AI trading software has given people access to a large range of securities and other investments. Folks who are interested in adopting AI trading system software should think about the basic features of their setup before committing to a product. Typically, traders should seek systems that have the following four components.

Multiplatform Integration

Whenever the system detects a sell or buy signal, you want it to jump on the opportunity as soon as possible. To that end, it needs to integrate with all of your platforms.

This is particularly true if you're using mixed strategies. If you're using strength in the Yen to hedge against U.S. equity weakness, for example, you might not find both of those trading options on one platform. The key to executing a strategy may be integration on two or more third-party systems. Consequently, AI needs integration with those platforms so it can check prices and execute trades.


Pulling in data is no small job, especially if you're making micro decisions based on macro trends. Your AI trading system software should pull data from an array of sources. Many of these will be publicly-available data sources, such as government-sponsored economic indicators and exchange-based indices. Fortunately, many of the integrated platforms will also provide data streams so you can often plug a huge amount of information into your AI's model.

Notably, you'll want to check the data's accuracy and latency. Even if the data is good, the trading world operates in milliseconds. If a platform delivers streams with, for example, a 20-minute delay, that could be brutal day trading and other timing-based strategies.


Even the best AI models are going to deviate from your trading style. Likewise, a model is only good if it's a good match for your goals. You want AI trading software that allows you to tightly customize the settings. For example, a person preparing for retirement in 10 years will likely want their setup to have less of an appetite for risk than someone who's trading during their summer break from college.


Whenever you devise a strategy, it's best to see how it would've performed against historical trends. In the business, this is backtesting. The goal of backtesting is to see if a strategy would've performed well in a different historical environment.

For example, a strategy focused on construction trends might have gone to zero during the 2007-09 financial crisis due to its focus on the real estate boom. Conversely, the same strategy would've performed fairly well during the peak COVID pandemic years. Backtesting across many decades improves a model's survivability. For more information on AI trading software, contact a professional near you.