feat: add ADX as 24th ML feature for trend strength learning

Migrate ADX from hard filter (ADX < 25 blocks entry) to ML feature so
the model can learn optimal ADX thresholds from data. Updates FEATURE_COLS,
build_features(), and corresponding tests from 23 to 24 features.

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

src/ml_features.py

@@ -11,6 +11,7 @@ FEATURE_COLS = [
     # 시장 미시구조: OI 변화율(z-score), 펀딩비(z-score)
     # parquet에 oi_change/funding_rate 컬럼이 없으면 dataset_builder에서 0으로 채움
     "oi_change", "funding_rate",
+    "adx",
 ]
 
 
@@ -39,7 +40,7 @@ def build_features(
 ) -> pd.Series:
     """
     기술 지표가 계산된 DataFrame의 마지막 행에서 ML 피처를 추출한다.
-    btc_df, eth_df가 제공되면 23개 피처를, 없으면 15개 피처를 반환한다.
+    btc_df, eth_df가 제공되면 24개 피처를, 없으면 16개 피처를 반환한다.
     signal: "LONG" | "SHORT"
     oi_change, funding_rate: 실제 값이 제공되면 사용, 없으면 0.0으로 채운다.
     """
@@ -133,5 +134,6 @@ def build_features(
     # 실시간에서 실제 값이 제공되면 사용, 없으면 0으로 채운다
     base["oi_change"]    = float(oi_change)    if oi_change    is not None else 0.0
     base["funding_rate"] = float(funding_rate) if funding_rate is not None else 0.0
+    base["adx"] = float(last.get("adx", 0))
 
     return pd.Series(base)

tests/test_ml_features.py

@@ -17,20 +17,21 @@ def _make_df(n=10, base_price=1.0):
         "ema21": closes, "ema50": closes, "atr": [0.01] * n,
         "stoch_k": [50.0] * n, "stoch_d": [50.0] * n,
         "vol_ma20": [1000.0] * n,
+        "adx": [20.0] * n,
     })
 
 
-def test_build_features_with_btc_eth_has_21_features():
+def test_build_features_with_btc_eth_has_24_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) == 23
+    assert len(features) == 24
 
-def test_build_features_without_btc_eth_has_13_features():
+def test_build_features_without_btc_eth_has_16_features():
     xrp_df = _make_df(10, base_price=1.0)
     features = build_features(xrp_df, "LONG")
-    assert len(features) == 15
+    assert len(features) == 16
 
 def test_build_features_btc_ret_1_correct():
     xrp_df = _make_df(10, base_price=1.0)
@@ -49,9 +50,9 @@ def test_build_features_rs_zero_when_btc_ret_zero():
     features = build_features(xrp_df, "LONG", btc_df=btc_df, eth_df=eth_df)
     assert features["xrp_btc_rs"] == 0.0
 
-def test_feature_cols_has_23_items():
+def test_feature_cols_has_24_items():
     from src.ml_features import FEATURE_COLS
-    assert len(FEATURE_COLS) == 23
+    assert len(FEATURE_COLS) == 24
 
 
 def make_df(n=100):