Machine Learning for Trading
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’.

● Python data science / ML libraries from pandas to TensorFlow and financial libraries from the Quantopian universe like zipline, alphalens and pyfolio.

https://github.com/stefan-jansen/machine-learning-for-trading
https://drive.google.com/open?id=1FJx6rwNvsBd8hp0ItW0chlvXEdazlWJ9

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.