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What we learned from running a quant crypto hedge fund



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"Lessons Learned from running a quant crypto fund" presented by Michael Feng, CEO and Co-founder of hummingbot
1. Crypto enables new quant strategies
2. Build a chain of production
3. Preventing overfitting is job #1
4. Establish a disaster response plan
5. Every model has an expiration date

Learn more about algo crypto trading: https://www.hummingbot.io

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What we learned from running a quant crypto hedge fund

  1. 1. What we learned from running a quant crypto hedge fund Michael Feng CoinAlpha January 24, 2018
  2. 2. Agenda About us Why quant crypto trading? Lessons learned Resources Q&A
  3. 3. About us
  4. 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
  5. 5. 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
  6. 6. 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%
  7. 7. Our new product High frequency market making bot Open source Professional-grade Cross-exchange market making DEX-compatible hummingbot.io
  8. 8. Why quant crypto trading?
  9. 9. Chart reading: the horse-drawn carriage of finance
  10. 10. Inefficient market Hundreds of exchanges globally New instruments appear daily No standard FIX protocol Lack of large institutional players Visible trends in historical data
  11. 11. Direct market access Alice Broker Market maker Exchange Bob Exchange commissions commissions payment for order flow bid-ask spread FIAT CRYPTO
  12. 12. No co-location Level playing field Free data Open APIs Will these characteristics persist as the crypto industry matures?
  13. 13. Lessons learned
  14. 14. 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
  15. 15. 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
  16. 16. 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!
  17. 17. 4. Establish a disaster response plan Falcon ETH trading Dec 15-22, 2017 1. Bot buys 2. Manual intervention 3. Bottom 4. Bot sells
  18. 18. 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
  19. 19. Resources
  20. 20. 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
  21. 21. 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)
  22. 22. 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
  23. 23. Q&A