In the previous article on studying to become a quant trader we touched on the importance of statistical and machine learning. Many of you contacted me in regard to the "state of the art" of such machine learning methods, and how they're applied in the quant finance world. In this article I want to outline the resources necessary to learn machine learning techniques so that you'll be better prepared for a role as a quant trader.
Statistical learning is extremely important in quant trading research. We can bring to bear the entire weight of the scientific method and hypothesis testing in order to rigourously assess the quant trading research process. For quantitative trading we are interested in testable, repeatable results that are subject to constant scrutiny. This allows easy replacement of trading strategies as and when performance degrades. Note that this is in stark contrast to the approach taken in "discretionary" trading where performance and risk are not often assessed in this manner.
The statistical approach to quant trading is designed to eliminate issues that surround discretionary methods. A great deal of discretionary technical trading is rife with cognitive biases, including loss aversion, confirmation bias and the bandwagon effect. Quant trading research uses alternative mathematical methods to mitigate such behaviours and thus enhance trading performance.
In order to carry out such a methodical process quant trading researchers possess a continuously skeptical mindset and any strategy ideas or hypotheses about market behaviour are subject to continual scrutiny. A strategy idea will only be put into a "production" environment after extensive statistical analysis, testing and refinement. This is necessary because the market has a rather low signal-to-noise ratio. This creates difficulties in forecasting and thus leads to a challenging trading environment.
The goal of quantitative trading research is to produce algorithms and technology that can satisfy a certain investment mandate. In practice this translates into creating trading strategies (and related infrastructure) that produce consistent returns above a certain pre-determined benchmark, net of costs associated with the trading transactions, while minimising "risk". Hence there are a few levers that can be pulled to enhance the financial objectives.
A great deal of attention is often given to the signal/alpha generator, i.e. "the strategy". The best funds and retail quants will spend a significant amount of time modelling/reducing transaction costs, effectively managing risk and determining the optimal portfolio. This article is primarily aimed at the alpha generator component of the stack, but please be aware that the other components are of equal importance if successful long-term strategies are to be carried out.
We will now investigate problems encountered in signal generation and how to solve them. The following is a basic list of such methods (which clearly overlap) that are often encountered in signal generation problems:
There are countless textbooks on statistical modelling, probability and machine learning. It is actually quite challenging to know where to begin. I myself have had to go through this process when transitioning from a physical modelling mindset (during my own PhD) towards a statistical approach while in industry. I described the two books I consider the "best" to get started in this field in the previous article, but to recap they are:
The first book doesn't require a great deal of mathematical sophistication. The necessary background includes typical college linear algebra, calculus and probability theory. The second book is more advanced and goes deeper into the theory. For that you should have some good grounding in probability theory, prior statistical methods and modelling.
These books will teach you about the following topics. By studying the books (and carrying out the associated "labs" in R) you will gain a solid insight into when certain algorithms are applicable.
To become an adept quantitative trading researcher it is essential to be familiar with the process of statistical modelling. An exhaustive knowledge of machine learning techniques is of lesser importance than a deeper understanding of the modelling process itself. Make sure to always keep in mind the core ideas of modelling assumptions, the bias-variance tradeoff, algorithm applicability and cognitive biases when carrying out quantitative trading research.comments powered by Disqus
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