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Machine Learning for Algorithmic Trading
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Table of Contents

Table of Contents

  1. Machine Learning for Trading - From Idea to Execution
  2. Market and Fundamental Data - Sources and Techniques
  3. Alternative Data for Finance - Categories and Use Cases
  4. Financial Feature Engineering - How to Research Alpha Factors
  5. Portfolio Optimization and Performance Evaluation
  6. The Machine Learning Process
  7. Linear Models - From Risk Factors to Return Forecasts
  8. The ML4T Workflow - From Model to Strategy Backtesting
  9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage
  10. Bayesian ML - Dynamic Sharpe Ratios and Pairs Trading
  11. (N.B. Please use the Look Inside option to see further chapters)

About the Author

Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems. Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank. He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.

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