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bitsian proof of spoof

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Analysis of spoof orders on crypto exchanges. Quantitative analysis of public market and trade data to develop evidence of spoof orders.

Published in: Data & Analytics
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bitsian proof of spoof

  1. 1. Nearly 100,000 confirmed incidents of spoofing based on one month - one exchange - three coin pairs Proof of Spoof Prepared for The Trading Show Chicago, USA - May 8th, 2019
  2. 2. Spoof?A spoof order is meant to manipulate the market while trading as little as possible most commonly done by displaying the bid or offer quantity that is removed before of the quantity is traded 2
  3. 3. We aim to improve the cryptocurrency ecosystem, making it easy for everyone make smart decisions and participate confidently 3I am Steven Brucato 30 years serving traders with technology at leading institutions
  4. 4. WHY IS BITSIAN DOING THIS RESEARCH? » We care: a better ecosystem is good for everyone » We can: we aggregate markets & have the talent » We should: good data means better decisions for you 4
  5. 5. Overview what you can expect in this presentation What did you find? How did you find it? What next? 5
  6. 6. Findings: breakdown of Spoof by Coin Three Pairs Jan 2018 Spoof BCH-BTC 24,110 ETH-BTC 19,525 LTC-BTC 55,054 Total 98,698 6
  7. 7. Findings: Spoof timeframes are predictable - High predictability between trade and spoof events - Trade trigger is at time 0 (bottom) with spoof event time shown vertically [graph] - Mean time from trade to spoof is 3 seconds 7
  8. 8. Findings: High correlation between trades and spoofs The number of spoof events detected by the filter correlates very highly with the number of trade events 8
  9. 9. Findings: time between spoofs is highly predictable Products with higher trade frequencies had shorter times between spoof events 9
  10. 10. Findings: the magnitude of spoof events is highly predictable The magnitude of spoof events - almost always consists of near 100% of the bid/offer quantity being removed - is the percentage of the display quantity increase 10
  11. 11. Findings: In summary » This is a predictable strategy » Not trying to conceal itself (no concerns for surveillance) » Slow process compared to spoofing in mature markets » All manipulative trading strategies have patterns » We have filters for multiple patterns » The analysis of this specific market is ongoing 11
  12. 12. How did you find Spoofing? Getting Started To get going, we had to first answer » what behaviors might be identifiable in market and/or trade data? » If we could find clear mathematical evidence of spoof/washtrading » which of these bad behaviors we wanted to identify 12
  13. 13. How did you find spoofing? Building tools to detect spoofing Data We started with historical market and trade data to develop filters that identify these behaviors Creating Filters Statistical analysis Machine learning Pruning Picking and choosing the best filter 13 We took a statistical approach for this market, using historical trade and market data (DOB) to create a filter to detect spoofing events events
  14. 14. How did you find spoofing? Looking closer » A single pattern where bid/offer quantity was removed from the market after a trade event » Differentiating intentional spoof vs organic cancelled orders Our filter » Ignored bid/offer qty reductions due to trade events » Compared trade events to spoof events » Measured magnitude of qty reduction pre vs post trade event » Measured time between trade events and the spoof events 14
  15. 15. What is Next? Transparency and Clarify makes everything better » Spoofing gets much more advanced » We have a variety of filters for that » Our real-time advanced filters help » identify more elaborate spoofing and other trading patterns » predict events slightly before they happen » Advanced filters will be implemented on live market data for » predictive inputs for our algorithms » clear info on real market dynamics for our customers 15
  16. 16. What is Next? Transparency and Clarify makes everything better » Our Why » By exposing bad behaviors we improve the overall quality of digital asset markets, making the ecosystem better. » Free Quarterly Studies » Many markets don’t have egregious spoofing. » We will publish activity on many markets to compare events 16
  17. 17. Improve the ecosystem Tools & information to help you make intelligent trade decisions 17 bitsian » steve@bitsian.io » www.bitsian.io » our booth

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