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/diag_expshared.py

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+"""Quick diagnostic: compare production exp_shared.run_backtest vs replicate_181 loop."""
+import sys
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import time, gc
+
+ROOT = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
+sys.path.insert(0, str(ROOT / 'nautilus_dolphin'))
+sys.path.insert(0, str(ROOT / 'nautilus_dolphin' / 'dvae'))
+
+import exp_shared
+from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
+from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
+
+print("JIT...")
+exp_shared.ensure_jit()
+
+# ── TEST A: production exp_shared.run_backtest (all agent changes) ─────────
+print("\n=== TEST A: exp_shared.run_backtest (production) ===")
+t0 = time.time()
+r = exp_shared.run_backtest(lambda kw: create_d_liq_engine(**kw), "prod_run",
+    extra_kwargs={'sp_maker_entry_rate': 1.0, 'sp_maker_exit_rate': 1.0, 'use_sp_slippage': False})
+print(f"  ROI={r['roi']:+.2f}%  T={r['trades']}  DD={r['dd']:.2f}%  t={time.time()-t0:.0f}s")
+
+# ── TEST B: replicate_181_gold.py style (NO float32, NO set_esoteric_hazard_multiplier) ─────────
+print("\n=== TEST B: replicate_181 style (no float32, no hazard call) ===")
+from exp_shared import load_data, ENGINE_KWARGS, META_COLS
+
+d = load_data()
+kw2 = ENGINE_KWARGS.copy()
+kw2.update({'sp_maker_entry_rate': 1.0, 'sp_maker_exit_rate': 1.0, 'use_sp_slippage': False})
+
+acb = AdaptiveCircuitBreaker()
+acb.preload_w750(d['date_strings'])
+
+eng2 = create_d_liq_engine(**kw2)
+eng2.set_ob_engine(d['ob_eng'])
+eng2.set_acb(acb)
+# NOTE: no set_esoteric_hazard_multiplier call
+
+t1 = time.time()
+daily_caps = []
+for pf in d['parquet_files']:
+    ds = pf.stem
+    df = pd.read_parquet(pf)  # float64, no casting
+    acols = [c for c in df.columns if c not in 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])
+
+    vol_ok = np.where(np.isfinite(dvol), dvol > d['vol_p60'], False)
+    eng2.process_day(ds, df, acols, vol_regime_ok=vol_ok)
+    daily_caps.append(eng2.capital)
+    del df; gc.collect()
+
+tr2 = eng2.trade_history
+roi2 = (eng2.capital - 25000.0) / 25000.0 * 100.0
+import math
+daily_pnls = [daily_caps[0]-25000.0] + [daily_caps[i]-daily_caps[i-1] for i in range(1,len(daily_caps))]
+peak, max_dd = 25000.0, 0.0
+for cap in daily_caps:
+    peak = max(peak, cap); max_dd = max(max_dd, (peak-cap)/peak*100.0)
+print(f"  ROI={roi2:+.2f}%  T={len(tr2)}  DD={max_dd:.2f}%  t={time.time()-t1:.0f}s")
+
+print("\nDONE")