01c19662cb
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.
72 lines
2.5 KiB
Python
Executable File
72 lines
2.5 KiB
Python
Executable File
"""BRONZE baseline with DAILY equity curve DD (same method as test_dliq_fix_verify.py).
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Expected: ROI=88.55%, DD=15.05%, T=2155
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"""
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import sys, time, math
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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from pathlib import Path
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import numpy as np
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_HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(_HERE.parent))
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from exp_shared import ENGINE_KWARGS, MC_BASE_CFG, load_data, load_forewarner
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from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
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from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
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def main():
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print("Loading data (gold method: float64, 2-file seg-based vol_p60)...")
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d = load_data()
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fw = load_forewarner()
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print()
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kw = ENGINE_KWARGS.copy()
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eng = NDAlphaEngine(**kw)
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acb = AdaptiveCircuitBreaker()
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acb.preload_w750(d['date_strings'])
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eng.set_ob_engine(d['ob_eng'])
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eng.set_acb(acb)
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if fw is not None:
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eng.set_mc_forewarner(fw, MC_BASE_CFG)
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eng.set_esoteric_hazard_multiplier(0.0)
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print(f" base_max_leverage = {eng.base_max_leverage}")
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t0 = time.time()
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daily_caps = []
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for pf_file in d['parquet_files']:
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ds = pf_file.stem
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df, acols, dvol = d['pq_data'][ds]
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vol_ok = np.where(np.isfinite(dvol), dvol > d['vol_p60'], 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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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, max_dd = 25000.0, 0.0
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for cap in daily_caps:
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peak = max(peak, cap)
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max_dd = max(max_dd, (peak - cap) / peak * 100.0)
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def _abs(t):
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return t.pnl_absolute if hasattr(t, 'pnl_absolute') else t.pnl_pct * 250.0
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wins = [t for t in tr if _abs(t) > 0]
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losses = [t for t in tr if _abs(t) <= 0]
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pf = sum(_abs(t) for t in wins) / max(abs(sum(_abs(t) for t in losses)), 1e-9)
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wr = len(wins) / n * 100.0 if n > 0 else 0.0
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print(f" ROI={roi:+.2f}% T={n} DD={max_dd:.2f}% PF={pf:.4f} WR={wr:.1f}% ({time.time()-t0:.0f}s)")
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print()
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print(f"BRONZE TARGET: ROI=88.55% T=2155 DD=15.05%")
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roi_ok = abs(roi - 88.55) < 1.0
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dd_ok = abs(max_dd - 15.05) < 0.5
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t_ok = n == 2155
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print(f"ROI match: {'PASS' if roi_ok else 'FAIL'} (diff={roi-88.55:+.2f}pp)")
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print(f"DD match: {'PASS' if dd_ok else 'FAIL'} (diff={max_dd-15.05:+.2f}pp)")
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print(f"T match: {'PASS' if t_ok else 'FAIL'} (got {n})")
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if __name__ == '__main__':
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main()
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