Our CTO & Co- Founder, Jesus Rodriguez unveils a series of new and unique analytics for order book datasets from cryptocurrency exchanges. We highlight some unique insights that arise when combining exchange order books and blockchain datasets. Finally, we show how order book metrics can be used

1. Patterns, Predictions
and Fascinating
Metrics from
Cryptocurrency
Order Books

2. Agenda
❖ The goal of order book analytics
❖ Traditional metrics
❖ The crypto asset angle
❖ Some provocative ideas

3. INTRODUCING
A few obvious questions to start…

4. Why should we pay attention
to the behavior of order books?

5. 5
Some Reasons to Consider
There is an average of over 12x more volume traded in exchanges than what is
reported on-chain
Binance Bittrex
Reported - On Chain

6. Exchange
Products
On-Chain
Activity
Crypto
Asset
Behavior
A Different Dimension to Understand Crypto-Assets

7. What’s wrong with the order
book of exchanges?

8. 841
Nothing…
But…
Cross exchange analysis
The combination of exchange activity
and order book analysis provides a
more complete version of the
behavior of a crypto asset
Detecting and understanding the
“non-conventional” patterns in the
order books of crypto-exchange(fake
volumes, wash trading, etc)

9. INTRODUCING
Traditional metrics for order book analysis…

10. Bid-Ask Spread:
The difference
between the bid
price and ask prices
for a particular
security

11. Market Depth: A
window that shows
the number of open
buy and sell orders
for a security or
currency at different
prices

12. INTRODUCING
What else can we do?

13. MORE SOPHISTICATED
ORDER BOOK ANALYSIS
SIGNALS THAT COMBINE
BLOCKCHAIN DATASETS AND
ORDER BOOK FEES
SIGNALS TAILORED TO THE
“VERY PARTICULAR”
BEHAVIORS OF CRYPTO
EXCHANGE ORDER BOOKS

14. Order Book
Data
Blockchain
Data
Machine
Learning
The Magic is
here!

15. INTRODUCING
What we did…

16. Order Book Data Streams
Blockchain Datasets
Machine Learning &
Statistical Models
Orderbook Intelligence

17. INTRODUCING
More sophisticated trading signals…

18. 18
The Reasoning
● There are decades of quantitative finance research based on order book
patterns
● Some highly used quant signals apply seamlessly to crypto assets

19. 19
Trades per Side
Measures the number (or volume) of trades where the buyers “crossed the spread” and
bought at the Ask price vs the number (or volume) of trades where sellers “crossed the
spread” and sold at the Bid price, per minute.

20. 20
Trades per Side Multi-Exchange

21. 21
Smart Price
A variation of mid-price where the average of bid and ask prices is weighted according to
their inverse volume.

22. INTRODUCING
Orderbook + Blockchain Analysis…

23. 23
The Reasoning
● Correlations between order book metrics and on-chain volume represents
relevant indicators in areas such as liquidity and risk.
● Flows of funds in and out of exchanges directly impact the behavior of
order books.
● Relationships between order book and on-chain inventories help
understand anomalies in crypto exchanges.

24. 24
To Do This We
Need to
Understand the On-
Chain Behavior of
Crypto Exchanges

25. 25
Exchange-On-Chain Market Depth
Shows the number of open buy and sell orders at different prices (bottom graph) and the
on-chain positions at those price ranges

26. 26
Order Book Spreads vs. On-Chain Flows (Binance)

27. 27
Order Book Spreads vs. On-Chain Flows (Bittrex)

28. 28
Order Book Spreads vs. On-Chain Inventory (Binance)

29. 29
Order Book Spreads vs. On-Chain Inventory (Bittrex)

30. INTRODUCING
Leveraging the power of machine learning…

31. 31
The Reasoning
● Machine learning and deep learning models can output results beyond
traditional statistical models
● Machine learning models can shade some light over the strange behavior
of crypto exchanges

32. Problem
Exchange order book
information is plagued
with bad data and wash
trading records
Solution
A machine learning
classifier can identify
potential fake trades and a
percentage of fake
volume.
Scenario: Fake Volume and Wash Trading Detection

33. 33
On-Chain vs. Reported Volume Ratio(Binance)

34. 34
On-Chain vs. Reported Volume Ratio(Bittrex)

35. Problem
Order books indicators are
some of the strongest
predictors of SHORT-TERM
price movements in an
asset, but most statistical
models fail to capture those
relationships
Solution
New generation deep
neural networks have
shown strong potential for
creating short term
prediction models across
different asset classes
Scenario: Price Prediction Direction

36. We are working on it!!
Next Webinar
Crypto asset predictions,
challenges and crazy
ideas using deep
learning

37. INTRODUCING
A quick message about IntoTheBlock…

38. 38
Summary
● Order book datasets provide an incredibly rich source of information about
crypto exchanges
● Order book records and a natural complement to on-chain datasets
● To obtain meaningful intelligence we need to look past the traditional
order book signals
● The combination of on-chain and order book dataset reveals fascinating
patterns about the behavior of crypto assets

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