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

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. 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
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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. 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
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5. Overview what you can expect in this presentation
What did you
find?
How did you
find it?
What next?
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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
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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
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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
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9. Findings: time between spoofs is highly predictable
Products with higher trade
frequencies had shorter
times between spoof events
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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
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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
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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
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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
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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. 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
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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
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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
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17. Improve the ecosystem
Tools & information to help you make intelligent trade
decisions
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bitsian
» steve@bitsian.io
» www.bitsian.io
» our booth