gru.py

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import datetime as dt
import sklearn as sk
import math

from sklearn.metrics import mean_absolute_percentage_error,r2_score, mean_squared_error ,mean_absolute_error
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense, Dropout, LSTM, GRU
from tensorflow.keras.models import Sequential

crypto_currency = 'BTC'
against_currency = 'USD'

start = dt.datetime(2019,1,1)
end = dt.datetime.now()

#btc = yf.Ticker('BTC')
#data = btc.history(period="max", auto_adjust=True)
da = pd.read_csv("BTC-USD.csv").values
data = pd.DataFrame(da, columns = ['Date','Open','High','Low','Close','Adj Close','Volume'])
#data = web.DataReader(f'{crypto_currency}-{against_currency}', 'yahoo',start,end)
print(type(data))
print(data)

#data

scalar = MinMaxScaler(feature_range=(0, 1))
scaled_data = scalar.fit_transform(data['Close'].values.reshape(-1,1))

prediction_days = 60
future_day = 30

x_train, y_train = [],[]

for x in range(prediction_days, len(scaled_data)-future_day):
    x_train.append(scaled_data[x-prediction_days:x, 0]) #days to be used
    y_train.append(scaled_data[x+future_day, 0])  #day predicted


x_train, y_train = np.array(x_train), np.array(y_train)  #for training the model
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))


#Neural Network
model = Sequential()

model.add(GRU(units=50, return_sequences= True, input_shape=(x_train.shape[1], 1))) #for memorizing the sequetial info and to feed the data back to neural network
model.add(Dropout(0.2)) #to prevent overfitting
model.add(GRU(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(GRU(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1)) #pricePrediction

model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train,y_train, epochs=25, batch_size=32)#train

#testing the model for data
test_start = dt.datetime(2021,1,1)
test_end = dt.datetime.now()

test_data = data
#test_data = web.DataReader(f'{crypto_currency}-{against_currency}','yahoo', test_start, test_end)
actual_prices = test_data['Close'].values
total_dataset = pd.concat((data['Close'], test_data['Close']), axis=0)

model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values
model_inputs = model_inputs.reshape(-1,1)
model_inputs = scalar.fit_transform(model_inputs)

#predictions
x_test = []

for x in range(prediction_days, len(model_inputs)):
    x_test.append(model_inputs[x-prediction_days:x, 0])

x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

prediction_prices = model.predict(x_test)
prediction_prices = scalar.inverse_transform(prediction_prices)

plt.plot(actual_prices, color = 'black', label = 'Actual Prices')
plt.plot(prediction_prices,color = 'red', label = 'Predicted Prices')
plt.title(f'{crypto_currency} price prediction')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend(loc='upper left')
plt.show()


#predict next day
real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs) + 1, 0]]
real_data = np.array(real_data)
real_data = np.reshape(real_data,(real_data.shape[0], real_data.shape[1], 1))


prediction = model.predict(model_inputs)
prediction = scalar.inverse_transform(prediction)
print(prediction)

#MPE
mape = sk.metrics.mean_absolute_percentage_error(actual_prices, prediction_prices)
print(mape)
#, *, sample_weight=None, multioutput='uniform_average'

coefficient_of_determination = r2_score(actual_prices, prediction_prices)
print(coefficient_of_determination)

mape = sk.metrics.mean_absolute_percentage_error(actual_prices, prediction_prices)
print(mape)