RNN for Bitcoin Price Prediction

Data: 8 years of Bitcoin prices. (2010-2018 Till Feb 10) Data Source Yahoo.

Solution: Using recurrent neural networks to predict Bitcoin prices till February 2018 using given data.

Data

Get Latest Data from https://finance.yahoo.com/quote/BTC-USD/history?p=BTC-USD

Pre-Requisits

Python 3.x.x
Training datasets
Libraries-
- tensorflow
- matploitlib
- keras
- pandas
- numpy

Visualising Data

#Importing libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


training_set=pd.read_csv('BTC-USD.csv')

Preprocessing the data

training_set1=training_set.iloc[:,1:2]        #selecting the second column
training_set1.head()                          #print first five rows
training_set1=training_set1.values            #converting to 2d array
training_set1                                 #print the whole data

#Scaling the data

from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()                           #scaling using normalisation 
training_set1 = sc.fit_transform(training_set1)
xtrain=training_set1[0:2694]                  #input values of rows [0-2694]		   
ytrain=training_set1[1:2695]                  #input values of rows [1-2695]

today=pd.DataFrame(xtrain)               #assigning the values of xtrain to today
tomorrow=pd.DataFrame(ytrain)            #assigning the values of xtrain to tomorrow
ex= pd.concat([today,tomorrow],axis=1)        #concat two columns 
ex.columns=(['today','tomorrow'])
xtrain = np.reshape(xtrain, (2694, 1, 1))     #Reshaping into required shape for Keras

Building the Recurrent Neraul Network

#importing keras and its packages

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM


regressor=Sequential()                                                      #initialize the RNN

regressor.add(LSTM(units=4,activation='sigmoid',input_shape=(None,1)))      #adding input layerand the LSTM layer 

regressor.add(Dense(units=1))                                               #adding output layers

regressor.compile(optimizer='adam',loss='mean_squared_error')               #compiling the RNN

regressor.fit(xtrain,ytrain,batch_size=32,epochs=2000)                      #fitting the RNN to the training set

Predictions

# Reading CSV file into test set
test_set = pd.read_csv('BTCtest.csv')
test_set.head()


real_stock_price = test_set.iloc[:,1:2]         #selecting the second column

real_stock_price = real_stock_price.values      #converting to 2D array

#getting the predicted BTC value of the first week of Dec 2017  
inputs = real_stock_price			
inputs = sc.transform(inputs)
inputs = np.reshape(inputs, (8, 1, 1))
predicted_stock_price = regressor.predict(inputs)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

Visualising the result

plt.plot(real_stock_price, color = 'green', label = 'Current BTC Value')
plt.plot(predicted_stock_price, color = 'red', label = 'Predicted BTC Value')
plt.title('BTC Value Prediction')
plt.xlabel('Days')
plt.ylabel('BTC Value')
plt.legend()
plt.show()

Reference-

kimanalytics - Recurrent Neural Network to Predict Tesla's Stock Prices

atuljha23/RNN-for-Bitcoin-Price-Prediction