Initial commit: Gold Trading Simulator with AI-powered analysis

backend/app/services/decisions.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List
+from datetime import datetime, timezone
+import threading
+
+
+class DecisionStore:
+    def __init__(self) -> None:
+        self._lock = threading.Lock()
+        self._items: List[Dict[str, Any]] = []
+
+    def add(self, item: Dict[str, Any]) -> None:
+        with self._lock:
+            self._items.append(item)
+            if len(self._items) > 1000:
+                # keep last 1000
+                self._items = self._items[-1000:]
+
+    def latest(self, limit: int = 50) -> List[Dict[str, Any]]:
+        with self._lock:
+            return list(reversed(self._items[-limit:]))
+
+
+# singleton store
+store = DecisionStore()
+
+
+def log_decision(
+    *,
+    symbol: str,
+    timeframe: str,
+    style: str,
+    recommendation: str,
+    confidence: float,
+    risk_level: str,
+    rationale: str,
+    inputs_hash: str | None = None,
+    cost: Dict[str, Any] | None = None,
+) -> Dict[str, Any]:
+    now = datetime.now(timezone.utc).isoformat()
+    item = {
+        "id": f"dec_{int(datetime.now(timezone.utc).timestamp()*1000)}",
+        "time": now,
+        "symbol": symbol,
+        "timeframe": timeframe,
+        "style": style,
+        "recommendation": recommendation,
+        "confidence": confidence,
+        "risk_level": risk_level,
+        "rationale": rationale,
+        "inputs_hash": inputs_hash,
+        "cost": cost or {},
+    }
+    store.add(item)
+    return item