Do Decimated Based Trading Model Outperform the Buy-and-Hold Investment Strategy

Trading Algorithms have significantly transformed the stock markets in recent years and practically replaced the technical analysts in internet trading to a considerable extent.However, due to the large computing requirements, these algorithms face issues in terms of speed and yield rates.To address these issues, this study introduces the algorithm, which uses decimated data to retain performance while reducing computational needs.This decimated method provides a viable solution to the computational issues encountered in online stock forecasting and devising a computerised trading strategy.The program follows stringent statistical criteria for selection of decimation rates, window size, and performance measures.Real-world stock market data from five well-known companies, notably Apple, Amazon, Alphabet, Meta, and Netflix, is used to assess the algorithm's efficacy in making investment decisions within a given time frame.The results indicate that the decimated algorithm has the potential to optimise stock returns in real time while demonstrating steady performance by using established concepts from information theory and making rational decisions about decimation rates.This research adds to the understanding of the dynamics of automated trading by shedding light on how to achieve optimal performance while minimising computational costs.