Algorithmic and High-Frequency Trading (Mathematics, Finance and Risk)
| Author | : | |
| Rating | : | 4.54 (779 Votes) |
| Asin | : | 1107091144 |
| Format Type | : | paperback |
| Number of Pages | : | 356 Pages |
| Publish Date | : | 2014-10-23 |
| Language | : | English |
DESCRIPTION:
Virtakuono said Good read. A very practically oriented and mathematically simple narrative. Plenty of examples of exactly solvable dynamic programming problems. Most chapters end with a discussion of practical implications of the calculations.. Definitely move along if you aren't an advanced calculus student that can write code using just proof Can be useful but you have to be an advanced calculus student and have to understand proofs well without practical exercises. As of this writing very little is available on their website and I had bought the book more than 9 months ago the exercises and examples are "still coming". You need more data than what is provided for sure unless you want a biased result. On the good side they have a few good ideas. I had no idea what optimal stopping even was before i read this book.. "Already a classic" according to AC. Undoubtedly one of the best books out there on this topic. It is on the mathematical end but rooted on data and realistic applications. Those who want to learn about the maths behind trading algorithms must start here.
Using a unique blend of microstructure theory, financial data analysis, and mathematical models, the authors walk the reader through the maze of the high-frequency markets, detailing how the exchanges work, and what kind of data they generate. It also requires specific mathematical tools, such as stochastic control, and understanding of how these tools are used to solve trading problems. Other books cover the mechanics and statistics of high-frequency market dynamics, but none covers the mathematical aspects to this depth. Human traders in financial markets are an endangered species, gradually replaced
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. 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. The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. 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. 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.
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. He consults for major banks and hedge funds focusing on implementing advance derivative valuation engines and algorithmic trading strategies. His recent interests include high-frequ