label GO2 """ import sys, time, math, argparse from pathlib import Path import numpy as np import pandas as pd # Force UTF-8 output (Windows cp1252 safety) if hasattr(sys.stdout, 'reconfigure'): sys.stdout.reconfigure(encoding='utf-8') sys.path.insert(0, str(Path(__file__).parent)) # ── JIT warmup ───────────────────────────────────────────────────────────── print("Compiling numba kernels...") t0c = time.time() from nautilus_dolphin.nautilus.alpha_asset_selector import compute_irp_nb, compute_ars_nb, rank_assets_irp_nb from nautilus_dolphin.nautilus.alpha_bet_sizer import compute_sizing_nb from nautilus_dolphin.nautilus.alpha_signal_generator import check_dc_nb _p = np.array([1.0, 2.0, 3.0], dtype=np.float64) compute_irp_nb(_p, -1); compute_ars_nb(1.0, 0.5, 0.01) rank_assets_irp_nb(np.ones((10, 2), dtype=np.float64), 8, -1, 5, 500.0, 20, 0.20) compute_sizing_nb(-0.03, -0.02, -0.05, 3.0, 0.5, 5.0, 0.20, True, True, 0.0, np.zeros(4, dtype=np.int64), np.zeros(4, dtype=np.int64), np.zeros(5, dtype=np.float64), 0, -1, 0.01, 0.04) check_dc_nb(_p, 3, 1, 0.75) print(f" JIT: {time.time() - t0c:.1f}s") from nautilus_dolphin.nautilus.alpha_orchestrator import NDAlphaEngine from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker # ── Config ───────────────────────────────────────────────────────────────── 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'} 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, 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, ) VD_THRESH = -0.02; VD_EXTREME = -0.05; CONVEXITY = 3.0 PAPER_CAPITAL = 10000.0 # user paper-trade capital SIM_CAPITAL = 25000.0 # engine simulation capital SCALE = PAPER_CAPITAL / SIM_CAPITAL # 0.40 # ── Data load ────────────────────────────────────────────────────────────── parquet_files = sorted(VBT_DIR.glob("*.parquet")) parquet_files = [p for p in parquet_files if 'catalog' not in str(p)] acb = AdaptiveCircuitBreaker() date_strings = [pf.stem for pf in parquet_files] acb.preload_w750(date_strings) 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)) if all_vols else 0.0 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 # ── Engine run ───────────────────────────────────────────────────────────── def run_engine(): import gc; gc.collect() engine = NDAlphaEngine(**ENGINE_KWARGS) 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=None) base_boost = acb_info['boost'] beta = acb_info['beta'] df, acols, dvol = pq_data[ds] 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 and base_boost > 1.0: ss = strength_cubic(float(vd)) 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; bid+=1 dstats.append({'date': ds, 'pnl': engine.capital - cs, 'cap': engine.capital}) tr = engine.trade_history w = [t for t in tr if t.pnl_absolute > 0] l = [t for t in tr if t.pnl_absolute <= 0] gw = sum(t.pnl_absolute for t in w) if w else 0.0 gl = abs(sum(t.pnl_absolute for t in l)) if l else 0.0 roi = (engine.capital - SIM_CAPITAL) / SIM_CAPITAL * 100 pf_val = gw / gl if gl > 0 else 999.0 dr = [s['pnl'] / SIM_CAPITAL * 100 for s in dstats] sharpe = np.mean(dr) / np.std(dr) * np.sqrt(365) if np.std(dr) > 0 else 0.0 peak_cap = SIM_CAPITAL; max_dd = 0.0 for s in dstats: peak_cap = max(peak_cap, s['cap']) dd = (peak_cap - s['cap']) / peak_cap * 100 max_dd = max(max_dd, dd) wr = len(w) / len(tr) * 100 if tr else 0.0 avg_win = float(np.mean([t.pnl_pct for t in w]) * 100) if w else 0.0 avg_loss= float(np.mean([t.pnl_pct for t in l]) * 100) if l else 0.0 return { 'roi': roi, 'pf': pf_val, 'dd': max_dd, 'sharpe': sharpe, 'trades': len(tr), 'capital': engine.capital, 'wr': wr, 'avg_win': avg_win, 'avg_loss': avg_loss, 'n_wins': len(w), 'n_losses': len(l), 'dstats': dstats, } # ── Main ─────────────────────────────────────────────────────────────────── if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--label', default='GO1', help='Run label (GO1 or GO2)') args = parser.parse_args() label = args.label.upper() print(f"\n{'='*70}") print(f" DOLPHIN NG -- OOD DRESS REHEARSAL [{label}]") print(f"{'='*70}") print(f" Data: {date_strings[0]} to {date_strings[-1]} ({len(date_strings)} days)") print(f" Capital: ${PAPER_CAPITAL:,.0f} paper (sim at ${SIM_CAPITAL:,.0f}, scale={SCALE:.2f})") print(f" OB: BASELINE (no OB engine)") print(f" Mode: Eigenvalue signal only") print(f"{'='*70}") t0 = time.time() r = run_engine() elapsed = time.time() - t0 net_pnl_paper = (r['capital'] - SIM_CAPITAL) * SCALE cap_paper = r['capital'] * SCALE print(f"\n [{label}] RESULT ({elapsed:.0f}s)") print(f" {'─'*66}") print(f" ROI: {r['roi']:+.2f}%") print(f" PF: {r['pf']:.3f}") print(f" Sharpe: {r['sharpe']:.2f}") print(f" Max DD: {r['dd']:.2f}%") print(f" Trades: {r['trades']} WR: {r['wr']:.1f}% AvgW: {r['avg_win']:+.3f}% AvgL: {r['avg_loss']:+.3f}%") print(f" W/L: {r['n_wins']}/{r['n_losses']}") print(f" Capital: ${cap_paper:,.2f} (net {net_pnl_paper:+,.2f} on $10k paper)") print(f" {'─'*66}") # Last 7 days breakdown tail = r['dstats'][-7:] print(f"\n Last {len(tail)} days daily P&L (paper $10k scale):") for s in tail: pnl_p = s['pnl'] * SCALE cap_p = s['cap'] * SCALE bar = '+' * int(abs(pnl_p) / 2) if pnl_p >= 0 else '-' * int(abs(pnl_p) / 2) sign = '+' if pnl_p >= 0 else '' print(f" {s['date']} {sign}${pnl_p:6.2f} cap ${cap_p:,.2f} {bar}") print(f"\n{'='*70}\n") # Save snapshot for GO2 delta calc import json snap = { 'label': label, 'dates': date_strings, 'roi': r['roi'], 'pf': r['pf'], 'sharpe': r['sharpe'], 'dd': r['dd'], 'trades': r['trades'], 'wr': r['wr'], 'capital': r['capital'], 'n_wins': r['n_wins'], 'n_losses': r['n_losses'], 'dstats': r['dstats'], } snap_path = Path(__file__).parent / f'ood_{label.lower()}_snap.json' with open(snap_path, 'w') as f: json.dump(snap, f, indent=2) print(f" Snapshot saved: {snap_path.name}") # If GO2, load GO1 snap and print delta if label == 'GO2': go1_path = Path(__file__).parent / 'ood_go1_snap.json' if go1_path.exists(): with open(go1_path) as f: g1 = json.load(f) new_days = [d for d in date_strings if d not in g1['dates']] d_roi = r['roi'] - g1['roi'] d_trades = r['trades'] - g1['trades'] d_wr = r['wr'] - g1['wr'] d_cap = (r['capital'] - g1['capital']) * SCALE print(f" {'='*66}") print(f" DELTA vs GO1") print(f" {'─'*66}") print(f" New days ingested: {new_days if new_days else '(none -- same dataset)'}") print(f" dROI: {d_roi:+.2f}%") print(f" dTrades: {d_trades:+d}") print(f" dWR: {d_wr:+.2f}%") print(f" dCapital:{d_cap:+,.2f} (paper $10k)") if new_days: print(f"\n New day(s) P&L:") new_dstats = [s for s in r['dstats'] if s['date'] in new_days] for s in new_dstats: pnl_p = s['pnl'] * SCALE sign = '+' if pnl_p >= 0 else '' print(f" {s['date']} {sign}${pnl_p:.2f} (paper)") print(f" {'='*66}\n") else: print(" [GO2] No GO1 snapshot found -- run GO1 first.")