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Machine Learning Applications in Algorithmic Trading

Machine Learning Applications in Algorithmic Trading. Ryan Brosnahan Ross Rothenstine. Goal. Create a learning stock trading algorithm that can produce consistent economic profit without excessive risk or hubris using techniques similar to those outlined by Berkeley Professor John Moody.

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Machine Learning Applications in Algorithmic Trading

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  1. Machine Learning Applications in Algorithmic Trading Ryan Brosnahan Ross Rothenstine

  2. Goal Create a learning stock trading algorithm that can produce consistent economic profit without excessive risk or hubris using techniques similar to those outlined by Berkeley Professor John Moody.

  3. Real Goal

  4. Introduction • Computational Mathematics is Hard! • Most Quants are Ph.D. • Requires multidisciplinary background • Expensive • Front-heavy Development Schedule

  5. Typical Scenario

  6. The Basic Steps • Acquire Data • Sanitize • Trading Strategy • Determine Risk • Entry, Exit • Execute Trade • Interface Exchange • Interface Clearing house

  7. Data • Time Scale • Latency • Sanitation • Multiple Sources • Data types • Economic • Sentiment • Price

  8. Price Data Sources

  9. Other Data Sources • Compustat • Bureau of Economic Analysis • Bureau of Labor Statistics • World Bank • Twitter API

  10. Algorithms • Implemented • Simple Moving Average • Seasonal Index • Planned • ARCH • Regression • Holt-Winters

  11. Considerations • Direct vs. Model Based Learning • SARSA, Q-Learning, RRL • Forecast Period • Estimating Differentials • Backward Euler Method, Finite Differences, Monte Carlo • Evaluating Performance • Sharpe Ratio vs. Sterling Ratio vs. Double Deviation Ratio

  12. Algorithm Management Simple Moving Average ARCH Linear Prediction Twitter Sentiment Seasonal Index SVD/PCA SVD/PCA

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