Computational Finance

This page contains resources about Computational Finance, Financial Engineering, Mathematical Finance and Quantitative Finance.

Subfields and Concepts

• Binomial Options Pricing Model
• Black–Scholes Model
• Capital Asset Pricing Model (CAPM)

Online courses

Video Lectures

• Computational Finance by Steven Skiena
• Financial Engineering and Risk Management Part I by Martin Haugh and Garud Iyengar
• Financial Engineering and Risk Management Part II by Martin Haugh and Garud Iyengar

Lectures Notes =

• Computational Finance by Christian Bayer and Antonis Papantoleon
• Introduction to computational finance and financial econometrics with R by Rric Zivot
• Computational Methods in Finance by Jonathan Goodman
• Financial Mathematics by Harald Lang
• Financial Mathematics I by Jitse Niesen
• Introduction to Financial Mathematics by P. V. Johnson
• Topics in Mathematics with Applications in Finance by Peter Kempthorne, Choongbum Lee, Vasily Strela and Jake Xia

Books and Book Chapters

See also Reading List.

• Lachowicz, P. (TBA). Python for Quants. Volume II. QuantAtRisk eBooks.
• Yan, Y. (2017). Python for Finance. 2nd Ed. Packt Publishing Ltd.
• Akansu, A. N., Kulkarni, S. R., & Malioutov, D. M. (Eds.). (2016). Financial Signal Processing and Machine Learning. John Wiley & Sons.
• Akansu, A. N., & Torun, M. U. (2015). A primer for financial engineering: financial signal processing and electronic trading. Academic Press.
• Lachowicz, P. (2015). Python for Quants. Volume I. QuantAtRisk eBooks.
• Hilpisch, Y. (2015). Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. John Wiley & Sons.
• John, C. (2014). Options, Futures and other Derivative Securities. 9th Ed. Prentice HaII.
• Hilpisch, Y. (2014). Python for Finance: Analyze Big Financial Data. O'Reilly Media.
• Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern portfolio theory and investment analysis. 9th Ed. John Wiley & Sons.
• Benninga, S. (2014). Financial modeling. MIT Press.
• Crack, T. F. (2014). Heard on the Street: Quantitative Questions from Wall Street Job Interviews. 15th Ed. Timothy Crack.
• Blyth, S. (2013). An introduction to quantitative finance. Oxford University Press.
• Joshi, M. S., & Paterson, J. M. (2013). Introduction to Mathematical Portfolio Theory. Cambridge University Press.
• Joshi, M. S., Denson, N., & Downes, A. (2013). Quant Job Interview: Questions and Answers. 2nd Ed. Pilot Whale Press.
• McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media.
• Steland, A. (2012). Financial statistics and mathematical finance: methods, models and applications. John Wiley & Sons.
• Hirsa, A. (2012). Computational methods in finance. CRC Press.
• Alhabeeb, M. J. (2012). Mathematical finance. John Wiley & Sons.
• Boyarshinov, V. (2012). Machine learning in computational finance: Practical algorithms for building artificial intelligence applications. LAP LAMBERT Academic Publishing.
• Joshi, M. S. (2011). More Mathematical Finance. Pilot Whale.
• Duffie, D. (2010). Dynamic asset pricing theory. Princeton University Press.
• Zhou, X. (2008). A Practical Guide to Quantitative Finance Interviews. 14th Ed. CreateSpace.
• Joshi, M. S. (2008). The concepts and practice of mathematical finance. 2nd Ed. Cambridge University Press.
• Wilmott, P. (2007). Paul Wilmott introduces quantitative finance. John Wiley & Sons.
• Seydel, R., & Seydel, R. (2006). Tools for computational finance. Springer.
• Higham, D. (2004). An introduction to financial option valuation: mathematics, stochastics and computation. Cambridge University Press.
• Joshi, M. S. (2004). More Mathematical Finance. Cambridge University Press.
• Joshi, M. S. (2004). The concepts and practice of mathematical finance. Cambridge University Press.
• Cuthbertson, K., & Nitzsche, D. (2004). Quantitative financial economics: stocks, bonds and foreign exchange. 2nd Ed. John Wiley & Sons.
• Glasserman, P. (2003). Monte Carlo methods in financial engineering. Springer Science & Business Media.
• Jackel, P. (2002). Monte Carlo methods in finance. John Wiley & Sons.
• Karatzas, I., & Shreve, S. E. (1998). Methods of mathematical finance (Vol. 39). Springer Science & Business Media.
• Luenberger, D. G. (1997). Investment science. Oxford University Press.
• Campbell, J. Y., Lo, A. W. C., & MacKinlay, A. C. (1997). The econometrics of financial markets. 2nd Ed. Princeton University Press.
• Baxter, M., & Rennie, A. (1996). Financial calculus: an introduction to derivative pricing. Cambridge University Press.
• Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press.

Software

• Financial Toolbox - MATLAB
• Computational Finance - MATLAB
• dawp - Python
• py4fi - Python
• Sage
• QuantLib - C#, Objective Caml, Java, Perl, Python, GNU R, Ruby, and Scheme

See also

• Probability and Statistics
• Stochastic Processes
• State Space Models / Time Series
• Nonlinear Dynamical Systems / Chaos Theory
• Monte Carlo Methods
• Statistical Signal Processing / Estimation Theory
• Digital Signal Processing
• Optimization / Operations Research
• Artificial Neural Networks

Other Resources

• QuantStart
• Quantopian
• What are the best blogs about quantitative trading? - Quora
• Algorithmic trading in less than 100 lines of Python code
• Awesome-Quant - Github
• Python for finance (Part I, Part II, Part III)
• Marco Avellaneda - List of lecture notes