TN-RSI: Trend-normalized RSI Indicator for Stock Trading Systems with Evolutionary Computation

RSI is a commonly used indicator preferred by stock traders. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. In this study, we developed a trading model using a modified RSI using trend-removed stock data. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. These parameters are optimized using genetic algorithms; then the trading performance is compared against B&H and standard RSI indicator usage. 9 different ETFs are selected for evaluating trading performance. The results indicate there is a performance improvement both in profit and success rates using this new model. As future work, other indicators might be modelled in a similar fashion in order to see if it is possible to find one indicator that can work under any market condition.