Upload Model1.py

Model1.py

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+import pandas as pd
+import numpy as np
+import time
+import requests
+import matplotlib.pyplot as plt
+
+from sklearn.preprocessing import MinMaxScaler
+
+import tensorflow as tf
+from keras.models import Sequential
+from keras.layers import Dense,Dropout,Bidirectional
+from keras.layers import LSTM
+
+data=requests.get('https://api.twelvedata.com/time_series?symbol=BTC/INR&timezone=Asia/Kolkata&start_date=2023-03-01 00:00:00&end_date=2023-03-20 00:00:00&order=ASC&interval=5min&outputsize=5000&apikey=e76157c75c3a42649e168c5c206e88ca').json()
+#print(data)
+data_final=pd.DataFrame(data['values'])
+#print(data_final)
+scaler=MinMaxScaler(feature_range=(0,1))
+scaled_data=scaler.fit_transform(data_final['close'].values.reshape(-1,1))
+timeinterval=24
+prediction=1
+
+x_train=[]
+y_train=[]
+
+for i in range(timeinterval,len(scaled_data)-prediction):
+  x_train.append(scaled_data[i-timeinterval:i,0])
+  y_train.append(scaled_data[i+prediction,0])
+
+x_train=np.array(x_train)
+y_train=np.array(y_train)
+x_train=np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))
+
+model=Sequential()
+
+model.add(Bidirectional(LSTM(256,return_sequences=True,input_shape=(x_train.shape[1],1),activation='relu')))
+model.add(Dropout(0.3))
+model.add(Bidirectional(LSTM(128,return_sequences=True,activation='tanh')))
+model.add(Dropout(0.2))
+model.add(Bidirectional(LSTM(64,activation='relu')))
+model.add(Dropout(0.1))
+model.add(Dense(1,activation='sigmoid'))
+model.compile(loss='mean_squared_error',optimizer='adam',metrics=['accuracy'])
+model.fit(x_train,y_train,epochs=15,batch_size=100)
+predict=[]
+exchange=[]
+
+i=0
+while(i<6):
+  testapi='https://api.twelvedata.com/time_series?symbol=BTC/INR&interval=5min&outputsize=3000&timezone=Asia/Kolkata&order=ASC&apikey=e76157c75c3a42649e168c5c206e88ca'
+  testdata=requests.get(testapi).json()
+  testdatafinal=pd.DataFrame(testdata['values'])
+
+  bitcoinprice=pd.to_numeric(testdatafinal['close'],errors='coerce').values
+  testinputs=testdatafinal['close'].values
+  testinputs=testinputs.reshape(-1,1)
+  modelinputs=scaler.fit_transform(testinputs)
+
+  x_test=[]
+  for x in range(timeinterval,len(modelinputs)):
+    x_test.append(modelinputs[x-timeinterval:x,0])
+
+  x_test=np.array(x_test)
+  x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))
+  prediction_price=model.predict(x_test)
+  prediction_price=scaler.inverse_transform(prediction_price)
+
+  """plt.plot(bitcoinprice,label='Bitcoin Prices')
+  plt.plot(prediction_price,label='Predicted Prices')
+  plt.title('Predicting Bitcoin Price')
+  plt.xlabel('5min Interval')
+  plt.ylabel('Price')
+  plt.legend()
+  plt.show()"""
+  exchangeapi='https://api.twelvedata.com/exchange_rate?symbol=BTC/INR&timezone=Asia/Kolkata&apikey=e76157c75c3a42649e168c5c206e88ca'
+  exchangedata=requests.get(exchangeapi).json()
+  print(exchangedata)
+
+  exchangefinal=exchangedata['rate']
+  exchange.append(exchangefinal)
+
+
+  lastdata=modelinputs[len(modelinputs)+1-timeinterval:len(modelinputs)+1,0]
+  lastdata=np.array(lastdata)
+  lastdata=np.reshape(lastdata,(1,lastdata.shape[0],1))
+  prediction=model.predict(lastdata)
+  prediction=scaler.inverse_transform(prediction)
+  predict.append(prediction[0][0])
+  print(prediction)
+  i+=1
+  print(i)
+  time.sleep(300)
+
+  """testapi='https://api.twelvedata.com/time_series?symbol=BTC/INR&interval=5min&outputsize=3000&apikey=e76157c75c3a42649e168c5c206e88ca'
+  testdata=requests.get(testapi).json()
+  testdatafinal=pd.DataFrame(testdata['values'])
+
+  bitcoinprice=pd.to_numeric(testdatafinal['close'],errors='coerce').values
+  testinputs=testdatafinal['close'].values
+  testinputs=testinputs.reshape(-1,1)
+  modelinputs=scaler.fit_transform(testinputs)
+
+  x_test=[]
+  for x in range(timeinterval,len(modelinputs)):
+    x_test.append(modelinputs[x-timeinterval:x,0])
+
+  x_test=np.array(x_test)
+  x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))
+  prediction_price=model.predict(x_test)
+  prediction_price=scaler.inverse_transform(prediction_price)
+
+plt.plot(bitcoinprice,label='Bitcoin Prices')
+plt.plot(prediction_price,label='Predicted Prices')
+plt.title('Predicting Bitcoin Price')
+plt.xlabel('5min Interval')
+plt.ylabel('Price')
+plt.legend()
+plt.show()
+predict=[]
+exchange=[]
+exchangeapi='https://api.twelvedata.com/exchange_rate?symbol=BTC/INR&timezone=Asia/Kolkata&apikey=e76157c75c3a42649e168c5c206e8'
+exchangedata=requests.get(exchangeapi).json()
+exchangefinal=pd.DataFrame(exchangedata['rate'])
+exchange.append(exchangefinal)
+
+
+
+
+lastdata=modelinputs[len(modelinputs)+1-timeinterval:len(modelinputs)+1,0]
+lastdata=np.array(lastdata)
+lastdata=np.reshape(lastdata,(1,lastdata.shape[0],1))
+prediction=model.predict(lastdata)
+prediction=scaler.inverse_transform(prediction)
+predict.append(prediction)
+print(prediction)"""
+
+
+
+exchange1api='https://api.twelvedata.com/exchange_rate?symbol=BTC/INR&timezone=Asia/Kolkata&apikey=e76157c75c3a42649e168c5c206e88ca'
+exchange1data=requests.get(exchange1api).json()
+exchange1final=exchange1data['rate']
+exchange.append(exchange1final)
+exchange.pop(0)
+print(exchange)
+print(predict)
+
+plt.plot(exchange,label='Bitcoin Price')
+plt.plot(predict,label='Predicted Prices')
+plt.title('Predicting Bitcoin Price')
+plt.xlabel('5min Interval')
+plt.ylabel('Price')
+plt.legend()
+plt.show()