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Q Research/scripts/export_metrics_prom.py

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+#!/usr/bin/env python3
+"""
+Export latest risk metrics row in Prometheus exposition format.
+
+Usage:
+  python scripts/export_metrics_prom.py --csv results/risk/metrics.csv --job risk_sim
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import pandas as pd
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Export risk metrics to Prometheus format.")
+    parser.add_argument("--csv", default="results/risk/metrics.csv")
+    parser.add_argument("--job", default="risk_sim")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    path = Path(args.csv)
+    if not path.exists():
+        raise SystemExit(f"metrics CSV not found: {path}")
+    df = pd.read_csv(path)
+    if df.empty:
+        raise SystemExit("metrics CSV is empty.")
+    latest = df.tail(1).iloc[0]
+    run_id = latest.get("run_id", "unknown")
+    def value(key: str, default: float = 0.0) -> float:
+        val = latest.get(key, default)
+        if isinstance(val, str) and not val:
+            return default
+        try:
+            if pd.isna(val):
+                return default
+        except TypeError:
+            pass
+        return val
+
+    metrics = {
+        "risk_rejects": value("rejects", 0),
+        "risk_kills": value("kills", 0),
+        "risk_latency_ms_avg": value("latency_ms_avg"),
+        "risk_latency_ms_p95": value("latency_ms_p95"),
+        "risk_total_pnl": value("total_pnl"),
+        "risk_max_gross_notional": value("max_gross_notional"),
+        "risk_max_symbol_exposure": value("max_symbol_exposure"),
+        "risk_max_drawdown_pct": value("max_drawdown_pct"),
+        "risk_live_sharpe_30d": value("rolling_sharpe_30d"),
+        "risk_live_drawdown_pct": value("live_drawdown_pct"),
+        "risk_live_latency_ms_p95": value("live_latency_ms_p95"),
+        "risk_slippage_bps": value("slippage_bps"),
+    }
+    labels = f'run="{run_id}",job="{args.job}"'
+    for name, value in metrics.items():
+        print(f'{name}{{{labels}}} {value}')
+
+
+if __name__ == "__main__":
+    main()