Evaluation of the Performance of a Dynamic Trading Strategy by Combining the Flag Pattern Detection Technique and an Exponential Moving Average with Cumulative Particle Motion Optimization

Designing trading systems with good returns is critical for capital market investors. Trading systems are often based on a combination of several tools to use their combined information. For the first time in Iran, the present study aimed to propose a pattern detection algorithm for a flag pattern based on Japanese candlestick charts and their arrangement. By recognizing the pattern and if the ۴- and ۱۰-day moving average is confirmed, a shopping position is developed, and the selling time is determined based on an optimized and dynamic process commensurate with price changes and the data scale. Our objective was to address the question of whether the returns resulting from this strategy have a more significant positive return compared to the purchase and maintenance strategy. The research sample included the daily information of ۱۶ active companies of basic metals in Tehran Stock Exchange during ۲۰۰۷-۲۰۱۹, extracted from the database of Novin Rahavard software. Data analysis was performed in MATLAB software, and the obtained experimental evidence was described using t-test. According to the results, the research strategy had a higher performance in terms of returns and risks compared to the market.

Keywords:

Particle Cumulative Algorithm , Optimization , Japanese Candlestick , ChartFlag Pattern Detection , moving average