Update app.py

app.py

@@ -7,6 +7,7 @@ import numpy as np
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.impute import SimpleImputer
 from scipy import stats
+import yfinance as yf
 
 # Fetch data
 def fetch_binance_data(symbol, timeframe, limit=2000):
@@ -86,7 +87,7 @@ def create_features_and_labels_with_advanced_features(btc, eth):
     features = np.vstack((btc_features, eth_features))
     return features, labels
 
-def get_data_predict(btc_ori, eth_ori, symbol='ETH/USDT', timeframe='4h', epsilon=2, normalized=False, limit=50):
+def get_data_predict(btc_ori, eth_ori, symbol='BCH/USDT', timeframe='4h', epsilon=2, normalized=False, limit=50):
     btc_data_ = fetch_binance_data('BTC/USDT', timeframe, limit=limit)
     eth_data_ = fetch_binance_data(symbol, timeframe, limit=limit)
     btc_data_ = remove_outliers(btc_data_, epsilon)
@@ -133,7 +134,7 @@ with open('model_n4h_cat.pkl','rb') as f:
 
 def predict_and_plot(timeframe, limit, epsilon, n_steps, ma):
     btc_ori = yf.download('BTC-USD', period=f'{limit}d', interval=timeframe)
-    eth_ori = yf.download('ETH-USD', period=f'{limit}d', interval=timeframe)
+    eth_ori = yf.download('BCH-USD', period=f'{limit}d', interval=timeframe)
     btc_data, eth_data, label = get_data_predict(btc_ori, eth_ori, symbol='ETH/USDT', timeframe=timeframe, epsilon=epsilon, normalized=True, limit=limit)
     model = model_n1d_cat if timeframe=='1d' else model_n4h_cat
     preds = predictions(model, btc_data, eth_data, name=timeframe, n_steps=n_steps)