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.

nautilus_dolphin/dvae/test_dliq_nostomp.py

@@ -0,0 +1,84 @@
+"""Quick test: D_LIQ with and without set_esoteric_hazard_multiplier(0.0)
+to confirm that's the root cause of 181.81% → 145.84% regression.
+"""
+import sys, time, math
+sys.stdout.reconfigure(encoding='utf-8', errors='replace')
+from pathlib import Path
+import numpy as np
+
+_HERE = Path(__file__).resolve().parent
+sys.path.insert(0, str(_HERE.parent))
+
+from exp_shared import ensure_jit, ENGINE_KWARGS, load_data, load_forewarner, MC_BASE_CFG
+from nautilus_dolphin.nautilus.proxy_boost_engine import create_d_liq_engine
+from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
+
+
+def _run(eng, d, fw, call_esof):
+    acb = AdaptiveCircuitBreaker()
+    acb.preload_w750(d['date_strings'])
+    eng.set_ob_engine(d['ob_eng'])
+    eng.set_acb(acb)
+    if fw is not None:
+        eng.set_mc_forewarner(fw, MC_BASE_CFG)
+    if call_esof:
+        eng.set_esoteric_hazard_multiplier(0.0)
+
+    # Print leverage state BEFORE trading
+    print(f"  base_max_leverage = {eng.base_max_leverage}")
+    print(f"  abs_max_leverage  = {eng.abs_max_leverage}")
+    print(f"  bet_sizer.max_lev = {eng.bet_sizer.max_leverage}")
+
+    t0 = time.time()
+    daily_caps, daily_pnls = [], []
+    for pf_file in d['parquet_files']:
+        ds = pf_file.stem
+        df, acols, dvol = d['pq_data'][ds]
+        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)
+
+    tr  = eng.trade_history
+    n   = len(tr)
+    roi = (eng.capital - 25000.0) / 25000.0 * 100.0
+    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)
+
+    def _abs(t):
+        return t.pnl_absolute if hasattr(t, 'pnl_absolute') else t.pnl_pct * 250.0
+    wins   = [t for t in tr if _abs(t) > 0]
+    losses = [t for t in tr if _abs(t) <= 0]
+    pf = sum(_abs(t) for t in wins) / max(abs(sum(_abs(t) for t in losses)), 1e-9)
+
+    lev_vals = [t.leverage for t in tr if hasattr(t, 'leverage') and t.leverage > 0]
+    avg_lev  = float(np.mean(lev_vals)) if lev_vals else 0.0
+    liq_stops = getattr(eng, 'liquidation_stops', 0)
+
+    print(f"  ROI={roi:+.2f}%  T={n}  DD={max_dd:.2f}%  PF={pf:.4f}  avg_lev={avg_lev:.2f}x  liq_stops={liq_stops}  ({time.time()-t0:.0f}s)")
+    return roi, n, max_dd, avg_lev
+
+
+def main():
+    ensure_jit()
+    d  = load_data()
+    fw = load_forewarner()
+
+    print("\n=== WITH set_esoteric_hazard_multiplier(0.0) ===")
+    eng_a = create_d_liq_engine(**ENGINE_KWARGS)
+    roi_a, n_a, dd_a, lev_a = _run(eng_a, d, fw, call_esof=True)
+
+    print("\n=== WITHOUT set_esoteric_hazard_multiplier(0.0) ===")
+    eng_b = create_d_liq_engine(**ENGINE_KWARGS)
+    roi_b, n_b, dd_b, lev_b = _run(eng_b, d, fw, call_esof=False)
+
+    print(f"\nGOLD TARGET: ROI=181.81%, T=2155, DD=17.65%, avg_lev=4.09x")
+    print(f"WITH stomp:    ROI={roi_a:+.2f}%  T={n_a}  DD={dd_a:.2f}%  avg_lev={lev_a:.2f}x")
+    print(f"WITHOUT stomp: ROI={roi_b:+.2f}%  T={n_b}  DD={dd_b:.2f}%  avg_lev={lev_b:.2f}x")
+
+
+if __name__ == '__main__':
+    main()