initial: import DOLPHIN baseline 2026-04-21 from dolphinng5_predict working tree
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
This commit is contained in:
hjnormey
260
nautilus_dolphin/run_final_metrics.py
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260
nautilus_dolphin/run_final_metrics.py
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import sys, time
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from pathlib import Path
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).parent))
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from nautilus_dolphin.nautilus.alpha_orchestrator import NDAlphaEngine
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from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
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from nautilus_dolphin.nautilus.ob_features import OBFeatureEngine
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from nautilus_dolphin.nautilus.ob_provider import MockOBProvider
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VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache")
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META_COLS = {'timestamp', 'scan_number', 'v50_lambda_max_velocity', 'v150_lambda_max_velocity',
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'v300_lambda_max_velocity', 'v750_lambda_max_velocity', 'vel_div',
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'instability_50', 'instability_150'}
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# Load parquet files correctly
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parquet_files = sorted(VBT_DIR.glob("*.parquet"))
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parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]
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print("Loading data...")
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all_vols = []
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for pf in parquet_files[:2]:
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df = pd.read_parquet(pf)
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if 'BTCUSDT' not in df.columns: continue
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pr = df['BTCUSDT'].values
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for i in range(60, len(pr)):
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seg = pr[max(0,i-50):i]
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if len(seg)<10: continue
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v = float(np.std(np.diff(seg)/seg[:-1]))
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if v > 0: all_vols.append(v)
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vol_p60 = float(np.percentile(all_vols, 60))
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pq_data = {}
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for pf in parquet_files:
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df = pd.read_parquet(pf)
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ac = [c for c in df.columns if c not in META_COLS]
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bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
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dv = np.full(len(df), np.nan)
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if bp is not None:
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for i in range(50, len(bp)):
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seg = bp[max(0,i-50):i]
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if len(seg)<10: continue
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dv[i] = float(np.std(np.diff(seg)/seg[:-1]))
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pq_data[pf.stem] = (df, ac, dv)
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# Initialize systems
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750([pf.stem for pf in parquet_files])
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ENGINE_KWARGS = dict(
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initial_capital=25000.0, vel_div_threshold=-0.02, vel_div_extreme=-0.05,
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min_leverage=0.5, max_leverage=5.0, leverage_convexity=3.0,
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fraction=0.20, fixed_tp_pct=0.0099, stop_pct=1.0, max_hold_bars=120,
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use_direction_confirm=True, dc_lookback_bars=7, dc_min_magnitude_bps=0.75,
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dc_skip_contradicts=True, dc_leverage_boost=1.0, dc_leverage_reduce=0.5,
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use_asset_selection=True, min_irp_alignment=0.45,
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use_sp_fees=True, use_sp_slippage=True,
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use_ob_edge=True, ob_edge_bps=5.0, ob_confirm_rate=0.40,
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lookback=100, use_alpha_layers=True, use_dynamic_leverage=True, seed=42,
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)
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def run_test(name, ob_engine=None):
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engine = NDAlphaEngine(**ENGINE_KWARGS)
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if ob_engine:
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engine.set_ob_engine(ob_engine)
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bar_idx = 0; peak_cap = engine.capital; max_dd = 0.0
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for pf in parquet_files:
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ds = pf.stem
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# ACB logic
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acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_engine)
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base_boost = acb_info['boost'] # This is now exactly (1.0 - cut)
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beta = acb_info['beta']
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df, acols, dvol = pq_data[ds]
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ph = {}
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for ri in range(len(df)):
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row = df.iloc[ri]; vd = row.get("vel_div")
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if vd is None or not np.isfinite(vd): bar_idx+=1; continue
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prices = {}
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for ac in acols:
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p = row[ac]
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if p and p > 0 and np.isfinite(p):
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prices[ac] = float(p)
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if ac not in ph: ph[ac] = []
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ph[ac].append(float(p))
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if len(ph[ac]) > 500: ph[ac] = ph[ac][-200:]
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if not prices: bar_idx+=1; continue
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vrok = False if ri < 100 else (np.isfinite(dvol[ri]) and dvol[ri] > vol_p60)
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# 3-scale meta-boost: ACB boost * (1 + beta * strength^3)
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if beta > 0 and base_boost > 1.0:
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ss = 0.0
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if vd < -0.02:
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raw = (-0.02 - float(vd)) / (-0.02 - -0.05)
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ss = min(1.0, max(0.0, raw)) ** 3.0
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engine.regime_size_mult = base_boost * (1.0 + beta * ss)
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else:
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engine.regime_size_mult = base_boost
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engine.process_bar(bar_idx=bar_idx, vel_div=float(vd), prices=prices, vol_regime_ok=vrok, price_histories=ph)
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# (no fraction reset needed)
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bar_idx += 1
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peak_cap = max(peak_cap, engine.capital)
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dd = (peak_cap - engine.capital) / peak_cap * 100
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max_dd = max(max_dd, dd)
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trades = engine.trade_history
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w = [t for t in trades if t.pnl_absolute > 0]
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l = [t for t in trades if t.pnl_absolute <= 0]
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gw = sum(t.pnl_absolute for t in w) if w else 0
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gl = abs(sum(t.pnl_absolute for t in l)) if l else 0
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roi = (engine.capital - 25000) / 25000 * 100
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pf_val = gw / gl if gl > 0 else 999
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wr = len(w) / len(trades) * 100 if trades else 0
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returns = [(t.pnl_absolute / (25000+sum(x.pnl_absolute for x in trades[:i]))) for i, t in enumerate(trades)]
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if not returns: returns = [0]
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mc_med_cagr, mc_tail_cagr, mc_p40, mc_med_dd = compute_mc_cagr(returns)
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print(f"\n{name}")
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print(f" ROI: {roi:+.2f}%")
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print(f" MaxDD (Emp): {max_dd:.2f}%")
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print(f" Trades: {len(trades)}")
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print(f" Win Rate: {wr:.1f}%")
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print(f" Prof Factor: {pf_val:.3f}")
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print(f" MC Med CAGR: {mc_med_cagr:+.1f}%")
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print(f" MC Med DD: {mc_med_dd:.1f}%")
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print(f" MC P(>40DD): {mc_p40:.1f}%")
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# Helper for Monte Carlo
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def compute_mc_cagr(returns, periods=252, n_simulations=1000):
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np.random.seed(42)
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dr = np.array(returns)
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if len(dr) == 0: return 0,0,0,0
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# Resample daily (simulated by block since returns are per trade, we approximate)
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sr = np.random.choice(dr, size=(n_simulations, periods), replace=True)
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eq = np.cumprod(1.0 + sr, axis=1)
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cagrs = (eq[:, -1] - 1.0) * 100
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med_cagr = np.median(cagrs)
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tail_cagr = np.percentile(cagrs, 5)
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max_dds = np.zeros(n_simulations)
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for i in range(n_simulations):
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curve = eq[i]
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peaks = np.maximum.accumulate(curve)
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dds = (peaks - curve) / peaks
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max_dds[i] = np.max(dds)
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p40 = np.mean(max_dds >= 0.40) * 100
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mdd = np.median(max_dds) * 100
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return med_cagr, tail_cagr, p40, mdd
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# 1. Test Baseline
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run_test("A. ACB v4 Baseline (No OB, Pure ACB)")
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# 2. Test Full Production (ACB Cut + OB Engine Hard Skip)
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mock = MockOBProvider(imbalance_bias=-0.09, depth_scale=1.0,
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assets=["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"],
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imbalance_biases={"BNBUSDT": 0.20, "SOLUSDT": 0.20})
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ob_engine = OBFeatureEngine(mock)
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ob_engine.preload_date("mock", mock.get_assets())
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run_test("B. Production Stack (ACB + OB Hard-Skip, 5.0x)", ob_engine)
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from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine as ThrottledEngine
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import json
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def run_throttled_test(name, ob_engine=None):
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engine = ThrottledEngine(**ENGINE_KWARGS)
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if ob_engine:
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engine.set_ob_engine(ob_engine)
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bar_idx = 0; peak_cap = engine.capital; max_dd = 0.0
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for pf in parquet_files:
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ds = pf.stem
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acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_engine)
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base_boost = acb_info['boost']
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beta = acb_info['beta']
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eso_path = VBT_DIR / f"ESOTERIC_data_{ds}.json"
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eso = {}
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if eso_path.exists():
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with open(eso_path, 'r') as f:
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eso = json.load(f)
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hazard = 0.0
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dow = eso.get('day_of_week', -1)
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if dow == 1: hazard = 1.0
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elif dow in [0, 2]: hazard = 0.5
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if eso.get('moon_illumination', 0.5) <= 0.05: hazard = 1.0
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engine.set_esoteric_hazard_multiplier(hazard)
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# Enable 6.0x dynamic
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engine.set_mc_forewarner_status(True)
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df, acols, dvol = pq_data[ds]
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ph = {}
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for ri in range(len(df)):
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row = df.iloc[ri]; vd = row.get("vel_div")
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if vd is None or not np.isfinite(vd): bar_idx+=1; continue
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prices = {}
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for ac in acols:
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p = row[ac]
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if p and p > 0 and np.isfinite(p):
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prices[ac] = float(p)
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if ac not in ph: ph[ac] = []
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ph[ac].append(float(p))
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if len(ph[ac]) > 500: ph[ac] = ph[ac][-200:]
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if not prices: bar_idx+=1; continue
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vrok = False if ri < 100 else (np.isfinite(dvol[ri]) and dvol[ri] > vol_p60)
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if beta > 0 and base_boost > 1.0:
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ss = 0.0
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if vd < -0.02:
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raw = (-0.02 - float(vd)) / (-0.02 - -0.05)
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ss = min(1.0, max(0.0, raw)) ** 3.0
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engine.regime_size_mult = base_boost * (1.0 + beta * ss)
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else:
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engine.regime_size_mult = base_boost
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engine.process_bar(bar_idx=bar_idx, vel_div=float(vd), prices=prices, vol_regime_ok=vrok, price_histories=ph)
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bar_idx += 1
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peak_cap = max(peak_cap, engine.capital)
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dd = (peak_cap - engine.capital) / peak_cap * 100
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max_dd = max(max_dd, dd)
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trades = engine.trade_history
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w = [t for t in trades if t.pnl_absolute > 0]
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l = [t for t in trades if t.pnl_absolute <= 0]
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gw = sum(t.pnl_absolute for t in w) if w else 0
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gl = abs(sum(t.pnl_absolute for t in l)) if l else 0
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roi = (engine.capital - 25000) / 25000 * 100
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pf_val = gw / gl if gl > 0 else 999
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wr = len(w) / len(trades) * 100 if trades else 0
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returns = [(t.pnl_absolute / (25000+sum(x.pnl_absolute for x in trades[:i]))) for i, t in enumerate(trades)]
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if not returns: returns = [0]
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mc_med_cagr, mc_tail_cagr, mc_p40, mc_med_dd = compute_mc_cagr(returns)
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print(f"\n{name}")
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print(f" ROI: {roi:+.2f}%")
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print(f" MaxDD (Emp): {max_dd:.2f}%")
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print(f" Trades: {len(trades)}")
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print(f" Win Rate: {wr:.1f}%")
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print(f" Prof Factor: {pf_val:.3f}")
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print(f" MC Med CAGR: {mc_med_cagr:+.1f}%")
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print(f" MC Med DD: {mc_med_dd:.1f}%")
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print(f" MC P(>40DD): {mc_p40:.1f}%")
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run_throttled_test("C. Ultimate Stack (Production OB + Dynamic 5-6x + EsoF Taper)", ob_engine)
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