A comprehensive financial analysis platform that combines real-time market data with state-of-the-art AI models from Hugging Face to provide actionable market insights and research capabilities.
- Real-time Market Analysis - Integration with multiple data sources for comprehensive market coverage
- AI-Powered Sentiment Analysis - Advanced NLP models to analyze news and social media sentiment
- Automated Report Generation - Intelligent summarization of earnings calls and financial reports
- Event Detection - Real-time monitoring of significant market events using Named Entity Recognition
- Interactive Dashboard - Streamlit-based visualization of market insights and sentiment analysis
- Market Data: Alpha Vantage, Yahoo Finance, Polygon
- News & Sentiment: NewsAPI, GDELT, Reddit/Twitter API
- Fundamentals: SEC EDGAR, Quandl, EOD Historical
- Alternative Data: CryptoCompare, Glassnode
- Sentiment Analysis: ProsusAI/finbert, distilroberta-finetuned-financial-news-sentiment
- Text Summarization: facebook/bart-large-cnn, google/pegasus-xsum
- Named Entity Recognition: dslim/bert-base-NER
- Time-Series Forecasting: huggingface/autonlp-timeseries-1
- Question Answering: deepset/roberta-base-squad2
# Clone the repository
git clone https://github.com/walterthesmart/Fin-Search.git
cd Fin-Search
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt- Set up your environment variables:
export HUGGINGFACE_API_KEY="your_key_here"
export ALPHA_VANTAGE_API_KEY="your_key_here"
export NEWS_API_KEY="your_key_here"- Run the sentiment analysis dashboard:
streamlit run app/dashboard.py- Access the dashboard at
http://localhost:8501
from pipelines.sentiment import analyze_sentiment
# Analyze sentiment for a specific ticker
sentiments = analyze_sentiment("AAPL")
print(f"Sentiment Analysis Results: {sentiments}")from pipelines.summarization import summarize_earnings_call
# Summarize an earnings call transcript
summary = summarize_earnings_call("path_to_transcript.txt")
print(f"Summary: {summary}").
├── data/ # Historical datasets
├── models/ # Fine-tuned Hugging Face models
├── pipelines/
│ ├── sentiment.py # News sentiment analysis
│ ├── summarization.py # Earnings call summaries
│ └── forecasting.py # Time-series predictions
├── app/ # Streamlit/FastAPI frontend
├── Dockerfile # Containerization
└── requirements.txt
The application can be deployed using Docker:
# Build Docker image
docker build -t Fin-Search .
# Run container
docker run -p 8501:8501 Fin-Search- Latency: Optimized with distilled models for real-time analysis
- Data Quality: Combined with technical indicators for validation
- Model Accuracy: Implemented fact-checking layer using SEC data
- Compliance: Focus on research insights rather than direct trading signals
This tool is designed for research purposes only. Users should:
- Consult with legal advisors before deployment
- Ensure compliance with financial regulations
- Not rely solely on this tool for trading decisions
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Hugging Face for providing state-of-the-art NLP models
- Various data providers for financial market data
- Open-source community for tools and libraries
Your Name - @kudiegomoney Project Link: https://github.com/walterthesmart/Fin-Search
