Hands-On Machine Learning for Algorithmic Trading

How to predict price movements using sklearn

We continue the price prediction example but now we binarize the outcome variable so that it takes on the value 1 whenever the 10-day return is positive and 0 otherwise; see the notebook logistic_regression.ipynb in the sub directory stock_price_prediction:

target = 'Returns10D'label = (y[target] > 0).astype(int).to_frame(target)

With this new categorical outcome variable, we can now train a logistic regression using the default L₂ regularization. For logistic regression, the regularization is formulated inversely to linear regression: higher values for λ imply less regularization and vice versa. We evaluate 11 parameter values using cross validation as follows:

nfolds = 250Cs = np.logspace(-