The role of data in the accuracy of AI models

Artificial intelligence has been a topic of much discussion in recent years as it becomes a larger and larger part of our daily lives: Examples include the rise of targeted ads, AI image generators and, most recently, the AI chatbot ChatGPT created by OpenAI. Its astonishing ability to produce lengthy and informative answers to all your questions in just one click – due to its access to a vast number of databases – has floored users. However, people are noticing that it can also periodically produce inaccurate answers, tripping users up on their quest for information. In the world of algorithmic trading, of which AI plays an integral part, inaccuracy is detrimental – so how must data be handled to avoid it?

Like ChatGPT, a good AI model needs access to high quantities of data to start providing reliable information, but how can we ensure that the results of a trading model are consistently accurate and mirror market trends? Well, to cite the well-known proverb, what goes in must come out. Quality is just as important as quantity here: If the data on the input side is taken from untrustworthy sources as well as processed poorly, one can be sure to end up with, in short, “garbage” on the output side.

The role of data when building AI models is the constant examination and processing of data quality behind the scenes. When any minor error presents itself, data scientists must step in to discover which factors may have produced this change. They must “teach” the model that certain variables cause changes or errors in the output, and the model “learns” over time to identify the kinds of variables which produce favourable results.

Unfortunately, many data scientists dedicate a disproportionate amount of time to the building of these models, and only a small fraction of time can be allotted to running them confidently: This is where Systemathics can help.

An important rule is never to trust data from a single source, even if the provider seems to be “safe”. Systemathics’ 24/7 data robots independently collect many different types of data from multiple sources – tick-by-tick, intraday, daily, corporate actions and sustainability data - before they are cleaned of invalid values, normalized and given a rating by built-in quality and trust indicators.

It is safe to say that without quality data readily available - and without proper methods in place to monitor its quality - there would be no AI models (at least, no accurate ones.) Systemathics ensures that normalized, fully validated data is uploaded to a private cloud where our users can easily access it at any time, allowing them to develop their trading models in record time while drastically reducing their operational risk.