Create mas.py

mas.py

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+import ccxt
+import pandas as pd
+import xgboost as xgb
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+import time
+
+exchange = ccxt.mexc({
+    'apiKey': 'YOUR_API_KEY',
+    'secret': 'YOUR_SECRET_KEY',
+    'enableRateLimit': True,
+})
+
+def fetch_ohlcv_data(symbol, timeframe, limit):
+    return exchange.fetch_ohlcv(symbol, timeframe, since=None, limit=limit)
+
+def data_to_dataframe(data):
+    df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    df.set_index('timestamp', inplace=True)
+    return df
+
+def prepare_dataset(df, lags):
+    X = pd.DataFrame()
+    y = pd.DataFrame()
+
+    for lag in range(1, lags + 1):
+        shifted_close = df['close'].shift(lag)
+        X[f'close_lag_{lag}'] = shifted_close
+
+    y = df['close'].shift(-1)
+    return X, y
+
+def train_xgboost_model(X_train, y_train):
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    params = {
+        'objective': 'reg:squarederror',
+        'eval_metric': 'rmse',
+    }
+
+    model = xgb.train(params, dtrain)
+    return model
+
+def predict_next_hour_price(df, model, lags):
+    X_test = pd.DataFrame()
+
+    for lag in range(1, lags + 1):
+        shifted_close = df['close'].shift(lag)
+        X_test[f'close_lag_{lag}'] = shifted_close
+
+    X_test = X_test.tail(1)
+    dtest = xgb.DMatrix(X_test)
+    next_hour_price = model.predict(dtest)
+    return next_hour_price
+
+def evaluate_prediction_accuracy(y_test, y_pred):
+    accuracy = r2_score(y_test, y_pred)
+    return accuracy
+
+symbol = 'BTC/USDT'
+timeframe = '1h'
+limit = 100000
+lags = 12
+test_size = 0.3
+
+# Fetch initial data
+ohlcv_data = fetch_ohlcv_data(symbol, timeframe, limit)
+df = data_to_dataframe(ohlcv_data)
+
+# Prepare dataset and train the model
+X, y = prepare_dataset(df, lags)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+model = train_xgboost_model(X_train, y_train)
+
+while True:
+    # Fetch new data every hour
+    ohlcv_data = fetch_ohlcv_data(symbol, timeframe, limit)
+    df = data_to_dataframe(ohlcv_data)
+
+    # Make prediction
+    latest_data = df.tail(lags)
+    predicted_price = predict_next_hour_price(latest_data, model, lags)
+
+    # Print predicted next hour price
+    print(f"Predicted next hour price: ${predicted_price[0]}")
+
+    # Sleep for 1 hour (3600 seconds)
+    time.sleep(3600)