  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.
