Neural Network-based ORB Strategies for Threshold Classification on Taiwan Futures Market

Opening Range Breakout (ORB) is a renowned technical analysis strategy in which two pre-determined thresholds are set up in the early stage after market opening to determine the direction of investment for the day. In the literature, ORB has been shown to produce significant profits on several stock markets. In this paper, we present novel ORB algorithms based on deep learning which take into account historical trends and price movement during the early market interval. The proposed scheme uses a multi-label classification framework with multi-layer perceptron neural models and convolutional neural network models to predict the most profitable thresholds. To generate ground truth values for evaluation, we compared two labeling methods (one-hot encoding and distributed encoding). In experiments based on an empirical dataset, the proposed strategy earned profits of 8126, annual returns of 14.003%, and a Sharpe ratio of 1.376. The proposed scheme outperformed the original ORB strategy by nearly 2 times on metrics mentioned above and decreased maximum drawdown by more than 60%. Experiment results revealed that the uppermost and lowermost classes of thresholds accounted for the majority of the predicted results. In other words, taking a long position at a lower boundary and a short position at a higher boundary increased the likelihood of generating higher profits, while reducing exposure to risk.