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
2.7 KiB
Python
Executable File
72 lines
2.7 KiB
Python
Executable File
"""Quick diagnostic: compare production exp_shared.run_backtest vs replicate_181 loop."""
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import sys
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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 time, 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("JIT...")
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exp_shared.ensure_jit()
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# ── TEST A: production exp_shared.run_backtest (all agent changes) ─────────
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print("\n=== TEST A: exp_shared.run_backtest (production) ===")
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t0 = time.time()
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r = exp_shared.run_backtest(lambda kw: create_d_liq_engine(**kw), "prod_run",
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extra_kwargs={'sp_maker_entry_rate': 1.0, 'sp_maker_exit_rate': 1.0, 'use_sp_slippage': False})
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print(f" ROI={r['roi']:+.2f}% T={r['trades']} DD={r['dd']:.2f}% t={time.time()-t0:.0f}s")
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# ── TEST B: replicate_181_gold.py style (NO float32, NO set_esoteric_hazard_multiplier) ─────────
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print("\n=== TEST B: replicate_181 style (no float32, no hazard call) ===")
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from exp_shared import load_data, ENGINE_KWARGS, META_COLS
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d = load_data()
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kw2 = ENGINE_KWARGS.copy()
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kw2.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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eng2 = create_d_liq_engine(**kw2)
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eng2.set_ob_engine(d['ob_eng'])
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eng2.set_acb(acb)
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# NOTE: no set_esoteric_hazard_multiplier call
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t1 = time.time()
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daily_caps = []
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for pf in d['parquet_files']:
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ds = pf.stem
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df = pd.read_parquet(pf) # float64, no casting
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acols = [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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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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vol_ok = np.where(np.isfinite(dvol), dvol > d['vol_p60'], False)
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eng2.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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daily_caps.append(eng2.capital)
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del df; gc.collect()
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tr2 = eng2.trade_history
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roi2 = (eng2.capital - 25000.0) / 25000.0 * 100.0
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import math
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daily_pnls = [daily_caps[0]-25000.0] + [daily_caps[i]-daily_caps[i-1] for i in range(1,len(daily_caps))]
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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); max_dd = max(max_dd, (peak-cap)/peak*100.0)
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print(f" ROI={roi2:+.2f}% T={len(tr2)} DD={max_dd:.2f}% t={time.time()-t1:.0f}s")
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print("\nDONE")
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