How to Build a Successful AI Trading Platform

The world is being automated. Today, all sectors of the economy are being automated. Even finance, a field that seemed immune to disruption is now seeing automation now more than ever. Today, many robo advisors have come up and are managing billions of dollars. Last month, Blackrock, the biggest fund manager in the world announced that it would invest in automated trading. Earlier on, Ray Dalio who runs the biggest hedge fund in the world announced that he would invest in artificial intelligence to boost his returns. As a trader, this is the same direction that you should be aiming at. In this article, We will highlight a few strategies to help you build a successful AI trading platform.

  • Not easy

We are in the early days of artificial intelligence. As such, building a successful artificial intelligence tool to automate your trading is not easy. In fact, you need to understand that it will take a significant amount of time to build and test a new AI platform. It will also take a lot of time to learn and come up with a code that will help you in this. Therefore, you need to understand that it won’t take a short period to develop this platform. To develop the AI platform, you also need to have some background in computer science. If you don’t, I recommend two things. First, you can partner with someone who has a background in software engineering. Second, you can use the ‘traditional’ algorithmic trading platforms which are provided by your broker.

  • Data Source

To develop a successful AI platform, you need high quality data. This data should be fast and accurate. In automated trading, traders deal with micro-seconds. The faster the data source the better it is for you. Therefore, you should ensure that you have the best data source that is not only fast but also accurate. You should now incorporate this data to your AI platform.

The data itself is not useful if you don’t know how to use it. In AI, you need to understand how to interpret the data and how to incorporate it to your trading thesis. There is a lot of data in the financial market. You should know the data that is useful and the one that is not.

  • Come up with the code

After finding a credible data source, you should now come up with a code. This should be a code that will control your trading. The code should be integrated with the data that you intend to use. In the code, you should have a set of parameters that will guide your trading. For instance, a code can state that when PMI manufacturing data exceeds 50, then a buy trade on DOW should be opened.

  • Test the code

Creating the code should take a few months. You should not develop the code in a hurry. This is because the code will determine your success. After creating the code, you should spend a few weeks or months testing it. This is probably one of the most important steps in this process. An AI platform that is not tested is a trap. After testing, you should now execute it on your trades.

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