Digital Personas are one of the TradeConnect network’s key features, so we thought it would be a good idea to brief you on just how they work.

We’re using machine learning and complex AI to analyse a decade’s worth of data acquired from ThinkMarkets to tag traders with a ‘score’ as they interact with the platform, which we’re calling Digital Personas. These will allow the system to match trades better, and improve the liquidity and efficiency of our network.

There are two key forms of these personas:

The first, the Trade Quality Persona, takes data from a user’s trades, including profit/loss and the way the trader interacts with the system, and pools the data to create a persona that represents and rates how a user trades. This score will adjust as a user continues to use the platform, and allows traders to guide the actions they take. As an example, a taker can choose to only accept trades that have a high-quality score based on the open times of the trade in order to reduce the spread and fees.

The Price-Matching Persona, our second structure, creates a score that takes note of the prices that a user trades in relation to the rest of the network. The score is given relative to how close to the ‘mid-point’ a user’s trade is, and will encourage participants to compete for the right to match trades. As the network grows this will result in effective pricing and improved liquidity.

Put simply, personas allow traders to operate with a level of precision and control never before seen in financial markets.

To learn more about the technology, read our whitepaper, available here.