In recent years, machine learning has been the buzz-word in algorithmic trading and quant firms. In their quest to seek the elusive alpha, a number of funds and trading firms have adopted to machine learning. This post gives a brief overview of the development of machine learning and its growing importance for quants and traders alike.
Machine Learning gains popularity in Algorithmic Trading
Machine learning techniques can be applied to trading using programming languages like Python, R, C++ etc. Machine learning packages/libraries are developed in-house by firms for their proprietary use or by third parties who make it freely available to the user community. In recent years, the number of machine learning packages has increased substantially which has helped the developer community in accessing various machine learning techniques and applying the same to their trading needs.
There are hundreds of ML algorithms, these algorithms can be classified into different types depending on how these work. For example, regression algorithms are used to model the relationship between variables; decision tree algorithms construct a model of decisions and are used in classification or regression problems (Machine Learning: An Introduction to Decision Trees). Of these, some algorithms have become popular among quants. Some of these include:
- Linear Regression
- Logistic Regression
- Random Forests (RM)
- Support Vector Machine (SVM)
- k-Nearest Neighbor (kNN)
- Classification and Regression Tree (CART)
- Deep learning
These ML algorithms are used by trading firms for different purposes. Some of these include:
- Analyzing historical market behavior using large data sets
- Determine optimal inputs (predictors) to a strategy
- Determining the optimal set of strategy parameters
- Making trade predictions etc.
Here are a couple of machine learning examples for our readers:
Machine Learning and Its Application in Forex Markets [WORKING MODEL]
Predictive Modeling in R for Algorithmic Trading
Resources to Study Machine Learning
Keeping oneself updated is of prime importance in today’s world. Professional quants and traders who intend to expand their knowledge can take up machine learning courses (part-time or full-time) which are offered by some well-known institutes. This can help enhance their career or provide them additional tools in the development of trading strategies for themselves or their firms.
Here’s a blog on ML resources – Free Resources to Learn Machine Learning for Trading
Other Research Areas
Machine learning techniques are applied in various markets like equities, derivative, Forex, etc. Machine learning enthusiast/Quants/Traders who intend to apply machine learning techniques to trading should also have some know-how on related subjects like Programming, Basic statistics, Market microstructure, Sentiment analysis, Technical analysis etc.
Machine Learning Competitions
There are a number of sites which host ML competitions. These competitions although not specifically targeted towards the application of ML in trading, can give good exposure to quants and traders to different ML problems via participation in competitions & forums and help expand their ML knowledge. Some of the popular ML competition hosting sites include:
- kaggle – (https://www.kaggle.com/)
- NUMERAI – (https://numer.ai/)
- Topcoder – (https://www.topcoder.com/)
- CrowdAnalytics – (https://www.crowdanalytix.com/)
- DrivenData – (https://www.drivendata.org/)
Funds using Machine Learning Techniques
Some established funds like Medallion fund, Citadel, D.E. Shaw are said to be using machine learning techniques for trading. However, the extent to which these ML techniques are applied in trading remains unknown to outsiders, and so does the contribution of machine learning strategies in the overall performance of these funds.
There are some hedge funds that have revealed extensive use of machine learning techniques as part of their core strategy. For example, Taaffeite Capital Management (http://taaffeitecm.com/). Taaffeite Capital trades in a fully systematic and automated fashion using proprietary machine learning systems. Here is a list of funds and trading firms that are using artificial intelligence or machine learning.
Future of Machine Learning in Trading
The rise of technology and electronic trading has only accelerated the rate of automated trading in recent years (Goldman Sachs automated trading replaces 600 traders with 200 engineers). Machine learning has found good adoption with global firms, both big and small. This will get further momentum as quants experiment with new developments in machine learning aided by superior hardware. This makes it imperative for quants and traders to gain a good understanding of machine learning to remain productive in the trading world.
If you want to learn various aspects of Algorithmic trading then check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!
Also, you can check our FREE introductory course, ‘Introduction to Machine Learning for Trading‘, it covers basics of various techniques used in Machine Learning for Trading, teaches you how to interpret the predictions made and code a trading strategy in Python with the help of interactive exercises and downloadable content.