Developed by Ayaan Iqbal, Raiyan Aaijaz and Mihir Kachroo
PoliLens is a stock analysis tool that tracks politicians' stock transactions and correlates them with legislation and news reports. The system leverages AI-powered NLP models to identify potential patterns and insights that may indicate politically motivated stock movements.
PoliLens combines data scraping, AI-powered analysis, and a web dashboard to provide meaningful insights.
| Feature | Description | Technology/Tools |
|---|---|---|
| Stock Transaction Tracking | Scrapes and stores politicians' stock transactions from public sources. | Python, BeautifulSoup, Requests |
| Legislation & News Correlation | Extracts and analyzes legislative bills and financial news for impact on stock movements. | NLP, Hugging Face Transformers |
| AI-Powered Insights | Uses machine learning models to detect patterns between stock trades and legislative actions. | TensorFlow, Scikit-learn |
| Web Dashboard | Displays key trends and findings interactively. | React, Node.js, Bootstrap, MongoDB (Currently in development) |
PoliLens consists of three core modules working together:
-
Data Scraper (Backend - Python)
- Scrapes stock trades from websites such as CapitolTrades and SmartInsider.
- Collects legislative and financial news data.
- Stores data in a structured format for further analysis.
-
AI & NLP Analysis
- Pre-trained NLP models analyze stock transactions concerning legislative activity.
- Generates insights on potential conflicts of interest or trading patterns.
-
Web Dashboard (MERN Stack)
- Displays AI-generated insights on a user-friendly interface.
- Allows users to explore connections between political activity and stock movements.
(Currently in development)
Prerequisites:
- Python 3.11 or higher
- MongoDB for database storage
git clone https://github.com/your-repo/polilens.git
cd polilensThis project is licensed under the MIT License. See the LICENSE file for details.