System architecture for on-line optimization of automated trading strategies

Authors:
Fabio Daros Freitas;Christian Daros Freitas;Alberto Ferreira De Souza
Affiliations:
Receita Federal do Brasil, Vitória E.S., Brazil;Receita Federal do Brasil, Vitória E.S., Brazil;DI, UFES, Vitória E.S., Brazil
Venue:
WHPCF '13 Proceedings of the 6th Workshop on High Performance Computational Finance
Year:
2013

Citing 4
Cited 0

Murdoch: publish/subscribe task allocation for heterogeneous agents

AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Algorithmic Trading

Computer
Fast learning and predicting of stock returns with virtual generalized random access memory weightless neural networks

Concurrency and Computation: Practice & Experience
An extended ASLD trading system to enhance portfolio management

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work proposes a new automated trading system (ATS) architecture that supports multiple strategies for multiple market conditions through hierarchical trading signals generation employing h-signals, which are trading signals that are generated using other trading signals. The central idea of the proposed system architecture is to decompose the trading problem into a set of tasks handled by distributed autonomous agents under a minimal central coordination. We implemented the proposed ATS using a software architecture that employed a publish/subscribe communication model. In the current stage of development, we are able to run our ATS in back-test mode with moving-average crossover strategies on minute-by-minute market databases. We achieved very satisfactory performance results, processing 306.791 database rows representing more than two years of data in only 47 seconds.