main.py

# Predicting Crypto Prices in Python
# https://www.youtube.com/watch?v=GFSiL6zEZF0

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

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

crypto_currency  = "MATIC"
against_currency = "USD"

start = dt.datetime(2015, 1, 1)
end = dt.datetime.now()

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

print(data.head())

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

prediction_days = 60
future_day = 0 # predict the price n days in the future

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])
    y_train.append(scaled_data[x+future_day, 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 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)

# Test the Model
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')

# 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("prediction:", prediction)

plt.show()