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Machine Learning for Finance in Python

via Datacamp

Overview

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from historical stock data, and how to create features and targets out of the historical stock data. You'll understand how to prepare our features for linear models, xgboost models, and neural network models. We will then use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. You will also learn how to evaluate the performance of the various models we train in order to optimize them, so our predictions have enough accuracy to make a stock trading strategy profitable.

Syllabus

Preparing data and a linear model
-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.

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.

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).

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.

Taught by

Nathan George

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