"""DOLPHIN MIG1.3 — MC-Forewarner Flow
Runs every 4 hours to evaluate structural market risk via Monte Carlo ML models.
Writes to Hazelcast DOLPHIN_FEATURES IMap key 'mc_forewarner_latest'.
"""
import sys, json, logging, argparse
from pathlib import Path
from datetime import datetime, timezone

from prefect import flow, task, get_run_logger
import hazelcast

HCM_DIR = Path(__file__).parent.parent

logging.basicConfig(level=logging.INFO)

# ── Tasks ───────────────────────────────────────────────────────────────────────

@task(name="run_mc_forewarner", retries=1, retry_delay_seconds=30)
def evaluate_market_structure() -> dict:
    log = get_run_logger()
    models_dir = HCM_DIR / 'nautilus_dolphin' / 'mc_results' / 'models'

    if not models_dir.exists() or not list(models_dir.glob('*.pkl')):
        log.warning("MC models not found — using deterministic FALLBACK_NO_DATA")
        return {
            'status': 'ORANGE',
            'catastrophic_prob': 0.20,
            'envelope_score': 0.80,
            'champion_probability': None,
            'predicted_roi': None,
            'predicted_roi_p10': None,
            'predicted_roi_p90': None,
            'predicted_max_dd': None,
            'parameter_risks': {},
            'warnings': ['MC models not found — using deterministic fallback'],
            'source': 'FALLBACK_NO_DATA',
        }

    try:
        sys.path.insert(0, str(HCM_DIR / 'nautilus_dolphin'))
        from mc.mc_ml import DolphinForewarner
        from mc.mc_sampler import MCSampler

        forewarner = DolphinForewarner(models_dir=str(models_dir))
        champion_config = MCSampler().generate_champion_trial()  # returns MCTrialConfig
        report = forewarner.assess(champion_config)

        prob      = float(report.catastrophic_probability)
        env_score = float(report.envelope_score)
        status    = 'GREEN' if prob < 0.10 else ('ORANGE' if prob < 0.30 else 'RED')

        log.info(f"MC-Forewarner real eval: status={status} prob={prob:.3f}")
        return {
            'status':               status,
            'catastrophic_prob':    prob,
            'envelope_score':       env_score,
            'champion_probability': float(report.champion_probability),
            'predicted_roi':        float(report.predicted_roi),
            'predicted_roi_p10':    float(report.predicted_roi_p10),
            'predicted_roi_p90':    float(report.predicted_roi_p90),
            'predicted_max_dd':     float(report.predicted_max_dd),
            'parameter_risks':      report.parameter_risks,
            'warnings':             report.warnings,
            'source': 'REAL_MODEL',
        }
    except Exception as e:
        log.error(f"MC-Forewarner evaluation failed: {e}")
        # Deterministic conservative fallback — never random
        return {
            'status': 'ORANGE',
            'catastrophic_prob': 0.20,
            'envelope_score': 0.80,
            'champion_probability': None,
            'predicted_roi': None,
            'predicted_roi_p10': None,
            'predicted_roi_p90': None,
            'predicted_max_dd': None,
            'parameter_risks': {},
            'warnings': [f'MC evaluation failed: {e}'],
            'source': 'FALLBACK_ERROR',
        }


@task(name="write_mc_hz", retries=3, retry_delay_seconds=5)
def write_mc_to_hz(result: dict):
    log = get_run_logger()
    try:
        client = hazelcast.HazelcastClient(cluster_name="dolphin", cluster_members=["localhost:5701"])
        imap = client.get_map('DOLPHIN_FEATURES').blocking()
        
        result['timestamp'] = datetime.now(timezone.utc).isoformat()
        # Include all ForewarningReport fields so TUI can show predicted bounds
        if 'predicted_roi' not in result:
            result.setdefault('predicted_roi',     None)
            result.setdefault('predicted_roi_p10', None)
            result.setdefault('predicted_roi_p90', None)
            result.setdefault('predicted_max_dd',  None)
            result.setdefault('champion_probability', None)
            result.setdefault('parameter_risks',   {})
            result.setdefault('warnings',          [])
        imap.put('mc_forewarner_latest', json.dumps(result))

        log.info(f"Successfully wrote MC-Forewarner state to HZ: {result['status']}")
        client.shutdown()
    except Exception as e:
        log.error(f"Failed to write MC-Forewarner to HZ: {e}")

# ── Flow ────────────────────────────────────────────────────────────────────────

@flow(name="mc-forewarner", log_prints=True)
def mc_forewarner_flow():
    log = get_run_logger()
    log.info(f"=== Starting MC-Forewarner Flow ===")
    
    result = evaluate_market_structure()
    write_mc_to_hz(result)
    
    return result

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--register', action='store_true')
    args = parser.parse_args()
    
    if args.register:
        from prefect.client.schemas.schedules import CronSchedule as CS
        deployment = mc_forewarner_flow.to_deployment(
            name="dolphin-mc-forewarner",
            schedule=CS(cron="0 * * * *", timezone="UTC"),
            work_pool_name="dolphin",
            tags=["mc-forewarner", "dolphin"],
        )
        deployment.apply()
        print("Registered: dolphin-mc-forewarner")
    else:
        import os
        os.environ.setdefault('PREFECT_API_URL', 'http://localhost:4200/api')
        mc_forewarner_flow()