Inspiration
The financial landscape is evolving rapidly, with vast amounts of data generated every second. We believe that generative AI holds the key to unlocking the true potential of this data, providing investors with unparalleled insights and a clearer vision of the future. Lunarity was born from the desire to democratize these powerful tools, making sophisticated investment strategies accessible to everyone.
What it does
Lunarity leverages cutting-edge AI to streamline the investment process by offering real-time sentiment analysis and portfolio optimization. Users can input their stock portfolio into the system, which then fetches and summarizes recent news articles related to the specific stocks using Google Gemini 1.5 Pro LLM. This provides investors with up-to-date, sentiment-driven insights that are crucial for making informed decisions. Additionally, the platform offers tools for portfolio optimization, suggesting adjustments based on the latest market trends and data analysis to enhance portfolio performance.
How we built it
We built Lunarity by integrating a range of technologies including Python for backend development, Flask as the web framework, and React for the frontend to ensure a responsive user experience. The application uses SQLite3 for database management, storing user portfolios and cached articles. TailwindCSS was used to style the front end, providing a modern and clean user interface. The core functionality of sentiment analysis is powered by Google Gemini 1.5 Pro LLM, which processes and analyzes the text from current news articles to extract mood and implications for the stocks in question.
Challenges we ran into
Integrating Gemini LLM with our backend posed a significant challenge due to inconsistent response formats initially encountered. This required us to refine our system instructions carefully to obtain the desired outputs reliably. Another major hurdle was sourcing timely and relevant news articles, as many sources were behind paywalls or subject to strict API rate limits. We addressed this by implementing a caching system to store articles temporarily, minimizing the need to refetch data and thus bypassing some of the limitations of free news APIs.
Accomplishments that we're proud of
We are particularly proud of how Lunarity integrates complex technologies to deliver a seamless and efficient user experience. The ability to provide instant, AI-driven insights based on the latest news impacts on stock performance is a significant breakthrough in personal investment tools. Moreover, our solution to the data sourcing challenge through effective caching has allowed us to maintain a high level of service without incurring prohibitive costs.
What we learned
Throughout the development of Lunarity, we gained invaluable insights into the integration of various technologies and the handling of AI tools like Google Gemini LLM. We learned the importance of adaptive problem-solving in dealing with data consistency issues and the practical limits of accessing external data sources. This project also enhanced our understanding of user interface design and the critical role of user experience in application development.
What's next for Lunarity
Moving forward, we plan to expand Lunarity’s capabilities by incorporating more advanced analytical tools and broader data sources. We aim to include predictive analytics using machine learning models to forecast stock trends based on historical data and current market conditions. Additionally, we plan to explore partnerships with financial news providers to overcome challenges related to news access. Enhancing the platform's scalability to support a growing user base and further refining the UI/UX to cater to diverse investor needs are also key objectives for the near future.
Log in or sign up for Devpost to join the conversation.