Inspiration: I wanted to create an AI-powered financial assistant that simplifies stock trend analysis, making data-driven investing more accessible.
What it does: It predicts stock prices based on historical data, allowing users to select a stock and time span to get structured price forecasts.
How I built it: I used Streamlit for the frontend, OpenAI API for AI-powered analysis, and Python for data processing. The tool integrates with financial datasets to generate insights.
Challenges I ran into: Optimizing API costs, handling rate limits, and ensuring accurate financial trend predictions were key challenges.
Accomplishments that I am proud of: I successfully integrated AI-driven predictions with a user-friendly interface and achieved readable, structured financial insights.
What I learned: I gained deeper insights into LLM-powered financial forecasting, cost-efficient API usage, and optimizing user experience in Streamlit.
What's next for Financial Data Analyzer: Expanding stock coverage, adding real-time data feeds, enhancing AI models for better accuracy, and introducing interactive visualizations.
Built With
- crewai
- openai
- python
- streamlit
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