The document discusses the challenges and methodologies in predicting the performance of crypto assets using deep learning techniques. It highlights two primary approaches: asset-based predictions targeting specific assets and factor-based predictions focusing on groups of assets, alongside various data sources for predictions such as order books and blockchain data. Additionally, it addresses common myths about predictive models and outlines the deep learning process for developing these models in the context of crypto-asset trading.

1. Crypto-Asset
predictions, challenges
and crazy ideas using
deep learning

2. Agenda
❖ A gentle introduction to predictions in capital markets
❖ Prediction In crypto assets
❖ Myths and realities of crypto asset predictions
❖ Time series vs. deep learning architectures
❖ Some ideas to explore

3. INTRODUCING
Predictions are the holy grail of machine
intelligence applied to financial markets…

4. INTRODUCING
Predictions focus on forecasting the
performance of one or a group of assets based
on a specific set of attributes….

5. INTRODUCING
There are two main schools of thought to
approach asset predictions in capital markets…

6. Asset-Based Factor-Based

7. Asset-Based
Predictions
Ex: Predict the price of Bitcoin
in the next 12 hours
Focus on predicting the
performance of a single asset
based on a specific criteria
Typically factors in specific
characteristics of the target
asset

8. 8
Asset-Based Predictions
Pros
• Rich models based on the specific characteristics of an asset
• Possible to achieve high levels of accuracy during short periods of
time
• Simpler to retrain and validate
Cons
• Exposure to a single asset within a market
• Hard to assemble in cohesive portfolios
• Becomes challenging to combine different predictions as part of
portfolios

9. Factor-Based
Predictions
Ex: Predict is cryptos with strong
momentum will outperform in
the next 24 hours
Focus on predicting the
performance of a group of assets
based on a set of factors
Typical factors include aspects
such as value, momentum, carry,
volatility, quality, etc…

10. 10
Factor-Based Predictions
Pros
• Minimum exposure to a single asset within a market
• Ability to formulate sophisticated thesis across an entire portfolio
• Exposure to non-correlated factors and hedging models
Cons
• Prediction models need to ignore unique characteristics of a
given asset
• Prediction models might miss unique investment opportunities in
a specific asset within the portfolio

11. Price Predictions in Crypto Assets

12. 12
Benefits and Challenges of Predictive Models for
Crypto Assets
Benefits
• Asymmetrical opportunity to uncover alpha generating strategies
• Unique data sources
• Irrational market with massive potential upsides
Challenges
• Small and inaccurate datasets
• Fake volumes and wash trading
• Lack of labeled datasets
• Limited non-correlated factors
• Inability to leverage existing quant models
• Black swan events
• Irrational markets

13. Different
Prediction
Data Sources
EXCHANGE
ORDER BOOKS
BLOCKCHAIN
RECORDS
DERIVATIVE
CONTRACTS
ALTERNATIVE
DATASETS
PROTOCOLS

14. Predictions Based on Order Book Datasets
14
Focus on predicting short term performance
fluctuations for an asset or a factor
Leverage attributes such as price, volume as well as
order book bids, asks and trades
Models are vulnerable to the poor quality of crypto
exchange order book datasets

15. Predictions Based on Blockchain Datasets
15
Focus on predicting medium to long term performance
trends on a crypto-asset or a factor
Leverage attributes such as hash rates, exchange flows,
wallet balances, etc.
Models are very unique to crypto assets and can’t take
advantage of existing quant research

16. Predictions Based on Derivative Contracts
16
Focus on predicting the short or medium-term
impact of derivative contracts in crypto markets
Current prediction models would necessarily be
asset based
Unique insights when combined with blockchain
datasets

17. Predictions Based on Protocol Contracts
17
Focus on medium to long term predictions of the
performance of specific protocol
By definition, prediction models will be asset based
Vulnerable to unexpected changes and versions of
a given protocol

18. Predictions Based on Alternative Asset Datasets
18
Focus on short term predictions based on sources like
Twitter, Telegram, News, etc.
Leverage natural language understanding techniques to
extract information from text data sources.
Largely used as a complement to more sophisticated
prediction models

20. 20
The Super Predictor Myth
Myth
• A single attribute can predict the price of a crypto
asset
• Ex: NVT score can predict the price of Bitcoin….
Reality
• Most prediction models require a combination of
many factors

21. 21
The Super Model Myth
Myth
• A single model can predict the price of a crypto asset
across different market conditions
• Ex: A model based on technical that can regularly predict
the price of Bitcoin
Reality
• Most models only work under specific market conditions
and they tend to overfit for those

22. 22
The Price-Volume Myth
Myth
• All characteristics of a crypto asset are captured in
price and volume sentiment
Reality
• Blockchchain and derivatives datasets offer unique
insights about crypto assets that are impossible to
capture in price and volume models

23. Predictive Model Approaches: Time Series vs. Machine Learning

24. Time Series
Prediction
Models
24
Statistical models that focus on predicting a variable
based on linear correlations with other attributes of
the dataset
The value of the predictor attributes in the future
should be known in advance
Most algorithms are based on variation of linear
regressions
Ex: predict the price of Bitcoin based on the expected
settlements in Bitcoin futures
Algorithms: ARIMA, ARIMAX, VARMAX

25. Machine
Learning
Prediction
Models
25
Neural network architectures that forecast a variable
based on hierarchical, non-linear relationships
between other predictors
Analyze complex relationships in sophisticated
datasets
Different schools of thought: supervised,
unsupervised, semi-supervised, reinforcement, etc
More prompt to discover alpha in efficient markets
Proven algorithms for financial markets: long-short-
term memory networks, convolutional neural
networks, multi-layer perceptrons

26. INTRODUCING
IntoTheBlock Predictions: Democratize machine
learning predictions for crypto-assets for the
mainstream investor

27. 27
What we are doing? : Building a series of
deep learning models based on different
datasets: order book, futures, blockchain
data

28. The Process
28
Deep
Learning
Process for
Crypto
Predictions
Prepare
dataset
Extract
features
Train model
Deploy
model
Evaluate
predictions

29. Example: CNN+LSTM
for Order Book Data
• The input is an image of the order
book from multiple exchanges
• A CNN layers detecting features for
the order book data
• A time distributed layer identifies
patterns for a specific timeframe
• A LSTM layer that aggregates all the
individual patterns into a target
prediction
29

30. Example: Multi-Layer
Perceptron for Order
Book Data
• The input is based on trades in the order
book of different exchanges
• A layer that obtain connections between
different features
• A series of layers that detect more
sophisticated patters
• Another layer that limits potential
overfitting error
• A prediction layer that aggregates the
patterns into potential predicitons

31. 31
Summary
● Crypto assets offer a blank canvas for prediction models
● There are different data sources of predictions for crypto assets: order
book, futures, blockchain, alternative …
● The two main schools of thought for prediction are : time series and
machine/deep learning
● ITB is working on prediction models for crypto assets based on deep
learning

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