DOLPHIN/nautilus_dolphin/mc_forewarning_service.py

"""
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.")