Stock market data tool integrated with Bloomberg
Overview: Our client, a financial services company, approached us with a challenge to improve their data analysis tools and provide automation to their team's daily work. They wanted a solution that would help their surveillance organization gain more insights into potential scenarios. Our approach was to create a solution that would enable them to predict MTD/YTD prices.
Approach: We worked in close collaboration with the client to analyze multiple numerical methods and compose different types of feature vectors to improve the prediction model's accuracy. We developed a script that extracts information and an algorithm that calculates the analysis of historical price changes.
We integrated multiple data sources into the solution, including the Bloomberg service terminal, to ensure we had the most accurate and up-to-date data. We developed an API and a user-friendly interface for filtering and searching the information we extracted and analyzed. We used Keras to predict MTD / YTD prices developing a stock prediction model with a neural network to predict the returns on stocks.
Tech stack: Python, Django,Vue.js, AWS, Keras.
Results: The performance of our solution was excellent, and we optimized it to achieve more than 80% accuracy in predicting prices. As a result, the tool became an essential asset for our client, empowering their surveillance organization to understand market trends better and predict future prices with greater accuracy.