Machine Learning for Finance in Python

Course Description

How to Predict Stock Prices with Machine Learning

Machine learning has a huge number of applications within the finance industry and is commonly used to predict stock values and maintain a strong stock portfolio. This course will teach you how to use Python to calculate technical indicators from historical stock data and create features and targets.

Build Your Knowledge of ML Models

Strong stock predictions start with good data preparation. You’ll learn how to prepare your financial data for ML algorithms and fit it into various models, including linear models, xgboost models, and neural network models.

The second chapter moves on to using Python decision trees to predict future values for your stock, and forest-based machine learning methods to enhance your predictions.

The second half of this course will cover how to scale your data for use in KNN and neural networks before using those tools to predict the future value of your stock. You’ll learn how to plot losses, measure performance, and visualize your prediction results.

Use the Sharpe Ratio to Build Your Ideal Portfolio

Machine learning can also help you find the optimal stock portfolio. You’ll learn how to use modern portfolio theory (MPT) and the Sharpe ratio as part of your process to predict the best portfolios. Once you’ve completed this course, you’ll also understand how to evaluate the performance of your machine learning-predicted portfolio.

You’ll use a variety of real-world data sets from NASDAQ and apply robust theories and techniques to them so that you can create your own predictions and optimize for your risk appetite and budget. ”

What You’ll Learn

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.

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 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.

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.