Dr. Anton Fokin The Svedberg Laboratory, Sweden R-Quant r o o t i n f i n a n c e TSL DAQ r o o t i n s c i e n c e.

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Dr. Anton Fokin The Svedberg Laboratory, Sweden R-Quant r o o t i n f i n a n c e TSL DAQ r o o t i n s c i e n c e

ROOT in Sweden New ROOT customers – The Svedberg Laboratory, Uppsala University SVEDAQ ++ – Division of Cosmic and Subatomic Physics, Lund University PHENIX CHIC collaboration, CHICSi project

CHICSi detector

SVEDAQ ++ Generic and expandable Object-oriented (C++) Lynx RTOS Event Builder ROOT on the client side On-line data analysis Friendly for users – ROOT Win95 GUI – ROOT macro processing Friendly for developers – ROOT class structure – ROOT documenting

ROOT & DAQ Event Building? Real Time Linux Lynx RTOS – Unix compatible OS for real time applications. – gcc support with custom libs Networking Threads – ROOT for Lynx?

ROOT and DAQ Networking – UDP sockets and multicasting Multithreading – Graphics in threads Windows NT GUI – Lots of people use NT on office computers Java support – Control experiments on the Internet

What is inside?

R-Quant classes Time series and technical analysis In finance people operate with either time series or cross-sectional data. A typical time series can contain several thousands of entries. Lots of specific statistical methods were developed for time series analysis. CINT seems to be a perfect macro processor to create new indicators.

R-Quant classes Optimization and portfolio management In finance people face (quadratic) optimization problems for sets of thousands variables with a number of constraints, therefore: Stochastic optimization (Simulating annealing + Metropolis) Genetic optimization

R-Quant classes Artificial Neural networks and genetic algorithms A set of generic classes supporting different network configurations TNeuron TInputNeuron TThresholdNeuron THiddenNeuron TOutputNeuron TNeuralLayer TNeuralNetwork TPerceptron TKohonenMap Optimization, visulaization and serialization

R-Quant classes Fuzzy logic and expert systems TFuzzyConstant TFuzzyVariable TFuzzyStatement TFuzzyRule TFuzzyExpert Forward (conclusion) and backward (explanation) chain techniques. Fuzzy input for neural network applications. CINT (C++) knowledge and action database even rate is low (sure) if event rate is low and experiment is fragmentation then beam is low (maybe) or detector is broken (maybe) if beam is low (maybe) and requested beam is high then wake up beam operators and ask to check if beam is high (sure) and event rate is low then wake up guy-who-knows and ask to come

Conclusions R-Quant is an open software project – Welcome to use – Welcome to join Contacts – – Thanks to ROOT, but NT/Java GUI is deadly important for such applications!