feat: Specialist Desks v2 — COT, Forward Curves, Surprise Index, Hawk/Dove scorer

- COT Positioning: CFTC disaggregated + financial futures (19 markets) via Socrata free API
  net MM position % OI + weekly change stored in cot_data table
- Forward Curves: yfinance front-month vs +3M slope (8 commodities)
  contango/backwardation/flat stored in forward_curve_data table
- Surprise Index: consensus_estimate + actual_value on specialist_reports
  auto-computes surprise_score = actual - consensus on save
- Hawk/Dove Text Scorer: GPT-4o-mini endpoint for CB statements
  score -1..+1, label, summary, key_phrases (forex/bonds: hawk/dove; commodities: bull/bear)
- AI context injection: COT net positioning, forward curve structure,
  surprise scores, upcoming consensus estimates injected into all desk blocks
- Frontend: COT panel (net% bars), Forward Curves panel, SurpriseInput
  on report cards, Hawk/Dove scorer in forex/bonds config tab
- auto_cycle.py: non-blocking COT + curve refresh before each cycle

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/services/forward_curve.py

@@ -0,0 +1,95 @@
+"""
+Forward Curve Service — contango / backwardation per commodity.
+Uses yfinance to compare front-month vs near-dated futures contracts.
+"""
+import logging
+from datetime import datetime, timedelta
+from typing import List, Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+# Month codes for futures tickers (standard CME/NYMEX/CBOT convention)
+_MONTH_CODES = {1:'F',2:'G',3:'H',4:'J',5:'K',6:'M',7:'N',8:'Q',9:'U',10:'V',11:'X',12:'Z'}
+
+# (base_ticker, label, asset_class, months_spread)
+_CURVE_SPECS = [
+    ("CL",  "WTI Crude",    "energy",  3),
+    ("NG",  "Natural Gas",  "energy",  3),
+    ("GC",  "Gold",         "metals",  3),
+    ("SI",  "Silver",       "metals",  3),
+    ("HG",  "Copper",       "metals",  3),
+    ("ZC",  "Corn",         "agri",    3),
+    ("ZW",  "Wheat",        "agri",    3),
+    ("ZS",  "Soybeans",     "agri",    3),
+]
+
+
+def _ticker_for_month(base: str, months_ahead: int) -> str:
+    target = datetime.now() + timedelta(days=months_ahead * 30)
+    code = _MONTH_CODES[target.month]
+    year = str(target.year)[-2:]
+    return f"{base}{code}{year}"
+
+
+def _get_price(ticker: str) -> Optional[float]:
+    try:
+        import yfinance as yf
+        data = yf.download(ticker, period="3d", progress=False, auto_adjust=True)
+        if data.empty:
+            return None
+        close = data["Close"].dropna()
+        if close.empty:
+            return None
+        val = float(close.iloc[-1])
+        # Handle multi-index DataFrames
+        if hasattr(val, '__iter__'):
+            val = list(val)[0]
+        return round(val, 4) if val and val > 0 else None
+    except Exception:
+        return None
+
+
+def fetch_forward_curves() -> List[Dict[str, Any]]:
+    """Fetch front-month and far-month prices to compute curve slope."""
+    results = []
+    for base, label, ac, spread in _CURVE_SPECS:
+        front_ticker = f"{base}=F"
+        front_price = _get_price(front_ticker)
+        if not front_price:
+            logger.warning(f"Forward curve: no front-month price for {label} ({front_ticker})")
+            continue
+
+        # Try M+spread, M+spread-1, M+spread+1 (some contract months are illiquid)
+        far_price = None
+        for offset in [0, -1, 1, -2, 2]:
+            far_ticker = _ticker_for_month(base, spread + offset)
+            far_price = _get_price(far_ticker)
+            if far_price:
+                break
+
+        slope_pct = None
+        structure = "unknown"
+        if far_price:
+            slope_pct = round((far_price - front_price) / front_price * 100, 2)
+            if slope_pct > 0.15:
+                structure = "contango"
+            elif slope_pct < -0.15:
+                structure = "backwardation"
+            else:
+                structure = "flat"
+
+        results.append({
+            "asset": label,
+            "asset_class": ac,
+            "front_price": round(front_price, 2),
+            "far_price": round(far_price, 2) if far_price else None,
+            "slope_pct": slope_pct,
+            "structure": structure,
+            "months_spread": spread,
+        })
+        logger.info(
+            f"Forward curve {label}: front={front_price:.2f} "
+            f"far={far_price} slope={slope_pct}% -> {structure}"
+        )
+
+    return results