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https://github.com/xavierchuan/FX-ML-Trading-Engine.git
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xiaochuan
80 lines
3.6 KiB
Python
80 lines
3.6 KiB
Python
#!/usr/bin/env python3
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"""
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Run multiple metric validators (risk, latency, pnl) in a single command.
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"""
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from __future__ import annotations
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import argparse
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import sys
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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="Watch aggregated risk/ops metrics.")
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parser.add_argument("--csv", default="results/risk/metrics.csv", help="Metrics CSV path.")
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parser.add_argument("--max-rejects", type=int, default=0)
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parser.add_argument("--max-latency-ms", type=float, default=500.0)
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parser.add_argument("--min-pnl", type=float, default=-3000.0)
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parser.add_argument("--max-exposure", type=float, default=2_000_000.0)
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parser.add_argument("--max-drawdown", type=float, default=0.1)
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parser.add_argument("--min-live-sharpe", type=float, default=1.4)
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parser.add_argument("--max-live-drawdown", type=float, default=0.05)
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parser.add_argument("--max-live-latency-ms", type=float, default=500.0)
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parser.add_argument("--max-slippage-bps", type=float, default=2.0)
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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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csv_path = Path(args.csv)
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if not csv_path.exists():
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raise SystemExit(f"metrics CSV not found: {csv_path}")
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df = pd.read_csv(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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rejects = latest.get("rejects", 0)
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status = str(latest.get("status", "unknown")).lower()
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latency = latest.get("latency_ms_avg", 0.0)
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pnl = latest.get("total_pnl", 0.0)
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max_exposure = latest.get("max_symbol_exposure", 0.0)
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drawdown = latest.get("max_drawdown_pct", 0.0)
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live_sharpe = latest.get("rolling_sharpe_30d", float("nan"))
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live_drawdown = latest.get("live_drawdown_pct", float("nan"))
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live_latency = latest.get("live_latency_ms_p95", float("nan"))
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slippage_bps = latest.get("slippage_bps", float("nan"))
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errors = []
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if rejects > args.max_rejects or status != "pass":
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errors.append(f"Rejects/status violation (rejects={rejects}, status={status})")
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if latency > args.max_latency_ms:
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errors.append(f"Latency {latency:.1f}ms > threshold {args.max_latency_ms}")
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if pnl < args.min_pnl:
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errors.append(f"Total PnL {pnl:.2f} < min {args.min_pnl}")
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if max_exposure > args.max_exposure:
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errors.append(f"Exposure {max_exposure:.2f} > max {args.max_exposure}")
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if drawdown > args.max_drawdown:
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errors.append(f"Drawdown {drawdown:.3f} > max {args.max_drawdown}")
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if not pd.isna(live_sharpe) and live_sharpe < args.min_live_sharpe:
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errors.append(f"Live Sharpe {live_sharpe:.2f} < min {args.min_live_sharpe}")
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if not pd.isna(live_drawdown) and live_drawdown > args.max_live_drawdown:
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errors.append(f"Live drawdown {live_drawdown:.3f} > max {args.max_live_drawdown}")
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if not pd.isna(live_latency) and live_latency > args.max_live_latency_ms:
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errors.append(f"Live latency p95 {live_latency:.1f}ms > max {args.max_live_latency_ms}")
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if not pd.isna(slippage_bps) and slippage_bps > args.max_slippage_bps:
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errors.append(f"Slippage {slippage_bps:.2f}bps > max {args.max_slippage_bps}")
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print(
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f"[watch_ops_metrics] run={latest.get('run_id')} status={status} "
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f"rejects={rejects} latency_avg={latency} pnl={pnl} exposure={max_exposure} drawdown={drawdown} "
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f"live_sharpe={live_sharpe} live_drawdown={live_drawdown} live_latency_p95={live_latency} slippage_bps={slippage_bps}"
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)
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if errors:
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raise SystemExit("; ".join(errors))
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if __name__ == "__main__":
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main()
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