Computational Finance
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This page contains resources about Computational Finance, including Financial Engineering, Mathematical Finance, Quantitative Finance and Financial Econometrics.
Contents
Subfields and Concepts[edit]
- Binomial Options Pricing Model
- Black–Scholes Model
- Capital Asset Pricing Model (CAPM)
- Markowitz Model / Mean-Variance Model
- Markov property
- Martingale property
- Efficient Market Hypothesis (EMH)
- Capital Market Line
- Financial Signal Processing
- Financial Portfolio Management / Asset Allocation
- Financial Risk Management
- Value at Risk (VaR)
- Sharpe ratio
- Dispersion
- Drawdown
- Maximum Drawdown
- Alpha
- Beta
Online courses[edit]
Video Lectures[edit]
- 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[edit]
- 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[edit]
See also Reading List.
- Lachowicz, P. (TBA). Python for Quants. Volume II. QuantAtRisk eBooks.
- 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.
- 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[edit]
- 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[edit]
- 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
See also[edit]
- 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[edit]
- 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