A Quantitative approach to create a Hybrid stock filtering platform

The world of investing and trading is vast and competitive, thus it is important to take into consideration multiple factors to gain an edge over other investors in order to be profitable. A stock screener is commonly used by investors and traders to filter stocks based on certain financial metrics. But to use these metrics available in the screener, one needs to have in depth knowledge to create a lucrative strategy. Attempting to use these metrics with lack of discipline and without taking into consideration multiple factors often leads to loss of hard earned money for novice investors. This problem is solved by creating a hybrid stock filtering system. The system is hybrid as it is created using a unique combination of fundamental, technical, machine learning and sentiment analysis. The users' of the platform can simply select from a list of hybrid strategies created that help in shortlisting of stocks. We aim to create a one-stop platform that beginner to experienced investors can use to filter out stocks. Apart from helping users' shortlist scrips through hybrid strategies, the entire procedure of data collection, cleaning, and visualization is automated. We demonstrate the complete methodology of our research along with in-depth explanations about our results and solutions to canvass the need of a hybrid stock filtering system for neophyte investors.