Graphical LASSO Algorithm

GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem. References “Sustik, M.A. and Calderhead, B., “GLASSOFAST: An efficient GLASSO implementation,” UTCS Technical Report TR-12-29, November 6, 2012.” “O. Banerjee, L. E. Ghaoui and A. d’Aspremont, “Model Selection Through Sparse Maximum Likelihood Estimation for multivariate Gaussian or Binary Data,” Journal of Machine Learning Research, 9, pp. 485-516, March 2008.”...

AlgoQuant

AlgoQuant is a collection of trading-bot building tools. It is an actively developed and ever more comprehensive library of code for building systems for algorithmic trading in all aspects. You can use it to build systems for backtesting, trading strategy generation, data analysis, research, and actual order execution. Out of the box, AlgoQuant supports data import, data filtering and cleaning, in-samples calibration, out-samples simulation and performance analysis. Beyond these, the main competitive advantage that AlgoQuant has over other software is the incorporation of SuanShu, a powerful modern library of mathematics, statistics, optimization and data mining. Unified Mathematical Analysis and Modeling Library AlgoQuant has a large collection of mathematical models, many from top academic journal publications, which a trader can use as building blocks to create his own trading models. For example, to build a mean reversion model, the trader can combine (D’Aspremont, 2011) to construct a maximally mean reverting portfolio of two assets and then trade the pair using (Elliott, van der Hoek, & Malcolm, 2005) pair trading strategy. Both mathematical modules are available in the library. In addition, AlgoQuant has a large number of algorithms that the trader can apply. For example, in the area of portfolio optimization, AlgoQuant covers Quadratic Programming (Markowitz or Modern Portfolio Theory), Second Order Conic Programming as well as Differential Evolution. In other words, using AlgoQuant, the trader does not need external math software like Excel, R, or MATLAB, and there are many readily available modules and algorithms to use. He can quickly build up very complicated mathematical strategies by combining together the components from the library. Uniqueness Numerical Method Inc. has a unique way...

Quantitative Trading

Quantitative trading is when traders design mathematical models to describe and predict market movements. Often these models are implemented on computer systems for automatic execution. Traders start with a vague trading intuition. Using mathematics, they turn the idea into a quantitative model for analysis, back testing and refinement. When this quantitative model proves to be likely profitable by passing the rigorous statistical tests, the traders implement them on computer systems for execution. Algorithmic trading is the process where ideas are turned into mathematical models and then coded into computer programs for systematic trading. It is a science where mathematics and computer sciences meet. Here is a collection of resources.   Tools AlgoQuant Quantitative Trading Strategies AlgoQuant Quantitative Models mean reversion trend following? NTU 2011 group projects NUS 2011 group projects Research ​Introduction to Algorithmic Trading Strategies literature historical data...

Demo

After the initial setup, you can run demo programs included in the sub-project algoquant-core. Source files that end in “…Demo.java” (e.g, RunAllDemo.java), are executable programs for demonstration. To run a demo program, you right click on it and hit Run File (or...

FAQs

Installation On your web site I can read, suanshu.net-2.0.0.zip and algoquant-0.5.0.zip. Should we download both, or does AlgoQuant suffice and we get SuanShu with the algoquant-0.x.x.zip? You just need to download algoquant-0.5.0.zip. It comes with the latest version of SuanShu as well. SuanShu.net is our experimental C# library and is free to download and use. Which version of Java do you recommend? We in house test all our code using a few thousands unit tests daily with 4 versions of jvm from Sun/Oracle: 1.6.x 32 and 64 bits, 1.7.x 32 and 64 bit. Your choice of jvm version is a personal decision. Although some of our developers use the IBM clone with eclipse, it is not part of our routine testing procedure. My personal preference is the Oracle 1.6.x 64 bit. We need 64 bit to process a large amount of data. We prefer 1.6.x because it is a much more mature product with history esp. amid-st the infamous security patches and testing/release policy from Oracle lately. mvn compile does not work! Please do mvn clean install before compile. When building AlgoQuant with Maven, I got errors with the plugin Surefire. E.g., Failed to execute goal org.apache.maven.plugins:maven-surefire-plugin:2.14:test (default-test) on project algoquant-core: Execution default-test of goal org.apache.maven.plugins:maven-surefire-plugin:2.14:test failed This problem seems to happen mostly on a Mac. Please try to use Surefire version 2.14.1 or above. You may try to run the maven build with option “-e”. You may try to skip the unit testing by option “-skipTests”. (In NetBeans, there is such an option under “Maven”.) If none of the above works, our wild guess is that your VM...