1) some thoughts on the liquidity metric:
First, it's definitely better than what you've had before! This table isn't crazy:
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Suprised to see a lot of exchanges above FTX. LATOKEN, really?
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As mentioned, we can only report what exchange report on their public API, and evaluate the data based on that. The only real way to test would be to send actual orders into the book and evaluate. We would appreciate any feedback based on that testing. 🙂
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One thing you can do is the BBO-trades test: see what % of an exchange's fills happen on the BBO at the time of the trade, and throw out any exchange with < 40% of volume trading on the BBO. This catches ~90% of fake exchanges.
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(1/3) We think this form of testing is heavily dependent on the threshold of time defined for "BBO". More sophisticated wash trading techniques focus on RNGing this element (shouldn't be too difficult to do), waiting for 1 sec or so, before trading themselves out.
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(2/3) If we went down this path, we would need to continuously adjust the "time" variable and make the model unusable because we would just get too many false positives. In essence, the detection of wash trading needs to be done via
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(3/3) Account-ID information and graph modeling the relationship between accounts and trades to fully appreciate if there is any malpractice here. We simply do not have that information to make very precise determination of wash trading, and can only guess/estimate.
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Totally hear you--but as it turns out right now most exchanges are lazy enough at faking volume that just doing something approximate is close enough. You definitely do get false positives, but if you set the bar at 40% then any exchange that's legit will pass.
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Fully agree. We have decided to take an alternate path of using the data that is on hand (web traffic, liquidity, time and sales) to predict volumes via a trained model and track reported vs predicted, and flag out errant exchanges instead. Will be launching this soon! 😀
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