Objective: (i) to demonstrate DC trading strategies can produce at least 12% yearly returns; (ii) to evaluate the performance of DC trading strategies on daily closing price data and stocks; (iii) to find out the best heuristic algorithm among GA and VSO for DC trading strategy.
Most of the financial forecasting methods use a traditional physical time scale, which is taken at fixed intervals (interval-based scale). In this paper, an intrinsic time (event-based scale) using Directional Changes (DC) is applied to focus only on important points that a crucial event happened, filtering out other irrelevant price details and noise. An improved version of the DC algorithms and DC formulas is proposed based on previous research papers. We will then take advantage of this directional changes paradigm to formulate different DC trading strategies. Previous research focuses on intraday tick data and 10-minute interval data in FX markets. To investigate whether the strategies work on non-high frequency data and other markets, we will apply the DC trading strategies to daily close price data and different stocks.
The goal of this paper is to demonstrate the DC trading strategies can make profitable returns, evaluate the performance of the DC trading strategies on daily closing price data and stocks, and find out the best heuristic algorithm among Virus Spread Optimization (VSO) and Genetic Algorithm (GA) for the DC trading strategy.The results show that the DC trading strategies can make promising returns on daily closing price data of stocks, but the performance is not stable. Also, the performance of GA is generally better than that of VSO, but more experiments with different parameter settings are needed to have a more comprehensive analysis.
Please refer to the Trading Optimization using Directional Changes and AI Technical Paper.pdf for details