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  3. Machine Learning for Finance in Python
  • 1

    Preparing data and a linear model

    Free

    In this chapter, we will learn how machine learning can be used in finance. We will also explore some stock data, and prepare it for machine learning algorithms. Finally, we will fit our first machine learning model -- a linear model, in order to predict future price changes of stocks.

  • 2

    Machine learning tree methods

    Learn how to use tree-based machine learning models to predict future values of a stock's price, as well as how to use forest-based machine learning methods for regression and feature selection.

  • 3

    Neural networks and KNN

    We will learn how to normalize and scale data for use in KNN and neural network methods. Then we will learn how to use KNN and neural network regression to predict the future values of a stock's price (or any other regression problem).

  • 4

    Machine learning with modern portfolio theory

    In this chapter, you'll learn how to use modern portfolio theory (MPT) and the Sharpe ratio to plot and find optimal stock portfolios. You'll also use machine learning to predict the best portfolios. Finally, you'll evaluate performance of the ML-predicted portfolios.

Initializing

Exercise

Gradient boosting feature importances

As with random forests, we can extract feature importances from gradient boosting models to understand which features are the best predictors. Sometimes it's nice to try different tree-based models and look at the feature importances from all of them. This can help average out any peculiarities that may arise from one particular model.

The feature importances are stored as a numpy array in the .feature_importances_ property of the gradient boosting model. We'll need to get the sorted indices of the feature importances, using np.argsort(), in order to make a nice plot. We want the features from largest to smallest, so we will use Python's indexing to reverse the sorted importances like feat_importances[::-1].

Instructions

100 XP
  • Reverse the sorted_index variable to go from greatest to least using python indexing.
  • Create the sorted feature labels list as labels by converting feature_names to a numpy array and indexing with sorted_index.
  • Create a bar plot of the xticks, and feature_importances indexed with the sorted_index variable, and labels as the xtick labels.