Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Three automated stock-trading agents: a comparative study
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A tree-based GA representation for the portfolio optimization problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Establishing a Framework for Dynamic Risk Management in `Intelligent' Aero-Engine Control
SAFECOMP '09 Proceedings of the 28th International Conference on Computer Safety, Reliability, and Security
Computational intelligence for evolving trading rules
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Evolutionary single-position automated trading
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Combining technical trading rules using particle swarm optimization
Expert Systems with Applications: An International Journal
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Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is susceptible to poor behavior in specific types of adverse market conditions. Naive combinations of such rules are not very effective in mitigating the weaknesses of component rules. We demonstrate that sophisticated approaches to combining these trading rules enable us to overcome these problems and gainfully utilize them in autonomous agents. We achieve this combination through the use of genetic algorithms and genetic programs. Further, we show that it is possible to use qualitative characterizations of stochastic dynamics to improve the performance of these agents by delineating safe, or feasible, regions. We present the results of experiments conducted within the Penn-Lehman Automated Trading project. In this way we are able to demonstrate that autonomous agents can achieve consistent profitability in a variety of market conditions, in ways that are human competitive.