Maximize Qdrant utilization: richer memory payloads, filtered retrieval, and qdrant health in metrics API

bot.py

@@ -125,9 +125,11 @@ def run_cycle():
 
                 # learning memory + notes + orchestration signal
                 qdrant_add_memory(symbol, f"{symbol} {decision['action']} conf={decision['confidence']} reason={decision['reason']}", {
+                    "memory_type": "execution",
                     "status": drow.status,
                     "action": decision["action"],
                     "confidence": decision["confidence"],
+                    "outcome_score": 1 if ok else -1,
                     "ts": datetime.utcnow().isoformat(),
                 })
                 trilium_log(
@@ -145,9 +147,11 @@ def run_cycle():
 
             # store non-trade decisions too for memory
             qdrant_add_memory(symbol, f"{symbol} decision={decision['action']} conf={decision['confidence']} status={drow.status}", {
+                "memory_type": "decision",
                 "status": drow.status,
                 "action": decision["action"],
                 "confidence": decision["confidence"],
+                "outcome_score": 0 if drow.status in {"skipped", "risk_blocked"} else (-1 if drow.status=="failed" else 1),
                 "ts": datetime.utcnow().isoformat(),
             })