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/profile_obf_gain.py

@@ -0,0 +1,48 @@
+import time
+import numpy as np
+import sys
+from pathlib import Path
+from unittest.mock import MagicMock
+
+# Correct sys.path
+ROOT_DIR = Path(__file__).parent.parent
+sys.path.insert(0, str(ROOT_DIR / "nautilus_dolphin"))
+sys.path.insert(0, str(ROOT_DIR))
+
+from nautilus_dolphin.nautilus.ob_features import OBFeatureEngine
+from nautilus_dolphin.nautilus.ob_provider import OBSnapshot
+
+def create_snap(asset):
+    return OBSnapshot(
+        timestamp=time.time(),
+        asset=asset,
+        bid_notional=np.random.rand(5) * 10000,
+        ask_notional=np.random.rand(5) * 10000,
+        bid_depth=np.random.rand(5),
+        ask_depth=np.random.rand(5)
+    )
+
+def benchmark():
+    provider = MagicMock()
+    assets = ["BTCUSDT", "ETHUSDT", "SOLUSDT", "BNBUSDT", "XRPUSDT"]
+    provider.get_snapshot.side_effect = lambda a, t: create_snap(a)
+    
+    engine = OBFeatureEngine(provider)
+    
+    # Warmup
+    for i in range(100):
+        engine.step_live(assets, i)
+        
+    # Test
+    iterations = 2000
+    start = time.perf_counter()
+    for i in range(iterations):
+        engine.step_live(assets, 100 + i)
+    end = time.perf_counter()
+    
+    duration = end - start
+    print(f"BASELINE: {iterations} iterations in {duration:.4f}s ({iterations/duration:.2f} Hz)")
+    return iterations / duration
+
+if __name__ == "__main__":
+    benchmark()