AI Stock prediction

A simple python application that tries to predict the closing price of a stock for the next day.

Table of contents

• Disclaimer
• Charts
• How to run
• Libaries
• Copyright and license

Disclaimer

ATTENTION: THIS APPLICATION IS NOT INTENDED TO BE A INVESTMENT ADVICE, THIS PROJECT ONLY SERVES TO ILLUSTRATE APPROACHES FROM PROGRAMMING IN ORDER TO BETTER UNDERSTAND AI.

Charts

How to run

These arguments indicate the basis on which the AI can be trained.

company = 'MSFT'
start_time = dt.datetime(2010,1,1)
end_time = dt.datetime(2020,12,31)
data = pdr.DataReader(company, 'yahoo', start_time, end_time)

This is where the model for the AI is created. The epochs and the batch_size can be different from stock to stock. Try out different values to achieve a "better result".

model = Sequential()
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)

After training the AI tries to predict the closing value for the upcoming day.

real_data = [model_inputs[len(model_inputs)+1 -pred_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(f"Prediction for the next day:{prediction}")

The pre-trained model can be saved to save time when using it later.

model.save('microsoft_model')
new_model =load_model('microsoft_model')

Libaries

• numpy
• matplotlib
• pandas
• pandas_datareader
• sklearn
• tensorflow

Copyright and license

Code and documentation copyright 2021 MrTob. Code released under the MIT License.

Enjoy 🤘