Machine learning and artificial intelligence stand to push algorithmic trading to new levels. Not only can more advanced strategies be employed and adapted in real time but new techniques like Natural Language Processing of news articles can offer even more avenues for getting special insight into market movements.
Algorithms can already make complex decisions and make them according to predetermined strategies and data, but with machine learning, these strategies can update themselves based on what is actually working. Instead of just “if/then” logic, an ML algorithm can assess multiple strategies and refine the next trades based upon the highest returns. While they still take work to set up, this means traders can have faith in their bot even as market conditions evolve beyond initial parameters.
One popular type of ML strategy is called naive Bayes. In this technique, learning algorithms make trades based on previous statistics and probability. For example, historical market data shows that Bitcoin goes up 70% after having three consecutive days in the red. A naive Bayes algorithm would see that the last three days have all been down and automatically place an order based on the likelihood it will rise today. These systems are highly customizable, and it will be up to every trader to set their own parameters for things like risk and reward ratios, but once you are happy with a balance, you can let it run with minimal interference.
Another benefit of ML is the ability for machines to be able to read and interpret news reports. By scanning for keywords and having the appropriate strategies lined up, these types of bots can make trades within seconds when positive or negative news breaks. Obviously, these will only be as accurate as the logic that goes into them — and are thus tricky to implement — but still offer an edge over other traders when properly set up.
Note that this is the cutting edge of a new branch in automated trading. So, bots designed to work this way may be harder to find, cost more to access or simply be less predictable than some of the more time-tested techniques.