Computational Finance



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

Subfields and Concepts

 * Binomial Options Pricing Model
 * Black–Scholes Model
 * Capital Asset Pricing Model (CAPM)
 * Modern Portfolio Theory (MPT)
 * Markowitz Model / Mean-Variance Model
 * Markov property
 * Martingale property
 * Efficient Market Hypothesis (EMH)
 * Capital Market Line
 * Financial Signal Processing
 * Financial Portfolio Management / Asset Allocation / Non-speculative Trading
 * Security Selection
 * Portfolio Construction
 * Speculative Trading
 * Intra-day Trading / Day Trading
 * Algorithmic Trading
 * Single-asset Trading
 * Multi-asset Trading
 * Inter-day trading
 * Swing trading
 * Financial Risk Management
 * Financial Risk Metrics
 * Value at Risk (VaR)
 * Conditional VaR (CVaR)
 * Sharpe ratio
 * Dispersion
 * Drawdown
 * Maximum Drawdown (MDD)
 * Alpha
 * Beta
 * Sortino ratio
 * Calmar ratio / Drawdown ratio

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
 * Machine Learning and Reinforcement Learning in Finance Specialization by Igor Halperin

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.
 * Dixon, M. F. (2020). Machine Learning in Finance: From Theory to Practice. Springer Nature.
 * Sargent, T., & Stachurski, J. (2015). Quantitative Economics with Python. (link)
 * de Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
 * 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.
 * Skoglund, J., & Chen, W. (2015). Financial risk management: Applications in market, credit, asset and liability management and firmwide risk. John Wiley & Sons.
 * Hull, J. (2015). Risk management and financial institutions. 4th Ed. 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.
 * Crouhy, M., Galai, D., & Mark, R. (2014). The essentials of risk management. 2nd Ed. McGraw-Hill.
 * Chatterjee, R. (2014). Practical methods of financial engineering and risk management: tools for modern financial professionals. Apress.
 * 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.
 * Verbeek, M. (2012). A guide to modern econometrics. 4th Ed. John Wiley & Sons.
 * 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.
 * Allen, S. (2012). Financial Risk Management: A Practitioner's Guide to Managing Market and Credit Risk. 2nd Ed. John Wiley & Sons.
 * Joshi, M. S. (2011). More Mathematical Finance. Pilot Whale.
 * Stefanica, D. (2011). A primer for the Mathematics of Financial Engineering. Fe Press.
 * Duffie, D. (2010). Dynamic asset pricing theory. Princeton University Press.
 * Tsay, R. S. (2010). Analysis of Financial Time Series. 3rd Ed. John Wiley & Sons.
 * Kennedy, D. (2010). Stochastic financial models. Chapman and Hall/CRC.
 * Jeanblanc, M., Yor, M., & Chesney, M. (2009). Mathematical methods for financial markets. Springer Science & Business Media.
 * Meucci, A. (2009). Risk and asset allocation. Springer Science & Business Media.
 * Wang, P. (2008). Financial econometrics. Routledge.
 * 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.
 * Brooks, C. (2008). Introductory econometrics for finance. 2nd Ed. Cambridge University Press.
 * Bacon, C. R. (2008). Practical Portfolio Performance Measurement and Attribution. 2nd Ed. John Wiley & Sons.
 * Fusai, G., & Roncoroni, A. (2008). Implementing models in quantitative finance: methods and cases. Springer Science & Business Media.
 * Wilmott, P. (2007). Paul Wilmott introduces quantitative finance. John Wiley & Sons.
 * Estrada, J. (2007). Finance in a Nutshell: A No-nonsense Companion to the Tools and Techniques of Finance. Pearson Education.
 * Brabazon, A., & O'Neill, M. (2006). Biologically inspired algorithms for financial modelling. Springer Science & Business Media.
 * Seydel, R., & Seydel, R. (2006). Tools for computational finance. Springer.
 * Brandimarte, P. (2006). Numerical methods in finance and economics: a MATLAB-based introduction. 2nd Ed. John Wiley & Sons.
 * 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.
 * Feibel, B. J. (2003). Investment performance measurement. John Wiley & Sons.
 * Jackel, P. (2002). Monte Carlo methods in finance. John Wiley & Sons.
 * Cuthbertson, K., & Nitzsche, D. (2001). Financial engineering: derivatives and risk management. John Wiley & Sons.
 * Grinold, R. C., & Kahn, R. N. (2000). Active portfolio management. 2nd Edition. McGraw-Hill Education.
 * Karatzas, I., & Shreve, S. E. (1998). Methods of mathematical finance. 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.

Scholarly Articles

 * Boyd, S., Busseti, E., Diamond, S., Kahn, R. N., Koh, K., Nystrup, P., & Speth, J. (2017). Multi-period trading via convex optimization. Foundations and Trends® in Optimization, 3(1), 1-76.
 * Feng, Y., & Palomar, D. P. (2016). A signal processing perspective on financial engineering. Foundations and Trends® in Signal Processing, 9(1–2), 1-231.
 * Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. The European Physical Journal B, 38(2), 363-371.

Software

 * Financial Toolbox - MATLAB
 * Computational Finance - MATLAB
 * dawp - Python
 * Sage
 * QuantLib - C#, Objective Caml, Java, Perl, Python, GNU R, Ruby, and Scheme
 * DX Analytics - Python
 * QuantEcon.py
 * zipline - Python
 * finmarketpy - Python
 * Lean - Python, C#, F#
 * backtrader - Python
 * TradingView

Other Resources

 * QuantStart
 * Quantopian
 * ARPM Lab Theory - Advanced Risk and Portfolio Management (ARPM)
 * What are the best blogs about quantitative trading? - Quora
 * Algorithmic trading in less than 100 lines of Python code
 * Awesome-Quant - Github
 * Marco Avellaneda - List of lecture notes
 * Neural networks for algorithmic trading: enhancing classic strategies - Blog post
 * Neural networks for algorithmic trading. Multivariate time series - Blog post
 * Neural networks for algorithmic trading. Simple time series forecasting - Blog post
 * Deep Learning the Stock Market - Blog post, with code
 * Yahoo! Finance - datasets
 * Quandl - datasets
 * Alpha Vantage - datasets
 * Quantopian Data - datasets
 * Quantiacs - Markets - datasets
 * PyFin (Medium) - blog
 * Python-for-Data-Science (GitHub) - code
 * Python for Finance - blog
 * Python for Finance: Stock Portfolio Analyses (Medium) - blog post
 * Python For Finance: Algorithmic Trading (DataCamp) - blog post
 * py4fi (GitHub) - code
 * Python for Finance (Part 1, Part 2, Part 3) - blog posts with code
 * QuantInsti’s Blog on Algo Trading and Quantitative Finance
 * quant-finance (GitHub) - code
 * Markowitz Portfolio Optimization with Python - blog post
 * Efficient Frontier Portfolio Optimisation in Python - blog post
 * Investment Portfolio Optimization - blog post
 * The Efficient Frontier: Markowitz portfolio optimization in Python - blog post
 * QuantAndFinancial - blog with code
 * portfolioopt (GitHub) - code
 * New-York-Stock-Exchange-Predictions-RNN-LSTM (GitHub) - code
 * Datasets on Finance (Kaggle)
 * Predict Stock Prices Using RNN (Part 1, Part 2) - blog post
 * Stock Market Predictions with LSTM in Python - blog post
 * Stock prediction LSTM using Keras (Kaggle)
 * Predict stock prices with LSTM (Kaggle)
 * The Trading Scientist - blog
 * Journal of Applied Econometrics Data Archive
 * EliteQuant (GitHub) - blog post
 * Reinforcement-learning-in-portfolio-management- (GitHub) - code
 * AI for portfolio management: from Markowitz to Reinforcement Learning - blog post