feat: enhance model training and deployment scripts with time-weighted sampling

- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training.
- Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance.
- Adjusted deployment scripts to support new backend options and improved error handling for model file transfers.
- Added new entries to the training log for better tracking of model performance metrics over time.
- Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.

scripts/train_and_deploy.sh

@@ -1,10 +1,10 @@
 #!/usr/bin/env bash
 # 맥미니에서 전체 학습 파이프라인을 실행하고 LXC로 배포한다.
-# 사용법: bash scripts/train_and_deploy.sh [LXC_HOST] [LXC_MODELS_PATH]
+# 사용법: bash scripts/train_and_deploy.sh [mlx|lgbm]
 #
 # 예시:
-#   bash scripts/train_and_deploy.sh
-#   bash scripts/train_and_deploy.sh root@10.1.10.24 /root/cointrader/models
+#   bash scripts/train_and_deploy.sh        # LightGBM (기본값)
+#   bash scripts/train_and_deploy.sh mlx    # MLX GPU 학습
 
 set -euo pipefail
 
@@ -19,37 +19,35 @@ else
     echo "경고: 가상환경을 찾을 수 없습니다 ($VENV_PATH). 시스템 Python을 사용합니다." >&2
 fi
 
-LXC_HOST="${1:-root@10.1.10.24}"
-LXC_MODELS_PATH="${2:-/root/cointrader/models}"
+BACKEND="${1:-lgbm}"
 
 cd "$PROJECT_ROOT"
 
-echo "=== [1/3] 데이터 수집 (XRP + BTC + ETH 3심볼) ==="
+echo "=== [1/3] 데이터 수집 (XRP + BTC + ETH 3심볼, 1년치) ==="
 python scripts/fetch_history.py \
     --symbols XRPUSDT BTCUSDT ETHUSDT \
     --interval 1m \
-    --days 90 \
+    --days 365 \
     --output data/xrpusdt_1m.parquet
 # 결과: data/combined_1m.parquet (타임스탬프 기준 병합)
 
 echo ""
 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="${TRAIN_BACKEND:-lgbm}"
+DECAY="${TIME_WEIGHT_DECAY:-2.0}"
 if [ "$BACKEND" = "mlx" ]; then
-    echo "  백엔드: MLX (Apple Silicon GPU)"
-    python scripts/train_mlx_model.py --data data/combined_1m.parquet
+    echo "  백엔드: MLX (Apple Silicon GPU), decay=${DECAY}"
+    python scripts/train_mlx_model.py --data data/combined_1m.parquet --decay "$DECAY"
 else
-    echo "  백엔드: LightGBM (CPU)"
-    python scripts/train_model.py --data data/combined_1m.parquet
+    echo "  백엔드: LightGBM (CPU), decay=${DECAY}"
+    python scripts/train_model.py --data data/combined_1m.parquet --decay "$DECAY"
 fi
 
 echo ""
 echo "=== [3/3] LXC 배포 ==="
-bash scripts/deploy_model.sh "$LXC_HOST" "$LXC_MODELS_PATH"
+bash scripts/deploy_model.sh "$BACKEND"
 
 echo ""
 echo "=== 전체 파이프라인 완료 ==="
 echo ""
 echo "봇 재시작이 필요하면:"
-echo "  ssh ${LXC_HOST} 'cd /root/cointrader && docker compose restart cointrader'"
+echo "  ssh root@10.1.10.24 'cd /root/cointrader && docker compose restart cointrader'"