1. Dr. Anton Fokin The Svedberg Laboratory, Sweden

2. Outline R-Quant is a software toolbox, which provides a financial researcher or quantitative investor with an advanced object oriented data analysis framework. R-Quant is a stand-alone extension of the ROOT and it is especially designed to work with financial objects. What is R-Quant Physics meets Finance R-Quant tools Conclusions

3. What is inside?

4. Where physics meets finance Quark-Gluon Plasma?

5. Where physics meets finance Or Market Crash? Phase transition?

6. R-Quant tools Data management Modern financial data management reflects all the features of full-scale data acquisition and storage systems we used to deal with in our experiments. In finance we talk about tens and hundreds of Gigabytes of off-line data. Real time data come with tick intervals ranging from seconds to hours.

7. R-Quant tools Base instruments – TAsset – TRiskFreeAsset TBankAccount TObligation – TRiskyAsset TStock TBond TFund Financial derivatives – TDerivativative TFutures TOption TSwap Inheritance trees representing base and derivative financial instruments make a perfect example of the object oriented software development.

8. R-Quant tools Asset pricing A number of standard pricing methods including Black-Scholes Finite difference Binomial Trinomial Monte-Carlo

9. R-Quant tools Time series and technical analysis Statistical tools for time series analysis. Tens of embedded indicators and signals. Opportunity to add new indicators via ROOT macro processor and script compiler using C++ interpreter as a script processor. Powered with ROOT visualization facility.

10. R-Quant tools Portfolio management and optimization Modern Portfolio Theory (MPT) Capital Asset Pricing Model (CAPM) Quadratic optimization problem for thousands of variables with a number of constraints ROOT MINUIT Simulated annealing with Metropolis algorithm Genetic optimization

11. R-Quant tools Artificial Neural Networks and Genetic Algorithms ANN are used in finance for pattern recognition and forecasting. ANN have the capability to learn underlying market dynamics from noisy and complex time series data. GA can help to build optimal neural network topologies, select good indicators, create new indicators from existing ones, etc.

12. R-Quant tools Fuzzy logic and expert systems In our experiments as well as in finance we use rather fuzzy definitions such as “high” or “low” which have different numerical value in different situations. R-Quant implements forward (conclusion) and backward (explanation) chain techniques. Fuzzy objects may also serve as inputs for neural network applications. Facts: dollar value goes up while operating on the US stock market Rule database: if stock market = US and dollar = up then interest rate = down if interest rate = down then stock market = up Conclusion: US stock market goes up

13. Conclusions R-Quant is an open software project – Welcome to use – Welcome to join Contacts – http://garbo.lucas.lu.se/~kosu_fokin/rquant.htm – Email:fokin@tsl.uu.se Thanks to ROOT