The project aims to develop a machine learning algorithm and a user-friendly query interface to address liquidity challenges in the Tel Aviv stock market. The algorithm will analyze stock data to recommend optimal times for buying and selling shares, minimizing the spread between transaction prices and actual share values. The query interface will allow users to filter transactions and query execution details like time taken and price margins. The project will utilize real transaction data from the Tel Aviv Stock Exchange, leveraging Python, cloud services, and various web technologies for implementation.