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
157
prod/diag_isolation.py
Executable file
157
prod/diag_isolation.py
Executable file
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"""
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diag_isolation.py — Isolate which agent change is causing the 12.83% failure.
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Tests:
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A. exp_shared.run_backtest as-is (hazard call + float32 + rolling vol_p60 + per-day OB)
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→ Expected: ~12.83%/1739 [already confirmed]
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B. Same as A but WITHOUT set_esoteric_hazard_multiplier call
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→ Expected: closer to 111%? This isolates the hazard call.
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C. Same as A but WITHOUT rolling vol_p60 (use static vol_p60 always)
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→ Expected: somewhere between A and B?
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Goal: confirm hazard call is the dominant regressor.
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"""
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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 math, gc
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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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print("exp_shared path:", exp_shared.__file__)
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print()
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# ── Shared data load ──────────────────────────────────────────────────────────
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exp_shared.ensure_jit()
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d = exp_shared.load_data()
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def run_variant(label, use_hazard_call, use_rolling_vol):
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print(f"\n{'='*60}")
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print(f" {label}")
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print(f" hazard_call={use_hazard_call} rolling_vol={use_rolling_vol}")
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print(f"{'='*60}")
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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(d['date_strings'])
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eng = create_d_liq_engine(**kw)
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eng.set_ob_engine(d['ob_eng'])
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eng.set_acb(acb)
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if use_hazard_call:
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eng.set_esoteric_hazard_multiplier(0.0)
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lev_after = getattr(eng, 'base_max_leverage', None)
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print(f" After hazard call: base_max_leverage={lev_after}")
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else:
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lev_now = getattr(eng, 'base_max_leverage', None)
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print(f" No hazard call: base_max_leverage={lev_now}")
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daily_caps, daily_pnls = [], []
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all_vols = []
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t0 = time.time()
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for i, pf in enumerate(d['parquet_files']):
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ds = pf.stem
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df = pd.read_parquet(pf)
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for c in df.columns:
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if df[c].dtype == 'float64':
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df[c] = df[c].astype('float32')
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acols = [c for c in df.columns if c not in exp_shared.META_COLS]
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if eng.ob_engine is not None:
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eng.ob_engine.preload_date(ds, d['OB_ASSETS'])
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bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
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dvol = np.zeros(len(df), dtype=np.float32)
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if bp is not None:
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rets = np.diff(bp.astype('float64')) / (bp[:-1].astype('float64') + 1e-9)
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for j in range(50, len(rets)):
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v = np.std(rets[j-50:j])
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dvol[j+1] = v
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if v > 0: all_vols.append(v)
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cap_before = eng.capital
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if use_rolling_vol and len(all_vols) > 1000:
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vp60 = np.percentile(all_vols, 60)
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else:
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vp60 = d['vol_p60']
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vol_ok = np.where(dvol > 0, dvol > vp60, 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 eng.ob_engine is not None:
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eng.ob_engine._preloaded_placement.clear()
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eng.ob_engine._preloaded_signal.clear()
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eng.ob_engine._preloaded_market.clear()
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eng.ob_engine._ts_to_idx.clear()
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del df
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gc.collect()
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if (i+1) % 10 == 0 or i == 0 or i == len(d['parquet_files'])-1:
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elapsed = time.time() - t0
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print(f" Day {i+1}/{len(d['parquet_files'])}: cap=${eng.capital:,.0f} trades={len(eng.trade_history)} ({elapsed:.0f}s)")
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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)")
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return {'label': label, 'roi': roi, 'trades': n, 'dd': max_dd}
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if __name__ == '__main__':
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results = []
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# Variant B: no hazard call, rolling vol (isolate hazard call effect)
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results.append(run_variant(
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"B: No hazard call, rolling vol",
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use_hazard_call=False,
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use_rolling_vol=True,
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))
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# Variant C: hazard call, static vol (isolate rolling vol effect)
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results.append(run_variant(
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"C: Hazard call, static vol",
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use_hazard_call=True,
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use_rolling_vol=False,
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))
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# Variant D: no hazard call, static vol (cleanest comparison to replicate style)
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results.append(run_variant(
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"D: No hazard call, static vol",
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use_hazard_call=False,
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use_rolling_vol=False,
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))
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print(f"\n{'='*60}")
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print(" ISOLATION SUMMARY")
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print(f"{'='*60}")
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print(f" {'Config':<45} {'ROI':>8} {'T':>6} {'DD':>7}")
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print(f" {'-'*70}")
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print(f" {'A: Hazard call + rolling vol (fork as-is)':<45} {'~+12.83%':>8} {'~1739':>6} {'~26.2%':>7} [prior run]")
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for r in results:
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print(f" {r['label']:<45} {r['roi']:>+7.2f}% {r['trades']:>6} {r['dd']:>6.2f}%")
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print(f" {'replicate style (no hazard, float64, static)':<45} {'~+111.0%':>8} {'~1959':>6} {'~16.9%':>7} [prior run]")
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print(f" {'GOLD target':<45} {'+181.81%':>8} {'2155':>6} {'17.65%':>7}")
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