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.
This commit is contained in:
hjnormey
138
prod/mc_forewarner_flow.py
Executable file
138
prod/mc_forewarner_flow.py
Executable file
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"""DOLPHIN MIG1.3 — MC-Forewarner Flow
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Runs every 4 hours to evaluate structural market risk via Monte Carlo ML models.
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Writes to Hazelcast DOLPHIN_FEATURES IMap key 'mc_forewarner_latest'.
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"""
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import sys, json, logging, argparse
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from pathlib import Path
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from datetime import datetime, timezone
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from prefect import flow, task, get_run_logger
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import hazelcast
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HCM_DIR = Path(__file__).parent.parent
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logging.basicConfig(level=logging.INFO)
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# ── Tasks ───────────────────────────────────────────────────────────────────────
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@task(name="run_mc_forewarner", retries=1, retry_delay_seconds=30)
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def evaluate_market_structure() -> dict:
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log = get_run_logger()
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models_dir = HCM_DIR / 'nautilus_dolphin' / 'mc_results' / 'models'
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if not models_dir.exists() or not list(models_dir.glob('*.pkl')):
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log.warning("MC models not found — using deterministic FALLBACK_NO_DATA")
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return {
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'status': 'ORANGE',
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'catastrophic_prob': 0.20,
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'envelope_score': 0.80,
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'champion_probability': None,
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'predicted_roi': None,
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'predicted_roi_p10': None,
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'predicted_roi_p90': None,
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'predicted_max_dd': None,
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'parameter_risks': {},
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'warnings': ['MC models not found — using deterministic fallback'],
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'source': 'FALLBACK_NO_DATA',
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}
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try:
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sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin'))
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from mc.mc_ml import DolphinForewarner
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from mc.mc_sampler import MCSampler
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forewarner = DolphinForewarner(models_dir=str(models_dir))
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champion_config = MCSampler().generate_champion_trial() # returns MCTrialConfig
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report = forewarner.assess(champion_config)
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prob = float(report.catastrophic_probability)
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env_score = float(report.envelope_score)
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status = 'GREEN' if prob < 0.10 else ('ORANGE' if prob < 0.30 else 'RED')
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log.info(f"MC-Forewarner real eval: status={status} prob={prob:.3f}")
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return {
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'status': status,
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'catastrophic_prob': prob,
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'envelope_score': env_score,
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'champion_probability': float(report.champion_probability),
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'predicted_roi': float(report.predicted_roi),
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'predicted_roi_p10': float(report.predicted_roi_p10),
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'predicted_roi_p90': float(report.predicted_roi_p90),
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'predicted_max_dd': float(report.predicted_max_dd),
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'parameter_risks': report.parameter_risks,
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'warnings': report.warnings,
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'source': 'REAL_MODEL',
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}
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except Exception as e:
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log.error(f"MC-Forewarner evaluation failed: {e}")
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# Deterministic conservative fallback — never random
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return {
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'status': 'ORANGE',
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'catastrophic_prob': 0.20,
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'envelope_score': 0.80,
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'champion_probability': None,
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'predicted_roi': None,
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'predicted_roi_p10': None,
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'predicted_roi_p90': None,
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'predicted_max_dd': None,
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'parameter_risks': {},
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'warnings': [f'MC evaluation failed: {e}'],
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'source': 'FALLBACK_ERROR',
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}
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@task(name="write_mc_hz", retries=3, retry_delay_seconds=5)
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def write_mc_to_hz(result: dict):
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log = get_run_logger()
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try:
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client = hazelcast.HazelcastClient(cluster_name="dolphin", cluster_members=["localhost:5701"])
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imap = client.get_map('DOLPHIN_FEATURES').blocking()
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result['timestamp'] = datetime.now(timezone.utc).isoformat()
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# Include all ForewarningReport fields so TUI can show predicted bounds
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if 'predicted_roi' not in result:
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result.setdefault('predicted_roi', None)
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result.setdefault('predicted_roi_p10', None)
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result.setdefault('predicted_roi_p90', None)
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result.setdefault('predicted_max_dd', None)
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result.setdefault('champion_probability', None)
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result.setdefault('parameter_risks', {})
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result.setdefault('warnings', [])
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imap.put('mc_forewarner_latest', json.dumps(result))
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log.info(f"Successfully wrote MC-Forewarner state to HZ: {result['status']}")
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client.shutdown()
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except Exception as e:
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log.error(f"Failed to write MC-Forewarner to HZ: {e}")
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# ── Flow ────────────────────────────────────────────────────────────────────────
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@flow(name="mc-forewarner", log_prints=True)
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def mc_forewarner_flow():
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log = get_run_logger()
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log.info(f"=== Starting MC-Forewarner Flow ===")
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result = evaluate_market_structure()
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write_mc_to_hz(result)
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return result
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--register', action='store_true')
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args = parser.parse_args()
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if args.register:
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from prefect.client.schemas.schedules import CronSchedule as CS
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deployment = mc_forewarner_flow.to_deployment(
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name="dolphin-mc-forewarner",
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schedule=CS(cron="0 * * * *", timezone="UTC"),
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work_pool_name="dolphin",
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tags=["mc-forewarner", "dolphin"],
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)
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deployment.apply()
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print("Registered: dolphin-mc-forewarner")
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else:
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import os
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os.environ.setdefault('PREFECT_API_URL', 'http://localhost:4200/api')
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mc_forewarner_flow()
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