feat(signal_v2-phase3b): chronos_predictor + 4 mock tests

ChronosPredictor wraps HuggingFace ChronosPipeline. Batch predict
returns ChronosPrediction(median, q10, q90, conf, as_of) per ticker.
Confidence = 1 - clamp(spread/2, 0, 1) where spread = (q90-q10) / |median|.
Lazy import of chronos lib (heavy). GPU auto-detect with FP16.

44 tests pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

signal_v2/tests/test_chronos_predictor.py

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+"""Tests for ChronosPredictor (model mock)."""
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture
+def mock_pipeline():
+    """Mock ChronosPipeline.from_pretrained returning a mock pipeline object."""
+    with patch("chronos.ChronosPipeline") as cls:
+        instance = MagicMock()
+        cls.from_pretrained.return_value = instance
+        yield instance
+
+
+@pytest.fixture
+def mock_torch_cpu():
+    with patch("torch.cuda.is_available", return_value=False):
+        yield
+
+
+def _daily_ohlcv(close_seq):
+    return [{"datetime": f"2026-05-{i+1:02d}", "open": c, "high": c, "low": c,
+             "close": c, "volume": 1000} for i, c in enumerate(close_seq)]
+
+
+def test_predict_batch_returns_prediction_dict(mock_pipeline, mock_torch_cpu):
+    """mock pipeline → dict[ticker, ChronosPrediction]. last_close=100, samples=102 → ~+2% return."""
+    import torch
+    samples = np.full((100,), 102.0)
+    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+
+    from signal_v2.chronos_predictor import ChronosPredictor, ChronosPrediction
+    predictor = ChronosPredictor(model_name="mock-model")
+    daily = {"005930": _daily_ohlcv([100] * 60)}
+    result = predictor.predict_batch(daily)
+    assert "005930" in result
+    pred = result["005930"]
+    assert isinstance(pred, ChronosPrediction)
+    assert abs(pred.median - 0.02) < 0.001
+
+
+def test_conf_high_when_distribution_narrow(mock_pipeline, mock_torch_cpu):
+    """좁은 distribution → conf ≈ 1."""
+    import torch
+    np.random.seed(42)
+    samples = np.random.normal(102.0, 0.1, 100)
+    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+
+    from signal_v2.chronos_predictor import ChronosPredictor
+    predictor = ChronosPredictor(model_name="mock-model")
+    daily = {"005930": _daily_ohlcv([100] * 60)}
+    result = predictor.predict_batch(daily)
+    assert result["005930"].conf > 0.8
+
+
+def test_conf_low_when_distribution_wide(mock_pipeline, mock_torch_cpu):
+    """넓은 distribution → conf ≈ 0."""
+    import torch
+    np.random.seed(42)
+    samples = np.random.normal(100.0, 30.0, 100)
+    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+
+    from signal_v2.chronos_predictor import ChronosPredictor
+    predictor = ChronosPredictor(model_name="mock-model")
+    daily = {"005930": _daily_ohlcv([100] * 60)}
+    result = predictor.predict_batch(daily)
+    assert result["005930"].conf < 0.3
+
+
+def test_return_computed_from_price_relative_to_last_close(mock_pipeline, mock_torch_cpu):
+    """price 예측 → last_close 대비 return 변환. last_close=100, samples=110 → return ~+10%."""
+    import torch
+    samples = np.full((100,), 110.0)
+    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+
+    from signal_v2.chronos_predictor import ChronosPredictor
+    predictor = ChronosPredictor(model_name="mock-model")
+    # last close in the seq = 100
+    daily = {"005930": _daily_ohlcv(list(range(41, 101)))}  # last = 100
+    result = predictor.predict_batch(daily)
+    assert abs(result["005930"].median - 0.10) < 0.001