Price PRedictions for Crypto-Assets Using Deep Learning
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This slide deck provides an overview of the universe of prediction techniques applied to cryptocurrencies. The content covers emerging prediction models in the deep learning field and how they apply to crypto-assets.
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Price PRedictions for Crypto-Assets Using Deep Learning
- 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…
- 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
- 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