Inspiration
Inspired by the intersection of quantitative finance and AI, we wanted to create a tool that translates complex financial data into actionable stock recommendations.
What it does
QuantAnalyzer.AI analyzes financial datasets and provides intuitive buy, sell, hold, or strong sell recommendations for stocks, making quantitative insights accessible to traders and investors.
How we built it
We built the frontend using Streamlit for an interactive and user-friendly experience. GeminiAPI serves as our LLM for analyzing financial data and generating stock recommendations, while ElevenLabs API converts results to natural-sounding speech. FastAPI was used to manage API key usage efficiently and handle backend requests.
Challenges we ran into
We faced challenges with API key limitations, and aligning frontend components with the AI outputs.
Accomplishments that we're proud of
Successfully set up a clean and interactive Streamlit frontend. Integrated GeminiAPI to analyze financial datasets effectively. Implemented ElevenLabs text-to-speech for audio output of recommendations. Built a FastAPI backend to manage multiple API keys seamlessly
What we learned
We gained hands-on experience with ElevenLabs API, learned to manage LLM-based analysis using GeminiAPI, and understood best practices for combining AI services with web applications.
What's next for QuantAnalyzer.AI
Develop a chatbot interface to answer user questions about financial recommendations. Implement a confidence scoring system to compare AI predictions against actual market performance. Expand the platform to cover real-time stock trends and multilingual support
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