feat: add OI derived features (oi_change_ma5, oi_price_spread) to dataset builder and ML features

Add two new OI-derived features to improve ML model's market microstructure
understanding:
- oi_change_ma5: 5-candle moving average of OI change rate (short-term trend)
- oi_price_spread: z-scored OI minus z-scored price return (divergence signal)

Both features use 96-candle rolling z-score window. FEATURE_COLS expanded from
24 to 26. Existing tests updated to reflect new feature counts.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

tests/test_dataset_builder.py

@@ -266,3 +266,74 @@ def test_stratified_undersample_preserves_signal():
     signal_indices = np.where(source == "signal")[0]
     for si in signal_indices:
         assert si in idx, f"signal 인덱스 {si}가 누락됨"
+
+
+def test_oi_derived_features_present():
+    """OI 파생 피처 2개가 결과에 포함되어야 한다."""
+    import numpy as np
+    import pandas as pd
+    from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
+
+    n = 300
+    np.random.seed(42)
+    df = pd.DataFrame({
+        "open":      np.random.uniform(1, 2, n),
+        "high":      np.random.uniform(2, 3, n),
+        "low":       np.random.uniform(0.5, 1, n),
+        "close":     np.random.uniform(1, 2, n),
+        "volume":    np.random.uniform(1000, 5000, n),
+        "oi_change": np.concatenate([np.zeros(100), np.random.uniform(-0.05, 0.05, 200)]),
+    })
+    d = _calc_indicators(df)
+    sig = _calc_signals(d)
+    feat = _calc_features_vectorized(d, sig)
+
+    assert "oi_change_ma5" in feat.columns, "oi_change_ma5 컬럼이 없음"
+    assert "oi_price_spread" in feat.columns, "oi_price_spread 컬럼이 없음"
+
+
+def test_oi_derived_features_nan_when_no_oi():
+    """oi_change 컬럼이 없으면 파생 피처도 nan이어야 한다."""
+    import numpy as np
+    import pandas as pd
+    from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
+
+    n = 200
+    np.random.seed(0)
+    df = pd.DataFrame({
+        "open":   np.random.uniform(1, 2, n),
+        "high":   np.random.uniform(2, 3, n),
+        "low":    np.random.uniform(0.5, 1, n),
+        "close":  np.random.uniform(1, 2, n),
+        "volume": np.random.uniform(1000, 5000, n),
+    })
+    d = _calc_indicators(df)
+    sig = _calc_signals(d)
+    feat = _calc_features_vectorized(d, sig)
+
+    assert feat["oi_change_ma5"].isna().all(), "oi_change 컬럼 없을 때 oi_change_ma5는 전부 nan이어야 함"
+    assert feat["oi_price_spread"].isna().all(), "oi_change 컬럼 없을 때 oi_price_spread는 전부 nan이어야 함"
+
+
+def test_oi_price_spread_is_continuous():
+    """oi_price_spread는 바이너리가 아닌 연속값이어야 한다."""
+    import numpy as np
+    import pandas as pd
+    from src.dataset_builder import _calc_features_vectorized, _calc_signals, _calc_indicators
+
+    n = 300
+    np.random.seed(42)
+    df = pd.DataFrame({
+        "open":      np.random.uniform(1, 2, n),
+        "high":      np.random.uniform(2, 3, n),
+        "low":       np.random.uniform(0.5, 1, n),
+        "close":     np.random.uniform(1, 2, n),
+        "volume":    np.random.uniform(1000, 5000, n),
+        "oi_change": np.random.uniform(-0.05, 0.05, n),
+    })
+    d = _calc_indicators(df)
+    sig = _calc_signals(d)
+    feat = _calc_features_vectorized(d, sig)
+
+    valid = feat["oi_price_spread"].dropna()
+    assert len(valid.unique()) > 2, "oi_price_spread는 연속값이어야 함 (2개 초과 유니크 값)"

tests/test_ml_features.py

@@ -21,17 +21,17 @@ def _make_df(n=10, base_price=1.0):
     })
 
 
-def test_build_features_with_btc_eth_has_24_features():
+def test_build_features_with_btc_eth_has_26_features():
     xrp_df = _make_df(10, base_price=1.0)
     btc_df = _make_df(10, base_price=50000.0)
     eth_df = _make_df(10, base_price=3000.0)
     features = build_features(xrp_df, "LONG", btc_df=btc_df, eth_df=eth_df)
-    assert len(features) == 24
+    assert len(features) == 26
 
-def test_build_features_without_btc_eth_has_16_features():
+def test_build_features_without_btc_eth_has_18_features():
     xrp_df = _make_df(10, base_price=1.0)
     features = build_features(xrp_df, "LONG")
-    assert len(features) == 16
+    assert len(features) == 18
 
 def test_build_features_btc_ret_1_correct():
     xrp_df = _make_df(10, base_price=1.0)
@@ -51,8 +51,9 @@ def test_build_features_rs_zero_when_btc_ret_zero():
     assert features["xrp_btc_rs"] == 0.0
 
 def test_feature_cols_has_24_items():
+    """Legacy test — updated to 26 after OI derived features added."""
     from src.ml_features import FEATURE_COLS
-    assert len(FEATURE_COLS) == 24
+    assert len(FEATURE_COLS) == 26
 
 
 def make_df(n=100):
@@ -139,3 +140,31 @@ def test_build_features_defaults_to_zero_when_not_provided(sample_df_with_indica
     feat = build_features(sample_df_with_indicators, signal="LONG")
     assert feat["oi_change"] == pytest.approx(0.0)
     assert feat["funding_rate"] == pytest.approx(0.0)
+
+
+def test_feature_cols_has_26_items():
+    from src.ml_features import FEATURE_COLS
+    assert len(FEATURE_COLS) == 26
+
+
+def test_build_features_with_oi_derived_params():
+    """oi_change_ma5, oi_price_spread 파라미터가 피처에 반영된다."""
+    xrp_df = _make_df(10, base_price=1.0)
+    btc_df = _make_df(10, base_price=50000.0)
+    eth_df = _make_df(10, base_price=3000.0)
+    features = build_features(
+        xrp_df, "LONG",
+        btc_df=btc_df, eth_df=eth_df,
+        oi_change=0.05, funding_rate=0.0002,
+        oi_change_ma5=0.03, oi_price_spread=0.12,
+    )
+    assert features["oi_change_ma5"] == pytest.approx(0.03)
+    assert features["oi_price_spread"] == pytest.approx(0.12)
+
+
+def test_build_features_oi_derived_defaults_to_zero():
+    """oi_change_ma5, oi_price_spread 미제공 시 0.0으로 채워진다."""
+    xrp_df = _make_df(10, base_price=1.0)
+    features = build_features(xrp_df, "LONG")
+    assert features["oi_change_ma5"] == pytest.approx(0.0)
+    assert features["oi_price_spread"] == pytest.approx(0.0)