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https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
cbe1c52e00
Rename all source files, scripts, tests, documentation, and configuration from Predix/predix to NexQuant/nexquant across the entire codebase.
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
#!/usr/bin/env python
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"""
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Option B: Evaluate Kronos-mini as a model alongside LightGBM.
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Computes IC (Information Coefficient) and hit rate for Kronos predictions
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vs actual realized returns. Results are printed for comparison with LightGBM.
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Usage:
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conda activate nexquant
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python scripts/kronos_model_eval.py
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python scripts/kronos_model_eval.py --pred 30 --context 512 --device cuda
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"""
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import argparse
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import json
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv(Path(__file__).parent.parent / ".env")
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import torch
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DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
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OUTPUT_DIR = Path("results/kronos")
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def main():
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parser = argparse.ArgumentParser(description="Evaluate Kronos as model (alongside LightGBM)")
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parser.add_argument("--context", type=int, default=512, help="Context window in bars")
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parser.add_argument("--pred", type=int, default=30, help="Prediction horizon in bars")
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parser.add_argument("--stride", type=int, default=None, help="Stride between evaluations (default: pred)")
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parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
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args = parser.parse_args()
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stride = args.stride or args.pred
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print(f"Kronos Model Evaluator (alongside LightGBM)")
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print(f" Context: {args.context} bars | Pred: {args.pred} bars | Device: {args.device}")
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print()
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if not DATA_PATH.exists():
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print(f"ERROR: Data not found at {DATA_PATH}")
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raise SystemExit(1)
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from rdagent.components.coder.kronos_adapter import evaluate_kronos_model
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print("Running evaluation (this may take several minutes)...")
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metrics = evaluate_kronos_model(
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hdf5_path=DATA_PATH,
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context_bars=args.context,
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pred_bars=args.pred,
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stride_bars=stride,
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device=args.device,
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)
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print("\n" + "=" * 50)
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print("Kronos-mini Model Evaluation Results")
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print("=" * 50)
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print(f" Predictions: {metrics['n_predictions']}")
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print(f" IC (mean): {metrics['IC_mean']:.4f}")
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print(f" IC (std): {metrics['IC_std']:.4f}")
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print(f" IC IR: {metrics['IC_IR']:.4f} (>0.5 = good)")
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print(f" Hit Rate: {metrics['hit_rate']:.2%} (>50% = directionally useful)")
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print("=" * 50)
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print()
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print("Reference: LightGBM baseline IC typically 0.01–0.05 on 1-min EUR/USD")
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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out = OUTPUT_DIR / f"kronos_eval_ctx{args.context}_pred{args.pred}.json"
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with open(out, "w") as f:
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json.dump({**metrics, "context_bars": args.context, "pred_bars": args.pred}, f, indent=2)
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print(f"\nResults saved to: {out}")
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if __name__ == "__main__":
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main()
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