Abstract: On over 800 pages, this thoroughly revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading. Four parts and 24 chapters cover:
- key aspects of data sourcing, financial feature engineering, and portfolio management,
- the design and evaluation of long-short strategies based on a broad range of ML algorithms,
- how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news,
- using deep learning models like CNN and RNN with financial and alternative data, how to generate synthetic data with GANs, and training a trading agent using deep reinforcement learning.
More details: https://github.com/stefan-jansen/machine-learning-for-trading
Bio: Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. 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 also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.