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
206
nautilus_dolphin/run_ob_variance_test.py
Executable file
206
nautilus_dolphin/run_ob_variance_test.py
Executable file
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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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import json
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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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from mc.mc_ml import DolphinForewarner
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from mc.mc_sampler import MCTrialConfig
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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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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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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_inst = OBFeatureEngine(mock)
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ob_engine_inst.preload_date("mock", mock.get_assets())
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def run_test(name, use_ob_engine=True):
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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=use_ob_engine, 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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# Re-evaluate config against ML forewarner
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forewarner = DolphinForewarner(models_dir=str(Path(__file__).parent / "mc_results" / "models"))
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config = MCTrialConfig(
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trial_id="LIVE",
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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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vd_trend_lookback=20, use_sp_fees=True, use_sp_slippage=True,
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sp_maker_entry_rate=0.62, sp_maker_exit_rate=0.5,
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use_asset_selection=True, min_irp_alignment=0.45,
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use_ob_edge=use_ob_engine, ob_edge_bps=5.0, ob_confirm_rate=0.40,
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ob_imbalance_bias=0.0, ob_depth_scale=1.0,
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lookback=100, use_alpha_layers=True, use_dynamic_leverage=True,
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acb_beta_high=1.5, acb_beta_low=0.2, acb_w750_threshold_pct=60.0
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)
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report = forewarner.assess(config)
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env = report.envelope_score
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cat = report.catastrophic_probability
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champ = report.champion_probability
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is_green = (env > 0.5 and champ > 0.6)
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print(f"\n[{name}]")
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print(f" MC-Forewarner Status: Env={env:.2f}, Cat={cat:.1%}, Champ={champ:.1%} -> GREEN? {is_green}")
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import gc
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gc.collect()
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engine = NDAlphaEngine(**ENGINE_KWARGS)
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# Apply our newly added dynamic capacity
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engine.set_mc_forewarner_status(is_green)
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print(f" Engine Ceiling set to: {engine.bet_sizer.max_leverage}x")
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ob_ref = ob_engine_inst if use_ob_engine else None
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if ob_ref:
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engine.set_ob_engine(ob_ref)
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bar_idx = 0; peak_cap = engine.capital; max_dd = 0.0
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daily_returns = []
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for pf in parquet_files:
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ds = pf.stem
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cs = engine.capital
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# ACB logic
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acb_info = acb.get_dynamic_boost_for_date(ds, ob_engine=ob_ref)
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base_boost = acb_info['boost']
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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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if beta > 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
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max_dd = max(max_dd, dd)
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# Daily Return
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daily_returns.append((engine.capital - cs) / cs if cs > 0 else 0)
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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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R = np.array(daily_returns)
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variance = np.var(R, ddof=1)
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# Geometric Growth Rate (Daily & Annualized)
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ggr_daily = np.exp(np.mean(np.log1p(R))) - 1
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ggr_ann = ((1 + ggr_daily)**365) - 1
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return {
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'name': name,
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'roi': roi,
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'pf': pf_val,
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'wr': wr,
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'max_dd': max_dd * 100,
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'trades': len(trades),
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'variance': variance,
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'ggr_ann': ggr_ann * 100
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}
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print("\n" + "="*80)
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print("OB FILTER IMPACT ON VARIANCE & GEOMETRIC GROWTH RATE (Dynamic MC-Forewarner Limits)")
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print("="*80)
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res1 = run_test("1. BEFORE OB FILTER (Baseline Alpha Layers)", use_ob_engine=False)
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res2 = run_test("2. AFTER OB FILTER (Full Stack OB Injection)", use_ob_engine=True)
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print("\n" + "="*80)
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print(" FINAL DIRECT COMPARISON (55-Day Vectorized Trajectory)")
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print("="*80)
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print(f" | {'Before OB Filter':>18} | {'After OB Filter':>18} | {'Delta':>10}")
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print("-" * 80)
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print(f" Wr (%) | {res1['wr']:>17.2f}% | {res2['wr']:>17.2f}% | {res2['wr']-res1['wr']:>+9.2f}%")
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print(f" PF | {res1['pf']:>18.3f} | {res2['pf']:>18.3f} | {res2['pf']-res1['pf']:>+10.3f}")
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print(f" Max DD | {res1['max_dd']:>17.2f}% | {res2['max_dd']:>17.2f}% | {res2['max_dd']-res1['max_dd']:>+9.2f}%")
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print(f" Daily Variance (s²)| {res1['variance']:>18.6f} | {res2['variance']:>18.6f} | {(res2['variance']/res1['variance'] - 1)*100:>+9.2f}%")
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print(f" Geometric Growth | {res1['ggr_ann']:>17.2f}% | {res2['ggr_ann']:>17.2f}% | {res2['ggr_ann']-res1['ggr_ann']:>+9.2f}%")
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print("="*80)
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