fix: sim portfolio asset_class fallback + consolidate risk into Risk Dashboard

- portfolio_risk.py: add _infer_asset_class() with ticker→asset_class map
  covering energy/metals/agri/indices/forex/rates futures, ETFs, forex pairs,
  exchange prefixes (NSE:). Fallback applied when JOIN finds no match (orphaned
  pattern_id after re-seed). Fixes "unknown 100%" shown in screenshot.

- RiskDashboard.tsx: add Portefeuille Réel / Simulé toggle at top.
  New SimRiskPanel component with KPI row + concentration bars + conflict cards
  + AI recommendations — all visible inline in Risk Dashboard.
  Red badge on Simulé tab when danger alerts exist.

- JournalDeBord.tsx: remove standalone Risque Sim. tab (moved to Risk Dashboard).
  Replace with a red banner in summary cards when conflicts are detected,
  pointing user to Risk Dashboard → Simulé.

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

backend/services/portfolio_risk.py

@@ -5,6 +5,55 @@ from services.database import get_conn
 
 _BEARISH_KEYWORDS = {"bear", "put", "short", "sell", "vente", "baissier"}
 
+# Ticker → asset_class mapping for common instruments (fallback when pattern not in DB)
+_TICKER_AC: Dict[str, str] = {
+    # Energy
+    "CL=F": "energy", "BZ=F": "energy", "NG=F": "energy", "RB=F": "energy",
+    "HO=F": "energy", "USO": "energy", "XLE": "energy", "XOP": "energy", "OIL": "energy",
+    # Metals
+    "GC=F": "metals", "SI=F": "metals", "HG=F": "metals", "PL=F": "metals",
+    "PA=F": "metals", "GLD": "metals", "SLV": "metals", "GDX": "metals", "GDXJ": "metals",
+    # Agriculture
+    "ZW=F": "agriculture", "ZC=F": "agriculture", "ZS=F": "agriculture",
+    "CT=F": "agriculture", "KC=F": "agriculture", "SB=F": "agriculture", "CC=F": "agriculture",
+    "WEAT": "agriculture", "CORN": "agriculture", "SOYB": "agriculture",
+    # Equity indices
+    "^GSPC": "indices", "^DJI": "indices", "^NDX": "indices", "^RUT": "indices",
+    "^VIX": "indices", "^FTSE": "indices", "^GDAXI": "indices", "^FCHI": "indices",
+    "^N225": "indices", "^HSI": "indices", "^NSEI": "indices", "^BSESN": "indices",
+    "^STOXX50E": "indices", "^IBEX": "indices",
+    "SPY": "indices", "QQQ": "indices", "IWM": "indices", "DIA": "indices",
+    "VXX": "indices", "UVXY": "indices", "SVXY": "indices",
+    # Forex
+    "EURUSD=X": "forex", "GBPUSD=X": "forex", "USDJPY=X": "forex",
+    "AUDUSD=X": "forex", "USDCAD=X": "forex", "USDCHF=X": "forex",
+    "NZDUSD=X": "forex", "EURGBP=X": "forex", "EURJPY=X": "forex",
+    "GBPJPY=X": "forex", "USDCNH=X": "forex",
+    "FXE": "forex", "UUP": "forex", "FXB": "forex", "FXY": "forex",
+    # Rates
+    "ZB=F": "rates", "ZN=F": "rates", "ZF=F": "rates", "ZT=F": "rates",
+    "TLT": "rates", "IEF": "rates", "SHY": "rates", "HYG": "rates",
+    "LQD": "rates", "EMB": "rates",
+}
+
+
+def _infer_asset_class(ticker: str) -> str:
+    """Derive asset_class from ticker when not stored in DB."""
+    t = (ticker or "").upper().strip()
+    if t in _TICKER_AC:
+        return _TICKER_AC[t]
+    if ":" in t:  # NSE:RELIANCE, BSE:TCS, etc.
+        return "equities"
+    if t.endswith("=X") and len(t) >= 7:  # forex pairs like EURUSD=X
+        return "forex"
+    if t.endswith("=F"):  # generic futures
+        return "energy"  # most unknown futures are commodities
+    if t.startswith("^"):  # index
+        return "indices"
+    if t.isalpha() and len(t) <= 5:  # short alpha = equity
+        return "equities"
+    return "unknown"
+
 
 def _direction(strategy: str) -> str:
     s = (strategy or "").lower()
@@ -12,7 +61,7 @@ def _direction(strategy: str) -> str:
 
 
 def get_open_simulation_trades() -> List[Dict[str, Any]]:
-    """Open trades enriched with asset_class from joined pattern table."""
+    """Open trades enriched with asset_class — via stored column, JOIN fallback, then ticker inference."""
     conn = get_conn()
     rows = conn.execute("""
         SELECT tep.*,
@@ -23,7 +72,13 @@ def get_open_simulation_trades() -> List[Dict[str, Any]]:
         ORDER BY tep.entry_date DESC
     """).fetchall()
     conn.close()
-    return [dict(r) for r in rows]
+    result = []
+    for r in rows:
+        d = dict(r)
+        if not d.get("asset_class"):
+            d["asset_class"] = _infer_asset_class(d.get("underlying", ""))
+        result.append(d)
+    return result
 
 
 def analyze_simulation_portfolio() -> Dict[str, Any]: