This quantitative study examines predicted stock price forecasts by two intuitive paradigms (Linear Regression and ARIMA) alongside a chosen technical indicator. The data used for this analysis was obtained from Yahoo Finance and the analysis was driven towards formalizing the statistical and regression paradigms in one experiment. To add to the comparison of models presented, the Russell price index (RSI), moving average convergence divergence (MACD), moving average 20 (MA20), and moving average 50 (MA50) were added to models to analyze the model’s predictability. The proposed design is captured in an interactive design and dashboards to allow interactive analyses in real-time online, where the research also considered the exchange conversion of USD-INR both on the regional usability side but also in application to user accessibility. For purposes of this research a seven-day forecast time horizon was chosen, which highlights short-term predictability, and in which the promises measuring the predictability of each model and their strength or weaknesses in both correctness, repeatability and usability. The research design also offered the research a platform to grade models on a graded model comparing a model across a Trimester on placements based on parameters, to offer a reflexive eye on comparative models based not only on models, but on usability for both investors/practitioners. The research strives for a user-friendly research design decision-making.