Autonomous Forex Trading Agents
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks
Foreign-Exchange-Rate Forecasting with Artificial Neural Networks
Optimization of the trading rule in foreign exchange using genetic algorithm
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An Automated Trading System with Multi-indicator Fusion Based on D-S Evidence Theory in Forex Market
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Modesty is the best policy: automatic discovery of viable forecasting goals in financial data
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
A GA combining technical and fundamental analysis for trading the stock market
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns.