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




from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense, Dropout, LSTM
from tensorflow.keras import Sequential


crypto_currency = 'BTC'
against_currency = 'USD'

start = dt.datetime(2016,1,1)
end = dt.datetime.now()

data = web.DataReader(f'{crypto_currency}-{against_currency}', 'yahoo', start,end)

# here we are preparing the data

scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
prediction_days = 365

x_train, y_train = [], []

for x in range(prediction_days, len(scaled_data)):
    x_train.append(scaled_data[x-prediction_days:x, 0])
    y_train.append(scaled_data[x, 0])

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


#create a neural network



model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))

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


# we are going to test the model here

test_start = dt.datetime(2020,1,1)
test_end = dt.datetime.now()
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 = scaler.fit_transform(model_inputs)

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 = scaler.inverse_transform(prediction_prices)

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


