feat: risk

backend/services/database.py

@@ -1681,6 +1681,10 @@ def add_position(pos: Dict[str, Any]) -> str:
     legs = pos.get("legs", [])
     num_contracts = sum(abs(leg.get("quantity", 1)) for leg in legs)
     ib_fees = compute_ib_fees(num_contracts)
+    # Normalize/infer at write time too (defense in depth) so the stored value is already
+    # canonical for any future reader that doesn't go through get_portfolio_exposure()'s
+    # own normalization — an empty/malformed asset_class no longer needs a later backfill.
+    asset_class = _normalize_asset_class(pos.get("asset_class") or "", pos.get("underlying") or "") or "autre"
 
     conn = get_conn()
     conn.execute("""INSERT INTO portfolio (
@@ -1692,7 +1696,7 @@ def add_position(pos: Dict[str, Any]) -> str:
         pos.get("title", pos.get("underlying", "")),
         pos.get("underlying", ""),
         pos.get("strategy", ""),
-        pos.get("asset_class", ""),
+        asset_class,
         pos.get("entry_date", datetime.utcnow().isoformat()[:10]),
         pos.get("expiry_date", ""),
         pos.get("expiry_days", 90),
@@ -2176,6 +2180,7 @@ _TICKER_ASSET_CLASS: dict = {
     # Energy
     "CL=F": "energy", "BZ=F": "energy", "NG=F": "energy", "RB=F": "energy", "HO=F": "energy",
     "XLE": "energy", "XOP": "energy", "USO": "energy", "UCO": "energy", "BOIL": "energy", "UNG": "energy",
+    "BNO": "energy",  # Brent ETF, used as the Options Lab proxy for the BRENT Watchlist entry
     # Metals
     "GC=F": "metals", "SI=F": "metals", "HG=F": "metals", "PA=F": "metals", "PL=F": "metals",
     "GLD": "metals", "IAU": "metals", "SLV": "metals", "GDX": "metals", "GDXJ": "metals",
@@ -2197,6 +2202,15 @@ _TICKER_ASSET_CLASS: dict = {
     # Forex
     "DX-Y.NYB": "forex", "UUP": "forex", "FXE": "forex", "FXY": "forex",
     "EUO": "forex", "YCS": "forex", "FXA": "forex", "FXB": "forex", "FXF": "forex",
+    # Rates & bonds — previously unmapped, so a position on any of these silently fell
+    # through to "autre" even though _RISK_FACTOR_MAP already has a "rates" bucket
+    # (récession/liquidité) that nothing could ever reach.
+    "ZB=F": "rates", "ZN=F": "rates", "ZF=F": "rates", "ZT=F": "rates",
+    "TLT": "rates", "IEF": "rates", "SHY": "rates", "HYG": "rates", "LQD": "rates", "EMB": "rates",
+    # Crypto — also previously unmapped; folded into "liquidité" in _RISK_FACTOR_MAP since
+    # crypto behaves as a risk-appetite/liquidity barometer rather than its own macro factor.
+    "BTC-USD": "crypto", "ETH-USD": "crypto", "BTC=F": "crypto", "ETH=F": "crypto",
+    "GBTC": "crypto", "IBIT": "crypto", "COIN": "crypto",
 }
 
 
@@ -2216,9 +2230,14 @@ def _asset_class_from_ticker(ticker: str) -> str:
             return "metals"
         if stem[:2] in ("ZC", "ZS", "ZW", "CC", "KC", "CT"):
             return "agriculture"
-    # Currency pairs
+        if stem[:2] in ("ZB", "ZN", "ZF", "ZT"):
+            return "rates"
+    # Currency pairs (this app's forex tickers always use the =X suffix, e.g. EURUSD=X)
     if "=X" in t or "/" in t:
         return "forex"
+    # Crypto tickers use the yfinance dash convention instead (BTC-USD, ETH-USD)
+    if t.endswith("-USD"):
+        return "crypto"
     return ""
 
 
@@ -2238,9 +2257,14 @@ def _normalize_asset_class(cls: str, ticker: str = "") -> str:
             return "equities"
         if any(k in c for k in ("forex", "currency", "fx", "devise", "change", "eur", "usd", "jpy", "dxy")):
             return "forex"
-        # Already a canonical value
-        if c in ("energy", "metals", "agriculture", "indices", "equities", "forex"):
-            return c
+        if any(k in c for k in ("rate", "bond", "treasury", "yield", "duration", "taux", "obligation")):
+            return "rates"
+        if any(k in c for k in ("crypto", "bitcoin", "btc", "ethereum", "eth", "token")):
+            return "crypto"
+        # Already a canonical value — includes asset_class_configs' "agri"/"bonds" desk keys
+        # as synonyms, since that registry uses a slightly different vocabulary than this one.
+        if c in ("energy", "metals", "agriculture", "agri", "indices", "equities", "forex", "rates", "bonds", "crypto"):
+            return {"agri": "agriculture", "bonds": "rates"}.get(c, c)
     # Fallback: infer from ticker
     return _asset_class_from_ticker(ticker)
 
@@ -4396,7 +4420,9 @@ _RISK_FACTOR_MAP = {
         "triggers": {"financial_crisis", "elections"},
     },
     "liquidité": {
-        "asset_classes": {"indices", "equities", "rates"},
+        # Crypto folded in here rather than given its own factor — it behaves as a
+        # risk-appetite/liquidity barometer more than a distinct macro theme.
+        "asset_classes": {"indices", "equities", "rates", "crypto"},
         "triggers": {"financial_crisis", "health_crisis"},
     },
     "dollar": {
@@ -4406,9 +4432,14 @@ _RISK_FACTOR_MAP = {
 }
 
 
-def _classify_risk_factors(asset_class: str, triggers: List[str]) -> List[str]:
-    """Return list of risk factors for a trade given its asset_class and pattern triggers."""
-    ac = (asset_class or "").lower()
+def _classify_risk_factors(asset_class: str, triggers: List[str], ticker: str = "") -> List[str]:
+    """Return list of risk factors for a trade given its asset_class and pattern triggers.
+    Routes asset_class through _normalize_asset_class() first (with a ticker fallback) so a
+    missing/malformed/differently-spelled value (e.g. an empty field, or a desk-registry
+    "agri"/"bonds" key instead of this map's "agriculture"/"rates") still resolves to a real
+    factor instead of silently falling through to "autre" — see _RISK_FACTOR_MAP's asset
+    classes above for the exhaustive set every recognized asset_class should now map into."""
+    ac = _normalize_asset_class(asset_class, ticker) or (asset_class or "").lower()
     trg_set = {t.lower() for t in (triggers or [])}
     factors = []
     for factor, cfg in _RISK_FACTOR_MAP.items():
@@ -4436,7 +4467,10 @@ def get_portfolio_exposure() -> Dict:
 
     for row in trades:
         t = dict(row)
-        ac = (t.get("asset_class") or "autre").lower()
+        # Normalized first (handles empty/malformed/differently-spelled values via the
+        # position's own ticker) — only a genuinely uncategorizable position falls to
+        # "autre" now, instead of any position whose asset_class field happened to be blank.
+        ac = _normalize_asset_class(t.get("asset_class") or "", t.get("underlying") or "") or "autre"
         cap = float(t.get("capital_invested") or 0)
         total_capital += cap
 
@@ -4460,7 +4494,7 @@ def get_portfolio_exposure() -> Dict:
         except Exception:
             pass
 
-        factors = _classify_risk_factors(ac, triggers)
+        factors = _classify_risk_factors(ac, triggers, t.get("underlying") or "")
         for f in factors:
             if f not in by_factor:
                 by_factor[f] = {"capital": 0.0, "trade_count": 0, "trades": []}
@@ -4702,7 +4736,7 @@ def compute_kelly_sizing(
     kelly_frac = kelly_full * fractional
 
     # Risk cluster adjustment: halve if saturated
-    ac = (p.get("asset_class") or "").lower()
+    ac = _normalize_asset_class(p.get("asset_class") or "") or ""
     triggers_raw = p.get("triggers") or "[]"
     try:
         triggers_list = json.loads(triggers_raw) if isinstance(triggers_raw, str) else triggers_raw