mirror of
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.
96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
#!/usr/bin/env python
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"""
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Option A: Generate Kronos predicted-return factor from EUR/USD 1-min data.
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Runs Kronos-mini inference in daily strides (96 bars/day) over all available
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OHLCV data and saves the resulting factor for use in NexQuant's factor pipeline.
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Usage:
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conda activate nexquant
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python scripts/kronos_factor_gen.py
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python scripts/kronos_factor_gen.py --context 512 --pred 96 --device cuda
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python scripts/kronos_factor_gen.py --device cpu # slower but no GPU needed
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"""
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import argparse
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import json
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from datetime import datetime
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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 pandas as pd
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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/factors")
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def main():
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parser = argparse.ArgumentParser(description="Generate Kronos predicted-return factor")
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parser.add_argument("--context", type=int, default=512, help="Context window in bars (max 512 for Kronos-mini)")
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parser.add_argument("--pred", type=int, default=96, help="Prediction horizon in bars (default: 96 = 1 trading day)")
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parser.add_argument("--stride", type=int, default=None, help="Stride between windows (default: same as --pred)")
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parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
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parser.add_argument("--output", type=str, default=None, help="Output parquet path (default: auto)")
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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 Factor Generator")
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print(f" Data: {DATA_PATH}")
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print(f" Context: {args.context} bars")
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print(f" Pred: {args.pred} bars ({args.pred} min = {args.pred/96:.1f} trading days)")
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print(f" Stride: {stride} bars")
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print(f" 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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print("Run data conversion first — see README Data Setup section.")
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raise SystemExit(1)
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from rdagent.components.coder.kronos_adapter import build_kronos_factor
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factor_df = build_kronos_factor(
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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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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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out_path = args.output or OUTPUT_DIR / f"kronos_pred_return_p{args.pred}.parquet"
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factor_df.to_parquet(out_path)
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print(f"\nFactor saved to: {out_path}")
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print(f"Shape: {factor_df.shape}")
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print(f"Non-NaN: {factor_df['KronosPredReturn'].notna().sum()}")
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print(f"\nSample (first 5):")
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print(factor_df.head())
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# Save metadata for nexquant.py top / best integration
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meta = {
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"factor_name": f"KronosPredReturn_p{args.pred}",
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"description": f"Kronos-mini predicted return, {args.pred}-bar horizon",
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"model": "NeoQuasar/Kronos-mini",
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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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"generated_at": datetime.now().isoformat(),
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"n_bars": len(factor_df),
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"n_non_nan": int(factor_df["KronosPredReturn"].notna().sum()),
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"parquet_path": str(out_path),
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}
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meta_path = out_path.with_suffix(".json")
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with open(meta_path, "w") as f:
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json.dump(meta, f, indent=2)
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print(f"Metadata saved to: {meta_path}")
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if __name__ == "__main__":
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main()
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