Machine Learning for Trading

Abstract: The rapid progress in machine learning (ML) and the massive increase in the availability and diversity of data has enabled novel approaches to quantitative investment. It has also increased the demand for the application of data science to develop both discretionary and algorithmic trading strategies.
In this workshop, we will cover popular use cases for ML in the investment industry, and how data science and ML fit into the workflow of developing a trading and investment strategy from the identification and combination of alpha factors to strategy backtesting and asset allocation.
We will see how a broad range of ML techniques can be used to extract tradeable signals. In particular, the rise of alternative data, i.e. sources beyond market and fundamental data, has created the need to apply deep learning for natural language processing and image classification. We will also take a look at how reinforcement learning can be used to train an agent interactively on market data.
The workshop uses Python and various standard data science and machine learning libraries like pandas, scikit-learn, gensim, spaCy as well as TensorFlow and Keras. The code examples will be presented using jupyter notebooks and are based on my book ‘Machine Learning for Algorithmic Trading’.

Bio: Stefan is founder, CEO and lead data scientist at Applied AI that provides data strategy consulting, machine learning solutions, as well as executive coaching and training for consumer, healthcare and financial industries. Prior to his current venture, he was co-founder and partner at an international investment firm, building the predictive analytics and investment research practice. Earlier, he was executive at a global fintech company with operations in 15 global markets.

A native German, he started his career as advisor to Central Banks in emerging markets and has worked in six languages across Asia, Africa, and Latin America. In 2007, he raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, an international organization for regulators that facilitates the adoption of financial technology to lower barriers to access.

Stefan holds a Master in Economics from FU Berlin with a Thesis on Early Warning Systems for Financial Crisis using Machine Learning, an MPA/ID from the Harvard Kennedy School, a CFA Charter, and has published through Harvard and Brookings. He teaches data science at General Assembly, has produced two courses with currently 13,000 students at DataCamp, and is the author of two courses on ‘Mastering Unsupervised Learning’ (forthcoming by Packt).