01c19662cb
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.
114 lines
3.7 KiB
Python
Executable File
114 lines
3.7 KiB
Python
Executable File
"""
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Live Monte Carlo Forewarning Service
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====================================
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Continously monitors the active Nautilus-Dolphin configuration
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against the pre-trained Monte Carlo operational envelope.
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Logs warnings and generates alerts if the parameters drift near
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the edge of the validated MC envelope, preventing catastrophic swans.
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"""
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import os
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import sys
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import time
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import json
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import logging
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from pathlib import Path
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from datetime import datetime
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# Adjust paths
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PROJECT_ROOT = Path(__file__).resolve().parent
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sys.path.insert(0, str(PROJECT_ROOT))
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sys.path.insert(0, str(PROJECT_ROOT.parent / 'external_factors'))
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from mc.mc_ml import DolphinForewarner
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from mc.mc_sampler import MCSampler
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# Configure Logging
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - [FOREWARNER] - %(levelname)s - %(message)s",
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handlers=[
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logging.StreamHandler(sys.stdout),
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logging.FileHandler(PROJECT_ROOT / "forewarning_service.log")
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]
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)
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MODELS_DIR = PROJECT_ROOT / "mc_results" / "models"
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CHECK_INTERVAL_SECONDS = 3600 * 4 # Check every 4 hours
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def get_current_live_config() -> dict:
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"""
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Simulates fetching the active trading system configuration.
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In full production, this would query Nautilus' live dictionary.
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For now, it pulls the baseline champion and applies any overrides.
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"""
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sampler = MCSampler()
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# Baseline champion config
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raw_config = sampler.generate_champion_trial().to_dict()
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# In a fully dynamic environment, we would overlay real-time changes
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# For demonstration, we simply return the dict
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return raw_config
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def determine_risk_level(report):
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"""
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Assess risk level per MONTE_CARLO_SYSTEM_ENVELOPE_SPEC.md mapping.
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"""
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env = report.envelope_score
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cat = report.catastrophic_probability
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champ = report.champion_probability
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if cat > 0.25 or env < -1.0:
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return "RED"
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elif env < 0 or cat > 0.10:
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return "ORANGE"
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elif env > 0 and champ > 0.4:
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return "AMBER"
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elif env > 0.5 and champ > 0.6:
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return "GREEN"
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else:
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return "AMBER" # Default transitional state
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def run_service():
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logging.info(f"Starting Monte Carlo Forewarning Service. Checking every {CHECK_INTERVAL_SECONDS} seconds.")
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if not MODELS_DIR.exists():
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logging.error(f"Models directory not found at {MODELS_DIR}. Ensure you've run 'python run_mc_envelope.py --mode train' first.")
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sys.exit(1)
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try:
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forewarner = DolphinForewarner(models_dir=str(MODELS_DIR))
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except Exception as e:
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logging.error(f"Failed to load ML models: {e}")
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sys.exit(1)
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while True:
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try:
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config_dict = get_current_live_config()
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report = forewarner.assess_config_dict(config_dict)
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level = determine_risk_level(report)
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log_msg = f"Check complete. Risk Level: {level} | Env_Score: {report.envelope_score:.3f} | Cat_Prob: {report.catastrophic_probability:.1%}"
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if level in ['ORANGE', 'RED']:
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logging.warning("!!! HIGH RISK CONFIGURATION DETECTED !!!")
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logging.warning(log_msg)
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if report.warnings:
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for w in report.warnings:
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logging.warning(f" -> {w}")
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else:
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logging.info(log_msg)
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except Exception as e:
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logging.error(f"Error during assessment loop: {e}")
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# Sleep till next cycle
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time.sleep(CHECK_INTERVAL_SECONDS)
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if __name__ == "__main__":
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try:
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run_service()
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except KeyboardInterrupt:
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logging.info("Forewarning service shutting down.")
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