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>

test/backtesting/test_kronos_adapter.py

@@ -0,0 +1,102 @@
+"""Tests for KronosAdapter — mock-based, no real model download needed."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+
+def _make_ohlcv(n: int = 600) -> pd.DataFrame:
+    """Synthetic 1-min OHLCV DataFrame."""
+    idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+    close = 1.1000 + np.cumsum(np.random.randn(n) * 0.0001)
+    df = pd.DataFrame({
+        "open": close + np.random.randn(n) * 0.00005,
+        "high": close + np.abs(np.random.randn(n) * 0.0001),
+        "low": close - np.abs(np.random.randn(n) * 0.0001),
+        "close": close,
+        "volume": np.abs(np.random.randn(n) * 100),
+    }, index=idx)
+    return df
+
+
+def test_ohlcv_conversion():
+    """_ohlcv_from_predix renames $ columns correctly."""
+    from rdagent.components.coder.kronos_adapter import _ohlcv_from_predix
+
+    idx = pd.MultiIndex.from_arrays(
+        [pd.date_range("2024-01-01", periods=3, freq="1min"), ["EURUSD"] * 3],
+        names=["datetime", "instrument"],
+    )
+    predix_df = pd.DataFrame({
+        "$open": [1.1, 1.2, 1.3],
+        "$high": [1.15, 1.25, 1.35],
+        "$low": [1.05, 1.15, 1.25],
+        "$close": [1.12, 1.22, 1.32],
+        "$volume": [100.0, 200.0, 300.0],
+    }, index=idx)
+
+    ohlcv = _ohlcv_from_predix(predix_df)
+    assert "close" in ohlcv.columns
+    assert "$close" not in ohlcv.columns
+    assert list(ohlcv.columns) == ["open", "high", "low", "close", "volume"]
+
+
+def test_kronos_adapter_load_skipped_without_repo(tmp_path, monkeypatch):
+    """KronosAdapter gracefully reports unavailable when repo is missing."""
+    import rdagent.components.coder.kronos_adapter as mod
+    monkeypatch.setattr(mod, "KRONOS_REPO", tmp_path / "nonexistent")
+    monkeypatch.setattr(mod, "_KRONOS_AVAILABLE", None)
+
+    from rdagent.components.coder.kronos_adapter import KronosAdapter, _ensure_kronos
+    available = _ensure_kronos()
+    assert available is False
+
+
+def test_build_kronos_factor_mock(tmp_path, monkeypatch):
+    """build_kronos_factor produces correct MultiIndex output with mocked predictor."""
+    import rdagent.components.coder.kronos_adapter as mod
+
+    # Mock the adapter so no real Kronos load happens
+    class MockAdapter:
+        def load(self): return self
+        def predict_next_bars(self, ohlcv_df, context_bars, pred_bars, **kw):
+            idx = pd.date_range(ohlcv_df.index[-1], periods=pred_bars + 1, freq="1min")[1:]
+            last_close = float(ohlcv_df["close"].iloc[-1])
+            return pd.DataFrame({
+                "open": last_close * (1 + np.random.randn(pred_bars) * 0.001),
+                "close": last_close * (1 + np.random.randn(pred_bars) * 0.001),
+                "high": last_close * 1.001,
+                "low": last_close * 0.999,
+                "volume": 100.0,
+            }, index=idx)
+
+    monkeypatch.setattr(mod, "KronosAdapter", lambda **kw: MockAdapter())
+
+    # Write minimal HDF5
+    n = 300
+    idx = pd.MultiIndex.from_arrays(
+        [pd.date_range("2024-01-01", periods=n, freq="1min"), ["EURUSD"] * n],
+        names=["datetime", "instrument"],
+    )
+    df = pd.DataFrame({
+        "$open": np.random.rand(n).astype("float32") + 1.1,
+        "$close": np.random.rand(n).astype("float32") + 1.1,
+        "$high": np.random.rand(n).astype("float32") + 1.11,
+        "$low": np.random.rand(n).astype("float32") + 1.09,
+        "$volume": np.random.rand(n).astype("float32") * 100,
+    }, index=idx)
+    h5_path = tmp_path / "intraday_pv.h5"
+    df.to_hdf(h5_path, key="data", mode="w")
+
+    result = mod.build_kronos_factor(
+        hdf5_path=h5_path,
+        context_bars=100,
+        pred_bars=20,
+        stride_bars=20,
+        device="cpu",
+    )
+
+    assert isinstance(result, pd.DataFrame)
+    assert result.index.names == ["datetime", "instrument"]
+    assert "KronosPredReturn" in result.columns
+    assert result["KronosPredReturn"].notna().sum() > 0