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.
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hjnormey
120
prod/test_gold_fork.py
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120
prod/test_gold_fork.py
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"""
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test_gold_fork.py — Definitive D_LIQ_GOLD replication test on agent-forked code.
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Tests the D_LIQ_GOLD engine using the PROVEN run_backtest() path from exp_shared_AGENT_fork.py
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(exact copy of agent-modified exp_shared.py).
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Gold target: ROI=181.81%, DD=17.65%, Calmar=10.30, T=2155
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Tolerance: ROI ±2pp, Trades ±5
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Specific agent claims verified here:
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A. Orchestrator ceiling_lev 6→10 (set_esoteric_hazard_multiplier)
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B. MC red: proportional 0.80x vs hard 5.0x cap
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C. exp_shared rolling vol_p60 vs static
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D. float32 casting, lazy OB loading
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RUNS ON: exp_shared_AGENT_fork.py (do not touch production exp_shared.py)
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"""
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import sys, time
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from pathlib import Path
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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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# ── Import the FORKED exp_shared (not the live one) ───────────────────────────
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import importlib.util, types
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def load_fork(fork_path: Path, module_name: str):
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spec = importlib.util.spec_from_file_location(module_name, fork_path)
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mod = importlib.util.module_from_spec(spec)
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sys.modules[module_name] = mod
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spec.loader.exec_module(mod)
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return mod
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print("Loading forked exp_shared...")
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exp = load_fork(
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ROOT / 'nautilus_dolphin' / 'dvae' / 'exp_shared_AGENT_fork.py',
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'exp_shared_fork'
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)
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from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine, LiquidationGuardEngine
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GOLD_ROI = 181.81
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GOLD_TRADES = 2155
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GOLD_DD = 17.65
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GOLD_CALMAR = 10.30
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TOL_ROI = 2.0 # ±2pp
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TOL_TRADES = 5 # ±5
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def run_test(label: str, factory, extra_kwargs=None):
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print(f"\n{'='*60}")
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print(f" TEST: {label}")
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print(f"{'='*60}")
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t0 = time.time()
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result = exp.run_backtest(factory, label, extra_kwargs=extra_kwargs)
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elapsed = time.time() - t0
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roi = result['roi']
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trades = result['trades']
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dd = result['dd']
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calmar = result.get('calmar', 0.0)
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roi_ok = abs(roi - GOLD_ROI) <= TOL_ROI
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trade_ok = abs(trades - GOLD_TRADES) <= TOL_TRADES
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print(f"\n ROI: {roi:+.2f}% (gold={GOLD_ROI:.2f}% diff={roi-GOLD_ROI:+.2f}pp) {'✓' if roi_ok else '✗'}")
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print(f" Trades: {trades} (gold={GOLD_TRADES} diff={trades-GOLD_TRADES:+d}) {'✓' if trade_ok else '✗'}")
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print(f" DD: {dd:.2f}% (gold={GOLD_DD:.2f}%)")
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print(f" Calmar: {calmar:.2f} (gold={GOLD_CALMAR:.2f})")
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print(f" Time: {elapsed:.0f}s")
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passed = roi_ok and trade_ok
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print(f"\n VERDICT: {'PASS ✓' if passed else 'FAIL ✗'}")
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return {'label': label, 'roi': roi, 'trades': trades, 'dd': dd,
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'calmar': calmar, 'elapsed': elapsed, 'pass': passed}
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if __name__ == '__main__':
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print("\n" + "="*60)
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print(" D_LIQ_GOLD REPLICATION — AGENT FORK TEST")
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print(f" Using: exp_shared_AGENT_fork.py")
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print(f" Gold: ROI={GOLD_ROI}% T={GOLD_TRADES} DD={GOLD_DD}% Calmar={GOLD_CALMAR}")
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print("="*60)
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print("\nWarming up JIT...")
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exp.ensure_jit()
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print(" JIT ready.")
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results = []
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# Test 1: D_LIQ_GOLD — perfect maker (closest to gold certification)
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results.append(run_test(
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"D_LIQ_GOLD perfect-maker (fork)",
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lambda kw: create_d_liq_engine(**kw),
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extra_kwargs={
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'sp_maker_entry_rate': 1.0,
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'sp_maker_exit_rate': 1.0,
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'use_sp_slippage': False,
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}
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))
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# Test 2: D_LIQ_GOLD stochastic (production mode, 0.62 fill)
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results.append(run_test(
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"D_LIQ_GOLD stochastic 0.62 (fork)",
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lambda kw: create_d_liq_engine(**kw),
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))
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# ── Summary ────────────────────────────────────────────────────────────────
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print(f"\n{'='*60}")
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print(" FORK TEST SUMMARY")
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print(f"{'='*60}")
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n_pass = sum(1 for r in results if r['pass'])
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for r in results:
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status = 'PASS ✓' if r['pass'] else 'FAIL ✗'
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print(f" {status} {r['label']:<40} ROI={r['roi']:+.2f}% T={r['trades']} Calmar={r['calmar']:.2f}")
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print(f"\n {n_pass}/{len(results)} tests passed")
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if n_pass == len(results):
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print("\n ✓ AGENT CHANGES VERIFIED — D_LIQ_GOLD REPLICATES ON FORK")
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else:
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print("\n ✗ REPLICATION FAILURE — agent changes compromise gold standard")
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