StockPredictor: Exploring the MLPRegressor

I want to predict stock prices using existing data. Optionally, I'll convert the problem into a classification problem ("to buy or not to buy). With the training data in place, I'll explore the Python package scikit-learn.

This article discusses some machine learning algorithms that can be used to predict a numerical value. Neural networks and K-nearest neighbors seems promising. I suspect non-linear relations between features and results, so I don't expect linear regression to work. However, I'll give it a try later.

Multilayer Perceptron Regressor

I based my program on the examples above, with my data from StockReader. 

After training the neural network on ~210 000 examples, I test it on ~50 000 examples. I compare how the neural network is doing with an dart throwing monkey (a simple prediction that the stock performances will be the average daily price increase of the stocks).

The initial output from training the neural network together with verifying it on the test data shows no improvement compared to the monkey. The Median Average Error for the Neural network was 0.095, compared to the benchmark of 0.064. 

The next step will be to investigate other parameters for the neural network, along with other algoritms. Since I will compare different models, I will need to introduce cross validation data. This is important, since I need to make sure to verify the model selection itself.

Useful link:

https://towardsdatascience.com/ml-preface-2-355b1775723e