awesome-high-frequency-trading
This is a curated list of high-impact tutorials, libraries, papers, books, courses and anything related to the high frequency trading. Feel free to make a pull request to contribute to this list.
1 market microstructure and algorithms trading(execution)
Market microstructure research primarily focuses on the structure of exchanges and trading venues (e.g. displayed and dark), the price discovery process, determinants of spreads and quotes, intraday trading behavior, and transaction costs
| Era | General Interest |
|---|---|
| 1970 - 1990 | Spreads, Quotes, Price Evolution, Risk Premium |
| 1990 - 2000 | Transaction Costs, Slippage, Cost Measurement, Friction |
| 2000 - 2010 | Algorithms, Pre-Trade, Black Box Models, Optimal Trading Strategies |
| 2010 - present | Market Fragmentation, High Frequency, Multi-Assets, and Portfolio Construction |
1.1 books
Empirical Market Microstructure
Empirical Market Microstructure is about the institutions that have evolved to handle our trading needs, the economic forces that guide our strategies, and statistical methods of using and interpreting the vast amount of information that these markets produce.
Algorithmic and High-Frequency Trading
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.
Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization
The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.
1.2 courses
FINM 37601: Mathematical Market Microstructure: An Optimized Approach @ The University of Chicago
This course is an introduction to mathematical theory of market microstructure, with key applications in solving optimal execution problems with inventory management. We will start from discussions of market design, global market structure, algorithmic trading and market making practices. We will then present traditional market microstructure theory in the context of dealer inventory management and information-based quoting and pricing. Latest literature about realized volatility calculations using high-frequency data will be reviewed. The subject of order book dynamics research with applications to market impact modeling will be discussed as well. Continuous-time stochastic control theory with applications to execution algorithm development and market making strategy design will be reviewed and discussed. The course also will go through some latest developments in algorithm and strategy development using machine learning techniques. The main goal of this course is to provide a clear discussion on key mathematical treatments and their practical applications of market microstructure problems related to price discovery and utility optimization for certain transaction processes with non-trivial transaction cost present.
46982: Market Microstructure and Algorithmic Trading @ Carnegie Mellon University
Trading is central to the investment process. This course presents foundational concepts and current issue relating to trading in financial markets including algorithmic and high frequency strategies, optimal order execution, execution quality analysis, the dynamics of limit order markets, the regulatory and institutional landscape, programming and IT infrastructure, and the economics of market microstructure. Important empirical methodologies and concepts such as price decomposition using vector autoregression, VWAP benchmarking, and PIN will be introduced. The course will consider trading in fixed income and futures markets as well as in equity markets. In hands-on course assignments, you will utilize the industry-standard Kdb software to work with actual intraday transactions and order flow data.
FIN 566 Algorithmic Market Microstructure @ the University of Illinois at Urbana-Champaign
This course introduces the modern theoretical, empirical and institutional foundations of market microstructure and trading activity, with an emphasis on applications to algorithmic and high-frequency trading. The first part of the course addresses market microstructure and the algorithmic implementation of traditional microstructure-inspired tasks such as minimizing execution costs. The second part of the course proceeds to examine actual algorithmic strategies, and ultimately high-frequency trading. Recurrent themes throughout the course will be the use of economic theory to simplify computationally challenging problems, and the use of theory-driven structural models to construct more robust trading algorithms.
1.3 papers
1.4 researchers
Joel Hasbrouck: @ Stern School of Business, New York University
Sebastian Jaimungal: @ Department of Statistical Sciences, University of Toronto
Alvaro Cartea: @ the Oxford-Man Institute of Quantitative Finance
Xin Guo: @ Department of Industrial Engineering and Operations Research, University of California, Berkeley
David Lariviere: @ Finance Department, University of Illinois at Urbana-Champaign
Dacheng Xiu @ Booth School of Business, University of Chicago
Dave Cliff @ Department of Computer Science, University of Bristol