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