Previous research primarily characterized price movements according to time intervals, resulting in temporal discontinuity and overlooking crucial activities in financial markets. Directional Change (DC) is an alternative approach to sampling price data, highlighting significant points while blurring out noise details in price movements. However, traditional DC treated the thresholds of upward and downward trends with distinct intrinsic patterns as equivalent and preset them as fixed values, which are dependent on the subjective judgment of traders. To enhance the generalization performance of this methodology, we improved DC by introducing a modified threshold selection technique. Specifically, we addressed upward and downward trends distinctly by incorporating a decay coefficient. Further, we simultaneously optimized the threshold and decay coefficient using the Bayesian Optimization Algorithm (BOA). Additionally, we recognized the abnormal market state by regime change detection based on the Hidden Markov Model (RCD-HMM) to reduce the risk. Our Intelligent Trading Algorithm (ITA) was constructed based on above methods and the experiments were carried out on tick data from diverse currency pairs in the forex market. The experimental results showed a significant increase in profit and reduction in risk of DC-based trading strategies, which demonstrated the effectiveness of our proposed methods.

翻译：以往研究主要根据时间间隔表征价格变动，导致时间不连续性并忽略了金融市场中的关键活动。方向变化（DC）是一种替代性的价格数据采样方法，在突出价格变动关键节点的同时过滤噪声细节。然而，传统方向变化将存在本质差异的上升趋势与下降趋势阈值视为等价，并预设为固定值，这依赖于交易者的主观判断。为提升该方法论的泛化性能，我们通过引入改进的阈值选择技术来优化方向变化。具体而言，我们通过引入衰减系数来区分处理上升与下降趋势，并利用贝叶斯优化算法（BOA）对阈值与衰减系数进行联合优化。此外，我们基于隐马尔可夫模型的制度变化检测（RCD-HMM）识别市场异常状态以降低风险。基于上述方法构建了智能交易算法（ITA），并在外汇市场多种货币对的逐笔成交数据上进行实验。实验结果表明，基于方向变化的交易策略在收益与风险控制方面均显著提升，验证了所提方法的有效性。