Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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A note on backtesting

The peculiarities of choosing training and testing sets are especially important in both systematic investing and algorithmic trading. The main way to test trading algorithms is a process called backtesting.

Backtesting means we train the algorithm on data from a certain time period and then test its performance on older data. For example, we could train on data from a date range of 2015 to 2018 and then test on data from 1990 to 2015. By doing this, not only is the model's accuracy tested, but the backtested algorithm executes virtual trades so its profitability can be evaluated. Backtesting is done because there is plenty of past data available.

With all that being said, backtesting does suffer from several biases. Let's take a look at four of the most important biases that we need to be aware of:

  • Look-ahead bias: This is introduced if future data is accidentally included at a point in the simulation where that data would not have been available yet. This can be caused...