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/mc_forewarning_service.py

@@ -0,0 +1,113 @@
+"""
+Live Monte Carlo Forewarning Service
+====================================
+
+Continously monitors the active Nautilus-Dolphin configuration
+against the pre-trained Monte Carlo operational envelope.
+
+Logs warnings and generates alerts if the parameters drift near
+the edge of the validated MC envelope, preventing catastrophic swans.
+"""
+
+import os
+import sys
+import time
+import json
+import logging
+from pathlib import Path
+from datetime import datetime
+
+# Adjust paths
+PROJECT_ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(PROJECT_ROOT))
+sys.path.insert(0, str(PROJECT_ROOT.parent / 'external_factors'))
+
+from mc.mc_ml import DolphinForewarner
+from mc.mc_sampler import MCSampler
+
+# Configure Logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - [FOREWARNER] - %(levelname)s - %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler(PROJECT_ROOT / "forewarning_service.log")
+    ]
+)
+
+MODELS_DIR = PROJECT_ROOT / "mc_results" / "models"
+CHECK_INTERVAL_SECONDS = 3600 * 4  # Check every 4 hours
+
+def get_current_live_config() -> dict:
+    """
+    Simulates fetching the active trading system configuration.
+    In full production, this would query Nautilus' live dictionary.
+    For now, it pulls the baseline champion and applies any overrides.
+    """
+    sampler = MCSampler()
+    # Baseline champion config
+    raw_config = sampler.generate_champion_trial().to_dict()
+    
+    # In a fully dynamic environment, we would overlay real-time changes
+    # For demonstration, we simply return the dict
+    return raw_config
+
+def determine_risk_level(report):
+    """
+    Assess risk level per MONTE_CARLO_SYSTEM_ENVELOPE_SPEC.md mapping.
+    """
+    env = report.envelope_score
+    cat = report.catastrophic_probability
+    champ = report.champion_probability
+
+    if cat > 0.25 or env < -1.0:
+        return "RED"
+    elif env < 0 or cat > 0.10:
+        return "ORANGE"
+    elif env > 0 and champ > 0.4:
+        return "AMBER"
+    elif env > 0.5 and champ > 0.6:
+        return "GREEN"
+    else:
+        return "AMBER" # Default transitional state
+
+def run_service():
+    logging.info(f"Starting Monte Carlo Forewarning Service. Checking every {CHECK_INTERVAL_SECONDS} seconds.")
+    if not MODELS_DIR.exists():
+        logging.error(f"Models directory not found at {MODELS_DIR}. Ensure you've run 'python run_mc_envelope.py --mode train' first.")
+        sys.exit(1)
+
+    try:
+        forewarner = DolphinForewarner(models_dir=str(MODELS_DIR))
+    except Exception as e:
+        logging.error(f"Failed to load ML models: {e}")
+        sys.exit(1)
+
+    while True:
+        try:
+            config_dict = get_current_live_config()
+            report = forewarner.assess_config_dict(config_dict)
+            level = determine_risk_level(report)
+
+            log_msg = f"Check complete. Risk Level: {level} | Env_Score: {report.envelope_score:.3f} | Cat_Prob: {report.catastrophic_probability:.1%}"
+            
+            if level in ['ORANGE', 'RED']:
+                logging.warning("!!! HIGH RISK CONFIGURATION DETECTED !!!")
+                logging.warning(log_msg)
+                if report.warnings:
+                    for w in report.warnings:
+                        logging.warning(f"  -> {w}")
+            else:
+                logging.info(log_msg)
+
+        except Exception as e:
+            logging.error(f"Error during assessment loop: {e}")
+
+        # Sleep till next cycle
+        time.sleep(CHECK_INTERVAL_SECONDS)
+
+if __name__ == "__main__":
+    try:
+        run_service()
+    except KeyboardInterrupt:
+        logging.info("Forewarning service shutting down.")