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QuantTrader/execution/metrics_logger.py

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+"""Append execution metrics to CSV for monitoring."""
+
+from __future__ import annotations
+
+import csv
+import os
+from dataclasses import asdict, dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+
+
+@dataclass
+class ExecutionMetric:
+    event: str
+    order_id: str
+    symbol: str
+    latency_ms: Optional[float]
+    status: str
+    timestamp: str
+
+
+class MetricsLogger:
+    def __init__(self, path: Optional[str] = None):
+        metrics_path = path or os.environ.get("EXECUTION_METRICS_PATH", "metrics/execution.csv")
+        self.path = Path(metrics_path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        if not self.path.exists():
+            with self.path.open("w", newline="", encoding="utf-8") as fh:
+                writer = csv.DictWriter(fh, fieldnames=list(ExecutionMetric.__annotations__.keys()))
+                writer.writeheader()
+
+    def log(self, metric: ExecutionMetric) -> None:
+        with self.path.open("a", newline="", encoding="utf-8") as fh:
+            writer = csv.DictWriter(fh, fieldnames=list(ExecutionMetric.__annotations__.keys()))
+            writer.writerow(asdict(metric))
+
+
+def log_event(event: str, order_id: str, symbol: str, status: str, start_ts: datetime, end_ts: datetime) -> None:
+    latency_ms = (end_ts - start_ts).total_seconds() * 1000.0
+    logger = MetricsLogger()
+    logger.log(
+        ExecutionMetric(
+            event=event,
+            order_id=order_id,
+            symbol=symbol,
+            latency_ms=latency_ms,
+            status=status,
+            timestamp=end_ts.isoformat(),
+        )
+    )