"""
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: configurable signal catalog driven by Technical Desk
  - reports  : institutional reports (COT, EIA) with high importance

Desk configs are loaded from ai_desks table at runtime.
"""
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."""
    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

    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}


# ── Semantic deduplication ────────────────────────────────────────────────────

def _semantic_dedup(
    title: str,
    source: str,
    date_str: str,
    summary: str,
    category: str,
    client: Any,
    dedup_lookback_days: int = 2,
    system_prompt_hint: str = "",
) -> bool:
    """
    Ask the AI whether this news already exists in recent market_events.
    Returns True if it's a duplicate (should be skipped).
    """
    from services.database import get_market_events_near_date
    dedup_categories = ["geopolitical", "fundamental", "report"]
    if category and category not in dedup_categories:
        dedup_categories.append(category)

    recent = get_market_events_near_date(date_str, days=dedup_lookback_days, categories=dedup_categories)
    if not recent:
        return False

    recent_block = "\n".join(
        f"  [{r['start_date']}] {r['name']} — {(r.get('description') or '')[:80]}"
        for r in recent[:15]
    )

    hint = f"\nNote du desk: {system_prompt_hint[:200]}" if system_prompt_hint else ""

    prompt = f"""Tu es un éditeur de base de données d'événements marchés.{hint}

NOUVELLE NEWS À VÉRIFIER:
- Titre: {title}
- Source: {source}
- Date: {date_str}
- Résumé: {summary[:300]}

ÉVÉNEMENTS EXISTANTS (±{dedup_lookback_days} jours):
{recent_block}

Cette news représente-t-elle le même fait qu'un événement déjà enregistré ?
Réponds JSON: {{"is_duplicate": true/false, "reason": "courte phrase"}}"""

    try:
        resp = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"},
            temperature=0.0,
            max_tokens=100,
        )
        parsed = json.loads(resp.choices[0].message.content)
        is_dup = bool(parsed.get("is_duplicate", False))
        if is_dup:
            logger.debug(f"[dedup] Skipping duplicate: '{title[:40]}' — {parsed.get('reason','')}")
        return is_dup
    except Exception as e:
        logger.debug(f"[dedup] AI check failed for '{title[:40]}': {e}")
        return False


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

def _check_news(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
    from services.data_fetcher import fetch_geo_news

    min_impact      = float(desk_cfg.get("min_impact", 0.55))
    lookback_hours  = int(desk_cfg.get("lookback_hours", 48))
    max_evaluate    = int(desk_cfg.get("max_evaluate", 15))
    dedup_enabled   = bool(desk_cfg.get("dedup_enabled", True))
    dedup_days      = int(desk_cfg.get("dedup_lookback_days", 2))
    system_prompt   = desk_cfg.get("_system_prompt", "")

    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_evaluate]
    if not candidates:
        return []

    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 []

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

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

        pub_date  = _parse_date(n.get("date", ""))
        news_summary = str(n.get("summary", ""))[:400]
        source    = n.get("source", "")

        # Semantic dedup before expensive classification call
        if dedup_enabled:
            if _semantic_dedup(
                title, source, pub_date, news_summary,
                category="geopolitical",
                client=client,
                dedup_lookback_days=dedup_days,
                system_prompt_hint=system_prompt,
            ):
                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: {source}
DATE: {n.get('date', '')}
RÉSUMÉ: {news_summary}
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":         source,
            "url":            n.get("url") or n.get("link", ""),
            "date":           pub_date,
            "original_score": round(float(n.get("impact_score", 0)), 3),
        }

        ev = {
            "name":            ev_name,
            "start_date":      pub_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(desk_cfg: Dict[str, Any]) -> List[Dict[str, Any]]:
    from services.database import get_recent_economic_surprises

    z_threshold = float(desk_cfg.get("z_threshold", 1.5))
    days        = int(desk_cfg.get("days", 7))

    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


# ── Technical signal detectors ────────────────────────────────────────────────

def _detect_ma_cross(ticker: str, df: Any, params: Dict, cutoff: str) -> List[Dict]:
    """Golden/Death cross detector for configured MA pairs."""
    import pandas as pd
    events = []
    pairs_cfg = params.get("pairs", [["MA50", "MA200"], ["MA50", "MA100"]])
    ma_map = {"MA20": 20, "MA50": 50, "MA100": 100, "MA200": 200}

    close = df["Close"].squeeze()

    # Pre-compute all required MAs
    needed: set = set()
    for pair in pairs_cfg:
        needed.update(pair)
    ma_series: Dict[str, Any] = {}
    for lbl in needed:
        period = ma_map.get(lbl)
        if period and len(df) >= period:
            ma_series[lbl] = close.rolling(period).mean()

    recent = df.tail(4)
    for i in range(1, len(recent)):
        date_str = str(recent.index[i])[:10]
        if date_str < cutoff:
            continue
        for fast_lbl, slow_lbl in pairs_cfg:
            if fast_lbl not in ma_series or slow_lbl not in ma_series:
                continue
            fp = ma_series[fast_lbl].iloc[-(len(recent) - i + 1)]
            fc = ma_series[fast_lbl].iloc[-(len(recent) - i)]
            sp = ma_series[slow_lbl].iloc[-(len(recent) - i + 1)]
            sc = ma_series[slow_lbl].iloc[-(len(recent) - i)]
            if any(pd.isna(v) for v in [fp, fc, sp, sc]):
                continue
            if fp < sp and fc >= sc:
                kind = "golden"
            elif fp > sp and fc <= sc:
                kind = "death"
            else:
                continue
            cross_label = "Golden Cross" if kind == "golden" else "Death Cross"
            events.append({
                "name":      f"{ticker} {fast_lbl}/{slow_lbl} {cross_label} ({date_str[:7]})",
                "date":      date_str,
                "direction": "bullish" if kind == "golden" else "bearish",
                "sub_type":  f"{fast_lbl}/{slow_lbl} Cross",
                "score":     0.65 if "MA200" in (fast_lbl, slow_lbl) else 0.45,
                "level":     "medium" if "MA200" in (fast_lbl, slow_lbl) else "short",
                "desc":      f"{cross_label}: {fast_lbl} {'au-dessus' if kind=='golden' else 'en-dessous'} de {slow_lbl} sur {ticker}.",
            })
    return events


def _detect_rsi_extreme(ticker: str, df: Any, params: Dict, cutoff: str) -> List[Dict]:
    """RSI oversold/overbought signal."""
    import pandas as pd
    period     = int(params.get("period", 14))
    oversold   = float(params.get("oversold", 30))
    overbought = float(params.get("overbought", 70))

    close = df["Close"].squeeze()
    if len(close) < period + 2:
        return []

    delta = close.diff()
    gain  = delta.clip(lower=0).rolling(period).mean()
    loss  = (-delta.clip(upper=0)).rolling(period).mean()
    rs    = gain / loss.replace(0, float("nan"))
    rsi   = 100 - (100 / (1 + rs))

    events = []
    recent = rsi.tail(3)
    for i in range(len(recent)):
        date_str = str(recent.index[i])[:10]
        if date_str < cutoff:
            continue
        val = recent.iloc[i]
        if pd.isna(val):
            continue
        if val <= oversold:
            direction, label = "bullish", "Oversold"
        elif val >= overbought:
            direction, label = "bearish", "Overbought"
        else:
            continue
        events.append({
            "name":      f"{ticker} RSI {label} ({date_str[:7]})",
            "date":      date_str,
            "direction": direction,
            "sub_type":  f"RSI {label}",
            "score":     0.50 if abs(val - 50) > 30 else 0.40,
            "level":     "short",
            "desc":      f"RSI({period}) à {val:.1f} sur {ticker} — signal {label.lower()} ({direction}).",
        })
    return events


def _detect_bb_squeeze(ticker: str, df: Any, params: Dict, cutoff: str) -> List[Dict]:
    """Bollinger Band squeeze detector."""
    import pandas as pd
    period          = int(params.get("period", 20))
    std_mult        = float(params.get("std", 2.0))
    width_threshold = float(params.get("width_threshold", 0.05))

    close = df["Close"].squeeze()
    if len(close) < period + 2:
        return []

    mid   = close.rolling(period).mean()
    std   = close.rolling(period).std()
    upper = mid + std_mult * std
    lower = mid - std_mult * std
    width = (upper - lower) / mid

    events = []
    recent = width.tail(3)
    for i in range(len(recent)):
        date_str = str(recent.index[i])[:10]
        if date_str < cutoff:
            continue
        w = recent.iloc[i]
        if pd.isna(w):
            continue
        if w <= width_threshold:
            events.append({
                "name":      f"{ticker} BB Squeeze ({date_str[:7]})",
                "date":      date_str,
                "direction": "neutral",
                "sub_type":  "BB Squeeze",
                "score":     0.45,
                "level":     "short",
                "desc":      f"Bandes de Bollinger({period},{std_mult}) très resserrées sur {ticker} — width={w:.3f}. Explosion de volatilité imminente.",
            })
    return events


def _detect_52w_extreme(ticker: str, df: Any, params: Dict, cutoff: str) -> List[Dict]:
    """New 52-week high/low detector."""
    import pandas as pd
    buffer_pct = float(params.get("buffer_pct", 0.5)) / 100

    close = df["Close"].squeeze()
    if len(close) < 252:
        return []

    high_52 = close.rolling(252).max()
    low_52  = close.rolling(252).min()

    events = []
    recent_close = close.tail(3)
    for i in range(len(recent_close)):
        date_str = str(recent_close.index[i])[:10]
        if date_str < cutoff:
            continue
        c   = recent_close.iloc[i]
        h52 = high_52.iloc[-(3 - i)]
        l52 = low_52.iloc[-(3 - i)]
        if pd.isna(c) or pd.isna(h52) or pd.isna(l52):
            continue
        if c >= h52 * (1 - buffer_pct):
            events.append({
                "name":      f"{ticker} Nouveau 52W High ({date_str[:7]})",
                "date":      date_str,
                "direction": "bullish",
                "sub_type":  "52W High",
                "score":     0.60,
                "level":     "medium",
                "desc":      f"{ticker} atteint un nouveau plus haut 52 semaines à {c:.2f} (précédent: {h52:.2f}).",
            })
        elif c <= l52 * (1 + buffer_pct):
            events.append({
                "name":      f"{ticker} Nouveau 52W Low ({date_str[:7]})",
                "date":      date_str,
                "direction": "bearish",
                "sub_type":  "52W Low",
                "score":     0.60,
                "level":     "medium",
                "desc":      f"{ticker} atteint un nouveau plus bas 52 semaines à {c:.2f} (précédent: {l52:.2f}).",
            })
    return events


# ── Source 3: Technical signals ───────────────────────────────────────────────

def _check_technical(desk_cfg: Dict[str, Any]) -> 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 []

    instruments    = desk_cfg.get("_instruments") or WATCH_INSTRUMENTS
    lookback_days  = int(desk_cfg.get("lookback_days", 7))
    signals_config = desk_cfg.get("signals", {})

    # Determine which signals are active
    def sig_cfg(sig_id: str) -> Optional[Dict]:
        c = signals_config.get(sig_id, {})
        return c if c.get("enabled", False) else None

    ma_cross_cfg   = sig_cfg("ma_cross")
    rsi_cfg        = sig_cfg("rsi_extreme")
    bb_cfg         = sig_cfg("bb_squeeze")
    extreme_52w    = sig_cfg("new_52w_extreme")

    if not any([ma_cross_cfg, rsi_cfg, bb_cfg, extreme_52w]):
        logger.info("[check_events/technical] no active signals in desk config")
        return []

    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="2y", interval="1d", progress=False, auto_adjust=True)
            if df is None or len(df) < 20:
                continue

            # Flatten MultiIndex if needed (yfinance ≥ 0.2 returns MultiIndex columns)
            if hasattr(df.columns, "levels"):
                df.columns = df.columns.get_level_values(0)

            detected: List[Dict] = []
            if ma_cross_cfg and len(df) >= 210:
                detected += _detect_ma_cross(ticker, df, ma_cross_cfg, cutoff)
            if rsi_cfg:
                detected += _detect_rsi_extreme(ticker, df, rsi_cfg, cutoff)
            if bb_cfg:
                detected += _detect_bb_squeeze(ticker, df, bb_cfg, cutoff)
            if extreme_52w and len(df) >= 252:
                detected += _detect_52w_extreme(ticker, df, extreme_52w, cutoff)

            for sig in detected:
                ev_name = sig["name"]
                if _is_dup(ev_name, existing):
                    continue

                source_ref = {
                    "title":          f"Technical signal: {ev_name}",
                    "source":         "yfinance/computed",
                    "url":            f"https://finance.yahoo.com/quote/{ticker}",
                    "date":           sig["date"],
                    "original_score": sig["score"],
                }

                ev = {
                    "name":            ev_name,
                    "start_date":      sig["date"],
                    "level":           sig["level"],
                    "category":        "technical",
                    "sub_type":        sig["sub_type"],
                    "description":     sig["desc"],
                    "market_impact":   f"Signal {sig['direction']} sur {ticker}",
                    "affected_assets": [ticker],
                    "impact_score":    sig["score"],
                    "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,
    # Legacy overrides (used when called from cycle_actions without a desk)
    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]:
    """
    Scans all (or selected) sources, creates market_events with source_refs,
    and immediately evaluates instrument impacts.
    Desk configs from ai_desks table override legacy params when available.
    """
    if sources is None:
        sources = ["news", "eco", "technical", "reports"]

    # Load desk configs (fall back to legacy params if no active desk found)
    try:
        from services.database import get_ai_desk_by_type
        news_desk  = get_ai_desk_by_type("news")
        tech_desk  = get_ai_desk_by_type("technical")
        eco_desk   = get_ai_desk_by_type("eco")
    except Exception as e:
        logger.warning(f"[check_events] Could not load desk configs: {e}")
        news_desk = tech_desk = eco_desk = None

    def _desk_cfg(desk: Optional[Dict], fallback: Dict) -> Dict:
        if not desk:
            return fallback
        cfg = dict(desk.get("config") or {})
        cfg["_instruments"] = desk.get("instruments") or None
        cfg["_system_prompt"] = desk.get("system_prompt") or ""
        return cfg

    news_cfg = _desk_cfg(news_desk, {
        "min_impact":         news_impact_min,
        "lookback_hours":     news_lookback_hours,
        "max_evaluate":       15,
        "dedup_enabled":      False,
        "dedup_lookback_days": 2,
    })
    eco_cfg = _desk_cfg(eco_desk, {
        "z_threshold": eco_z_threshold,
        "days":        eco_days,
    })
    tech_cfg = _desk_cfg(tech_desk, {
        "lookback_days": technical_lookback_days,
        "signals": {
            "ma_cross":        {"enabled": True, "pairs": [["MA50", "MA200"], ["MA50", "MA100"]]},
            "rsi_extreme":     {"enabled": True, "period": 14, "oversold": 30, "overbought": 70},
            "bb_squeeze":      {"enabled": True, "period": 20, "std": 2.0, "width_threshold": 0.05},
            "new_52w_extreme": {"enabled": True, "buffer_pct": 0.5},
        },
    })

    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(news_cfg)
        except Exception as e:
            logger.error(f"[check_events] news source error: {e}")

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

    if "technical" in sources:
        try:
            results["technical"] = _check_technical(tech_cfg)
        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