Forecasting the value of stocks and other financial instruments traded on a stock exchange is known as stock market prediction. Technical analysis and trading are two areas where it may be helpful. Trading, for example, requires the execution of hundreds of transactions per second, which is impossible to do manually. For this reason, Algorithm Trading has recently gained traction in the financial sector, and with it, the use of signal processing filters to foretell how the market will behave in the future and inform algorithm development. This research delves into the use of the Kalman Filter and the Linear Regression Filter in Algorithmic Trading, specifically studying its utilization in signal processing. The purpose of this study is to explore the use of Machine Learning Algorithms in the stock prediction process, specifically in predicting the actual movements of stocks.