7e4e9315c2
- 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
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
"""
|
|
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()
|