feat: implement BTC/ETH correlation features for improved model accuracy

- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy.
- Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH.
- Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features.
- Updated various scripts and modules to support the new feature set and data handling.
- Enhanced training and deployment scripts to accommodate the new dataset structure.

This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.

tests/test_dataset_builder.py

@@ -29,12 +29,16 @@ def test_returns_dataframe(sample_df):
 
 
 def test_has_required_columns(sample_df):
-    """FEATURE_COLS + label 컬럼이 모두 있어야 한다."""
-    from src.ml_features import FEATURE_COLS
+    """기본 13개 피처 + label 컬럼이 모두 있어야 한다."""
+    BASE_FEATURE_COLS = [
+        "rsi", "macd_hist", "bb_pct", "ema_align",
+        "stoch_k", "stoch_d", "atr_pct", "vol_ratio",
+        "ret_1", "ret_3", "ret_5", "signal_strength", "side",
+    ]
     result = generate_dataset_vectorized(sample_df)
     if len(result) > 0:
         assert "label" in result.columns
-        for col in FEATURE_COLS:
+        for col in BASE_FEATURE_COLS:
             assert col in result.columns, f"컬럼 없음: {col}"
 
 
@@ -45,6 +49,30 @@ def test_label_is_binary(sample_df):
         assert set(result["label"].unique()).issubset({0, 1})
 
 
+def test_generate_dataset_vectorized_with_btc_eth_has_21_feature_cols():
+    """BTC/ETH DataFrame을 전달하면 결과 컬럼이 21개 피처 + label이어야 한다."""
+    import pandas as pd
+    import numpy as np
+    from src.dataset_builder import generate_dataset_vectorized
+    from src.ml_features import FEATURE_COLS
+
+    np.random.seed(42)
+    n = 500
+    closes = np.cumprod(1 + np.random.randn(n) * 0.001) * 1.0
+    xrp_df = pd.DataFrame({
+        "open": closes * 0.999, "high": closes * 1.005,
+        "low": closes * 0.995, "close": closes,
+        "volume": np.random.rand(n) * 1000 + 500,
+    })
+    btc_df = xrp_df.copy() * 50000
+    eth_df = xrp_df.copy() * 3000
+
+    result = generate_dataset_vectorized(xrp_df, btc_df=btc_df, eth_df=eth_df)
+    if not result.empty:
+        assert set(FEATURE_COLS).issubset(set(result.columns))
+        assert len(result.columns) == len(FEATURE_COLS) + 1  # +1 for label
+
+
 def test_matches_original_generate_dataset(sample_df):
     """벡터화 버전과 기존 버전의 샘플 수가 유사해야 한다.