FinSent-Agent

A sophisticated financial sentiment analysis AI-agent that fetches real-time market news, analyzes sentiment using FinBERT, and provides AI-powered summaries to help with investment decisions.

Features

• Real-time News Fetching: Automatically retrieves latest trending financial news from multiple sources
• Advanced Sentiment Analysis: Uses FinBERT (Financial BERT) model for accurate financial sentiment scoring and embedding
• Semantic Search: FAISS-powered similarity search to find relevant articles
• AI-Powered Summaries: OpenAI GPT integration for intelligent market sentiment summaries
• Interactive Queries: Ask specific questions about market sentiment and get tailored insights

Technology Stack

• Python 3.11+
• Transformers & FinBERT: Financial sentiment analysis
• FAISS: Vector similarity search for semantic matching
• OpenAI GPT: Advanced natural language summarization
• Sentence Transformers: Text embedding generation
• pandas: Data manipulation and analysis
• News API: Real-time news data ingestion

Prerequisites

• Python 3.11 or higher
• OpenAI API key
• News API key

Quick Start

1. Clone the Repository

git clone https://github.com/skondaparthi/FinSent-Agent.git
cd FinSent-Agent

2. Set Up Virtual Environment

python3 -m venv finsentenv
source finsentenv/bin/activate  # On macOS/Linux
# or
finsentenv\Scripts\activate     # On Windows

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key_here
NEWS_API_KEY=your_news_api_key_here

5. Run the Agent

python main.py

📁 Project Structure

FinSent-Agent/
├── main.py                # Main application entry point
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (create this)
├── Finsent.ipynb          # Google Colab playground (experimental notebook)
├── ingest/
│   └── news_api.py        # News data ingestion module using NewsAPI
├── models/
│   ├── finbert_model.py   # FinBERT sentiment analysis
│   ├── faiss_model.py     # FAISS similarity search
│   └── llm_model.py       # OpenAI integration
└── README.md              # This file

Configuration

API Keys Setup

1. OpenAI API Key: Get your OpenAI key at OpenAI Platform
2. News API Key: Get NewsAPI key at NewsAPI.org

Add these to your .env file:

OPENAI_API_KEY=sk-your-actual-openai-key
NEWS_API_KEY=your-actual-news-api-key

Usage Examples

Basic Usage

Run the script and enter your query when prompted:

Enter a query to summarize market news: Tesla stock analysis

Example Queries

• "Should I invest in Tesla right now?"
• "AI market sentiment analysis"
• "Cryptocurrency market trends"
• "Tech stock outlook"
• "Federal Reserve interest rate impact"

How It Works

1. News Ingestion: Fetches latest financial news from News API
2. Sentiment Scoring: FinBERT analyzes each article for financial sentiment
3. Embedding Generation: Creates vector embeddings for semantic search
4. FAISS Indexing: Builds searchable index for similarity matching efficiently
5. Query Processing: Finds articles relevant to your specific query
6. AI Summarization: OpenAI GPT generates comprehensive market insights

Output Example

Summary of Market News:
The current market sentiment surrounding Tesla appears predominantly positive, despite some underlying concerns. Recent headlines highlight optimism about potential investment returns, with Elon Musk suggesting that a $150,000 investment could lead to millionaire status. Additionally, there's a recognition that Tesla is evolving beyond just a car manufacturer, which could open up new revenue streams. However, analysts express caution regarding dwindling sales and critical revenue sources, indicating a mixed outlook. Given this context, investing in Tesla could be worthwhile for those who believe in its long-term vision and innovations, but potential investors should remain aware of the challenges the company faces in the short term.

Advanced Features

• Semantic Search: Find articles similar to your specific interests
• Multi-source Analysis: Aggregates sentiment from diverse news sources
• Customizable Analysis: Adjust the number of articles analyzed (k parameter)
• Real-time Processing: Always uses the latest available market news

Troubleshooting

Common Issues

1. Missing API Keys: Ensure your .env file contains valid API keys
2. Package Conflicts: Use a fresh virtual environment
3. FAISS Installation: On some systems, use pip install faiss-cpu==1.10.0 (M-Series Macs)
4. Tokenizer Warnings: These are handled automatically in the code

Error Solutions

• ImportError: Run pip install -r requirements.txt in your virtual environment
• API Errors: Check your API keys and quotas
• No Articles Found: Try broader search terms or check News API status, or change date so that it is within one month of current date (NewsAPI limitation)

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• FinBERT: For financial sentiment analysis capabilities
• FAISS: For efficient similarity search
• OpenAI: For advanced text generation
• News API: For real-time news data

Support

If you encounter any issues or have questions:

1. Check the troubleshooting section above
2. Open an issue on GitHub
3. Review the documentation for each component

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Happy Trading!