Spaces:
Running
Running
| title: Alpha Predict | |
| emoji: 🚀 | |
| colorFrom: red | |
| colorTo: red | |
| sdk: docker | |
| app_port: 8501 | |
| tags: | |
| - financial-analysis | |
| - nlp | |
| - sentiment-analysis | |
| - finbert | |
| - streamlit | |
| pinned: false | |
| short_description: Market Sentiment & Volatility Prediction using FinBERT | |
| # 🚀 Alpha Predict: Market Sentiment Engine | |
| Alpha Predict is an AI-driven financial analysis tool that leverages **FinBERT** (Financial BERT) to quantify market sentiment from real-time and historical headlines. It correlates sentiment with **S&P 500 (SPY)** performance and the **VIX (Fear Index)** to provide a holistic view of market psychology. | |
| ## 🧠 Core Features | |
| * **NLP Sentiment Analysis:** Uses `ProsusAI/finbert` to perform high-fidelity sentiment classification on thousands of market headlines. | |
| * **Hybrid Data Fetching:** Integrated with **Finnhub API** for live market news and price action, with a **robust CSV fallback** mechanism for maximum uptime. | |
| * **Predictive Indicators:** Analyzes "Panic Interaction" (Sentiment x Volatility) to detect market dislocations. | |
| * **Interactive Analytics:** Visualizes the relationship between news sentiment trends and price movements via Streamlit. | |
| ## 🛠️ Technical Stack | |
| * **UI Framework:** Streamlit | |
| * **Model:** FinBERT (Hugging Face Transformers) | |
| * **Data Providers:** Finnhub API, Yahoo Finance (via backup) | |
| * **Deployment:** Docker / Hugging Face Spaces | |
| ## 📂 Project Structure | |
| * `app.py`: Main entry point for the Streamlit dashboard. | |
| * `src/data_fetcher.py`: Handles API interactions and data resilience. | |
| * `src/processor.py`: Feature engineering and sentiment batch processing. | |
| * `data/`: Secure storage for historical backup data to ensure 100% availability. | |
| ## 🚦 Getting Started | |
| 1. **API Keys:** Ensure your `FINNHUB_API_KEY` is set in the Hugging Face Space Secrets. | |
| 2. **Processing:** Upon launch, the app will fetch the last 45-60 days of data. | |
| 3. **Inference:** FinBERT runs batch inference on the latest headlines to calculate the `Sent_Mean` index. | |
| --- | |
| **Note:** *This project was developed for academic purposes to demonstrate the application of Transformer-based models in quantitative finance.* |