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
148
prod/test_4asset_ob.py
Executable file
148
prod/test_4asset_ob.py
Executable file
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"""
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test_4asset_ob.py — Test whether OB_ASSETS=4 (matching gold test) restores T=2155.
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Gold test used: OB_ASSETS = sorted(list(all_assets)) = [BNBUSDT, BTCUSDT, ETHUSDT, SOLUSDT]
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Reconstruction tests used: OB_ASSETS = ["BTCUSDT", "ETHUSDT"] only
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"""
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import sys, time, math
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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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ROOT = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
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sys.path.insert(0, str(ROOT / 'nautilus_dolphin'))
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sys.path.insert(0, str(ROOT / 'nautilus_dolphin' / 'dvae'))
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import exp_shared
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from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
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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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print("Ensuring JIT...", flush=True)
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exp_shared.ensure_jit()
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VBT_DIR = exp_shared.VBT_DIR
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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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date_strings = [p.stem for p in parquet_files]
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print(f"Found {len(parquet_files)} parquet files", flush=True)
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# Compute static vol_p60 (same as gold test - from first 2 files, offset 60)
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all_vols = []
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for pf in parquet_files[:2]:
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tmp = pd.read_parquet(pf)
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if 'BTCUSDT' in tmp.columns:
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bp = tmp['BTCUSDT'].values
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diffs = np.diff(bp) / bp[:-1]
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for i in range(60, len(diffs)):
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all_vols.append(np.std(diffs[i-60:i]))
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del tmp
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vol_p60_static = float(np.percentile(all_vols, 60)) if all_vols else 0.0002
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print(f"Static vol_p60 (gold-style, 2 files, offset 60): {vol_p60_static:.8f}", flush=True)
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# Load data (float64, old style)
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print("Loading parquet data (float64)...", flush=True)
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pq_data = {}
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for pf in parquet_files:
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ds = pf.stem
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df = pd.read_parquet(pf)
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acols = [c for c in df.columns if c not in exp_shared.META_COLS]
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bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
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dvol = np.full(len(df), np.nan)
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if bp is not None:
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diffs = np.zeros(len(bp), dtype=np.float64)
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diffs[1:] = np.diff(bp) / bp[:-1]
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for j in range(50, len(bp)):
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dvol[j] = np.std(diffs[j-50:j])
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pq_data[ds] = (df, acols, dvol)
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print(f"Loaded {len(pq_data)} days.", flush=True)
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# Get all assets from data (like gold test)
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sample_df = pd.read_parquet(parquet_files[0])
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all_assets_in_data = set(c for c in sample_df.columns if c not in exp_shared.META_COLS)
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OB_ASSETS_4 = sorted(list(all_assets_in_data))
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print(f"All assets from data: {OB_ASSETS_4}", flush=True)
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def run_test(label, ob_assets):
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print(f"\n{'='*65}", flush=True)
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print(f" {label}", flush=True)
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print(f" OB_ASSETS={ob_assets}", flush=True)
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print(f"{'='*65}", flush=True)
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_mock_ob = MockOBProvider(
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imbalance_bias=-0.09, depth_scale=1.0, assets=ob_assets,
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imbalance_biases={"BTCUSDT":-0.086,"ETHUSDT":-0.092,"BNBUSDT":+0.05,"SOLUSDT":+0.05},
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)
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ob_eng = OBFeatureEngine(_mock_ob)
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ob_eng.preload_date("mock", ob_assets)
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kw = exp_shared.ENGINE_KWARGS.copy()
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kw.update({'sp_maker_entry_rate': 1.0, 'sp_maker_exit_rate': 1.0, 'use_sp_slippage': False})
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750(date_strings)
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eng = create_d_liq_engine(**kw)
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eng.set_ob_engine(ob_eng)
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eng.set_acb(acb)
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# Apply ceiling=6.0 patch (cert conditions)
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import types, math as _math
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ceiling_lev = 6.0
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def patched_hazard(self, hazard_score):
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floor_lev = 3.0; c_lev = ceiling_lev; range_lev = c_lev - floor_lev
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target_lev = c_lev - (hazard_score * range_lev)
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step = range_lev / 8.0
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stepped_lev = _math.ceil(target_lev / step) * step
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self.base_max_leverage = max(floor_lev, min(c_lev, stepped_lev))
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self.bet_sizer.max_leverage = self.base_max_leverage
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if hasattr(self, '_day_mc_status') and self._day_mc_status == 'RED':
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self.bet_sizer.max_leverage = self.base_max_leverage * 0.8
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eng.set_esoteric_hazard_multiplier = types.MethodType(patched_hazard, eng)
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eng.set_esoteric_hazard_multiplier(0.0)
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print(f" base_max={eng.base_max_leverage} abs_max={eng.abs_max_leverage}", flush=True)
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daily_caps, daily_pnls = [], []
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t0 = time.time()
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for i, pf in enumerate(parquet_files):
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ds = pf.stem
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df, acols, dvol = pq_data[ds]
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cap_before = eng.capital
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vol_ok = np.where(np.isfinite(dvol), dvol > vol_p60_static, False)
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eng.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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daily_caps.append(eng.capital)
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daily_pnls.append(eng.capital - cap_before)
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if (i+1) % 20 == 0 or i == len(parquet_files)-1:
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print(f" Day {i+1}/{len(parquet_files)}: cap=${eng.capital:,.0f} T={len(eng.trade_history)} ({time.time()-t0:.0f}s)", flush=True)
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tr = eng.trade_history
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n = len(tr)
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roi = (eng.capital - 25000.0) / 25000.0 * 100.0
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peak_cap, max_dd = 25000.0, 0.0
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for cap in daily_caps:
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peak_cap = max(peak_cap, cap)
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max_dd = max(max_dd, (peak_cap - cap) / peak_cap * 100.0)
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elapsed = time.time() - t0
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print(f"\n RESULT: ROI={roi:+.2f}% T={n} DD={max_dd:.2f}% ({elapsed:.0f}s)", flush=True)
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print(f" GOLD: ROI=+181.81% T=2155 DD=17.65%", flush=True)
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print(f" T match: {'PASS' if abs(n-2155)<=10 else 'FAIL'} ROI match: {'PASS' if abs(roi-181.81)<=5 else 'FAIL'}", flush=True)
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return n, roi
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# Run 1: 2 assets (current reconstruction style)
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n2, roi2 = run_test("2 assets [BTCUSDT, ETHUSDT]", ["BTCUSDT", "ETHUSDT"])
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# Run 2: 4 assets (gold test style)
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n4, roi4 = run_test(f"4 assets {OB_ASSETS_4}", OB_ASSETS_4)
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print(f"\n{'='*65}", flush=True)
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print(f"COMPARISON:", flush=True)
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print(f" 2 assets: T={n2} ROI={roi2:+.2f}%", flush=True)
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print(f" 4 assets: T={n4} ROI={roi4:+.2f}%", flush=True)
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print(f" GOLD: T=2155 ROI=+181.81%", flush=True)
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print(f" T diff 2->4: {n4-n2:+d}", flush=True)
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