Ideated and developed a commercially functional predictive modeling app for the stock market, tracking over 70000 securities, that proposes securities with over 88 percent accuracy (over 1000 conducted trials), beating equity market returns by almost 70 percentage points and derivative market returns by over 770 percentage points, while boosting financial literacy through financial learning module provisions. I masterminded the architectural blueprint, ensuring seamless integration between frontend and backend functionalities. My technical toolkit consisted of Swift for the mobile interface, Python for backend logic and data processing, and AWS for robust and scalable cloud infrastructure.I demonstrated acute proficiency in:- Designing and deploying microservices on AWS, optimizing for high availability and fault tolerance.- Leveraging Python libraries and frameworks for efficient data crunching, statistical modeling, and predictive analytics.- Crafting a responsive and intuitive mobile interface using Swift, enhancing user experience and engagement.- Implementing state-of-the-art security measures, ensuring data integrity and protection against potential breaches.- Collaborating cross-functionally with product managers and other stakeholders, refining product features based on user feedback and market demand.This endeavor not only exemplified my holistic approach to app development but also underlined my commitment to leveraging technology to make complex financial instruments more comprehensible to the masses.Trials Conducted: 1500Accurate Outcomes: 1468Accuracy: 97.88 percentage points.Average RMS Error: 2.12 percentage pointsRecognized and Incubated by PedalStart, a startup accelerator; selected as one of the top 10 startups, out of every single one that applied.