cpp.py

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.models import Sequential

crypto_currency = 'BNB'
fiat_currency = 'USD'

start = dt.datetime(2018,1,1)
end = dt.datetime.now()

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

# Prepare data
scaler =  MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
prediction_days = 60

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 the 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)

#Testing the Model
test_start = dt.datetime(2020,1,1)
test_end = dt.datetime.now()

test_data = web.DataReader(f'{crypto_currency}-{fiat_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()

# 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(real_data)
prediction = scaler.inverse_transform(prediction)
print()