Our initial report focused on web data statistics, which we broke down into unique visitor counts in which the site was directly accessed, to hone in on actual traders and not referral traffic.
This report pointed us in the right direction for our next report in November, which added mobile app and API data to try and capture more volume avenues. We interviewed and got screen shots from a ton of exchanges which we used to estimate data on the one’s that did not cooperate. Once we had web volume and mobile volume numbers and a “total user volume” metric, the rest we attributed to API trading.
During this time, we were recording order books, spreads and trade history of many of these exchanges for hours on end. We found an endless stream of insantly matched exact orders on many of these exchanges.
From here, we took the exchanges with higher than 80% API trading numbers and begun comparing different volume metrics and graphs we have against exchanges with similar “total user volume”, and begun to see stark differences.
The patterns of a wash trading exchange became clear and so we broke this data down even further to determine true volume based off outliers when comparing their data, in addition to our recorded live data. Determining these outliers is not so different than current trade surveillance software.