Evaluating the effectiveness and sensitivity of forex trading robots

Abstract

Robot trading, also known as algorithmic trading, has been widely used in financial markets in recent decades. The wide application of robot trading has brought significant benefits to transaction efficiency. A lot of developers have designed trading robots which can simulate their trading strategies, and they have claimed that these robots can keep making profits continuously in the place of human traders. However, their performance is usually not as satisfactory as human traders. Two factors could lead to this failure in trading: (1) programs cannot simulate all human behavior; and (2) most robots are over- sensitive, which may reduce their performance. To solve those problems, therefore, evaluating the effectiveness and sensitivity of trading robots is necessary.

The contribution of this research includes a study of trading robots and their algorithms, trading robot experiment design, data analysis and improvements to program design. The study focuses on the conceptual mechanism of trading robots, which includes trading applications and robots deployment.

This paper reports that forex trading robots are suitable for forex rates prediction. The evidence shows that trading robots can capture the underlying “rules” of the forex market trend by using time series, technical indicators and other factors. Traditional standards for robot trading analysis are used to evaluate the accuracy of forecasting currency price changes when traders are using their software for real trading. The results indicate that practical forecasting can be completed and paper profits be obtained, by using five different trading robots. However, the collection of sensitive analysis is incompatible with efficiency testing. Most testing results were collected by chart review, and the test results show that the effectiveness and sensitivity of robot trading are both interrelated and contradictory. The more sensitive trading robots take more trading opportunities, but this reduces performance.

Further research is discussed in this paper. In future work, more data segments will be required in the analysis, and all experimental data should be analyzed in different time frames on the currency of JPY / USD.