fix: weekend-aware cycle — IVGate, pandas MultiIndex, ticker aliases, day/session in AI prompt
- auto_cycle.py: detect weekend/market session, build cycle_meta with day_of_week/is_weekend/market_note; IVGate skips iv_rank>=99 on weekends to avoid artificial weekend option premium cascade; inject portfolio context (open trades + price moves + concentration) before AI scoring; pass portfolio_context_block + run_id to both AI scorer and suggester - ai_analyzer.py: _build_temporal_news_block injects market session banner (WEEKEND warning, pre/after-market note, or open session label) so AI knows markets are closed and defers execution to Monday - iv_engine.py: add WHEAT/EUR/USD ticker aliases; skip saving IV snapshots on weekends to protect history; resolve aliases before slash-format conversion in _resolve_ticker - technical_indicators.py: fix pandas MultiIndex from yfinance>=0.2 (droplevel+squeeze); use period proportional to lookback instead of fixed period=1d - database.py: asset_class ticker-based fallback (_asset_class_from_ticker); one-time backfill migration for all NULL asset_class rows; ai_call_logs table + save/get helpers; normalize_ticker public function Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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OpenSquared
@@ -970,9 +970,13 @@ def partition_news_by_age(news: List[Dict], delta_minutes: float) -> Dict[str, L
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def _build_temporal_news_block(partitioned: Dict[str, List], cycle_meta: Dict) -> str:
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"""Build the news section of the IA prompt with temporal framing."""
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"""Build the news section of the IA prompt with temporal framing + market session context."""
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delta_min = cycle_meta.get("delta_minutes", 180)
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calib = cycle_meta.get("calibration_label", "")
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day_of_week = cycle_meta.get("day_of_week", "")
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is_weekend = cycle_meta.get("is_weekend", False)
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market_session = cycle_meta.get("market_session", "")
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market_note = cycle_meta.get("market_note", "")
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def _fmt_news(articles: List[Dict], limit: int = 6) -> str:
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lines = []
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@@ -989,7 +993,27 @@ def _build_temporal_news_block(partitioned: Dict[str, List], cycle_meta: Dict) -
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recent = partitioned.get("recent_24h", [])
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older = partitioned.get("older", [])
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# Market session banner
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if is_weekend:
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session_banner = (
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f"\n## 📅 STATUT DES MARCHÉS — {day_of_week.upper()}\n"
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f"⚠️ WEEKEND — Marchés FERMÉS. {market_note}\n"
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"→ Les patterns peuvent être identifiés mais NE PEUVENT PAS être exécutés avant lundi matin.\n"
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"→ Utilise ce cycle pour préparer des ordres à cours limité pour l'ouverture de lundi.\n"
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"→ Les prix affichés sont ceux de la CLÔTURE DE VENDREDI — ne pas interpréter les mouvements intraday.\n"
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"→ La volatilité implicite des options est artificiellement élevée ce weekend (prime weekend des market makers) — les IVR affichés sont surestimés.\n"
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)
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elif market_session in ("pre_market", "after_hours", "overnight"):
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session_banner = (
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f"\n## 📅 STATUT DES MARCHÉS — {day_of_week} {market_session.upper()}\n"
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f"{market_note}\n"
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"→ Les prix peuvent ne pas refléter la session régulière — liquidité réduite.\n"
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)
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else:
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session_banner = f"\n## 📅 STATUT DES MARCHÉS — {day_of_week} | {market_session.upper()}\n{market_note}\n" if day_of_week else ""
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block = f"""
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{session_banner}
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## ⏱ CONTEXTE TEMPOREL DU CYCLE
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- Dernier cycle il y a : {delta_min:.0f} minutes
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- Calibration : {calib}
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@@ -230,12 +230,41 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
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else:
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_calib_label = f"Long terme ({_delta_minutes/60:.0f}h) — marchés ont eu le temps d'intégrer"
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# ── Market calendar context ────────────────────────────────────────
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from datetime import timezone as _tz
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_now_utc = _now.replace(tzinfo=_tz.utc) if _now.tzinfo is None else _now.astimezone(_tz.utc)
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_weekday = _now_utc.weekday() # 0=Mon … 6=Sun
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_day_names = ["Lundi", "Mardi", "Mercredi", "Jeudi", "Vendredi", "Samedi", "Dimanche"]
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_is_weekend = _weekday >= 5
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# CME equity/energy/metals futures: closed Fri 17:00 ET → Sun 18:00 ET
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# Rough ET offset (UTC-5 winter / UTC-4 summer) — good enough for intent
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_et_hour = (_now_utc.hour - 5) % 24
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if _is_weekend:
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_market_session = "closed"
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_market_note = f"{_day_names[_weekday]} — marchés US et CME fermés. Derniers prix disponibles: vendredi clôture."
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elif _et_hour < 4:
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_market_session = "overnight"
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_market_note = "Nuit US (overnight) — liquidité réduite, spreads larges."
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elif _et_hour < 9:
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_market_session = "pre_market"
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_market_note = "Pré-marché US — prix indicatifs, faible liquidité."
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elif _et_hour < 16:
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_market_session = "open"
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_market_note = "Marché US ouvert."
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else:
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_market_session = "after_hours"
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_market_note = "After-hours US — prix indicatifs, liquidité réduite."
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cycle_meta = {
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"current_cycle_ts": _now.isoformat(),
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"last_cycle_ts": _last_cycle_ts_str,
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"delta_minutes": round(_delta_minutes, 1),
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"interval_hours": _interval_hours,
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"calibration_label": _calib_label,
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"day_of_week": _day_names[_weekday],
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"is_weekend": _is_weekend,
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"market_session": _market_session,
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"market_note": _market_note,
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}
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logger.info(
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f"[Cycle {run_id[:16]}] Cycle meta: delta={_delta_minutes:.0f}min depuis dernier cycle"
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@@ -1075,6 +1104,10 @@ def _apply_iv_gate(
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except Exception:
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pass
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# Detect weekend: IVR=100 on weekends is artificial (weekend premium) — ignore rank
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from datetime import datetime as _dt_cls
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_weekend_now = _dt_cls.utcnow().weekday() >= 5
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all_blocked: List[Dict] = []
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for sp in scored:
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@@ -1093,6 +1126,11 @@ def _apply_iv_gate(
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continue
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iv_rank = snap.get("iv_rank")
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# On weekends, ATM IV is inflated by weekend premium (market makers can't hedge).
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# IVR=99-100 on a weekend is almost always artificial — don't block on it.
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if _weekend_now and iv_rank is not None and iv_rank >= 99.0:
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iv_rank = None
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snap = {**snap, "iv_rank": None}
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iv_current_pct = snap.get("iv_current_pct")
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iv_min_52w = snap.get("iv_min_52w_pct")
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iv_max_52w = snap.get("iv_max_52w_pct")
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@@ -1069,6 +1069,45 @@ def _normalize_yf_ticker(ticker: str) -> str:
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return t
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# Common AI hallucinations / wrong ticker formats → canonical Yahoo Finance symbols
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TICKER_ALIASES: dict = {
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# Commodities
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"WHEAT": "ZW=F", "CORN_FUTURES": "ZC=F", "SOYBEANS": "ZS=F", "SOYBEAN": "ZS=F",
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"CRUDE": "CL=F", "OIL": "CL=F", "WTI": "CL=F", "BRENT": "BZ=F",
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"CRUDE OIL": "CL=F", "NATURAL GAS": "NG=F",
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"GOLD": "GC=F", "SILVER": "SI=F", "COPPER": "HG=F", "PLATINUM": "PL=F",
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"SUGAR": "SB=F", "COFFEE": "KC=F", "COCOA": "CC=F", "COTTON": "CT=F",
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# Forex — "/" format → yfinance format
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"EUR/USD": "EURUSD=X", "USD/EUR": "EURUSD=X",
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"USD/JPY": "USDJPY=X", "JPY/USD": "USDJPY=X",
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"GBP/USD": "GBPUSD=X", "USD/GBP": "GBPUSD=X",
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"USD/CHF": "USDCHF=X", "CHF/USD": "USDCHF=X",
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"AUD/USD": "AUDUSD=X", "USD/CAD": "USDCAD=X",
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# Indices
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"SP500": "^GSPC", "S&P500": "^GSPC", "S&P 500": "^GSPC",
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"NASDAQ": "QQQ", "NASDAQ100": "^NDX",
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"DOW": "DIA", "DOW JONES": "DIA",
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"RUSSELL2000": "IWM", "RUSSELL 2000": "IWM",
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}
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def normalize_ticker(ticker: str) -> str:
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"""Normalize AI-generated ticker strings to valid Yahoo Finance symbols."""
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if not ticker:
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return ticker
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t = ticker.strip()
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upper = t.upper()
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# Check alias map (case-insensitive)
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if upper in TICKER_ALIASES:
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return TICKER_ALIASES[upper]
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# Forex: "EUR/USD" style not caught above
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if "/" in t:
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parts = t.upper().split("/")
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if len(parts) == 2 and all(2 <= len(p) <= 4 for p in parts):
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return parts[0] + parts[1] + "=X"
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return t
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_TICKER_ASSET_CLASS: dict = {
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# Energy
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"CL=F": "energy", "BZ=F": "energy", "NG=F": "energy", "RB=F": "energy", "HO=F": "energy",
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@@ -48,9 +48,21 @@ IV_WATCHLIST = [
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]
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# Common AI-hallucinated ticker names → canonical Yahoo Finance tickers
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_TICKER_ALIASES: Dict[str, str] = {
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"WHEAT": "ZW=F", "CORN_FUTURES": "ZC=F", "SOYBEANS": "ZS=F", "SOYBEAN": "ZS=F",
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"CRUDE": "CL=F", "OIL": "CL=F", "WTI": "CL=F", "BRENT": "BZ=F",
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"GOLD": "GC=F", "SILVER": "SI=F", "COPPER": "HG=F",
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"SP500": "^GSPC", "S&P500": "^GSPC", "NASDAQ": "QQQ",
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}
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def _resolve_ticker(ticker: str) -> str:
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"""Return the optionable proxy ticker for a given symbol."""
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t = ticker.upper().strip()
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# Normalize alias names (WHEAT → ZW=F, etc.)
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if t in _TICKER_ALIASES:
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t = _TICKER_ALIASES[t]
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# Normalize slash-format forex (EUR/USD → EURUSD=X) before proxy lookup
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if '/' in t:
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parts = t.split('/')
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@@ -412,8 +424,8 @@ def get_full_iv_snapshot(ticker: str) -> Dict[str, Any]:
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rank_data: Dict[str, Any] = {}
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if iv_current:
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if live_iv:
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# Only persist to history when we have a fresh live IV
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if live_iv and date.today().weekday() < 5:
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# Only persist weekday IV — weekend premium inflates IV and corrupts history
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save_iv_snapshot(proxy, today, iv_current, term.get("iv_30d"), term.get("iv_60d"), term.get("iv_90d"))
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rank_data = get_iv_rank_percentile(proxy, iv_current)
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@@ -498,7 +510,9 @@ def get_iv_context_for_prompt(tickers: List[str]) -> str:
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continue
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from services.database import get_iv_rank_percentile, save_iv_snapshot
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today = date.today().isoformat()
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save_iv_snapshot(proxy, today, iv, None, None, None)
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# Don't save weekend IV — market premium inflates it, would corrupt history
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if date.today().weekday() < 5:
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save_iv_snapshot(proxy, today, iv, None, None, None)
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rank = get_iv_rank_percentile(proxy, iv)
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iv_rank = rank.get("iv_rank")
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@@ -105,13 +105,16 @@ def compute_indicators(ticker: str, horizon_days: int, enabled_indicators: Optio
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lookback = cal["ma_slow"] * 2 + 50
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try:
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df = yf.download(ticker, period=f"{lookback}d", interval="1d", progress=False, auto_adjust=True)
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# yfinance ≥0.2 returns MultiIndex columns when group_by is not set — flatten
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if df is not None and isinstance(df.columns, pd.MultiIndex):
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df.columns = df.columns.droplevel(1)
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except Exception as e:
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return {"error": f"yfinance download failed: {e}"}
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if df is None or len(df) < cal["ma_slow"]:
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return {"error": f"Not enough data for {ticker} (got {len(df) if df is not None else 0} rows)"}
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closes = df["Close"].dropna()
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closes = df["Close"].squeeze().dropna()
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price = float(closes.iloc[-1])
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enabled = set(enabled_indicators) if enabled_indicators else {"rsi", "ma", "bollinger", "atr"}
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