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_bronze_daily_dd.py

@@ -0,0 +1,71 @@
+"""BRONZE baseline with DAILY equity curve DD (same method as test_dliq_fix_verify.py).
+Expected: ROI=88.55%, DD=15.05%, T=2155
+"""
+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 ENGINE_KWARGS, MC_BASE_CFG, load_data, load_forewarner
+from nautilus_dolphin.nautilus.esf_alpha_orchestrator import NDAlphaEngine
+from nautilus_dolphin.nautilus.adaptive_circuit_breaker import AdaptiveCircuitBreaker
+
+
+def main():
+    print("Loading data (gold method: float64, 2-file seg-based vol_p60)...")
+    d  = load_data()
+    fw = load_forewarner()
+    print()
+
+    kw = ENGINE_KWARGS.copy()
+    eng = NDAlphaEngine(**kw)
+    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)
+    eng.set_esoteric_hazard_multiplier(0.0)
+
+    print(f"  base_max_leverage = {eng.base_max_leverage}")
+
+    t0 = time.time()
+    daily_caps = []
+    for pf_file in d['parquet_files']:
+        ds = pf_file.stem
+        df, acols, dvol = d['pq_data'][ds]
+        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)
+
+    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)
+    wr = len(wins) / n * 100.0 if n > 0 else 0.0
+
+    print(f"  ROI={roi:+.2f}%  T={n}  DD={max_dd:.2f}%  PF={pf:.4f}  WR={wr:.1f}%  ({time.time()-t0:.0f}s)")
+    print()
+    print(f"BRONZE TARGET: ROI=88.55%  T=2155  DD=15.05%")
+    roi_ok = abs(roi - 88.55) < 1.0
+    dd_ok  = abs(max_dd - 15.05) < 0.5
+    t_ok   = n == 2155
+    print(f"ROI match: {'PASS' if roi_ok else 'FAIL'}  (diff={roi-88.55:+.2f}pp)")
+    print(f"DD  match: {'PASS' if dd_ok  else 'FAIL'}  (diff={max_dd-15.05:+.2f}pp)")
+    print(f"T   match: {'PASS' if t_ok   else 'FAIL'}  (got {n})")
+
+
+if __name__ == '__main__':
+    main()