Many quantitative funds rely on data collection from buying user trades from brokerages while other funds attempt to make money off of small differences in price over minuscule amounts of time. This paper attempts to architect a python[1] system by which we analyze signals from Markov Chains[2], Polynomial Regression, and Sentiment Analysis latter passed into Decision Trees[3] for a holistic evaluation based solely on publicly available information.