Designing Strategies for Autonomous Stock Trading Agents using a Random Forest Approach

Machine learning-based autonomous agents are valuable for back-testing stock trading strategies, including algorithmic trading. Several studies in the financial literature have proposed artificial intelligence-based algorithms that support decision making for financial investment, but few studies have provided systematic processes for designing intelligent trading agents. This paper overviews the steps involved in designing agents that forecast stock prices in a trading strategy. These steps include data preprocessing, time-series segmentation, dimensionality reduction, clustering, and others. Our main contributions are: (i) a systematic process that guides the design and development of trading agents, and (ii) a random forest forecasting model.

@article{Aloud2021,
  title     = {Designing Strategies for Autonomous Stock Trading Agents using a Random Forest Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {7},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Monira Aloud},
  doi       = {10.14569/IJACSA.2021.0120788},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120788}
}