During my internship, I worked on developing sophisticated analysis tools for monitoring political trading activities and market movements. The project involved creating AI-powered systems to analyze social media sentiment, track SEC form submissions, and identify correlations between political events and market trends.
Phase 1: Political Trading Analysis System
Developed a system to analyze the trading patterns of political figures, focusing on senators' and governors' investment actions, trading strategies, and the correlation between political actions and stock performance.
Case Study: Market Influence Analysis
Analyzed significant market movements triggered by social media posts from influential figures.
HHH (Home Construction ETF)
Commentary on interest rates and Federal Reserve policy created notable market movements in the housing sector.
FNMA (Fannie Mae)
Tracked market response to commentary on Fannie Mae, showing impact on government-sponsored enterprises.
Social Media Sentiment
Analyzed the correlation between social media posts and market movements, demonstrating immediate effects.
Phase 2: Congressional Financial Data Integration
Developed a comprehensive monitoring system that automated data collection from multiple government portals, including:
- House Stock Watcher & Senate Financial Disclosures Database
- Office of Government Ethics (OGE) data and Periodic Transaction Reports (PTRs)
- US Senate Lobby Disclosure API
Phase 3: Social Media Sentiment Analysis
Implemented AI-powered sentiment analysis by integrating real-time data from the News API and the X/Twitter API. This allowed for correlation analysis between public sentiment and market movements, culminating in comprehensive analysis reports.
Skills Developed
- AI/ML
- Data Analysis
- Government APIs
- Data Integration
- News API
- Twitter/X API
- Sentiment Analysis
- Market Analysis
- Python