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.

prod/replicate_181_gold.py

@@ -0,0 +1,95 @@
+import sys
+from pathlib import Path
+import json
+import numpy as np
+import time
+
+HCM_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
+sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin'))
+sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin' / 'dvae'))
+
+# We'll monkeypatch run_backtest to add progress bars
+import exp_shared
+
+def replicate():
+    print("="*60)
+    print("EXACT REPLICATION OF D_LIQ_GOLD PERFECT MAKER (181.81%)")
+    print("="*60)
+    exp_shared.ensure_jit()
+    
+    perfect_kwargs = {
+        'seed': 42,
+        'sp_maker_entry_rate': 1.0,
+        'sp_maker_exit_rate': 1.0,
+        'use_sp_fees': True,
+        'use_sp_slippage': False,
+    }
+
+    print("Running D_liq (8x/9x) configuration in PERFECT MAKER mode...")
+    # Using the monkeypatched version that prints daily progress to avoid 'locked up' appearance
+    from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
+    
+    # Manually running loop here for transparency
+    from exp_shared import load_data, ENGINE_KWARGS, MC_BASE_CFG
+    from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
+    import pandas as pd
+    import gc
+
+    d = load_data()
+    kw = ENGINE_KWARGS.copy()
+    kw.update(perfect_kwargs)
+    
+    acb = AdaptiveCircuitBreaker()
+    acb.preload_w750(d['date_strings'])
+    
+    eng = create_d_liq_engine(**kw)
+    eng.set_ob_engine(d['ob_eng'])
+    eng.set_acb(acb)
+    
+    daily_caps, daily_pnls = [], []
+    total_days = len(d['parquet_files'])
+    
+    t_start = time.time()
+    for i, pf in enumerate(d['parquet_files']):
+        ds = pf.stem
+        # Print progress every 5 days or first/last
+        if i == 0 or i == total_days-1 or (i+1) % 10 == 0:
+            elapsed = time.time() - t_start
+            print(f"  Day {i+1}/{total_days}: {ds} | Cap: ${eng.capital:,.2f} | Trades: {len(eng.trade_history)} | Elapsed: {elapsed:.0f}s")
+        
+        df = pd.read_parquet(pf)
+        acols = [c for c in df.columns if c not in exp_shared.META_COLS]
+        
+        bp = df['BTCUSDT'].values if 'BTCUSDT' in df.columns else None
+        dvol = np.full(len(df), np.nan)
+        if bp is not None:
+            diffs = np.zeros(len(bp), dtype=np.float64)
+            diffs[1:] = np.diff(bp) / bp[:-1]
+            for j in range(50, len(bp)):
+                dvol[j] = np.std(diffs[j-50:j])
+
+        cap_before = eng.capital
+        vol_ok = np.where(np.isfinite(dvol), dvol > d['vol_p60'], False)
+        eng.process_day(ds, df, acols, vol_regime_ok=vol_ok)
+        daily_caps.append(eng.capital)
+        daily_pnls.append(eng.capital - cap_before)
+        
+        del df
+        gc.collect()
+
+    tr = eng.trade_history
+    n = len(tr)
+    roi = (eng.capital - 25000.0) / 25000.0 * 100.0
+    
+    print("\nREPLICATION RESULTS:")
+    print(f"  ROI:    {roi:.2f}% (Target: 181.81%)")
+    print(f"  Trades: {n} (Target: 2155)")
+    print(f"  Final Cap: ${eng.capital:,.2f}")
+    
+    if abs(roi - 181.81) < 0.2:
+        print("\nGOLD CERTIFICATION: SUCCESS ✓")
+    else:
+        print(f"\nGOLD CERTIFICATION: FAILED ✗ (Diff: {roi-181.81:+.2f}pp)")
+
+if __name__ == "__main__":
+    replicate()