awesome-rl-for-trading

awesome reinforcement learning in the context of quantitative trading domain

outline

• 1 Books & Tutorials
  • 1.1 Books
  • 1.2 Tutorials
• 2 Papers
  • 2.1 rl for market timing
  • 2.2 rl for portfolio optimization
  • 2.3 rl for algorithm trading
• 3 Researchers & Organizations

1 Books & Tutorials

1.1 Books

[1] Algorithmic and High-Frequency Trading

The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you.

[2] Machine Learning in Finance: From Theory to Practice

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.

Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications.

1.2 Tutorials

2 Papers

2.1 rl for market timing

Paper Title	Year	Venue	Materials
Using Data Augmentation Based Reinforcement Learning for Daily Stock Trading	2020	Electronics	pdf
Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading	2020	Expert Systems with Applications	pdf

2.2 rl for portfolio optimization

Paper Title	Year	Venue	Materials
Deep reinforcement learning for portfolio management of markets with a dynamic number of assets	2020	ESA	pdf
Model-based deep reinforcement learning for dynamic portfolio optimization	2019	arXiv	pdf

2.3 rl for algorithm trading

3 Researchers & Organizations

• Sebastian Jaimungal: @ Department of Statistical Sciences, University of Toronto
• Matthew F. Dixon: @ Department of Applied Math, Illinois Institute of Technology