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feat: integrate Kronos-mini OHLCV foundation model (Option A + B)
Add Kronos-mini (4.1M params, AAAI 2026, MIT) as: - Option A: predicted-return alpha factor via rolling daily inference (kronos_factor_gen.py — stride=96 bars/day, ~2k inference calls) - Option B: standalone model evaluator alongside LightGBM (kronos_model_eval.py — IC / hit-rate vs actual realized returns) KronosAdapter wraps NeoQuasar/Kronos-mini + Kronos-Tokenizer-2k, auto-detects GPU, gracefully degrades if ~/Kronos repo is missing. Factor output: MultiIndex (datetime, instrument) with KronosPredReturn. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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TPTBusiness
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#!/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 Predix's factor pipeline.
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Usage:
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conda activate predix
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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 predix.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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#!/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 predix
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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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