"""
Isolated cycle action: Check New Market Events.

Scans 4 sources and creates market_events for significant findings:
  - news     : geopolitical/macro news (RSS feeds, rule-scored)
  - eco      : FRED economic releases with high surprise z-score
  - technical: MA50/MA100/MA200 crossovers on key instruments
  - reports  : institutional reports (COT, EIA) with high importance

After each event is created, instrument impacts are evaluated immediately
via the AI (impact_service.evaluate_event_impacts).
"""
import json
import logging
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional

logger = logging.getLogger(__name__)

WATCH_INSTRUMENTS = [
    "SPY", "QQQ", "IWM", "EEM", "EFA",
    "GLD", "SLV", "USO", "TLT", "HYG",
    "USDJPY=X", "EURUSD=X", "VXX", "NVDA", "BTC-USD",
]

SUBTYPE_FROM_SERIES = {
    "UNRATE": "NFP", "PAYEMS": "NFP",
    "CPIAUCSL": "CPI", "CPILFESL": "CPI",
    "A191RL1Q225SBEA": "GDP", "GDP": "GDP",
    "FEDFUNDS": "FOMC", "DFF": "FOMC",
    "PCEPILFE": "PCE", "PCEPI": "PCE",
    "BAMLH0A0HYM2": "Credit", "BAMLC0A0CM": "Credit",
}


# ── Helpers ───────────────────────────────────────────────────────────────────

def _get_api_key() -> str:
    import os
    key = os.environ.get("OPENAI_API_KEY", "")
    if not key:
        from services.database import get_config
        key = get_config("openai_api_key") or ""
    return key


def _existing_event_keys() -> set:
    from services.database import get_all_market_events
    return {ev["name"].lower()[:50] for ev in get_all_market_events()}


def _is_dup(name: str, existing: set) -> bool:
    return name.lower()[:50] in existing


def _parse_date(raw: str) -> str:
    if not raw:
        return datetime.utcnow().strftime("%Y-%m-%d")
    try:
        return datetime.fromisoformat(raw[:19]).strftime("%Y-%m-%d")
    except Exception:
        pass
    try:
        from email.utils import parsedate_to_datetime
        return parsedate_to_datetime(raw).strftime("%Y-%m-%d")
    except Exception:
        pass
    return raw[:10] if len(raw) >= 10 else datetime.utcnow().strftime("%Y-%m-%d")


def _save_and_evaluate(ev: Dict, existing: set) -> Optional[Dict]:
    """Save a market_event and immediately evaluate instrument impacts. Returns created dict or None."""
    from services.database import save_market_event
    try:
        event_id = save_market_event(ev)
        existing.add(ev["name"].lower()[:50])
        logger.info(f"[check_events] ✓ saved event #{event_id}: {ev['name']}")
    except Exception as e:
        logger.error(f"[check_events] save failed for '{ev['name']}': {e}")
        return None

    # Evaluate instrument impacts immediately
    try:
        from services.impact_service import evaluate_event_impacts
        evaluate_event_impacts(event_id, force=False)
    except Exception as e:
        logger.warning(f"[check_events] impact eval failed for #{event_id}: {e}")

    return {"name": ev["name"], "category": ev.get("category", ""), "date": ev.get("start_date", ""), "event_id": event_id}


# ── Source 1: Geopolitical / macro news ──────────────────────────────────────

def _check_news(
    min_impact: float = 0.55,
    lookback_hours: int = 48,
    max_to_evaluate: int = 15,
) -> List[Dict[str, Any]]:
    from services.data_fetcher import fetch_geo_news

    api_key = _get_api_key()
    if not api_key:
        logger.warning("[check_events/news] no OpenAI key — skipping")
        return []

    try:
        all_news = fetch_geo_news()
    except Exception as e:
        logger.warning(f"[check_events/news] fetch failed: {e}")
        return []

    cutoff_dt = datetime.utcnow() - timedelta(hours=lookback_hours)
    candidates = []
    for n in all_news:
        if (n.get("impact_score") or 0) < min_impact:
            continue
        pub_date = _parse_date(n.get("date", ""))
        try:
            if datetime.fromisoformat(pub_date) < cutoff_dt:
                continue
        except Exception:
            pass
        candidates.append(n)

    candidates = candidates[:max_to_evaluate]
    if not candidates:
        return []

    existing = _existing_event_keys()
    created: List[Dict] = []

    try:
        from openai import OpenAI
        client = OpenAI(api_key=api_key)
    except Exception as e:
        logger.warning(f"[check_events/news] OpenAI init failed: {e}")
        return []

    for n in candidates:
        title = n.get("title", "")
        if not title or _is_dup(title, existing):
            continue

        prompt = f"""Tu es un analyste macro. Cette news représente-t-elle un événement marché structurant qui mérite un enregistrement permanent ?

TITRE: {title}
SOURCE: {n.get('source', '')}
DATE: {n.get('date', '')}
RÉSUMÉ: {str(n.get('summary', ''))[:400]}
SCORE IMPACT (règle): {n.get('impact_score', 0):.2f}

Réponds OUI seulement si c'est un fait avéré, pas une rumeur ou une opinion, et qu'il a un impact macro ou géopolitique mesurable.

FORMAT JSON STRICT:
{{
  "qualifies": true/false,
  "reason": "une phrase",
  "name": "Nom court (≤ 60 chars)",
  "category": "geopolitical|event_calendar|fundamental|report",
  "sub_type": "ex: Conflit, Tarifs, Sanctions, OPEC+, Crise...",
  "description": "1-2 phrases analytiques",
  "affected_assets": ["SPY","GLD",...],
  "impact_score": 0.5,
  "level": "short|medium|long"
}}"""

        try:
            resp = client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[{"role": "user", "content": prompt}],
                response_format={"type": "json_object"},
                temperature=0.1,
                max_tokens=350,
            )
            parsed = json.loads(resp.choices[0].message.content)
        except Exception as e:
            logger.debug(f"[check_events/news] AI call failed for '{title[:40]}': {e}")
            continue

        if not parsed.get("qualifies"):
            continue

        ev_name = (parsed.get("name") or title)[:60]
        if _is_dup(ev_name, existing):
            continue

        source_ref = {
            "title": title,
            "source": n.get("source", ""),
            "url": n.get("url") or n.get("link", ""),
            "date": _parse_date(n.get("date", "")),
            "original_score": round(float(n.get("impact_score", 0)), 3),
        }

        ev = {
            "name":           ev_name,
            "start_date":     _parse_date(n.get("date", "")),
            "level":          parsed.get("level", "short"),
            "category":       parsed.get("category", "geopolitical"),
            "sub_type":       parsed.get("sub_type", ""),
            "description":    parsed.get("description", title),
            "market_impact":  "",
            "affected_assets": parsed.get("affected_assets", []),
            "impact_score":   float(parsed.get("impact_score", 0.6)),
            "source_refs":    [source_ref],
            "origin":         "detector_news",
        }
        result = _save_and_evaluate(ev, existing)
        if result:
            result["source"] = "news"
            created.append(result)

    return created


# ── Source 2: Eco calendar — FRED surprises ───────────────────────────────────

def _check_eco(z_threshold: float = 1.5, days: int = 7) -> List[Dict[str, Any]]:
    from services.database import get_recent_economic_surprises

    try:
        releases = get_recent_economic_surprises(days=days, min_zscore=z_threshold)
    except Exception as e:
        logger.warning(f"[check_events/eco] query failed: {e}")
        return []

    existing = _existing_event_keys()
    created: List[Dict] = []

    for rel in releases:
        z        = abs(rel.get("surprise_zscore") or 0)
        s_pct    = rel.get("surprise_pct") or 0
        ev_date  = (rel.get("event_date") or "")[:10]
        s_id     = rel.get("series_id", "")
        ev_name_base = rel.get("event_name", s_id)
        direction = rel.get("surprise_direction", "neutral")

        sign = "+" if s_pct >= 0 else ""
        ev_name = f"{ev_name_base} — Surprise {sign}{s_pct:.1f}% ({ev_date[:7]})"

        if _is_dup(ev_name, existing):
            continue

        sub_type = SUBTYPE_FROM_SERIES.get(s_id, s_id[:10]) if s_id else ev_name_base[:10]
        level    = "long" if z >= 3 else ("medium" if z >= 2 else "short")
        assets   = rel.get("assets_impacted") or []
        if isinstance(assets, str):
            try:
                assets = json.loads(assets)
            except Exception:
                assets = []

        source_ref = {
            "title":  f"FRED release: {ev_name_base} ({ev_date})",
            "source": "FRED",
            "url":    f"https://fred.stlouisfed.org/series/{s_id}" if s_id else "",
            "date":   ev_date,
            "original_score": round(min(0.95, 0.35 + z * 0.15), 3),
        }

        ev = {
            "name":           ev_name,
            "start_date":     ev_date,
            "level":          level,
            "category":       "event_calendar",
            "sub_type":       sub_type,
            "description":    (
                f"Surprise {direction} {sign}{s_pct:.1f}% vs baseline "
                f"(z-score: {z:.1f}σ). "
                f"Réel: {rel.get('actual_value', '?')} {rel.get('actual_unit', '')} "
                f"/ Prévision: {rel.get('forecast_value', '?')}."
            ),
            "market_impact":  "",
            "affected_assets": assets,
            "impact_score":   min(0.95, 0.35 + z * 0.15),
            "actual_value":   str(rel.get("actual_value", "")),
            "expected_value": str(rel.get("forecast_value", "")),
            "surprise_pct":   float(s_pct),
            "source_refs":    [source_ref],
            "origin":         "detector_eco",
        }
        result = _save_and_evaluate(ev, existing)
        if result:
            result["source"] = "eco"
            created.append(result)

    return created


# ── Source 3: MA crossovers (technical) ──────────────────────────────────────

def _check_technical(instruments: List[str] = None, lookback_days: int = 7) -> List[Dict[str, Any]]:
    try:
        import yfinance as yf
        import pandas as pd
    except ImportError:
        logger.warning("[check_events/technical] yfinance/pandas not available")
        return []

    if instruments is None:
        instruments = WATCH_INSTRUMENTS

    existing  = _existing_event_keys()
    created: List[Dict] = []
    cutoff    = (datetime.utcnow() - timedelta(days=lookback_days)).strftime("%Y-%m-%d")

    for ticker in instruments:
        try:
            df = yf.download(ticker, period="1y", interval="1d", progress=False, auto_adjust=True)
            if df is None or len(df) < 210:
                continue

            close = df["Close"].squeeze()
            df["ma50"]  = close.rolling(50).mean()
            df["ma100"] = close.rolling(100).mean()
            df["ma200"] = close.rolling(200).mean()

            recent = df.tail(lookback_days + 2)

            for i in range(1, len(recent)):
                date_str = str(recent.index[i])[:10]
                if date_str < cutoff:
                    continue

                prev = recent.iloc[i - 1]
                curr = recent.iloc[i]

                def cross(fp, fc, sp, sc):
                    if any(pd.isna(v) for v in [fp, fc, sp, sc]):
                        return None
                    if fp < sp and fc >= sc:
                        return "golden"
                    if fp > sp and fc <= sc:
                        return "death"
                    return None

                pairs = [
                    ("MA50", "MA200", prev["ma50"], curr["ma50"], prev["ma200"], curr["ma200"]),
                    ("MA50", "MA100", prev["ma50"], curr["ma50"], prev["ma100"], curr["ma100"]),
                ]

                for fast_lbl, slow_lbl, fp, fc, sp, sc in pairs:
                    kind = cross(fp, fc, sp, sc)
                    if kind is None:
                        continue

                    cross_label = "Golden Cross" if kind == "golden" else "Death Cross"
                    ev_name = f"{ticker} {fast_lbl}/{slow_lbl} {cross_label} ({date_str[:7]})"

                    if _is_dup(ev_name, existing):
                        continue

                    direction = "bullish" if kind == "golden" else "bearish"
                    level     = "medium" if slow_lbl == "MA200" else "short"

                    source_ref = {
                        "title":  f"Technical signal: {ev_name}",
                        "source": "yfinance/computed",
                        "url":    f"https://finance.yahoo.com/quote/{ticker}",
                        "date":   date_str,
                        "original_score": 0.65 if slow_lbl == "MA200" else 0.45,
                    }

                    ev = {
                        "name":           ev_name,
                        "start_date":     date_str,
                        "level":          level,
                        "category":       "technical",
                        "sub_type":       f"{fast_lbl}/{slow_lbl} Cross",
                        "description":    (
                            f"{cross_label} : {fast_lbl} passe "
                            f"{'au-dessus' if kind == 'golden' else 'en-dessous'} "
                            f"de la {slow_lbl} sur {ticker}. "
                            f"Signal {direction} de tendance "
                            f"{'long terme' if slow_lbl == 'MA200' else 'moyen terme'}."
                        ),
                        "market_impact":  f"Signal {direction} sur {ticker}",
                        "affected_assets": [ticker],
                        "impact_score":   0.65 if slow_lbl == "MA200" else 0.45,
                        "source_refs":    [source_ref],
                        "origin":         "detector_technical",
                    }
                    result = _save_and_evaluate(ev, existing)
                    if result:
                        result["source"] = "technical"
                        created.append(result)

        except Exception as e:
            logger.debug(f"[check_events/technical] {ticker} failed: {e}")

    return created


# ── Source 4: Institutional reports ──────────────────────────────────────────

def _check_reports(days: int = 7, min_importance: int = 3) -> List[Dict[str, Any]]:
    from services.database import get_conn

    try:
        cutoff = (datetime.utcnow() - timedelta(days=days)).strftime("%Y-%m-%d")
        conn   = get_conn()
        rows   = conn.execute(
            """SELECT * FROM institutional_reports
               WHERE report_date >= ? AND importance >= ?
               ORDER BY importance DESC, report_date DESC
               LIMIT 15""",
            (cutoff, min_importance),
        ).fetchall()
        conn.close()
        reports = [dict(r) for r in rows]
    except Exception as e:
        logger.warning(f"[check_events/reports] query failed: {e}")
        return []

    existing = _existing_event_keys()
    created: List[Dict] = []

    for rpt in reports:
        title    = rpt.get("title", "")
        rpt_type = rpt.get("report_type", "Report")
        rpt_date = (rpt.get("report_date") or "")[:10]

        if not title or _is_dup(title, existing):
            continue

        ev_name = title[:60]
        summary = rpt.get("ai_summary") or rpt.get("trading_implications", "")
        try:
            kp = json.loads(rpt.get("key_points_json") or "[]")
            if kp:
                summary = " ".join(kp[:2]) + " " + summary
        except Exception:
            pass

        assets: List[str] = []
        for sig_col, asset_list in [
            ("signal_energy",  ["USO", "XOM"]),
            ("signal_metals",  ["GLD", "SLV"]),
            ("signal_indices", ["SPY", "QQQ"]),
            ("signal_forex",   ["EURUSD=X", "USDJPY=X"]),
        ]:
            if rpt.get(sig_col, "neutral") not in ("neutral", "", None):
                assets.extend(asset_list)

        source_ref = {
            "title":  title,
            "source": rpt.get("source", rpt_type),
            "url":    "",
            "date":   rpt_date,
            "original_score": round(min(0.9, 0.3 + rpt.get("importance", 2) * 0.12), 3),
        }

        ev = {
            "name":            ev_name,
            "start_date":      rpt_date,
            "level":           "medium" if rpt.get("importance", 2) >= 4 else "short",
            "category":        "report",
            "sub_type":        rpt_type.upper(),
            "description":     summary[:500] or f"Rapport {rpt_type} du {rpt_date}.",
            "market_impact":   rpt.get("trading_implications", ""),
            "affected_assets": list(set(assets)),
            "impact_score":    min(0.9, 0.3 + rpt.get("importance", 2) * 0.12),
            "source_refs":     [source_ref],
            "origin":          "detector_report",
        }
        result = _save_and_evaluate(ev, existing)
        if result:
            result["source"] = "reports"
            created.append(result)

    return created


# ── Main entry point ──────────────────────────────────────────────────────────

def check_new_market_events(
    sources: Optional[List[str]] = None,
    news_impact_min: float = 0.55,
    news_lookback_hours: int = 48,
    eco_z_threshold: float = 1.5,
    eco_days: int = 7,
    technical_lookback_days: int = 7,
    report_days: int = 7,
    report_min_importance: int = 3,
) -> Dict[str, Any]:
    """
    Isolated cycle action — scans all (or selected) sources, creates
    market_events with source_refs, and immediately evaluates instrument impacts.
    """
    if sources is None:
        sources = ["news", "eco", "technical", "reports"]

    results: Dict[str, Any] = {
        "news": [], "eco": [], "technical": [], "reports": [],
        "total_created": 0,
        "ran_at": datetime.utcnow().isoformat(),
    }

    if "news" in sources:
        try:
            results["news"] = _check_news(min_impact=news_impact_min, lookback_hours=news_lookback_hours)
        except Exception as e:
            logger.error(f"[check_events] news source error: {e}")

    if "eco" in sources:
        try:
            results["eco"] = _check_eco(z_threshold=eco_z_threshold, days=eco_days)
        except Exception as e:
            logger.error(f"[check_events] eco source error: {e}")

    if "technical" in sources:
        try:
            results["technical"] = _check_technical(lookback_days=technical_lookback_days)
        except Exception as e:
            logger.error(f"[check_events] technical source error: {e}")

    if "reports" in sources:
        try:
            results["reports"] = _check_reports(days=report_days, min_importance=report_min_importance)
        except Exception as e:
            logger.error(f"[check_events] reports source error: {e}")

    results["total_created"] = sum(len(results[s]) for s in ["news", "eco", "technical", "reports"])
    logger.info(
        f"[check_events] Done — {results['total_created']} new events: "
        f"news={len(results['news'])} eco={len(results['eco'])} "
        f"technical={len(results['technical'])} reports={len(results['reports'])}"
    )
    return results