Algorithmic trading and high-frequency trading are relatively unpopular in the industry, and we have no practical experience or experts to consult, so we can only turn to literature and books. In the course of our Xi studies, the monograph Algorithmic and High-Frequency Trading by Professors Cartea, Jaimungal and Penalva aroused great interest: on the one hand, it has a description of the microstructure of trading;On the other hand, the use of mathematical tools such as stochastic optimal control reveals the beautiful and profound connotation of algorithmic trading. It is precisely because of the perfect combination of these two that we feel it is necessary to introduce this book to the Chinese mathematical and financial circles, especially the worldMaster's and Ph.D. students with a background in mathematics who intend to pursue a career in high-frequency trading in the future
Chen Wenbin, Cheng Jin, Pan Hanshuang
Algorithmic and High-Frequency Trading.
Yu Kwong Wah Building on January 21, 2021
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About the translator
Chen Wenbin, Professor, School of Mathematical Sciences, Fudan University, Ph.D. supervisor. Research interests: numerical methods of partial differential equations, large-scale parallel computing algorithms and their fast algorithms. He has published more than 110 articles in the fields of electromagnetic field calculation, thin film growth, groundwater, etc. Assistant Editorial Board Member of Communications in Nonlinear Science and Numerical Simulation. He has participated in the National 973 "Large-scale Scientific Computing" and "High-Performance Scientific Computing Research", the National Natural Science Major Research Program and the National Natural Science Special Project, and presided over 3 national natural science projects. He is currently the principal investigator of the master's program in finance at the School of Mathematical Sciences, Fudan University. It has also completed key research projects such as Shanghai Zhonghui Yida, China Debt Valuation Subsidiaries, Shanghai ** Stock Exchange, and Beijing Huironghe Company.
Cheng JinHe is currently the director of the Shanghai Key Laboratory of Modern Applied MathematicsChairman of Shanghai Society of Industrial and Applied Mathematics;Fellow, Institute of Physics, UK;Executive Member of the International Anti-Problem Coalition, etc. He used to be the vice president of the Chinese Mathematical Society and a member of the expert review team of the Department of Mathematics and Physics of the National ** CommitteeHe is a panel member of NSF in the United States, and an editorial board member of many internationally renowned journals. He has published more than 120 articles in academic journals at home and abroad. In 2019, he won the first prize of the Shanghai Natural Science Award, the second prize of the Shanghai Natural Science Award in 2020, and the first prize of the Shanghai Teaching Achievement Award in 2022. A number of important advances have been made in the theoretical analysis of partial differential equation inverse problems and efficient inversion algorithms for general inverse problems. In terms of application, it has carried out effective cooperation with Nippon Steel, Huawei and other domestic and foreign enterprises, and has achieved outstanding results and has been well received by the industry.
Pan FrostHe holds a bachelor's degree in mathematics from Fudan University and a master's degree in computational mathematics from Fudan University.
Trading algorithmsThe design requires complex mathematical models, solid analysis of financial data, and an in-depth understanding of how markets and exchanges operate. In this textbook, the authors develop models for algorithmic trading in the following scenarios: execution of large orders, market making, target VWAPs and other schemes, trading pairs or asset collections, and execution of trades in dark pools. These models are based on how the exchange works, whether the algorithm trades with more knowledgeable traders (adverse selection), and the type of information available to market participants at both UHF and LF.
Algorithms and High-Frequency Trading, translated by Álvaro Cartea, et al.: Chen Wenbin, Cheng Jin, Pan Hanshuang. Beijing: Science Press, 20212]It is the first book to combine complex mathematical modeling, empirical facts, and financial economics, taking readers from basic concepts to the frontiers of research and practice. If you need to understand how modern electronic markets work, what information provides trading advantages, and how other market participants affect the profitability of algorithms, then this book is for you. The book is divided into three parts, guiding the reader from how electronic exchanges work, to the economics behind them, to the related mathematics, and finally to the models and problems of algorithmic trading.
The first part begins with a description of the basic elements of the electronic market, and the main ways in which people participate in the market: as an active trader, using the information advantage to profit from potentially short-lived profit opportunities, or as a market maker, at the same time selling with favorable ***.
The book would be incomplete if the textbook on algorithmic trading was not motivated by the information seen by participants in the electronic market. Therefore, there is a need to leave room for discussion of data and empirical implications. This data allows us to present the context that determines the ultimate fate of the algorithm. By looking at the details of the volume, volume, and limit order books, the reader will have a basic understanding of some of the key issues that any algorithm needs to consider, such as the information in trading, the nature of movement, volume, volatility, spreads, and more.
The second part develops mathematical tools for analyzing trading algorithms. The chapters on stochastic optimal control and stopping provide a practical approach to the less standard material in financial mathematics textbooks. This section has also been written in order to equip readers who have not been exposed to these techniques to understand the mathematical models behind certain algorithmic trading strategies.
of the bookThe third part delves into the modeling of algorithmic trading strategies. The first two chapters deal with the optimal execution strategy, in which a large number of positions must be liquidated or acquired within a pre-specified window and traded continuously using only market orders.
Chapter 6 covers the classic execution issue of an investor's trade affecting the asset** and adjusts the urgency with which she wishes to enforce the procedure.
In Chapter 7, we develop three execution models in which investors: i) execute procedures, as long as the ** of the asset does not breach the critical boundary;ii) incorporate order flows into her strategy to take advantage of trends in the mid-price, which are caused by unilateral pressure from buyers or sellers in the market;and iii) trading in a public place and in a dark pool.
In Chapter 8, we assume that the investor's goal is to execute a large number of positions within the trading window, but only with limit orders, or with limit and market orders. In addition, we demonstrate an execution strategy in which the investor also tracks a specific schedule of the liquidation procedure.
Chapter 9 deals with execution algorithms that target volume-based schedules. We have developed strategies for investors who want to track the total volume of transactions in the market, with the following objectives: percentage of volume, percentage of cumulative volume, and volume-weighted average**, also known as VWAP. The last three chapters cover a variety of topics in algorithmic trading.
Chapter 10 shows how market makers choose to post limit orders in their ledgers. The model we developed looked at how the strategy depends on different factors including market makers' aversion to inventory risk, adverse selection, and short-term trends in the mid-price dynamics.
Finally, Chapter 11 is dedicated to statistical arbitrage and pair trading, and Chapter 12 shows how the volume information provided in the limit order book can be used to improve the execution algorithm.
This book assumes that readers have basic knowledge of continuous-time finance, but assumes that they are unaware of stochastic optimal control and stopping. In order to maintain the independence of this book, we have included the random calculus tool and the desired results in the appendix. The processing of the material should be of interest to a wide range of readers, and it is ideal for graduate programs in algorithmic trading at the master's or doctoral level. This book is also perfect for those who are already working in the financial field and want to combine their industry knowledge and expertise with a powerful mathematical model of algorithmic trading
This article is excerpted from Algorithmic and High-Frequency Trading [Álvaro Cartea, et al.: Translated by Chen Wenbin, Cheng Jin, and Pan Hanshuang. Beijing: Science Press, 20212] The "Translator's Preface", "Author's Profile" and "Foreword" of the book, with deletions and modifications, are titled by the editor.
isbn978-7-03-067995-6
Editor in charge: Wang Liping, Jia Xiaorui.
Editor of this article: Liu Sidan).
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