There are countless Quantitative Finance courses, tutorials, articles available online, but for some, having a book is still a necessity to learn. This is an up-to-date list of recommended books.
- An Introduction to Quantitative Finance (2013)
- A Practical Guide To Quantitative Finance Interviews (2008)
- A First Course in Quantitative Finance (2018)
- Paul Wilmott Introduces Quantitative Finance (2007)
- Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return (2018)
- Quantitative Financial Analytics: The Path To Investment Profits (2017)
- Frequently Asked Questions in Quantitative Finance (2009)
- 150 Most Frequently Asked Questions on Quant Interviews (Pocket Book Guides for Quant Interviews) (2013)
- Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis (Chapman & Hall/CRC The R Series) (2018)
- Mastering R for Quantitative Finance (2015)
- Python for Finance: Apply powerful finance models and quantitative analysis with Python, 2nd Edition (2017)
- A Primer For The Mathematics Of Financial Engineering, Second Edition (Financial Engineering Advanced Background Series) (2011)
- Essential Quantitative Methods: For Business, Management and Finance (2016)
The worlds of Wall Street and The City have always held a certain allure, but in recent years have left an indelible mark on the wider public consciousness and there has been a need to become more financially literate.
This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews.
A First Course in Quantitative Finance (2018)
This new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools.
Paul Wilmott Introduces Quantitative Finance, Second Edition is an accessible introduction to the classical side of quantitative finance specifically for university students. Adapted from the comprehensive, even epic, works Derivatives and Paul Wilmott on Quantitative Finance, Second Edition, it includes carefully selected chapters to give the student a thorough understanding of futures, options and numerical methods.
Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return (2018)
R is a free, open source programming language that’s become a popular standard for financial and economic analysis. Quantitative Investment Portfolio Analytics In R is your guide to getting started with modeling portfolio risk and return in R. Even if you have no experience with the software, you’ll be fluent in R at a basic level after reading this short primer. The chapters provide step-by-step instructions for tapping into R’s powerful capabilities for portfolio analytics.
This book provides a comprehensive treatment of the important aspects of investment theory, security analysis, and portfolio selection, with a quantitative emphasis not to be found in most other investment texts. The statistical analysis framework of markets and institutions in the book meets the need for advanced undergraduates and graduate students in quantitative disciplines, who wish to apply their craft to the world of investments.
Getting agreement between finance theory and finance practice is important like never before. In the last decade the derivatives business has grown to a staggering size, such that the outstanding notional of all contracts is now many multiples of the underlying world economy.
150 Most Frequently Asked Questions on Quant Interviews (Pocket Book Guides for Quant Interviews) (2013)
Topics: • Mathematics, calculus, differential equations • Covariance and correlation matrices. Linear algebra • Financial instruments: options, bonds, swaps, forwards, futures • C++, algorithms, data structures • Monte Carlo simulations. Numerical methods • Probability.
Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis (Chapman & Hall/CRC The R Series) (2018)
Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications.
Use R to optimize your trading strategy and build up your own risk management system About This BookLearn to manipulate, visualize, and analyze a wide range of financial data with the help of built-in functions and programming in RUnderstand the concepts of financial engineering and create