An Algorithmic Approaches for Maximizing Return on Investment (ROI) Using Simple Moving Average (SMAC)

This paper investigates the LEGO toy sets as a good investment and proposes a Simple Moving Average Crossover (SMAC) for maximum return on investment (ROI). Recently, LEGO has provided better-quality toy sets, enabling everyone to experience better gaming and collection value. This increased the value of LEGO toys and made the investors rethink their investments, allowing them to gain colossal profits. This research focuses on the actual and trading prices of the various LEGO sets in the market. Furthermore, this research aims to design a return on investment algorithm and propose a maximum return for LEGO collectibles items. However, in this research, we also develop a working prototype for evaluation and data visualization for the proposed trading algorithm. This algorithm is used to calculate ROI for LEGO collectible items and its effective chosen strategy for maximizing ROI for LEGO collectible items. Based on the findings of the study, investing in LEGO sets is a low-risk option that does not require extensive financial knowledge. This makes an attractive investment opportunity, simplifying the investment process and making it possible to earn high returns for LEGO enthusiasts and investors. The future scope of this research may incorporate machine learning or deep learning techniques, such as linear regression or random forest, for highly accurate predictions.