Optimal Technical Indicator-based Trading Strategies Using NSGA-II
This work addresses stock trading optimization for investors, but it is incremental as it applies an existing algorithm to a specific domain.
The paper tackled the problem of optimizing stock trading strategies by using NSGA-II to find optimal combinations of technical indicators, aiming to maximize Sharpe ratio and minimize Maximum Drawdown, with results showing considerable improvement in stable economic periods.
This paper proposes non-dominated sorting genetic algorithm-II (NSGA-II ) in the context of technical indicator-based stock trading, by finding optimal combinations of technical indicators to generate buy and sell strategies such that the objectives, namely, Sharpe ratio and Maximum Drawdown are maximized and minimized respectively. NSGA-II is chosen because it is a very popular and powerful bi-objective evolutionary algorithm. The training and testing used a rolling-based approach (two years training and a year for testing) and thus the results of the approach seem to be considerably better in stable periods without major economic fluctuations. Further, another important contribution of this study is to incorporate the transaction cost and domain expertise in the whole modeling approach.