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What we learned from running a quant crypto hedge fund
- 1. What we learned from running a quant crypto hedge fund Michael Feng CoinAlpha January 24, 2018
- 2. Agenda About us Why quant crypto trading? Lessons learned Resources Q&A
- 3. About us
- 4. CoinAlpha Mission: To give individuals financial superpowers using blockchain technology Deep domain expertise in trading, corporate finance, software engineering, and data science Based in Mountain View, CA
- 6. Our hedge fund CoinAlpha Falcon, LP Oct 2017 to Sept 2018 BTC/USD, ETH/USD Directional (momentum, mean reversion) Mid-frequency (1-3 days) 22 ($600k) 1% base / 20% performance None (Ethereum smart contract) Name Track record Portfolio Strategy Frequency Investors Fees Administrator
- 7. Fund performance Falcon BTC ETH Return 21.8% 13.3% -28.9% Sharpe ratio 0.5 0.2 -0.3 Sortino ratio 0.74 0.27 -0.50 Max drawdown -40.5% -68.5% -83.2%
- 8. Our new product High frequency market making bot Open source Professional-grade Cross-exchange market making DEX-compatible hummingbot.io
- 10. Chart reading: the horse-drawn carriage of finance
- 11. Inefficient market Hundreds of exchanges globally New instruments appear daily No standard FIX protocol Lack of large institutional players Visible trends in historical data
- 12. Direct market access Alice Broker Market maker Exchange Bob Exchange commissions commissions payment for order flow bid-ask spread FIAT CRYPTO
- 13. No co-location Level playing field Free data Open APIs Will these characteristics persist as the crypto industry matures?
- 14. Lessons learned
- 15. 1. Crypto enables new quant strategies Directional momentum mean reversion correlation sentiment event-driven Overfitting Arbitrage jurisdiction spot vs futures CEX vs DEX borrow/lend Saturation Market making liquid, high volume assets Illiquid, low volume assets cross-exchange Inventory Type Variations Primary risk
- 16. 2. Build a chain of production Data curation Feature analysis Strategy definition Backtesting Deployment Monitoring Collect, clean, index, store data Discover features by labeling and weighting data Develop a general model that explains the features Assess the profitability of a model using historical data Roll out the strategy into live trading Evaluate the performance of all live strategies and allocate capital between them
- 17. 3. Preventing overfitting is job #1 Why it happens ● Humans naturally find patterns ● Time-series data is not independent ● When in doubt, add more features! How to prevent it ● Separate tasks ● Split data into training/validation/test ● Paper trade before doing live trading ● Be skeptical!
- 18. 4. Establish a disaster response plan Falcon ETH trading Dec 15-22, 2017 1. Bot buys 2. Manual intervention 3. Bottom 4. Bot sells
- 19. 5. Every model has an expiration date Market regime pre May 2018 Trends last for days/weeks Higher volatility Long term up and down cycles Market regime post May 2018 Trends last for hours/days Lower volatility Long-term downtrend
- 20. Resources
- 21. Books Advances in Financial Machine Learning by Martin Lopez de Prado Flash Boys by Michael Lewis The Quants by Scott Patterson When Genius Failed by Roger Lowenstein
- 22. Courses Machine Learning (Coursera, free) Intro to Machine Learning for Coders (fast.ai, free) Artificial Intelligence for Trading (Udacity, paid) Cryptocurrency Trading (Blockchain at Berkeley, free)
- 23. Open source projects hummingbot: high frequency market making bot ccxt: trading API for multiple crypto exchanges gekko: TA-based trading bot and backtesting tool backtrader: Python backtesting library catalyst: Crypto fork of Quantopian Zipline
- 24. Q&A