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

Subfields and Concepts Edit

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

Online courses Edit

Video Lectures


Lectures Notes

Books and Book Chapters Edit

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.
  • 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 Edit

See also Edit

Other Resources Edit