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>

backend/services/technical_indicators.py

@@ -105,13 +105,16 @@ def compute_indicators(ticker: str, horizon_days: int, enabled_indicators: Optio
     lookback = cal["ma_slow"] * 2 + 50
     try:
         df = yf.download(ticker, period=f"{lookback}d", interval="1d", progress=False, auto_adjust=True)
+        # yfinance ≥0.2 returns MultiIndex columns when group_by is not set — flatten
+        if df is not None and isinstance(df.columns, pd.MultiIndex):
+            df.columns = df.columns.droplevel(1)
     except Exception as e:
         return {"error": f"yfinance download failed: {e}"}
 
     if df is None or len(df) < cal["ma_slow"]:
         return {"error": f"Not enough data for {ticker} (got {len(df) if df is not None else 0} rows)"}
 
-    closes = df["Close"].dropna()
+    closes = df["Close"].squeeze().dropna()
     price = float(closes.iloc[-1])
     enabled = set(enabled_indicators) if enabled_indicators else {"rsi", "ma", "bollinger", "atr"}