The usual reason cited for adopting artificial intelligence and machine learning in one’s day trading is that these tools can improve your trading results. The stated rationale is that both one’s strategies and one’s minute by minute decision making can be improved by analysis of large amounts of complex data from markets. Such data includes market sentiment, news feeds, price movements, market volume, and standard technical indicators. When adopting any new tool for your day trading it helps to understand how it works and can help your trading as well as any pitfalls that are being glossed over by those promoting a new approach to day trading. How does leveraging AI and machine learning in day trading work?

Introduction to AI and Machine Learning in Trading

By paying attention to price fluctuations, market volume, sentiment indicators, news feeds, and underlying fundamentals a day trader can develop reliable trading strategies. By using discipline in choosing, entering, managing, and exiting their trades it is likely that the day trader will profit more often than not on any given trade. Any information that improves one’s analysis of a rapidly moving market or discovers information hidden in massive amounts of data ought to lead to greater trading success. The rationale for using AI or artificial intelligence and machine learning is that this approach allows for faster analysis of more data than a day trader can handle without such tools. By using computer programs that learn as they go and improve their performance the expectation is that a day trader will see better and better trading results.

AI-Powered Trading Bots: How They Work

Automated trading has been around for a while. The most effective programs have been those owned and managed by folks with huge amounts of computer power as well as the money to track results and pay programmers to continually improve such programs. Although these programs can be very useful there can also result in big losses. That is because ever since the advent of computers we have known that if the initial assumptions or the programming of a computer program are problematic, the results will be awful. The old saying in the programming world is “garbage in, garbage out.” The idea behind artificial intelligence programs is that they learn as they go and correct their mistakes. They also work who massive amounts of data, more than a human being could process over the fractions of a second that these programs do their analysis. Thus the benefits of a well designed AI or machine learning tool are analysis of massive amounts of market information, faster processing of information, and discovery of tradable patterns that would not otherwise be evident with these tools.

No matter what strategies a day trader uses or how good their results are, it is always important to track one’s results and adjust such strategies accordingly. This need does not go away with machine learning and AI. If you are paying for a service or AI software with the hope of getting better trading results, you need to pay attention to whether your investment is paying off. Are you getting sufficiently more profitable trading that the extra expense in making your foray into AI profitable is worth it!

Real-Time Data Analysis with Machine Learning

What has been true in the computer world for years has been that while these programs can do repetitive calculations much faster than humans can, the results are only as good as the input data and the programming. A day trader at their trade station has to follow the market, pay attention to one or more technical indicators, follow the news, and consider how all of what they are paying attention to results in useful trading cues. Compared to the original trading algorithms, newer AI and machine learning programs are able to handle massively greater amounts of data, process decisions faster, and make sense of what they are processing without always having to regurgitate the data to a human analyst in order to improve their approach to trading. In an ideal world AI assisted day trading should result in greater profits. However, as these programs become common they end up competing against one another. Thus one’s performance relative to the larger market may not improve but merely prevent trading losses.

The Role of Big Data in AI-Driven Day Trading

Day traders use technical analysis tools like moving averages to make sense of price movements, recognize trends, and predict trend reversals. Such tools reveal things about the market that are not always easily recognized by looking at the raw data. The “big data” approach takes this a couple of steps farther. Programs that are able to process huge amounts of data in real time commonly recognize trends, reversals, and other information that would not be intuitive even to the savviest trader. This opens the door to creating strategies that can harvest profits from previously unrecognized market opportunities.

Challenges and Risks of Using AI in Day Trading

AI programs do not arrive at your computer and start from scratch. They are “trained” using the kind of data that that they will use later on. In the case of day trading this is market data from futures, options, stocks, or currencies. To a degree the usefulness of your AI program to your purposes will be only as good as the data on which it trained. All too often an AI program suffers from what is called overfitting. It only works on the data set on which it trained. Thus it will not provide useful analysis in your real world of day trading. As fast as computer programs are at processing data they are still computer programs and not intuitive human beings. Thus it is important to track your trades using this approach to make certain that the analysis that you are getting is making sense and creating profits sufficient to justify the time, energy, and money you are investing in this approach to day trading.