Books
Search runs across title, subtitle, short description, and author names. Topics mirror the full instrument and desk coverage of the library—see also topic hubs.
14 titles match these filters
Michael Gorelick · Iman Ozsvald · 2019 · O Reilly
This comprehensive volume delves into high-performance computing (HPC) systems and their applications, particularly in quantitative finance and technology. It covers modern architectures, programming models, and performance optimization techniques essential for practitioners in the field.
Manish Yarats · David Mann · 2019 · Springer
Machine Learning for Trading provides practitioners with a comprehensive guide to applying machine learning techniques in financial trading. The book covers essential quantitative finance concepts and the integration of technology to enhance trading strategies.
Dylan Toscano · 2018 · John Wiley & Sons
This book provides an in-depth exploration of quantitative finance through an object-oriented programming lens using Python. It covers essential topics such as derivatives and quantitative methods, making it suitable for practitioners in finance and technology.
Richard S. Sutton · Andrew G. Barto · 2018 · MIT Press
This comprehensive text on reinforcement learning provides an in-depth exploration of key algorithms and concepts within the field, focusing on both theoretical foundations and practical applications. It covers a range of topics, including online learning algorithms, function approximation, and the intersection of reinforcement learning with psychology and neuroscience.
Yves Hilpisch · 2018 · John Wiley & Sons
This comprehensive guide to Python for finance equips analysts and technologists with the skills needed to harness data-driven techniques in financial contexts. Covering a range of topics from Monte Carlo simulations to portfolio optimisation, the book provides practical examples and frameworks essential for modern quantitative finance.
Kevin Mandia · 2018 · John Wiley & Sons
This comprehensive guide addresses the critical intersection of cybersecurity and financial services, offering frameworks and best practices essential for risk managers and technology professionals. It outlines strategies to mitigate risks associated with cyber threats in the financial sector.
Michael Eling · Nadine Schnell · 2016 · John Wiley & Sons
This book addresses the evolving landscape of cyber risk and its implications for the insurance industry. It explores the challenges faced by insurers in underwriting cyber risks and the development of appropriate insurance products.
Ian Goodfellow et al. · 2016 · MIT Press
Deep Learning provides a comprehensive introduction to the field of deep learning, covering essential mathematical concepts, techniques employed in industry, and research perspectives. It addresses various applications, including natural language processing and computer vision, making it a vital resource for analysts in quantitative finance and technology.
Thomas Philippon · 2016 · Nyu Stern
This book explores the transformative impact of financial technology on traditional banking systems and the broader financial landscape. It delves into the mechanisms of disintermediation and the implications for investors, policymakers, and technologists.
Arvind Narayanan et al. · 2016 · Arvatech
This comprehensive overview of cryptocurrencies and blockchain technology explores their implications for finance and beyond. It addresses the technological foundations, economic principles, and future prospects of these emerging assets, making it essential for traders, investors, and policymakers alike.
Andreas Mueller · 2016 · O Reilly
Advanced Python for Data Science provides a comprehensive guide for practitioners looking to leverage Python in quantitative finance and data analysis. The book covers advanced techniques and libraries essential for data manipulation, statistical analysis, and machine learning applications in finance.
Budish et al. · 2015 · University Of Chicago Press
This book provides a comprehensive analysis of the design and functioning of futures markets, focusing on market microstructure and the technological frameworks that underpin these systems. It is particularly relevant for traders, researchers, and exchange operators seeking to understand the complexities of futures trading.
Gareth James et al. · 2013 · Springer
An Introduction to Statistical Learning offers a comprehensive overview of statistical learning techniques, essential for analysing complex data sets in various fields including finance. The book covers key topics such as linear regression, classification, and tree-based methods, with practical applications demonstrated using R.
Steven S. Skiena · 2008 · Springer
The Algorithm Design Manual by Steven S. Skiena serves as a comprehensive guide for understanding and applying algorithm design techniques. It covers essential topics such as data structures, dynamic programming, and heuristics, tailored for practitioners in quantitative finance and technology sectors.