Inspiration
Inspiration for Gemini Finance Oracle came from the need to simplify financial analysis and decision-making. This is Multi-modal AI App that will take all forms of financial information and provide investment insights to the user.
We wanted to create a tool that leverages advanced AI capabilities to provide accurate and actionable financial insights, making it accessible to both novice and expert users.
What it does
Gemini Finance Oracle is an intelligent financial assistant that analyzes stock market data, news articles, and sentiment scores to provide comprehensive financial insights. Users can ask questions about specific stocks, get the latest news sentiment analysis to make informed investment decisions.
📈 Dashboard
Provides a comprehensive view of financial data, including sentiment news updates, comprehensive reports and Buying/Selling Recommendation all in one place.
💬 Ask Question on Earnings Call
Allows users to get detailed insights and analysis from company earnings calls audio, helping them understand the financial health and future prospects of a company.
❓ Ask Question on Annual Report
Enables users to query the annual reports of companies to extract key information, financial metrics, and performance indicators.
✅ Chat with Expert
Connects users with financial experts for personalized advice for finance gurus such as Warren Buffet,Charlie Munger and Peter Lynch and deeper insights into market trends and investment strategies.
📊 Chat with Data
Offers an interactive way to explore and analyze financial data, allowing users to ask specific questions and receive data-driven responses.
How we built it
We built **Gemini Finance Oracle* using a combination of Python, Streamlit, and Google Vertex AI. The application integrates with various APIs to fetch real-time stock data and news articles. We used Vertex AI for generating natural language responses based on the data, and Streamlit to create an interactive and user-friendly web interface. Additionally, we employed pandas for data manipulation, matplotlib for plotting charts, and yfinance for accessing financial data.
Challenges we ran into
One of the major challenges we faced was ensuring the accuracy and relevance of the AI-generated responses. Training the model to understand and analyze complex financial data was a difficult task. We also encountered issues with integrating multiple APIs and handling the large volume of data efficiently. Ensuring the application remains responsive and user-friendly was another significant challenge.
Accomplishments that we're proud of
We are proud of successfully creating a comprehensive financial assistant that can provide real-time insights and answer user queries effectively. Integrating advanced AI models to generate accurate and useful financial advice is a significant achievement. Additionally, we are proud of the user-friendly interface we developed, which makes complex financial analysis accessible to everyone.
What we learned
Throughout this project, we learned a lot about natural language processing, financial data analysis, and the intricacies of integrating various APIs. We also gained valuable experience in building scalable and responsive web applications using Streamlit. This project has deepened our understanding of AI and its applications in the financial domain.
What's next for Gemini Finance Oracle
In the future, we plan to enhance Gemini Finance Oracle by incorporating more advanced AI models and expanding its data sources to include more diverse financial instruments. We aim to improve the accuracy of sentiment analysis and provide more personalized insights based on user preferences. Additionally, we are looking to integrate predictive analytics to help users forecast market trends and make more informed investment decisions.
Log in or sign up for Devpost to join the conversation.