  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.
