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Quant in Crypto Land
The document discusses the unique characteristics and opportunities of quantitative strategies in the cryptocurrency space, highlighting the challenges posed by small datasets and market inefficiencies. It emphasizes the potential of decentralized finance (DeFi) and new deep learning methods for developing informed trading strategies. Additionally, it addresses the importance of adapting traditional quant strategies to fit the dynamic nature of crypto assets.
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Quant in Crypto Land
- 1. Quant in CryptoLand Jesus Rodriguez IntoTheBlock
- 2. QUANT IN CRYPTOLAND ITB Analytics Platform ● Over 150 customers that are currently distributing its data under different integration models. ● Analyzing over 50TB of data ● Leveraging machine learning to derive actionable insights about crypto assets FINTECH AND NEWS SITES (45) WALLETS (5) DATA PROVIDERS (9) BLOCKCHAIN PROJECTS (5) EXCHANGES (35)
- 3. Quant Intelligent ServicesUnit Traction ● ITB has partnered with the top quant funds in the industry to provide alpha- generating intelligence. ● Over $500M deployed in ITB crypto quant strategies ● ITB receives a % of the profits generated by each quant fund’s strategy that uses ITB services. QUANT IN CRYPTO LAND
- 4. Agenda ● Unique characteristicsof quant strategies in crypto ● Unique sources of alpha ● DeFi as a new frontier for HFTs ● Inefficiencies in crypto ● NFTs as a new form of asset class ● Why most crypto quant strategies fail? ● Cutting edge deep learning and crypto quant strategies QUANT IN CRYPTO LAND
- 5. Some factors thatmakes crypto unique for quant strategies...
- 6. QUANT IN CRYPTOLAND Unique Sources of Alpha New Programmable Financial Primitives Regularly Inefficient Market Four Core Factors New Types of Digital Assets 1 2 3 4
- 7. Unique Sources ofAlpha… QUANT IN CRYPTO LAND
- 8. Blockchain datasets representa very unique dataset to understand the behavior of groups of investors… QUANT IN CRYPTO LAND
- 9. Some ideas? QUANT INCRYPTO LAND
- 10. Support and ResistanceBased on Wallet Holdings Ex: Bitcoin QUANT IN CRYPTO LAND
- 11. Event Signals Basedon Flows Into Exchanges Ex: Bitcoin QUANT IN CRYPTO LAND
- 12. Or Miners Ex: Bitcoin QUANTIN CRYPTO LAND
- 13. Patterns About HoldingTime, Geography of Relationships of Individual Investor Positions Ex: Bitcoin QUANT IN CRYPTO LAND
- 14. And many more... QUANTIN CRYPTO LAND
- 15. Blockchain datasets canprovide a unique set of features to quant models that can lead to robust alpha capture… QUANT IN CRYPTO LAND
- 16. New Financial Primitives… QUANTIN CRYPTO LAND
- 17. QUANT IN CRYPTOLAND Decentralized finance(DeFi) enables the representations of financial primitives as decentralized programmable smart contract… Lending Market Making Capital Formation Asset Composition Insurance Derivatives
- 18. Programmable, on-chain, financialprimitives represent a new frontier for high frequency trading… QUANT IN CRYPTO LAND
- 19. The DeFi HFTPerspective QUANT IN CRYPTO LAND Block Time Speed Factor Trade Transparency Factor Cost Factor MEV Factor
- 20. The DeFi HFTPerspective QUANT IN CRYPTO LAND Block Time Speed Factor Trade Transparency Factor Cost Factor MEV Factor The HFT DeFi Transactions are limited by the processing time of each block The Result Speed advantage is relatively limited
- 21. The DeFi HFTPerspective QUANT IN CRYPTO LAND Block Time Speed Factor Trade Transparency Factor Cost Factor MEV Factor The HFT DeFi All trades are visible to all competing parties The Result Every single HFT trade can be copied or subject to competition
- 22. The DeFi HFTPerspective QUANT IN CRYPTO LAND Block Time Speed Factor Trade Transparency Factor Cost Factor MEV Factor The HFT DeFi There is a cost associated with transaction execution The Result Cost becomes an economic factor evaluating the viability of specific trades
- 23. The DeFi HFTPerspective QUANT IN CRYPTO LAND Block Time Speed Factor Trade Transparency Factor Cost Factor MEV Factor The HFT DeFi Trades are dependent on miner’s criteria for placing transactions in a block The Result Perfectly viable HFT trades can fail because of the mining criteria
- 24. Regularly Inefficient Market… QUANTIN CRYPTO LAND
- 25. QUANT IN CRYPTOLAND Information Asymmetry Insiders in crypto projects have regular access to material, market influencing information News Overreaction News in crypto cause dispositional movements in the market Massive Event Driven Trades New token listings, project updates… Crypto Market Inefficiencies CeFi-DeFi Arbitrages Asset prices in DeFi and CeFi exchanges are regularly influenced by different factors Cross Protocol Arbitrages Lending, borrowing, market making dynamics are drastically different across different protocols
- 26. Some examples... QUANT INCRYPTO LAND
- 27. Crazy Correlations withTwitter Sentiment Ex: Cardano QUANT IN CRYPTO LAND
- 28. DeFi Borrow-Lending RatesArbitrage QUANT IN CRYPTO LAND
- 29. New types ofdigital assets… QUANT IN CRYPTO LAND
- 30. Non-Fungible tokens area typical case of a crypto-based asset class that trades based on different dynamics than the underlying platform QUANT IN CRYPTO LAND
- 31.
- 32. Inefficient Indexes QUANT INCRYPTO LAND
- 33. Crazy Volume QUANT INCRYPTO LAND
- 34. Challenges of CryptoQuant Strategies… QUANT IN CRYPTO LAND
- 35. Most crypto quantstrategies run into very peculiar challenges… QUANT IN CRYPTO LAND
- 36. Challenge #1 Small datasets QUANTIN CRYPTO LAND
- 37. QUANT IN CRYPTOLAND Datasets are really small compared to traditional capital markets Crypto is Different Most ML quant models struggle learning with small datasets The availability of existing high quality datasets is very unstable
- 38. Challenge #2 Regular “outlier”events QUANT IN CRYPTO LAND
- 39. QUANT IN CRYPTOLAND Outlier events happen regularly(ex: Bitcoin recent rally, March crash, the raise of DeFi…) Crypto is Different ML quant models struggle to learn patterns they haven’t seen in training datasets Some outlier events introduce noise in training datasets
- 40. Challenge #3 The regularretraining dilemma QUANT IN CRYPTO LAND
- 41. QUANT IN CRYPTOLAND Outlier events cause ML crypto quant strategies to fail and force them to incorporate new records in the training dataset Crypto is Different Training ML crypto quant models with the latest data is a recipe for overfitting Outlier training records could distort the behavior of crypto quant strategies
- 42. Challenge #4 Data qualityand reliability QUANT IN CRYPTO LAND
- 43. QUANT IN CRYPTOLAND Crypto datasets are plagued with “bad data”(ex: wash trades, fake volume…) Crypto is Different Crypto data providers are unstable and fail all the time ML crypto quant models fail with bad or missing data
- 44. Challenge #5 Anonymous blockchainrecords QUANT IN CRYPTO LAND
- 45. QUANT IN CRYPTOLAND Anonymous blockchain records introduce a misleading level of uniformity in training datasets in ML crypto quant models Crypto is Different There is no consistency in the way crypto exchanges interact with the underlying blockchains There is no linear correlation between blockchain activity and trading activity
- 46. Challenge #6 Simple modelfallacy QUANT IN CRYPTO LAND
- 47. QUANT IN CRYPTOLAND Crypto is too inefficient for simple ML quant strategies Crypto is Different It’s hard to model the behavior of crypto assets with a handful of parameters Quant research based on simple ML models results impractical in the crypto space
- 48. Some cutting-edge ideasthat might influence quant strategies in crypto… QUANT IN CRYPTO LAND
- 49. Crypto is coincidingwith the renaissance of deep learning methods which can have a disproportional impact in quant strategies in the space… QUANT IN CRYPTO LAND
- 50. QUANT IN CRYPTOLAND Graph Neural Networks Blockchain data is fundamentally hierarchical and well suited for GNN models Transformers for Time Series Traditional time series forecasting models will struggle with the complexities of the crypto market. Transformer models applied to time series datasets can have strong predictive capabilities Self-Supervised Learning The anonymity of blockchain datasets it’s a challenge for most supervised learning methods. Self-supervised learning techniques can organically learn patterns using unlabeled blockchain datasets Crypto Market Inefficiencies Reinforcement Learning for On- Chain Data The on-chain transparency of DeFi trades makes it a good candidate for reinforcement learning models
- 51. Some practical lessonsabout building quant strategies in crypto… QUANT IN CRYPTO LAND
- 52. QUANT IN CRYPTOLAND The Simplest Trades Work Market inefficiencies cause very simple trading strategies to work in crypto Ex: basis trading. Assume Very Fragile Quant Infrastructures Poor quality datasets, regular exchange failures. Traditional Quant Strategies can be Adapted to Crypto Many traditional order book or derivative strategies can be retrained for crypto assets. Crypto Market Inefficiencies Plenty of Opportunities for New Strategies DeFi, new derivatives open the door to new types of strategies.
- 53. Summary ● Crypto representsa new landscape for quant strategies ● The immaturity of the crypto space creates very unique challenges for quant strategies ● DeFi represents a new frontier for HFT quant strategies ● NFTs are a great example of new trading dynamics ● New deep learning methods could have an impact in crypto quant strategies QUANT IN CRYPTO LAND
- 54.