This is a submission for the Bright Data AI Web Access Hackathon
What I Built
I developed an AI-powered stock analysis system that aggregates technical, fundamental, and sentiment signals to help investors make more informed decisions. The system analyzes various factors such as recent news, technical analysis, financial fundamentals, social sentiment, and insider activity to generate an investment score. The AI model provides explanations and confidence levels for each analysis, assisting users in making smarter investment choices based on multi-dimensional data.
Key Features:
- News Analysis: Sentiment analysis of the latest news related to a stock.
- Technical Analysis: Evaluation based on technical indicators and price trends.
- Fundamental Analysis: Assessment of the stock’s financial health, including balance sheets and earnings reports.
- Social Sentiment: Insights from social media sentiment and discussions.
- Insider Activity: Monitoring of insider trading and stock movements by company executives.
Demo
You can explore the project and access the full code repository here. Below are some screenshots showing the solution in action:
How I Used Bright Data's Infrastructure
To gather real-time data from various web sources, I used Bright Data's Managed Proxy Network (MCP) to aggregate and scrape technical, financial, and sentiment data. The Bright Data infrastructure allowed me to gather accurate and up-to-date stock information across multiple platforms and news sources without hitting rate limits or facing IP bans, making the data aggregation process both seamless and reliable.
Key benefits of Bright Data in my project:
- Scalability: Easily access a wide variety of data from multiple websites simultaneously.
- Reliability: Ensure that data is consistently updated and available without interruption.
- Data Enrichment: Retrieve a rich mix of both structured and unstructured data from multiple sources to enhance my AI analysis.
Performance Improvements
The integration of real-time web data through Bright Data significantly improved the performance of my stock analysis application. By leveraging fresh data, the AI model can generate timely investment recommendations based on current market trends, rather than relying on outdated datasets. This ensures that users have the most accurate and up-to-date information when making investment decisions, giving them a competitive edge in fast-moving markets.
In comparison to traditional approaches that use static datasets or delayed reports, the Bright Data-powered system delivers a more dynamic, responsive, and actionable analysis, improving both the speed and accuracy of stock predictions.
Top comments (1)
Live Demo: You can try out the project here 👉 effervescent-sherbet-2800a4.netlif...
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