feat: implement ML filter with LightGBM for trading signal validation

- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.

Made-with: Cursor

tests/test_ml_features.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import numpy as np
+import pytest
+from src.ml_features import build_features, FEATURE_COLS
+
+
+def make_df(n=100):
+    """테스트용 최소 DataFrame 생성"""
+    np.random.seed(42)
+    close = 100 + np.cumsum(np.random.randn(n) * 0.5)
+    df = pd.DataFrame({
+        "open":   close * 0.999,
+        "high":   close * 1.002,
+        "low":    close * 0.998,
+        "close":  close,
+        "volume": np.random.uniform(1000, 5000, n),
+    })
+    return df
+
+
+def test_build_features_returns_series():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    features = build_features(df_ind, signal="LONG")
+    assert isinstance(features, pd.Series)
+
+
+def test_build_features_has_all_cols():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    features = build_features(df_ind, signal="LONG")
+    for col in FEATURE_COLS:
+        assert col in features.index, f"피처 누락: {col}"
+
+
+def test_build_features_no_nan():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    features = build_features(df_ind, signal="LONG")
+    assert not features.isna().any(), f"NaN 존재: {features[features.isna()]}"
+
+
+def test_side_encoding():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    long_feat  = build_features(df_ind, signal="LONG")
+    short_feat = build_features(df_ind, signal="SHORT")
+    assert long_feat["side"] == 1
+    assert short_feat["side"] == 0