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>

scripts/kronos_model_eval.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+"""
+Option B: Evaluate Kronos-mini as a model alongside LightGBM.
+
+Computes IC (Information Coefficient) and hit rate for Kronos predictions
+vs actual realized returns. Results are printed for comparison with LightGBM.
+
+Usage:
+    conda activate predix
+    python scripts/kronos_model_eval.py
+    python scripts/kronos_model_eval.py --pred 30 --context 512 --device cuda
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+load_dotenv(Path(__file__).parent.parent / ".env")
+
+import torch
+
+DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
+OUTPUT_DIR = Path("results/kronos")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Evaluate Kronos as model (alongside LightGBM)")
+    parser.add_argument("--context", type=int, default=512, help="Context window in bars")
+    parser.add_argument("--pred", type=int, default=30, help="Prediction horizon in bars")
+    parser.add_argument("--stride", type=int, default=None, help="Stride between evaluations (default: pred)")
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
+    args = parser.parse_args()
+
+    stride = args.stride or args.pred
+
+    print(f"Kronos Model Evaluator (alongside LightGBM)")
+    print(f"  Context: {args.context} bars  |  Pred: {args.pred} bars  |  Device: {args.device}")
+    print()
+
+    if not DATA_PATH.exists():
+        print(f"ERROR: Data not found at {DATA_PATH}")
+        raise SystemExit(1)
+
+    from rdagent.components.coder.kronos_adapter import evaluate_kronos_model
+
+    print("Running evaluation (this may take several minutes)...")
+    metrics = evaluate_kronos_model(
+        hdf5_path=DATA_PATH,
+        context_bars=args.context,
+        pred_bars=args.pred,
+        stride_bars=stride,
+        device=args.device,
+    )
+
+    print("\n" + "=" * 50)
+    print("Kronos-mini Model Evaluation Results")
+    print("=" * 50)
+    print(f"  Predictions:  {metrics['n_predictions']}")
+    print(f"  IC (mean):    {metrics['IC_mean']:.4f}")
+    print(f"  IC (std):     {metrics['IC_std']:.4f}")
+    print(f"  IC IR:        {metrics['IC_IR']:.4f}  (>0.5 = good)")
+    print(f"  Hit Rate:     {metrics['hit_rate']:.2%}  (>50% = directionally useful)")
+    print("=" * 50)
+    print()
+    print("Reference: LightGBM baseline IC typically 0.01–0.05 on 1-min EUR/USD")
+
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    out = OUTPUT_DIR / f"kronos_eval_ctx{args.context}_pred{args.pred}.json"
+    with open(out, "w") as f:
+        json.dump({**metrics, "context_bars": args.context, "pred_bars": args.pred}, f, indent=2)
+    print(f"\nResults saved to: {out}")
+
+
+if __name__ == "__main__":
+    main()