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
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prod/diag_5day.py
Executable file
96
prod/diag_5day.py
Executable file
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"""5-day diagnostic: compare TEST A vs TEST B daily capital to find where they diverge."""
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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, math
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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 exp_shared import load_data, ENGINE_KWARGS, META_COLS
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exp_shared.ensure_jit()
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# ── TEST A: exp_shared.run_backtest style (set hazard + per-day OB + rolling vol) ────────────
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print("\n=== TEST A: 5 days (hazard call + per-day OB clear + rolling vol_p60) ===")
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d = load_data()
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kw = 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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eng.set_esoteric_hazard_multiplier(0.0)
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print(f" After hazard call: base_max={eng.base_max_leverage} sizer={eng.bet_sizer.max_leverage} abs={eng.abs_max_leverage}")
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all_vols = []
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for i, pf in enumerate(d['parquet_files'][:5]):
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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 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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vp60 = np.percentile(all_vols, 60) if len(all_vols) > 1000 else d['vol_p60']
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n_vol_ok = np.sum(np.where(dvol > 0, dvol > vp60, False))
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vol_ok = np.where(dvol > 0, dvol > vp60, False)
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n_before = len(eng.trade_history)
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eng.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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n_after = len(eng.trade_history)
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print(f" Day {i+1} {ds}: cap=${eng.capital:,.0f} trades_today={n_after-n_before} total={n_after} vol_ok_bars={n_vol_ok}/{len(df)} vp60={vp60:.6f} base_max={eng.base_max_leverage:.1f}")
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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; gc.collect()
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# ── TEST B: replicate style (no hazard call, static vol, float64) ─────────────────────────────
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print("\n=== TEST B: 5 days (no hazard call, static vol_p60, float64) ===")
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d2 = 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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acb2 = AdaptiveCircuitBreaker()
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acb2.preload_w750(d2['date_strings'])
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eng2 = create_d_liq_engine(**kw2)
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eng2.set_ob_engine(d2['ob_eng'])
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eng2.set_acb(acb2)
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print(f" No hazard call: base_max={eng2.base_max_leverage} sizer={eng2.bet_sizer.max_leverage} abs={eng2.abs_max_leverage}")
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for i, pf in enumerate(d2['parquet_files'][:5]):
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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 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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n_vol_ok = np.sum(np.where(np.isfinite(dvol), dvol > d2['vol_p60'], False))
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vol_ok = np.where(np.isfinite(dvol), dvol > d2['vol_p60'], False)
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n_before = len(eng2.trade_history)
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eng2.process_day(ds, df, acols, vol_regime_ok=vol_ok)
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n_after = len(eng2.trade_history)
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print(f" Day {i+1} {ds}: cap=${eng2.capital:,.0f} trades_today={n_after-n_before} total={n_after} vol_ok_bars={n_vol_ok}/{len(df)} base_max={eng2.base_max_leverage:.1f}")
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del df; gc.collect()
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print("\nDONE")
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