Evolutionary ORB-based model with protective closing strategies

Opening range breakout (ORB) is a well-known intraday trading strategy via technical analysis. ORB lacks robustness against market uncertainties (e.g., information from contradictory sources), and does not consider all relevant market characteristics. Furthermore, the closing strategies in generic ORB are not well defined. In this study, we developed an evolutionary ORB-based model, which utilized historical data to optimize thresholds in order to enhance profitability, and developed protective closing strategies aimed at to prevent unacceptable losses. Selecting appropriate thresholds and parameters for ORB is a non-trivial task, due to the fact that the search space exceeds sixty-five thousand options. We used evolutionary computation to derive rational strategies and parameters for ORB. The proposed framework based on a genetic algorithm optimizes the parameters related to threshold selection and protective closing strategies. In experiments, this resulted in annual returns of 9.3% (representing a 2.8% improvement over the original strategy) and Sharpe ratio of 2.5 (an improvement of 1.0), while reducing the maximum drawdown by half. The proposed scheme also reduced computational overhead by 89% compared to a grid search.