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.

scripts/train_and_deploy.sh

@@ -16,19 +16,24 @@ PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
 
 cd "$PROJECT_ROOT"
 
-echo "=== [1/3] 데이터 수집 ==="
-python scripts/fetch_history.py --symbol XRPUSDT --interval 1m --days 90 --output data/xrpusdt_1m.parquet
+echo "=== [1/3] 데이터 수집 (XRP + BTC + ETH 3심볼) ==="
+python scripts/fetch_history.py \
+    --symbols XRPUSDT BTCUSDT ETHUSDT \
+    --interval 1m \
+    --days 90 \
+    --output data/xrpusdt_1m.parquet
+# 결과: data/combined_1m.parquet (타임스탬프 기준 병합)
 
 echo ""
-echo "=== [2/3] 모델 학습 ==="
+echo "=== [2/3] 모델 학습 (21개 피처: XRP 13 + BTC/ETH 상관관계 8) ==="
 # TRAIN_BACKEND=mlx 로 설정하면 Apple Silicon GPU(Metal)를 사용한다 (기본: lgbm)
 BACKEND="${TRAIN_BACKEND:-lgbm}"
 if [ "$BACKEND" = "mlx" ]; then
     echo "  백엔드: MLX (Apple Silicon GPU)"
-    python scripts/train_mlx_model.py --data data/xrpusdt_1m.parquet
+    python scripts/train_mlx_model.py --data data/combined_1m.parquet
 else
     echo "  백엔드: LightGBM (CPU)"
-    python scripts/train_model.py --data data/xrpusdt_1m.parquet
+    python scripts/train_model.py --data data/combined_1m.parquet
 fi
 
 echo ""