The project aims to create a sentiment-driven automated trading system for Ada Analytics. The system will leverage publicly available financial disclosures and social media sentiment data to identify high-potential trading opportunities. By integrating custom algorithms, the system will predict market movements and execute trades, incorporating risk management features to ensure optimal performance. The project provides an opportunity for learners to apply their knowledge of data analysis, algorithm development, and financial markets. The tasks are closely related and can be completed by a team of learners from a finance or data science program. The project is designed to be completed in under 80 hours, focusing on developing a prototype that demonstrates the core functionalities of the trading system.

The deliverables for this project include a functional prototype of the automated trading system. The prototype should demonstrate the ability to process financial disclosures and social media sentiment data to identify trading opportunities. Additionally, the system should include custom algorithms for market prediction and trade execution, along with risk management features. A final report detailing the system's design, functionality, and performance metrics should also be provided.