2. Agenda
2
â The challenges of centralized exchanges for crypto
analytics
â Why understanding centralized exchanges is a machine
learning problem?
â What machine learning can tell us about centralized
exchanges ?
4. Centralized Exchanges
4
â Are one of the most important components of the crypto
ecosystem
â Represent a significant portion of the daily trading volume
â Exchange inflows and outflows represent an important
metric of the behavior of the crypto ecosystem
6. Blockchain Analytics Nightmare
6
â Exchange trades are not always recorded in public
blockchains
â Volume manipulations
â Wallet anonymity
â Transaction composition patterns
8. 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
16. Ensemble Learning
⢠A machine learning technique in
which multiple learners try to
master the same task and are
combined in a single master
model
⢠Ensemble models in machine
learning combine the decisions
from multiple models to
improve the overall
performance
⢠Work efficiently on diverse
datasets
18. Inside
IntoTheBlockâs
Exchange Classifier
ITB Exchange
Intelligence
Obtain
features from
the main
actors in
Ethereum
blockchain Create
ensemble
of models
Train on the
Ethereum
blockchain
Predict and
evaluate the
ensemble in the
Ethereum
blockchainGeneralize
the ensemble
to other
blockchains
Test and predict
on non-Ethereum
blockchains
Add blockchain
specific
heuristics
Optimize
model
19. What we Learned by Analyzing Crypto
Exchanges Using Machine Learning and
Fascinating Data Visualizations
39. 3940
Some Challenges Worth Mentioning
â Continuous Training: There is not enough labeled datasets about
centralized exchanges for most cryptocurrencies.
â Architecture Changes: Exchanges regularly change their transaction
processing patterns.
â Privacy/Encryption: Blockchains like Monero or zCash obfuscate information
related to exchanges.
40. 4041
Summary
â ML First: Analyzing crypto exchanges is a machine learning problem
â More is Better: Combining many machine learning models into ensembles is
an efficient technique to tackle the classification of centralized exchanges
â The Core Architecture: The are four key components of centralized
exchanges: hot wallets, cold wallets, deposit addresses and withdrawal
addresses.
â Lots of Insights: The analysis of centralized exchanges reveals fascinating
insights that can provide an edge to researchers and traders in crypto
markets