7th Webinar: Patterns, Predictions and Fascinating Metrics from Cryptocurrency Order Books
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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
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7th Webinar: Patterns, Predictions and Fascinating Metrics from Cryptocurrency Order Books
- 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
- 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