Machine Learning for Algorithmic Trading

Algorithmic trading has the potential to be an incredibly effective investment strategy when implemented correctly. Smart algo traders know this, and they do their best to ensure they've got every advantage they can get before they let their algorithms loose on the markets. The reward is certainly worth it, especially when you leverage cutting-edge tools to make your algorithms smarter and faster than everyone else's.

But how do you go about fine-tuning your algorithms to perfection? It all comes down to the programming techniques you use in creating those algos. As one of the most revolutionary of those techniques, machine learning can play a major role in revolutionizing the effectiveness of your trading algorithms. Here's how machine learning can radically enhance your algorithmic trading.

What is Machine Learning?

Believe it or not, the concept of machine learning has been around for a long time. Originally developed in the 1950s by IBM, the term was meant to be synonymous with artificial intelligence. But in practice, machine learning isn't about creating super-powered science-fiction computers that you can carry on a conversation with. Instead, machine learning today is a branch of computer programming that seeks to develop technologies that let computers learn from the data they're given and then make predictions or decisions based on that data.

But it's more complicated than that. In real-world situations, machine learning is an incredible tool for data analysis. It's especially great at identifying patterns in very large data sets, as a computer can be programmed to look through that data and find correlations in a fraction of the time that a human user can. Taking it another step forward, machine learning can cross-reference new data against the information it's already processed and predict when another pattern is likely to occur.

Why use Machine Learning in Algorithmic Trading?

You can see quite quickly how machine learning comes in handy for algorithmic trading. With the ability to quickly identify historical patterns and then compare new market data against those patterns, an algorithm that uses machine learning has a high likelihood of predicting when market conditions will lead to price changes in an asset. Algorithms that are programmed to do so can then capitalize on these emergent patterns by triggering buy or sell orders at quantities and thresholds specified by users.

Machine learning in algorithmic trading really comes into its own when high-volume trading strategies are employed. Because algorithms can run data analysis and comparison in a fraction of the time that a human user can, algo trading is incredibly quick. As a result, a good algorithm can execute dozens or even hundreds of transactions in a very short period of time, maximizing profit potential. And the best part is that the more exposure these algos have to market data, the more accurate and precise their predictions are likely to become.

Machine learning is also a powerful tool for creating algorithmic strategies that trade less frequently than intraday. Machine learning programs can be written to determine meta-weights of multi-strategy portfolios, forecast market volatility, and identify complementary assets within a strategy.  

Common use cases for machine learning in algorithmic trading 

How do algo traders typically leverage the power of machine learning? In practice, there are more than a few ways, including: 

Pattern Recognition

We touched on this above, but it bears repeating: Machine learning is ideal for pattern recognition. Providing computers access to massive amounts of historical market data can "train" algorithms to recognize when a live asset is set to change in value by identifying when that asset is exhibiting activity that matches one of those historical patterns. This can be leveraged as an early-warning system for traders or as part of a more automated system that then triggers a relevant buy/sell order for that asset.

Sentiment Analysis

Investor sentiment plays a major role in market movements. Monitoring and assessing this sentiment has often been thought to be impossible to automate, but new machine learning approaches that leverage the artificial intelligence subfield of natural language processing (NLP) can categorize these previously challenging aspects. NLP works by analyzing the sentiments as expressed on platforms like social media into positive, negative, and neutral and then makes predictions based on the prevailing sentiment of that asset or market.

Data Prediction

Being able to predict more long-term asset values is an advantage for many traders. Machine-learning algorithms can aid those investors by certifying the accuracy of any predictions they make. Machine-learning algos can be programmed to take multiple factors into account above and beyond historical data patterns. Other predictors, such as prevailing sentiment, can also be incorporated into data prediction models for even greater accuracy.

Risk Assessment

Accurate risk assessment is a major boon to anyone who wishes to be a successful investor. Algorithms using machine learning techniques are capable of processing mass volumes of data, and this provides avenues to assess risk based on historical performance. These algos can be used to forecast how the market will change in the future. Investors can then analyze these forecasts and use this data to make proactive decisions to mitigate their risk exposure.

Interactive Chatbots

Traders and investors looking for up-to-the-minute information on a market are increasingly turning to automated interactive chatbots for the info they need. These chatbots have backend algorithms that gather and analyze market data and then present that data to traders on demand much quicker and more accurately than a human analyst. The more these chatbots are used, the better they get at providing relevant, concise, and actionable data to users.

Getting Started with Machine Learning

At this point, you're likely convinced that leveraging machine learning for algo trading is a good idea. The question now is how to learn how to integrate these techniques. This will require you to look for good resources to educate yourself on how machine learning works in further detail. Some of these resources can be found online, including open-source ones on GitHub like the second edition of Machine Learning for Algorithmic Trading. If you're looking for more organized approaches, consider a course like Machine Learning for Trading Specialization from Coursera.

Knowing where to find the knowledge you need is just the first step. If you're serious about machine learning and algo trading, you're also going to need detailed knowledge of general computer programming skills to be effective.

As a subset of artificial intelligence, machine learning is poised to revolutionize asset investment. Understanding how machine learning algorithms work is the first step in using them in your own trading activity. 

If you don't already have these skills, a good place to begin is by learning Python. This programming language is one of the most popular and widely used for developing trading algorithms. It's open source, free for commercial use, and has a burgeoning online community of experts willing to answer questions. You can read about how to apply Python in creating trading strategies on our resources page. If you are a focused or serious investor looking to generate returns through algorithmic trading, it might be a good idea to go with established platforms. Composer offers a customized, flexible, and backtested strategy that takes care of the coding while you focus on strategy creation. Get started here.