Stock Prices Prediction Using Neural Network Models (Backpropagation, RNN LSTM, RBF) implemented in keras with Tensorflow backend to predict the daily closing price.
snn = stocknn().RNN()
snn = snn.preprocess('AAPL.csv', test_size=0.2)
snn = snn.train(batch_size=32, epochs=50)
or
snn = stocknn().BKP().preprocess('AAPL.csv', test_size=0.5).train(batch_size=16, epochs=25)
model = snn.save_model('AAPL')
mape = snn.test(model)[0]
pred = snn.predict(100)[1]
[Subclasses]:
stocknn().RNN() Recurrent Neural Networks.
stocknn().RBF() Radial Basis Function Networks.
stocknn().BKP() Back-propagation Networks.
All datasets are obtained using pystocklib.
- Keras
- Pandas
- numpy
- scikit-learn
- matplotlib
- PetraVidnerova