1 Machine Learning for Trading – From Idea to Execution
Algorithmic trading relies on computer programs that execute algorithms to automate some or all elements of a trading strategy. Algorithms are a sequence of steps or rules designed to achieve a goal. They can take many forms and facilitate optimization throughout the investment process, from idea generation to asset allocation, trade execution, and risk management.
Machine learning (ML) involves algorithms that learn rules or patterns from data to achieve a goal such as minimizing a prediction error. The examples in this book will illustrate how ML algorithms can extract information from data to support or automate key investment activities. These activities include observing the market and analyzing data to form expectations about the future and decide on placing buy or sell orders, as well as managing the resulting portfolio to produce attractive returns relative to the risk.
Ultimately, the goal of active investment management is to generate alpha, defined as portfolio returns in excess of the benchmark used for evaluation. The fundamental law of active management postulates that the key to generating alpha is having accurate return forecasts combined with the ability to act on these forecasts (Grinold 1989; Grinold and Kahn 2000).
This law defines the information ratio (IR) to express the value of active management as the ratio of the return difference between the portfolio and a benchmark to the volatility of those returns. It further approximates the IR as the product of the following:
- The information coefficient (IC), which measures the quality of forecasts as their rank correlation with outcomes
- The square root of the breadth of a strategy expressed as the number of independent bets on these forecasts
The competition of sophisticated investors in financial markets implies that making precise predictions to generate alpha requires superior information, either through access to better data, a superior ability to process it, or both.
This is where ML comes in: applications of ML for trading (ML4T) typically aim to make more efficient use of a rapidly persifying range of data to produce both better and more actionable forecasts, thus improving the quality of investment decisions and results.
Historically, algorithmic trading used to be more narrowly defined as the automation of trade execution to minimize the costs offered by the sell-side. This book takes a more comprehensive perspective since the use of algorithms in general and ML in particular has come to impact a broader range of activities, from generating ideas and extracting signals from data to asset allocation, position-sizing, and testing and evaluating strategies.
This chapter looks at industry trends that have led to the emergence of ML as a source of competitive advantage in the investment industry. We will also look at where ML fits into the investment process to enable algorithmic trading strategies. More specifically, we will be covering the following topics:
- Key trends behind the rise of ML in the investment industry
- The design and execution of a trading strategy that leverages ML
- Popular use cases for ML in trading
You can find links to additional resources and references in the README file for this chapter in the GitHub repository (https://github.com/PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Second-Edition).