Álvaro Cartea, University College London
Álvaro Cartea is a Reader in Financial Mathematics at University College London. Before joining UCL, he was Associate Professor of Finance at Universidad Carlos III, Madrid (2009–2012) and from 2002 to 2009 he was a Lecturer (with tenure) in the School of Economics, Mathematics and Statistics at Birkbeck, University of London. He was previously JP Morgan Lecturer in Financial Mathematics at Exeter College, Oxford.
Sebastian Jaimungal, University of Toronto
Sebastian Jaimungal is an Associate Professor and Chair of Graduate Studies in the Department of Statistical Sciences, University of Toronto, where he teaches in the PhD and Masters in Mathematical Finance programs. He consults for major banks and hedge funds focusing on implementing advance derivative valuation engines and algorithmic trading strategies. He is also an associate editor for the SIAM Journal on Financial Mathematics, the International Journal of Theoretical and Applied Finance, the journal Risks and the Argo newsletter. Jaimungal is Vice Chair for the SIAM activity group on Financial Engineering and Mathematics, and his research has been widely published in academic and practitioner journals. His recent interests include high-frequency and algorithmic trading, applied stochastic control, mean-field games, real options, and commodity models and derivative pricing.
José Penalva, Universidad Carlos III de Madrid
José Penalva is an Associate Professor at the Universidad Carlos III de Madrid, where he teaches in the PhD and Masters in Finance programs, as well as at the undergraduate level. He is currently working on information models and market microstructure and his research has been published in Econometrica and other top academic journals.
发表于2024-12-22
Algorithmic and High-Frequency Trading 2024 pdf epub mobi 电子书
图书标签: HFT 量化交易 quant 量化 交易 金融 Finance Trade
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
课本、。就是各种解PDE。。
评分非常适合研究生读的高频交易书籍,第一部分讲基于逆向选择的市场微结构,第二部分讲连续时间的随机最优控制,最后再讲算法交易模型,填补了这方面的空白:经济系学生一般不了解基于HJB方程的金融数学方法,而金融学生一般不了解信息经济学的背景知识。
评分参考,知乎有中文专栏整理的读书笔记
评分第一本真正意义上的高频交易的综述
评分看过此书会解PDE!
Algorithmic and High-Frequency Trading 2024 pdf epub mobi 电子书