main.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.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM

"""
pip install numpy
pip install matplotlib, pandas (-datareader), tensorflow, scikit-learn
"""

# Load Data
company = 'SBUX'

start = dt.datetime(2012,1,1)
end = dt.datetime(2020,1,1)

data = web.DataReader(company, '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))

# Build The Model
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_sequence=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1)) # Prediction of next closing value.

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


''' Test the Model Accuracy on Existing Data '''
# Load Test Data
test_start = dt.datetime(2020,1,1)
test_end = dt.datetime.now()

test_data = web.DataReader(company, '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.transform(model_inputs)


# Make Prediction on Test Data

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

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

predicted_prices = model.predict(x_test)
predicted_prices = scaler.inverse_transform(predicted_prices)

# Plot the Test Predictions
plt.plot(actual_prices, color="black", label="Actual " + company + "Price")
plt.plot(predicted_prices, color="red", label="Predicted " + company + "Price")
plt.title("Share Price")
plt.xlabel('Time')
plt.ylabel(company + ' Share Price')
plt.legend()
plt.show()