Bitcoin Price Predictions and Machine Learning: Some New Ideas and Results
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We have continued our work experimenting with cutting edge machine learning models for price predictions in the crypto space and have learned a lot of new things. During this session we covered: - The challenges of crypto-asset prediction models - New trends and ideas we are excited about - Some techs to follow - A brief note about DeFi and crypto-quant models Find out more at https://app.intotheblock.com/
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Bitcoin Price Predictions and Machine Learning: Some New Ideas and Results
- 1. Crypto Asset Predictions: New Ideas and Lessons Learned
- 2. Agenda ● The challenges of crypto asset prediction models ● New trends and ideas we are excited about ● Some techs to follow ● A brief note about DeFi and crypto-quant models
- 3. A quick update about ITB
- 4. New Customers Have Gone Live with ITB Analytics
- 5. Price Predictions for Dash are Live and Kicking
- 6. 6 Coming Up: Crypto vs. Capital Markets Release
- 8. This session is about new and practical ideas and challenges in our journey trying to predict the behavior of crypto assets…
- 9. IntoTheBlock Today ITB Data Platform ITB Analytics ITB Quant Services
- 10. Let’s start with a simple question…
- 11. We are seeing constant breakthroughs in all sectors of artificial intelligence so why not capital markets?
- 12. What Makes Capital Markets Challenging as a Deep Learning Problem? The Stability Challenge • Markets are not static entities and are constantly changing The Training Challenge • In markets, the past is not a reflection of the future The Irrationality Factor • Markets are regularly vulnerable to irrational human behaviors
- 13. Crypto brings a new series of challenges to price predictive models and quant strategies…
- 14. A Different Set of Challenges in Crypto Asset Predictions Benefits Challenges
- 16. How Do You Retrain a Prediction Model to Handle This…?
- 17. How About This…?
- 18. Despite the challenges, crypto is a phenomenal playground for predictive models…
- 19. Understanding crypto asset predictions as a time-series forecasting problem…
- 20. Some Key Characteristics of Crypto as a Time- Series Forecasting Problem Multi-Variable •Many, interrelated variables can lead to predictions •Ex: order books spreads, blockchain exchange flow average… Multi-Steps •Prediction problems target multiple steps into the future •Ex: Predicting Bitcoin prices every 5 mins, 10 mins, 60 mins… Multi-Series •Datasets includes thousands of related time-series •Ex: order books, blockchain transactions… Seasonal •Vulnerable to seasonality and market events •Ex: Launch of a new token, protocol changes…
- 21. The Evolution of Time-Series Forecasting Methods Statistical Methods Traditional Machine Learning Deep Learning Complexity of the Environment Lack of Interpretability
- 22. ● ARIMA, DeepAR+, Prophet ● Easy to implement and fast to execute ● Poor resiliency to market fluctuations ● Limited number of potential predictors ● Hard to estimate predictors ahead of time 22 Time Series Forecasting Models
- 23. ● Computationally expensive to execute at scale ● Difficult to interpret ● Many of the benefits such as automated feature extraction are hard to materialize ● Great to tackle sophisticated theses 23 Deep Learning Models
- 24. 24 Crypto and Time-Series Forecasting Methods Statistical Methods Machine Learning Methods Deep Learning Methods
- 25. Some new ideas about crypto-asset predictions we are excited about…
- 26. Two Fundamental Challenges… Accelerate Experimentation • Rapid experimentation is essential to uncover alpha in crypto markets Incorporate Advanced Research Methods • Research in times-series forecasting methods is growing fast but remains difficult to adapt
- 28. Accelerating Experimentation with Crypto Predictive Models
- 29. A Different Mindset to Tackle Crypto Asset Predictions
- 30. Two Trends to Be Excited About Representation Learning AutoML + Neural Architecture Search
- 32. Automated Machine Learning + Neural Architecture Search
- 33. Putting it All Together
- 34. Super Cutting Edge WAYMO UBER ENGINEERING GOOGLE AI BLOG
- 35. Evaluating advanced time-series forecasting research ideas…
- 36. Deep Learning Breakthroughs are Reaching Major Milestones
- 37. And Those Techniques Are Being Adapted to the Time-Series Forecasting Space
- 38. And Those Ideas are Being Tested
- 39. Temporal Fusion Transformers: GPT-3 for Time Series
- 40. Some new technologies we have adopted and are excited about….
- 41. Building a State of Art Infrastructure for Crypto-Asset Predictions AutoML Stacks • Streamline the execution of many experiments in predictive models • Ex: Azure AutoML, AWS AutoPilot, Amazon AutoGluon… MLOPs & Interpretability Stacks • Facilitate the interpretability of complex deep learning models • Ex: Fiddler AI… Feature Stores • Store and manage features used in crypto prediction models • Ex: Tecton, AWS SageMaker Feature Store, HopsWorks…
- 42. A quick note about DeFi and crypto- predictive models…
- 43. DeFi represents a new and more transparent playground for high frequency trading strategies….
- 44. From Programmability to Intelligence Programmability Intelligence Value
- 45. The Emergence of Intelligent DeFi Protocols Intelligent AMMs Alpha Generating Protocols Intelligent Robo Advisors
- 46. 46 ● The future of crypto asset predictions is based on deep learning models ● Representation learning and AutoML are trends that can streamline productivity in the creation of crypto quant strategies ● Time-series forecasting research is accelerating at a rapid pace ● DeFi represents a new playground for intelligent quant strategies Summary
- 47. 47 Next Webinar