Algorithmic trading system:design and applications

Abstract

This paper provides an overview of research and development in algorithmic trading and discusses key issues involved in the current effort on its improvement, which would be of great value to traders and investors. Some current systems for algorithmic trading are introduced, together with some illustrations of their functionalities. We then present our platform named FiSimn and discuss its overall design as well as some experimental results in user strategy comparisons.

Keywords

algorithmic trading / portfolio optimization / news retrieval / decision making / system design

References

[1] Eriksson S, Roding C. Algorithmic trading uncovered – impacts on an electronic exchange of increasing automation in futures trading. Royal Institute of Technology, Stockholm, 2007

[2] Market Risk and Algorithmic Trading. AMD White Paper

[3] Berkowitz S, Logue D, Noser E. The Total Cost of Transactions on the NYSE. Journal of Finance, 1988, 41: 97-112

[4] MacKinlay A C, Ramaswamy K. Index-Futures Arbitrage and the Behavior of Stock Index Futures Prices. The Review of Financial Studies, 1988, 1(2): 137-158

[5] Hogan S, Jarrow R,Warachka M. Statistical arbitrage and tests of market efficiency. Working paper, 2002

[6] Hogan S, Jarrow R, Teo M. Testing market efficiency using statistical arbitrage with application to momentum and value strategies. Journal of Financial Economics, 2004, 73: 525-565

[7] Gordon Baker, Shashi Tiwari. Algorithmic Trading: Perceptions and Challenges. Working Paper, 2004

[8] Leigh Tesfatsion. Introduction to the special issue on agent-based computational economics. Journal of Economic Dynamics and Control, 2001

[9] Leigh Tesfatsion. Agent-based computational economics: modeling economies as complex adaptive systems. Information Sciences, 2003, 149(4): 263-269

[10] Shu-Heng Chen. Computational Intelligence in Agent-Based Computational Economics. Computational Intelligence: A Compendium, 2008, 517-594

[11] Blake LeBaron. Agent-based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control, 1998, 24: 679-702

[12] Aki-Hiro Sato. Frequency analysis of tick quotes on the foreign exchange market and agent-based modeling: A spectral distance approach. Physica A: Statistical Mechanics and Its Applications, 2007, 382: 258-270

[13] DempsterMA H, Romahi Y S. Intraday FX Trading: An Evolutionary Reinforcement Learning Approach, 2002

[14] Christopher Neely, Paul Weller, and Rob Dittmar. Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of Financial and Quantitative Analysis, 1997, 32(4): 405-426

[15] Shu-Heng Chen and Chia-Hsuan Yeh, Genetic Programming in the Agent-Based Artificial Stock Market. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, 1999, 2: 834-841

[16] Shu-Heng Chen. Trends in Agnet-Based Computational Modeling of Macroeconomics. New Generation Comput, 2004, 23(1)

[17] Christopher J, Neely, Paul A, Weller. Predicting Exchange Rate Volatility: Genetic Programming vs. GARCH and RiskMetrics. Working paper of The Federal Reserve Bank of ST. Louis, 2001

[18] Hendershott T. Electronic Trading in Financial Markets. IT Professional, 2003, 5(4): 10-14

[19] Michael J. Kearns, Luis E. Ortiz. The Penn-Lehman Automated Trading Project. IEEE Intelligent Systems, 2003, 18(6): 22-31

[20] Holland J and Miller J. Artificial adaptive agents in economic theory. American Economic Review: Papers and Proceedings, 1991, 81(2): 365-370

[21] Palmer G, Arthur B, Holland J, LeBaron B, and Tayler P. Artificial economic life: A simple model of the stock market. Physica D, 1994, 75: 264-274

[22] Shanghai Stock Exchange. NGTS Part Tech Guide SSE D0602, 5-6

[23] Shanghai Stock Exchange. NGTS Report Trading Model, 6-10

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg