This document discusses challenges with measuring liquidity and detecting bad behaviors in crypto exchanges. Traditionally, order books and web activity are analyzed but are easy to manipulate. The document proposes analyzing exchanges at the blockchain level by using machine learning to classify exchange wallet addresses and transactions. This could provide more robust liquidity measures like inflows/outflows correlating with order books, exchange inventory levels, and liquidity provider interactions. Five new ideas for blockchain-based analysis are described to tackle exchange liquidity in a way that is difficult for bad actors to manipulate.

1. Liquidity and Fake
Volumes in Crypto
Exchanges: Some
New Ideas

2. Agenda
● Liquidity and bad behaviors in crypto
exchanges
● The challenges with the traditional
method
● The blockchain angle
● Some new ideas for measuring liquidity
in detecting bad behaviors in crypto
exchanges

3. Why crypto exchange
liquidity matters?

4. Crypto
Exchange
Liquidity
Crypto exchanges regularly manipulate key
market indicators
Liquidity is a key indicator of the viability of
an exchange
Key indicator to detect bad behaviors
Fundamental to trading strategies

5. Fake volumes in exchanges are
one of the biggest challenges in
the crypto market

6. All Sorts of Bad Behaviors
Wash
Trading
Spoofing Front
Running
Tape
Painting

7. Wash Trading
• Simulate buy or sell
activity by trading with
the same person or entity
• Often used to generate
fake volumes or trigger
commission fees

8. Spoofing
• Fake large orders to
create the illusion of buy
or sell pressure
• Its used to manipulate
market sentiment

9. Front Running
• Used by exchanges to
take advantage of buy or
sell orders before its
customers can
• Very common in high
frequency trading
scenarios

10. Painting the Tape
• Similar to wash trading
but with multiple
participants
• Multiple players create
the illusion of market
activity by trading among
themselves

11. Exchange fake volumes and
liquidity remain a white whale in
the crypto market

12. The issue
became front
and center
after Bitwise’s
SEC report

13. The report outlined some of the main
market manipulations in crypto
exchanges….

14. But the analytical techniques used
were, in and out itself, very easy to
manipulate

15. Since then, the market has been
obsessed to create an effective
methodology to measure real exchange
volumes and liquidity

16. 16
Most Methodologies Focus on Two Core Datasets
Order
Books
Web
Activity

17. Those are valid ideas but have a few
challenges…

18. Both datasets are relatively trivial to
manipulate…

19. Order books analysis is effective to
detect patterns and anomalous
activity but an inefficient metric of
liquidity…

20. It becomes a race. For any new
liquidity methodologies, bad actors
can create new manipulations that
adapt to it...

21. The exchange liquidity challenge can’t be solved
with a single score and it rather requires a
combination of solutions each one tackling
specific dimensions of the problem….

22. Let’s look at a different dimension
of the problem….

23. Real centralized crypto exchanges need
to interact with blockchains to transfer
funds in and out of its accounts…

24. And that activity is hard to fake
and manipulate…

25. The challenge is that centralized
exchanges remain black boxes that
result incredibly hard to analyze…

26. But what if we could understand
crypto exchanges at the
blockchain level?….

27. Used to
transfer
funds out
of the
exchange
Used to
transfer
funds into
an exchange
Used to secure
the storage of
crypto-assets.
The main
interaction point
between
external parties
and an
exchange.
Hot
Wallets
Cold
Wallets
Withdrawal
Addresses
Deposit
Addresses

28. Using Machine Learning Classification Methods we can
Get a Solid Understanding of the Architecture of a
Centralized Exchange

29. Single-Hot Wallet Architecture: Binance

30. Dual-Hot Wallet Architecture: Poloniex

31. How can blockchain analysis become a
robust measure of exchange liquidity?

32. 5 new ideas for analyzing exchange
liquidity and bad behaviors based on
exchange data…

33. Idea #1
Measuring inflows and outflows
becomes an effective way to detect
liquidity patterns…

35. Idea #2
Exchange netflows should maintains
a regular correlation with the
corresponding orderbooks.
Variations of that rule could be signs
of anomalous activity…

37. Idea #3
The inventory held in exchange’s
deposit addresses, hot wallets and
cold wallets is a relevant measure of
liquidity…

39. Idea #4
Exchanges should regularly interact
with known liquidity providers

40. Liquidity
Provider

41. Idea #5
Withdrawal addresses in an
exchange should have a uniform
liquidity distribution

43. 43
● Exchange liquidity and fake volumes remain real challenges in the crypto
space
● Addressing this challenge will require a compendium of different solutions
instead of a magic metric
● Machine learning classification methods enable the deanonymization of a
relevant percentage of the blockchain records related to crypto exchanges
● Deanonymized blockchain datasets offer a unique angle for analyzing
liquidity and fake volumes
Summary

45. INTRODUCING
Jesus Rodriguez
jr@intotheblock.io
medium.com/@jrodthoughts
intotheblock.com
Thank You!