d256b65d30
Stack: FastAPI + React/TypeScript + SQLite + GPT-4o Features: Radar géopolitique, Marchés, Régime Macro, Journal de Bord MTM, Rapport IA, Super Contexte (base de raisonnement évolutive), Boucle feedback IA. Deploy: Docker + docker-compose + nginx pour openfin.open-squared.tech Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
698 lines
30 KiB
Python
698 lines
30 KiB
Python
"""
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Auto-cycle orchestration — runs every N hours (configurable).
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Cycle steps:
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1. Fetch current context (news, quotes, macro, geo)
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2. Ask GPT-4o to suggest new patterns
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3. Filter: keep only those with Jaccard keyword similarity < threshold vs existing
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4. Save filtered patterns to DB
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5. Score ALL patterns (existing + new)
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6. Log: pattern scores, trade entry prices, geo alert, macro snapshot
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7. Generate GPT-4o commentary: why are top/bottom trades performing this way?
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8. Update cycle_runs with results + commentary
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"""
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import logging
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import threading
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import uuid
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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logger = logging.getLogger(__name__)
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# ── Global scheduler state ────────────────────────────────────────────────────
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_stop_event = threading.Event()
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_cycle_thread: Optional[threading.Thread] = None
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_cycle_lock = threading.Lock() # prevents concurrent cycles
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_current_status: Dict[str, Any] = {
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"running": False,
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"last_run_id": None,
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"last_run_at": None,
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"next_run_at": None,
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"enabled": False,
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"interval_hours": 3,
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}
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# ── Helpers ───────────────────────────────────────────────────────────────────
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def _jaccard(a: List[str], b: List[str]) -> float:
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sa = {x.lower() for x in (a or [])}
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sb = {x.lower() for x in (b or [])}
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if not sa and not sb:
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return 0.0
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union = sa | sb
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return len(sa & sb) / len(union) if union else 0.0
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def _max_similarity_vs_existing(candidate_kws: List[str], existing: List[Dict]) -> float:
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return max((_jaccard(candidate_kws, p.get("keywords") or []) for p in existing), default=0.0)
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# ── Core cycle logic ──────────────────────────────────────────────────────────
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def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
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"""
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Execute one full auto-cycle. Thread-safe (skips if already running).
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Returns a summary dict.
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"""
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if not _cycle_lock.acquire(blocking=False):
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logger.info("Auto-cycle skipped — another cycle is already running")
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return {"skipped": True, "reason": "already_running"}
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run_id = datetime.utcnow().isoformat()
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summary: Dict[str, Any] = {
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"run_id": run_id,
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"trigger": trigger,
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"patterns_suggested": 0,
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"patterns_added": 0,
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"patterns_scored": 0,
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"geo_score": None,
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"dominant_regime": None,
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"commentary": None,
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"status": "error",
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}
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try:
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from services.database import (
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get_config, get_custom_patterns, save_custom_pattern,
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save_pattern_scores, log_macro_regime, log_geo_alert, log_trade_entries,
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add_cycle_run, update_cycle_run, save_reasoning_trace,
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get_latest_portfolio_lessons,
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)
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from services.data_fetcher import fetch_geo_news, get_all_quotes, get_macro_gauges, score_macro_scenarios
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from services.geo_analyzer import compute_geo_risk_score
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from services.ai_analyzer import (
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suggest_patterns_from_market_context, score_patterns_with_context,
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ai_score_news_batch, _chat, DEFAULT_ANALYSIS_TEMPLATE,
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)
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# Check AI key
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ai_key = get_config("openai_api_key") or ""
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if not ai_key:
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logger.warning("Auto-cycle: no OpenAI key configured, skipping AI steps")
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return {**summary, "status": "no_ai_key"}
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import os
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os.environ["OPENAI_API_KEY"] = ai_key
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sim_threshold = float(get_config("auto_cycle_similarity_threshold") or "0.30")
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add_cycle_run(run_id, trigger=trigger)
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_current_status["running"] = True
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_current_status["last_run_id"] = run_id
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# ── Step 0: Load portfolio lessons + Super Contexte ──────────────────
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portfolio_lessons = get_latest_portfolio_lessons()
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# Load Super Contexte (accumulated knowledge base)
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try:
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from services.database import get_latest_reasoning_state
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_reasoning_state = get_latest_reasoning_state()
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if _reasoning_state:
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if portfolio_lessons is None:
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portfolio_lessons = {}
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portfolio_lessons["super_context"] = (
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f"[Super Contexte v{_reasoning_state.get('version',1)} "
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f"du {(_reasoning_state.get('created_at','')[:16])}]\n"
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+ _reasoning_state.get("narrative", "")[:800]
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)
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_synth = _reasoning_state.get("synthesis") or {}
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portfolio_lessons["strategic_priorities"] = _synth.get("strategic_priorities", [])
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portfolio_lessons["recurring_mistakes"] = [
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m.get("mistake", "") for m in _synth.get("recurring_mistakes", [])[:3]
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]
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logger.info(
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f"[Cycle {run_id[:16]}] Super Contexte v{_reasoning_state.get('version')} loaded "
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f"({_reasoning_state.get('reports_used',0)} rapports, "
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f"{_reasoning_state.get('trades_analyzed',0)} trades)"
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)
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except Exception as _e:
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logger.warning(f"[Cycle] Could not load Super Contexte: {_e}")
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if portfolio_lessons:
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age_hours = 0
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try:
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from datetime import datetime as _dt
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created = _dt.fromisoformat(portfolio_lessons["created_at"])
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age_hours = (_dt.utcnow() - created).total_seconds() / 3600
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except Exception:
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pass
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logger.info(
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f"[Cycle {run_id[:16]}] Portfolio lessons loaded "
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f"(report from {portfolio_lessons.get('created_at','?')[:10]}, "
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f"{age_hours:.0f}h ago, avg_pnl={portfolio_lessons['stats'].get('avg_pnl_pct')}%)"
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)
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else:
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logger.info(f"[Cycle {run_id[:16]}] No portfolio report yet — cycle runs without performance feedback")
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# ── Step 1: Fetch context ─────────────────────────────────────────────
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logger.info(f"[Cycle {run_id[:16]}] Step 1: fetching context")
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from routers.geopolitical import _news_cache # type: ignore
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news = _news_cache.get("data") or fetch_geo_news()
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news = ai_score_news_batch(news)
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_news_cache["data"] = news
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geo_score_obj = compute_geo_risk_score(news)
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geo_score_val = int(geo_score_obj.get("score") or 0)
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summary["geo_score"] = geo_score_val
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quotes = get_all_quotes()
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gauges = get_macro_gauges()
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scenarios = score_macro_scenarios(gauges)
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macro_regime = {"gauges": gauges, "scenarios": scenarios}
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dominant = scenarios.get("dominant", "incertain")
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summary["dominant_regime"] = dominant
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# ── Step 2: Suggest new patterns ──────────────────────────────────────
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logger.info(f"[Cycle {run_id[:16]}] Step 2: suggesting patterns")
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try:
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from services.data_fetcher import get_economic_calendar
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calendar = get_economic_calendar()
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suggestions = suggest_patterns_from_market_context(
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news, quotes, calendar, macro_regime=macro_regime, geo_score=geo_score_obj,
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portfolio_lessons=portfolio_lessons,
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)
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except Exception as e:
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logger.warning(f"[Cycle] Suggestion step failed: {e}")
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suggestions = []
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summary["patterns_suggested"] = len(suggestions)
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logger.info(f"[Cycle {run_id[:16]}] Suggested {len(suggestions)} patterns from AI")
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# ── Step 3: Filter by similarity ──────────────────────────────────────
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existing = get_custom_patterns()
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logger.info(f"[Cycle {run_id[:16]}] Step 3: {len(existing)} existing patterns, threshold={sim_threshold}")
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added_count = 0
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for s in suggestions:
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kws = s.get("keywords") or []
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sim = _max_similarity_vs_existing(kws, existing)
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if sim < sim_threshold:
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# Capture returned ID so the pattern has a valid id for scoring
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assigned_id = save_custom_pattern(s)
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s["id"] = assigned_id
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existing.append(s)
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added_count += 1
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logger.info(f"[Cycle] Added pattern '{s.get('name')}' id={assigned_id} (sim={sim:.2f})")
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# ── Save suggestion reasoning trace ───────────────────────────
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_top_news_ctx = [
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{"title": n.get("title", "")[:120], "impact": round(float(n.get("impact_score") or 0), 2), "source": n.get("source", "")}
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for n in sorted(news, key=lambda x: -(float(x.get("impact_score") or 0)))[:8]
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]
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save_reasoning_trace(
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run_id=run_id,
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trace_type="suggestion",
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pattern_id=assigned_id,
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input_context={
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"geo_score": geo_score_val,
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"macro_dominant": dominant,
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"macro_scores": scenarios.get("scores", {}),
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"top_news": _top_news_ctx,
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"cycle_run_id": run_id,
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},
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output={
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"name": s.get("name"),
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"description": s.get("description"),
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"macro_fit": s.get("macro_fit"),
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"expected_move_pct": s.get("expected_move_pct"),
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"probability": s.get("probability"),
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"horizon_days": s.get("horizon_days"),
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"suggested_trades": s.get("suggested_trades", []),
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"keywords": s.get("keywords", []),
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"triggers": s.get("triggers", []),
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},
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reasoning_summary=(s.get("macro_fit") or s.get("description") or "")[:300],
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geo_score=geo_score_val,
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macro_dominant=dominant,
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)
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else:
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logger.debug(f"[Cycle] Filtered '{s.get('name')}' — sim={sim:.2f} >= {sim_threshold}")
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summary["patterns_added"] = added_count
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# ── Step 4: Score ALL patterns ────────────────────────────────────────
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# Verify all patterns have IDs before scoring (guard against stale data)
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patterns_with_id = [p for p in existing if p.get("id")]
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patterns_without_id = [p.get("name", "?") for p in existing if not p.get("id")]
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if patterns_without_id:
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logger.warning(f"[Cycle] {len(patterns_without_id)} patterns have no id, skipping: {patterns_without_id}")
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logger.info(f"[Cycle {run_id[:16]}] Step 4: scoring {len(patterns_with_id)} patterns (of {len(existing)} total)")
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template = get_config("analysis_template") or DEFAULT_ANALYSIS_TEMPLATE
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try:
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scored = score_patterns_with_context(
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patterns=patterns_with_id,
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recent_news=news,
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quotes_by_class=quotes,
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geo_score=geo_score_obj,
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template=template,
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top_n=len(patterns_with_id),
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category_filter=None,
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macro_regime=macro_regime,
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portfolio_lessons=portfolio_lessons,
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)
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scored_with_id = [s for s in scored if s.get("pattern_id")]
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scored_without_id = [s for s in scored if not s.get("pattern_id")]
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if scored_without_id:
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logger.warning(f"[Cycle] {len(scored_without_id)} scored results have no pattern_id — they will NOT be saved to history")
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logger.info(f"[Cycle {run_id[:16]}] Scoring returned {len(scored)} results ({len(scored_with_id)} with valid id)")
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except Exception as e:
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logger.error(f"[Cycle] Scoring failed: {e}", exc_info=True)
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scored = []
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summary["patterns_scored"] = len(scored)
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# ── Enrich scored patterns with original data not in GPT-4o response ─
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# GPT-4o scoring doesn't return expected_move_pct or suggested_trades —
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# copy from the original pattern so log_trade_entries can use them.
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_pmap = {p.get("id"): p for p in patterns_with_id}
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for s in scored:
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orig = _pmap.get(s.get("pattern_id", ""), {})
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if orig:
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if not s.get("expected_move_pct") and orig.get("expected_move_pct"):
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s["expected_move_pct"] = orig["expected_move_pct"]
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logger.debug(f"[Cycle] Enriched '{orig.get('name')}' expected_move_pct={orig['expected_move_pct']}")
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if not s.get("trade_rankings") and not s.get("suggested_trades"):
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s["suggested_trades"] = orig.get("suggested_trades", [])
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# ── Step 5: Log everything ────────────────────────────────────────────
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logger.info(f"[Cycle {run_id[:16]}] Step 5: logging")
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scoring_run_id = save_pattern_scores(scored, meta={
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"geo_score": geo_score_val,
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"total": len(scored),
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"cycle_run_id": run_id,
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"trigger": trigger,
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})
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# ── Save scoring reasoning traces (one per scored pattern) ────────────
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_macro_scores_ctx = scenarios.get("scores", {})
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_asset_bias_ctx = scenarios.get("asset_bias", {}).get(dominant, {})
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for sp in scored:
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pid = sp.get("pattern_id", "")
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if not pid:
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continue
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orig = _pmap.get(pid, {})
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save_reasoning_trace(
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run_id=scoring_run_id,
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trace_type="scoring",
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pattern_id=pid,
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input_context={
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"geo_score": geo_score_val,
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"macro_dominant": dominant,
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"macro_scores": _macro_scores_ctx,
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"asset_class": sp.get("asset_class") or orig.get("asset_class"),
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"asset_bias": _asset_bias_ctx.get(sp.get("asset_class") or orig.get("asset_class", ""), "neutral"),
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"expected_move_pct": sp.get("expected_move_pct") or orig.get("expected_move_pct"),
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"cycle_run_id": run_id,
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},
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output={
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"score": sp.get("score"),
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"confidence": sp.get("confidence"),
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"buckets": sp.get("buckets", []),
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"key_catalyst": sp.get("key_catalyst"),
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"summary": sp.get("summary"),
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"trade_rankings": sp.get("trade_rankings", []),
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"recommended_trade": sp.get("recommended_trade", {}),
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"has_strong_contra": sp.get("has_strong_contra", False),
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"geo_trigger": sp.get("geo_trigger"),
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},
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reasoning_summary=((sp.get("key_catalyst") or "") + " | " + (sp.get("summary") or ""))[:400],
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geo_score=geo_score_val,
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macro_dominant=dominant,
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)
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logger.info(f"[Cycle {run_id[:16]}] Saved {len([s for s in scored if s.get('pattern_id')])} reasoning traces")
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top_patterns_log = sorted(
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[{"pattern_id": sp.get("pattern_id"), "name": sp.get("geo_trigger"), "score": sp.get("score", 0)}
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for sp in scored if sp.get("score", 0) > 0],
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key=lambda x: -x["score"]
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)[:10]
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log_geo_alert(geo_score=geo_score_val, top_patterns=top_patterns_log,
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news_count=len(news), run_id=scoring_run_id)
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log_trade_entries(run_id=scoring_run_id, scored_patterns=scored, quotes=quotes)
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gauges_summary = {
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k: {"value": v.get("value"), "change_pct": v.get("change_pct"), "label": v.get("label")}
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for k, v in gauges.items()
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if v.get("value") is not None or v.get("change_pct") is not None
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}
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log_macro_regime(dominant=dominant, scores=scenarios.get("scores", {}),
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reasons=scenarios.get("reasons", {}), gauges_summary=gauges_summary)
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# Update macro cache so the UI sees fresh data immediately
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from routers.market_data import _macro_cache # type: ignore
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import datetime as _dt
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_macro_cache["data"] = {"gauges": gauges, "scenarios": scenarios,
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"fetched_at": datetime.utcnow().isoformat(), "cached": False}
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_macro_cache["ts"] = _dt.datetime.utcnow()
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# ── Step 6: GPT-4o cycle commentary ──────────────────────────────────
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logger.info(f"[Cycle {run_id[:16]}] Step 6: generating commentary")
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commentary = _generate_cycle_commentary(
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scored=scored, dominant=dominant, scenarios=scenarios,
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geo_score_val=geo_score_val, news=news, gauges=gauges,
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)
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# Attach lessons metadata to commentary so UI can display it
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if commentary and portfolio_lessons:
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try:
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import json as _json
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c = _json.loads(commentary) if isinstance(commentary, str) else commentary
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c["lessons_from_report"] = portfolio_lessons.get("created_at", "")[:16].replace("T", " ")
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c["lessons_headline"] = portfolio_lessons.get("headline", "")[:100]
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commentary = _json.dumps(c, ensure_ascii=False)
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except Exception:
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pass
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summary["commentary"] = commentary
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# ── Finalize ──────────────────────────────────────────────────────────
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summary["status"] = "completed"
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update_cycle_run(
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run_id,
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completed_at=datetime.utcnow().isoformat(),
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patterns_suggested=summary["patterns_suggested"],
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patterns_added=summary["patterns_added"],
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patterns_scored=summary["patterns_scored"],
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geo_score=geo_score_val,
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dominant_regime=dominant,
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commentary=commentary,
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status="completed",
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)
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_current_status["last_run_at"] = datetime.utcnow().isoformat()
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logger.info(f"[Cycle {run_id[:16]}] Completed — {added_count} new patterns, {len(scored)} scored")
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# ── Step 7: Auto portfolio snapshot ──────────────────────────────────
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# Generate (or refresh) the portfolio report so the NEXT cycle has
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# fresh performance lessons. Runs in background to not block the cycle.
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import threading
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threading.Thread(
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target=_auto_portfolio_snapshot,
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args=(ai_key,),
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daemon=True,
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name=f"portfolio-snapshot-{run_id[:8]}",
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).start()
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except Exception as e:
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logger.error(f"[Cycle {run_id[:16]}] Fatal error: {e}", exc_info=True)
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try:
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from services.database import update_cycle_run
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update_cycle_run(run_id, status="error", completed_at=datetime.utcnow().isoformat())
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except Exception:
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pass
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finally:
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_current_status["running"] = False
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_cycle_lock.release()
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return summary
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def _generate_cycle_commentary(
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scored: List[Dict], dominant: str, scenarios: Dict,
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geo_score_val: int, news: List[Dict], gauges: Dict,
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) -> Optional[str]:
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"""Ask GPT-4o to explain current performance of top/bottom patterns."""
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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()),
|
|
}
|