feat: implement ML filter with LightGBM for trading signal validation

- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.

Made-with: Cursor

scripts/fetch_history.py

@@ -0,0 +1,69 @@
+"""
+바이낸스 선물 REST API로 과거 캔들 데이터를 수집해 parquet으로 저장한다.
+사용법: python scripts/fetch_history.py --symbol XRPUSDT --interval 1m --days 90
+"""
+import asyncio
+import argparse
+from datetime import datetime, timedelta
+import pandas as pd
+from binance import AsyncClient
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+
+async def fetch_klines(symbol: str, interval: str, days: int) -> pd.DataFrame:
+    client = await AsyncClient.create(
+        api_key=os.getenv("BINANCE_API_KEY", ""),
+        api_secret=os.getenv("BINANCE_API_SECRET", ""),
+    )
+    try:
+        start_ts = int((datetime.utcnow() - timedelta(days=days)).timestamp() * 1000)
+        all_klines = []
+        while True:
+            klines = await client.futures_klines(
+                symbol=symbol,
+                interval=interval,
+                startTime=start_ts,
+                limit=1500,
+            )
+            if not klines:
+                break
+            all_klines.extend(klines)
+            last_ts = klines[-1][0]
+            if last_ts >= int(datetime.utcnow().timestamp() * 1000):
+                break
+            start_ts = last_ts + 1
+            print(f"수집 중... {len(all_klines)}개")
+    finally:
+        await client.close_connection()
+
+    df = pd.DataFrame(all_klines, columns=[
+        "timestamp", "open", "high", "low", "close", "volume",
+        "close_time", "quote_volume", "trades",
+        "taker_buy_base", "taker_buy_quote", "ignore",
+    ])
+    df = df[["timestamp", "open", "high", "low", "close", "volume"]].copy()
+    for col in ["open", "high", "low", "close", "volume"]:
+        df[col] = df[col].astype(float)
+    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+    df.set_index("timestamp", inplace=True)
+    return df
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--symbol",   default="XRPUSDT")
+    parser.add_argument("--interval", default="1m")
+    parser.add_argument("--days",     type=int, default=90)
+    parser.add_argument("--output",   default="data/xrpusdt_1m.parquet")
+    args = parser.parse_args()
+
+    df = asyncio.run(fetch_klines(args.symbol, args.interval, args.days))
+    df.to_parquet(args.output)
+    print(f"저장 완료: {args.output} ({len(df)}행)")
+
+
+if __name__ == "__main__":
+    main()