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Foreign exchange is the largest financial market in the world, and it is alsoone of the most volatile markets. Technical analysis plays an important role inthe forex market and trading algorithms are designed utilizing machine learningtechniques. Most literature used historical price information and technicalindicators for training. However, the noisy nature of the market affects theconsistency and profitability of the algorithms. To address this problem, wedesigned trading rule features that are derived from technical indicators andtrading rules. The parameters of technical indicators are optimized to maximizetrading performance. We also proposed a novel cost function that computes therisk-adjusted return, Sharpe and Sterling Ratio (SSR), in an effort to reducethe variance and the magnitude of drawdowns. An automatic robotic trading(RoboTrading) strategy is designed with the proposed Genetic AlgorithmMaximizing Sharpe and Sterling Ratio model (GA-MSSR) model. The experiment wasconducted on intraday data of 6 major currency pairs from 2018 to 2019. Theresults consistently showed significant positive returns and the performance ofthe trading system is superior using the optimized rule-based features. Thehighest return obtained was 320% annually using 5-minute AUDUSD currency pair.Besides, the proposed model achieves the best performance on risk factors,including maximum drawdowns and variance in return, comparing to benchmarkmodels. The code can be accessed athttps://github.com/zzzac/rule-based-forextrading-system

GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading