"""
Auto-cycle orchestration — runs every N hours (configurable).

Cycle steps:
  1. Fetch current context (news, quotes, macro, geo)
  2. Ask GPT-4o to suggest new patterns
  3. Filter: keep only those with Jaccard keyword similarity < threshold vs existing
  4. Save filtered patterns to DB
  5. Score ALL patterns (existing + new)
  6. Log: pattern scores, trade entry prices, geo alert, macro snapshot
  7. Generate GPT-4o commentary: why are top/bottom trades performing this way?
  8. Update cycle_runs with results + commentary
"""
import logging
import threading
import uuid
from datetime import datetime
from typing import Any, Dict, List, Optional

logger = logging.getLogger(__name__)

# ── Global scheduler state ────────────────────────────────────────────────────

_stop_event = threading.Event()
_cycle_thread: Optional[threading.Thread] = None
_cycle_lock = threading.Lock()   # prevents concurrent cycles
_current_status: Dict[str, Any] = {
    "running": False,
    "last_run_id": None,
    "last_run_at": None,
    "next_run_at": None,
    "enabled": False,
    "interval_hours": 3,
}


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

def _jaccard(a: List[str], b: List[str]) -> float:
    sa = {x.lower() for x in (a or [])}
    sb = {x.lower() for x in (b or [])}
    if not sa and not sb:
        return 0.0
    union = sa | sb
    return len(sa & sb) / len(union) if union else 0.0


def _max_similarity_vs_existing(candidate_kws: List[str], existing: List[Dict]) -> float:
    return max((_jaccard(candidate_kws, p.get("keywords") or []) for p in existing), default=0.0)


# ── Core cycle logic ──────────────────────────────────────────────────────────

def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
    """
    Execute one full auto-cycle. Thread-safe (skips if already running).
    Returns a summary dict.
    """
    if not _cycle_lock.acquire(blocking=False):
        logger.info("Auto-cycle skipped — another cycle is already running")
        return {"skipped": True, "reason": "already_running"}

    run_id = datetime.utcnow().isoformat()
    summary: Dict[str, Any] = {
        "run_id": run_id,
        "trigger": trigger,
        "patterns_suggested": 0,
        "patterns_added": 0,
        "patterns_scored": 0,
        "geo_score": None,
        "dominant_regime": None,
        "commentary": None,
        "status": "error",
    }

    try:
        from services.database import (
            get_config, get_custom_patterns, save_custom_pattern,
            save_pattern_scores, log_macro_regime, log_geo_alert, log_trade_entries,
            add_cycle_run, update_cycle_run, save_reasoning_trace,
            get_latest_portfolio_lessons,
        )
        from services.data_fetcher import fetch_geo_news, get_all_quotes, get_macro_gauges, score_macro_scenarios
        from services.geo_analyzer import compute_geo_risk_score
        from services.ai_analyzer import (
            suggest_patterns_from_market_context, score_patterns_with_context,
            ai_score_news_batch, _chat, DEFAULT_ANALYSIS_TEMPLATE,
        )

        # Check AI key
        ai_key = get_config("openai_api_key") or ""
        if not ai_key:
            logger.warning("Auto-cycle: no OpenAI key configured, skipping AI steps")
            return {**summary, "status": "no_ai_key"}

        import os
        os.environ["OPENAI_API_KEY"] = ai_key

        sim_threshold = float(get_config("auto_cycle_similarity_threshold") or "0.30")

        add_cycle_run(run_id, trigger=trigger)
        _current_status["running"] = True
        _current_status["last_run_id"] = run_id

        # ── Step 0: Load portfolio lessons + Super Contexte ──────────────────
        portfolio_lessons = get_latest_portfolio_lessons()

        # Load Super Contexte (accumulated knowledge base)
        try:
            from services.database import get_latest_reasoning_state
            _reasoning_state = get_latest_reasoning_state()
            if _reasoning_state:
                if portfolio_lessons is None:
                    portfolio_lessons = {}
                portfolio_lessons["super_context"] = (
                    f"[Super Contexte v{_reasoning_state.get('version',1)} "
                    f"du {(_reasoning_state.get('created_at','')[:16])}]\n"
                    + _reasoning_state.get("narrative", "")[:800]
                )
                _synth = _reasoning_state.get("synthesis") or {}
                portfolio_lessons["strategic_priorities"] = _synth.get("strategic_priorities", [])
                portfolio_lessons["recurring_mistakes"] = [
                    m.get("mistake", "") for m in _synth.get("recurring_mistakes", [])[:3]
                ]
                logger.info(
                    f"[Cycle {run_id[:16]}] Super Contexte v{_reasoning_state.get('version')} loaded "
                    f"({_reasoning_state.get('reports_used',0)} rapports, "
                    f"{_reasoning_state.get('trades_analyzed',0)} trades)"
                )
        except Exception as _e:
            logger.warning(f"[Cycle] Could not load Super Contexte: {_e}")

        if portfolio_lessons:
            age_hours = 0
            try:
                from datetime import datetime as _dt
                created = _dt.fromisoformat(portfolio_lessons["created_at"])
                age_hours = (_dt.utcnow() - created).total_seconds() / 3600
            except Exception:
                pass
            logger.info(
                f"[Cycle {run_id[:16]}] Portfolio lessons loaded "
                f"(report from {portfolio_lessons.get('created_at','?')[:10]}, "
                f"{age_hours:.0f}h ago, avg_pnl={portfolio_lessons['stats'].get('avg_pnl_pct')}%)"
            )
        else:
            logger.info(f"[Cycle {run_id[:16]}] No portfolio report yet — cycle runs without performance feedback")

        # ── Step 1: Fetch context ─────────────────────────────────────────────
        logger.info(f"[Cycle {run_id[:16]}] Step 1: fetching context")
        from routers.geopolitical import _news_cache  # type: ignore
        news = _news_cache.get("data") or fetch_geo_news()
        news = ai_score_news_batch(news)
        _news_cache["data"] = news

        geo_score_obj = compute_geo_risk_score(news)
        geo_score_val = int(geo_score_obj.get("score") or 0)
        summary["geo_score"] = geo_score_val

        quotes = get_all_quotes()

        gauges = get_macro_gauges()
        scenarios = score_macro_scenarios(gauges)
        macro_regime = {"gauges": gauges, "scenarios": scenarios}
        dominant = scenarios.get("dominant", "incertain")
        summary["dominant_regime"] = dominant

        # ── Step 2: Suggest new patterns ──────────────────────────────────────
        logger.info(f"[Cycle {run_id[:16]}] Step 2: suggesting patterns")
        try:
            from services.data_fetcher import get_economic_calendar
            calendar = get_economic_calendar()
            suggestions = suggest_patterns_from_market_context(
                news, quotes, calendar, macro_regime=macro_regime, geo_score=geo_score_obj,
                portfolio_lessons=portfolio_lessons,
            )
        except Exception as e:
            logger.warning(f"[Cycle] Suggestion step failed: {e}")
            suggestions = []

        summary["patterns_suggested"] = len(suggestions)
        logger.info(f"[Cycle {run_id[:16]}] Suggested {len(suggestions)} patterns from AI")

        # ── Step 3: Filter by similarity ──────────────────────────────────────
        existing = get_custom_patterns()
        logger.info(f"[Cycle {run_id[:16]}] Step 3: {len(existing)} existing patterns, threshold={sim_threshold}")
        added_count = 0
        for s in suggestions:
            kws = s.get("keywords") or []
            sim = _max_similarity_vs_existing(kws, existing)
            if sim < sim_threshold:
                # Capture returned ID so the pattern has a valid id for scoring
                assigned_id = save_custom_pattern(s)
                s["id"] = assigned_id
                existing.append(s)
                added_count += 1
                logger.info(f"[Cycle] Added pattern '{s.get('name')}' id={assigned_id} (sim={sim:.2f})")
                # ── Save suggestion reasoning trace ───────────────────────────
                _top_news_ctx = [
                    {"title": n.get("title", "")[:120], "impact": round(float(n.get("impact_score") or 0), 2), "source": n.get("source", "")}
                    for n in sorted(news, key=lambda x: -(float(x.get("impact_score") or 0)))[:8]
                ]
                save_reasoning_trace(
                    run_id=run_id,
                    trace_type="suggestion",
                    pattern_id=assigned_id,
                    input_context={
                        "geo_score": geo_score_val,
                        "macro_dominant": dominant,
                        "macro_scores": scenarios.get("scores", {}),
                        "top_news": _top_news_ctx,
                        "cycle_run_id": run_id,
                    },
                    output={
                        "name": s.get("name"),
                        "description": s.get("description"),
                        "macro_fit": s.get("macro_fit"),
                        "expected_move_pct": s.get("expected_move_pct"),
                        "probability": s.get("probability"),
                        "horizon_days": s.get("horizon_days"),
                        "suggested_trades": s.get("suggested_trades", []),
                        "keywords": s.get("keywords", []),
                        "triggers": s.get("triggers", []),
                    },
                    reasoning_summary=(s.get("macro_fit") or s.get("description") or "")[:300],
                    geo_score=geo_score_val,
                    macro_dominant=dominant,
                )
            else:
                logger.debug(f"[Cycle] Filtered '{s.get('name')}' — sim={sim:.2f} >= {sim_threshold}")

        summary["patterns_added"] = added_count

        # ── Step 4: Score ALL patterns ────────────────────────────────────────
        # Verify all patterns have IDs before scoring (guard against stale data)
        patterns_with_id = [p for p in existing if p.get("id")]
        patterns_without_id = [p.get("name", "?") for p in existing if not p.get("id")]
        if patterns_without_id:
            logger.warning(f"[Cycle] {len(patterns_without_id)} patterns have no id, skipping: {patterns_without_id}")

        logger.info(f"[Cycle {run_id[:16]}] Step 4: scoring {len(patterns_with_id)} patterns (of {len(existing)} total)")
        template = get_config("analysis_template") or DEFAULT_ANALYSIS_TEMPLATE
        try:
            scored = score_patterns_with_context(
                patterns=patterns_with_id,
                recent_news=news,
                quotes_by_class=quotes,
                geo_score=geo_score_obj,
                template=template,
                top_n=len(patterns_with_id),
                category_filter=None,
                macro_regime=macro_regime,
                portfolio_lessons=portfolio_lessons,
            )
            scored_with_id = [s for s in scored if s.get("pattern_id")]
            scored_without_id = [s for s in scored if not s.get("pattern_id")]
            if scored_without_id:
                logger.warning(f"[Cycle] {len(scored_without_id)} scored results have no pattern_id — they will NOT be saved to history")
            logger.info(f"[Cycle {run_id[:16]}] Scoring returned {len(scored)} results ({len(scored_with_id)} with valid id)")
        except Exception as e:
            logger.error(f"[Cycle] Scoring failed: {e}", exc_info=True)
            scored = []

        summary["patterns_scored"] = len(scored)

        # ── Enrich scored patterns with original data not in GPT-4o response ─
        # GPT-4o scoring doesn't return expected_move_pct or suggested_trades —
        # copy from the original pattern so log_trade_entries can use them.
        _pmap = {p.get("id"): p for p in patterns_with_id}
        for s in scored:
            orig = _pmap.get(s.get("pattern_id", ""), {})
            if orig:
                if not s.get("expected_move_pct") and orig.get("expected_move_pct"):
                    s["expected_move_pct"] = orig["expected_move_pct"]
                    logger.debug(f"[Cycle] Enriched '{orig.get('name')}' expected_move_pct={orig['expected_move_pct']}")
                if not s.get("trade_rankings") and not s.get("suggested_trades"):
                    s["suggested_trades"] = orig.get("suggested_trades", [])

        # ── Step 5: Log everything ────────────────────────────────────────────
        logger.info(f"[Cycle {run_id[:16]}] Step 5: logging")
        scoring_run_id = save_pattern_scores(scored, meta={
            "geo_score": geo_score_val,
            "total": len(scored),
            "cycle_run_id": run_id,
            "trigger": trigger,
        })

        # ── Save scoring reasoning traces (one per scored pattern) ────────────
        _macro_scores_ctx = scenarios.get("scores", {})
        _asset_bias_ctx = scenarios.get("asset_bias", {}).get(dominant, {})
        for sp in scored:
            pid = sp.get("pattern_id", "")
            if not pid:
                continue
            orig = _pmap.get(pid, {})
            save_reasoning_trace(
                run_id=scoring_run_id,
                trace_type="scoring",
                pattern_id=pid,
                input_context={
                    "geo_score": geo_score_val,
                    "macro_dominant": dominant,
                    "macro_scores": _macro_scores_ctx,
                    "asset_class": sp.get("asset_class") or orig.get("asset_class"),
                    "asset_bias": _asset_bias_ctx.get(sp.get("asset_class") or orig.get("asset_class", ""), "neutral"),
                    "expected_move_pct": sp.get("expected_move_pct") or orig.get("expected_move_pct"),
                    "cycle_run_id": run_id,
                },
                output={
                    "score": sp.get("score"),
                    "confidence": sp.get("confidence"),
                    "buckets": sp.get("buckets", []),
                    "key_catalyst": sp.get("key_catalyst"),
                    "summary": sp.get("summary"),
                    "trade_rankings": sp.get("trade_rankings", []),
                    "recommended_trade": sp.get("recommended_trade", {}),
                    "has_strong_contra": sp.get("has_strong_contra", False),
                    "geo_trigger": sp.get("geo_trigger"),
                },
                reasoning_summary=((sp.get("key_catalyst") or "") + " | " + (sp.get("summary") or ""))[:400],
                geo_score=geo_score_val,
                macro_dominant=dominant,
            )
        logger.info(f"[Cycle {run_id[:16]}] Saved {len([s for s in scored if s.get('pattern_id')])} reasoning traces")

        top_patterns_log = sorted(
            [{"pattern_id": sp.get("pattern_id"), "name": sp.get("geo_trigger"), "score": sp.get("score", 0)}
             for sp in scored if sp.get("score", 0) > 0],
            key=lambda x: -x["score"]
        )[:10]

        log_geo_alert(geo_score=geo_score_val, top_patterns=top_patterns_log,
                      news_count=len(news), run_id=scoring_run_id)

        log_trade_entries(run_id=scoring_run_id, scored_patterns=scored, quotes=quotes)

        gauges_summary = {
            k: {"value": v.get("value"), "change_pct": v.get("change_pct"), "label": v.get("label")}
            for k, v in gauges.items()
            if v.get("value") is not None or v.get("change_pct") is not None
        }
        log_macro_regime(dominant=dominant, scores=scenarios.get("scores", {}),
                         reasons=scenarios.get("reasons", {}), gauges_summary=gauges_summary)

        # Update macro cache so the UI sees fresh data immediately
        from routers.market_data import _macro_cache  # type: ignore
        import datetime as _dt
        _macro_cache["data"] = {"gauges": gauges, "scenarios": scenarios,
                                "fetched_at": datetime.utcnow().isoformat(), "cached": False}
        _macro_cache["ts"] = _dt.datetime.utcnow()

        # ── Step 6: GPT-4o cycle commentary ──────────────────────────────────
        logger.info(f"[Cycle {run_id[:16]}] Step 6: generating commentary")
        commentary = _generate_cycle_commentary(
            scored=scored, dominant=dominant, scenarios=scenarios,
            geo_score_val=geo_score_val, news=news, gauges=gauges,
        )
        # Attach lessons metadata to commentary so UI can display it
        if commentary and portfolio_lessons:
            try:
                import json as _json
                c = _json.loads(commentary) if isinstance(commentary, str) else commentary
                c["lessons_from_report"] = portfolio_lessons.get("created_at", "")[:16].replace("T", " ")
                c["lessons_headline"] = portfolio_lessons.get("headline", "")[:100]
                commentary = _json.dumps(c, ensure_ascii=False)
            except Exception:
                pass
        summary["commentary"] = commentary

        # ── Finalize ──────────────────────────────────────────────────────────
        summary["status"] = "completed"
        update_cycle_run(
            run_id,
            completed_at=datetime.utcnow().isoformat(),
            patterns_suggested=summary["patterns_suggested"],
            patterns_added=summary["patterns_added"],
            patterns_scored=summary["patterns_scored"],
            geo_score=geo_score_val,
            dominant_regime=dominant,
            commentary=commentary,
            status="completed",
        )
        _current_status["last_run_at"] = datetime.utcnow().isoformat()
        logger.info(f"[Cycle {run_id[:16]}] Completed — {added_count} new patterns, {len(scored)} scored")

        # ── Step 7: Auto portfolio snapshot ──────────────────────────────────
        # Generate (or refresh) the portfolio report so the NEXT cycle has
        # fresh performance lessons. Runs in background to not block the cycle.
        import threading
        threading.Thread(
            target=_auto_portfolio_snapshot,
            args=(ai_key,),
            daemon=True,
            name=f"portfolio-snapshot-{run_id[:8]}",
        ).start()

    except Exception as e:
        logger.error(f"[Cycle {run_id[:16]}] Fatal error: {e}", exc_info=True)
        try:
            from services.database import update_cycle_run
            update_cycle_run(run_id, status="error", completed_at=datetime.utcnow().isoformat())
        except Exception:
            pass
    finally:
        _current_status["running"] = False
        _cycle_lock.release()

    return summary


def _generate_cycle_commentary(
    scored: List[Dict], dominant: str, scenarios: Dict,
    geo_score_val: int, news: List[Dict], gauges: Dict,
) -> Optional[str]:
    """Ask GPT-4o to explain current performance of top/bottom patterns."""
    try:
        from services.database import get_trade_entry_prices
        from services.ai_analyzer import _chat

        # Get recent trade P&L for context (last 7 days)
        entries = get_trade_entry_prices(7)
        trade_summary = []
        for e in entries[:15]:
            trade_summary.append({
                "pattern": e.get("pattern_name", ""),
                "underlying": e.get("underlying", ""),
                "strategy": e.get("strategy", ""),
                "score_at_entry": e.get("score_at_entry", 0),
                "entry_date": e.get("entry_date", ""),
            })

        # Top 5 scored patterns now
        top_scored = sorted(scored, key=lambda x: -(x.get("score") or 0))[:5]
        top_scored_summary = [
            {"name": s.get("geo_trigger"), "score": s.get("score"), "summary": s.get("summary", "")[:120]}
            for s in top_scored
        ]

        # Top recent news headlines
        top_news = [{"title": n.get("title", ""), "impact": n.get("impact_score", 0)}
                    for n in sorted(news, key=lambda x: -(x.get("impact_score") or 0))[:5]]

        import json

        def gv(key: str) -> str:
            v = gauges.get(key, {}).get("value")
            return str(round(v, 2)) if v is not None else "N/A"

        def gc(key: str) -> str:
            v = gauges.get(key, {}).get("change_pct")
            return f"{v:+.2f}%" if v is not None else "N/A"

        prompt = f"""Tu es un stratège macro-géopolitique senior qui analyse la performance de notre système de détection de patterns.

CONTEXTE DU CYCLE (maintenant):
- Régime dominant: {dominant.upper()} (score: {scenarios.get('scores', {}).get(dominant, 0)}%)
- Score risque géopolitique: {geo_score_val}/100
- VIX: {gv('vix')} | Pente 10Y-3M: {gv('slope_10y3m')}% | DXY: {gc('dxy')} | Brent: {gc('brent')}
- Cuivre: {gc('copper')} | Or: {gc('gold')} | S&P vs 200j: {gv('spx_vs_200d')}%

TOP 5 PATTERNS ACTUELLEMENT LES MIEUX SCORÉS:
{json.dumps(top_scored_summary, ensure_ascii=False, indent=2)}

TRADES LOGUÉS CES 7 DERNIERS JOURS:
{json.dumps(trade_summary, ensure_ascii=False, indent=2)}

NEWS GÉOPOLITIQUES À FORT IMPACT:
{json.dumps(top_news, ensure_ascii=False, indent=2)}

Écris un COMMENTAIRE DE CYCLE concis (4-6 phrases) pour un trader options:
1. Le régime macro confirme-t-il les patterns qui scorent le mieux ?
2. Y a-t-il des news ou événements qui expliquent un écart avec nos prévisions ?
3. Quels patterns/trades méritent attention (confirmation ou invalidation) ?
4. Une recommandation tactique pour le prochain cycle (3h)

Réponds UNIQUEMENT en JSON: {{"commentary": "<ton texte 4-6 phrases>", "key_risk": "<risque principal en 1 phrase>", "top_pattern": "<nom du pattern le plus pertinent maintenant>"}}"""

        result = _chat(
            "Tu es un stratège macro senior. Analyse concise et actionnable. JSON uniquement.",
            prompt,
            model="gpt-4o",
            json_mode=True,
            max_tokens=500,
        )
        if result and result.get("commentary"):
            return json.dumps(result, ensure_ascii=False)
    except Exception as e:
        logger.warning(f"[Cycle] Commentary generation failed: {e}")
    return None


# ── Auto portfolio snapshot ───────────────────────────────────────────────────

def _auto_portfolio_snapshot(ai_key: str) -> None:
    """
    Called in a background thread at the end of each cycle.
    Fetches live prices, checks if enough trades are priced (P&L ≠ 0),
    and if so generates a GPT-4o portfolio report so the NEXT cycle has
    fresh performance lessons. Skipped silently if not enough data.
    """
    try:
        import os
        os.environ["OPENAI_API_KEY"] = ai_key

        from services.database import (
            get_mtm_trades_with_traces, save_ai_report, get_latest_portfolio_lessons,
        )

        data = get_mtm_trades_with_traces(days=30, limit_movers=5)
        winners = data.get("winners", [])
        losers  = data.get("losers",  [])
        priced  = data.get("priced_count", 0)

        # Need at least 3 priced trades with actual movement to make analysis meaningful
        meaningful = [
            t for t in (winners + losers)
            if t.get("pnl_pct") is not None and abs(t.get("pnl_pct", 0)) > 0.05
        ]
        if len(meaningful) < 3:
            logger.info(
                f"[AutoSnapshot] Skipping GPT-4o report: only {len(meaningful)} trades "
                f"with meaningful P&L movement (need ≥ 3)"
            )
            return

        avg_pnl = data.get("avg_pnl_pct")
        stats = {
            "total_trades": data["total_trades"],
            "priced_count": priced,
            "avg_pnl_pct": avg_pnl,
        }

        # Build prompt (reuse same logic as reasoning.py generate endpoint)
        from routers.reasoning import _trade_summary_block, _bucket_summary, _rankings_summary
        from services.ai_analyzer import _chat

        winners_block = _trade_summary_block("TOP GAINS", winners)
        losers_block  = _trade_summary_block("TOP PERTES", losers)
        avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"

        prompt = f"""Tu es un stratège macro-géopolitique senior. Rapport synthétique post-cycle automatique.

═══ STATISTIQUES GLOBALES ═══
Période : 30 derniers jours | Trades total: {data['total_trades']} | Pricés: {priced} | P&L moyen: {avg_str}

═══ {winners_block}

═══ {losers_block}

Génère un rapport JSON :
{{
  "headline": "<1 phrase résumant la performance>",
  "regime_assessment": "<alignement régime macro avec nos thèses ?>",
  "winners_analysis": "<pourquoi ces trades ont marché — 2-3 phrases>",
  "losers_analysis": "<pourquoi ces trades ont déçu — 2-3 phrases>",
  "key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
  "blind_spots": "<ce que le scoring n'a pas bien capturé>",
  "next_cycle_priorities": "<3 priorités pour le prochain cycle>",
  "risk_watch": "<1-2 risques à surveiller>"
}}"""

        result = _chat(
            "Tu es un stratège macro senior. Rapport post-cycle concis. JSON uniquement.",
            prompt,
            model="gpt-4o",
            json_mode=True,
            max_tokens=800,
        )
        if not result:
            logger.warning("[AutoSnapshot] GPT-4o returned empty response")
            return

        report_id = save_ai_report(
            days=30, stats=stats, winners=winners, losers=losers, report=result,
            report_type="portfolio",
        )
        logger.info(
            f"[AutoSnapshot] Portfolio report #{report_id} saved automatically "
            f"({len(meaningful)} meaningful trades, avg P&L {avg_str})"
        )
    except Exception as e:
        logger.error(f"[AutoSnapshot] Failed: {e}", exc_info=True)


# ── Scheduler ─────────────────────────────────────────────────────────────────

def _scheduler_loop(stop_event: threading.Event):
    """Background loop that runs the cycle at the configured interval."""
    import time
    from services.database import get_config

    while not stop_event.is_set():
        try:
            interval_hours = float(get_config("auto_cycle_hours") or "3")
        except Exception:
            interval_hours = 3.0

        _current_status["interval_hours"] = interval_hours
        next_run = datetime.utcnow().isoformat()
        _current_status["next_run_at"] = next_run
        logger.info(f"[Scheduler] Next cycle in {interval_hours}h")

        # Wait for the interval (or until stop is signalled)
        stop_event.wait(timeout=interval_hours * 3600)

        if stop_event.is_set():
            break

        # Check if still enabled
        try:
            enabled = (get_config("auto_cycle_enabled") or "false").lower() == "true"
        except Exception:
            enabled = False

        if enabled:
            logger.info("[Scheduler] Running scheduled auto-cycle")
            try:
                run_cycle_once(trigger="auto")
            except Exception as e:
                logger.error(f"[Scheduler] Cycle error: {e}", exc_info=True)


def start_scheduler():
    """Start the background scheduler thread if auto_cycle is enabled."""
    global _cycle_thread, _stop_event
    from services.database import get_config

    enabled = (get_config("auto_cycle_enabled") or "false").lower() == "true"
    _current_status["enabled"] = enabled

    if not enabled:
        logger.info("[Scheduler] Auto-cycle disabled — skipping scheduler start")
        return

    if _cycle_thread and _cycle_thread.is_alive():
        logger.info("[Scheduler] Already running")
        return

    _stop_event = threading.Event()
    _cycle_thread = threading.Thread(
        target=_scheduler_loop,
        args=(_stop_event,),
        daemon=True,
        name="auto-cycle-scheduler",
    )
    _cycle_thread.start()
    logger.info("[Scheduler] Auto-cycle scheduler started")


def stop_scheduler():
    """Stop the background scheduler thread."""
    global _cycle_thread
    _stop_event.set()
    if _cycle_thread:
        _cycle_thread.join(timeout=5)
    _current_status["enabled"] = False
    logger.info("[Scheduler] Stopped")


def restart_scheduler():
    """Restart the scheduler — call after config changes."""
    stop_scheduler()
    _stop_event.clear()
    start_scheduler()


def trigger_manual():
    """Run one cycle immediately in a background thread (non-blocking)."""
    t = threading.Thread(target=run_cycle_once, args=("manual",), daemon=True, name="auto-cycle-manual")
    t.start()
    return t


def get_status() -> Dict[str, Any]:
    from services.database import get_config, get_cycle_runs
    try:
        interval_hours = float(get_config("auto_cycle_hours") or "3")
        enabled = (get_config("auto_cycle_enabled") or "false").lower() == "true"
        sim_threshold = float(get_config("auto_cycle_similarity_threshold") or "0.30")
        min_ev = float(get_config("min_ev_threshold") or "0.0")
        min_score = int(get_config("min_score_threshold") or "0")
    except Exception:
        interval_hours, enabled, sim_threshold, min_ev, min_score = 3.0, False, 0.30, 0.0, 0

    recent = get_cycle_runs(limit=1)
    last = recent[0] if recent else None

    return {
        **_current_status,
        "enabled": enabled,
        "interval_hours": interval_hours,
        "similarity_threshold": sim_threshold,
        "min_ev_threshold": min_ev,
        "min_score_threshold": min_score,
        "last_cycle": last,
        "scheduler_alive": bool(_cycle_thread and _cycle_thread.is_alive()),
    }