Welcome to the Market Anomaly Detection project! This application combines machine learning, real-time market data, and AI-powered reasoning to predict market anomalies and provide detailed insights on predictions.
| Dashboard | Market Trends | Prediction Insights |
|---|---|---|
This project analyzes real-time market data, predicts potential market anomalies (such as stock crashes), and explains predictions using Retrieval-Augmented Generation (RAG) powered by Groq AI.
💡 Key Objective: To empower investors with actionable insights and help them make informed decisions.
✅ Market Anomaly Detection: A Random Forest model predicts potential market crashes based on real-time data.
✅ AI-Powered Insights: Predictions are explained using Groq RAG, incorporating news and feature analysis.
✅ Real-Time Data Integration: Uses Yahoo Finance API to fetch live stock data.
✅ Dynamic Data Visualization: Interactive charts visualize stock trends and model predictions.
✅ Customizable Parameters: Users can select stock symbols and timeframes.
| Component | Technology |
|---|---|
| Backend | Python, Flask, Yahoo Finance API, Groq API |
| Frontend | React, Next.js, TailwindCSS, Chart.js |
| Machine Learning | Random Forest Model (SMOTE for imbalanced data) |
| API Communication | Flask REST API |
| Environment Management | dotenv |
- Model Used:
RandomForestClassifier - Preprocessing: Feature scaling, SMOTE for class balancing
- Prediction Output:
1→ Market Crash0→ No Crash
- XAU BGNL (Gold Spot)
- BDIY (Baltic Dry Index)
- DXY (Dollar Index)
- VIX (CBOE Volatility Index)
- JPY (Japanese Yen)
- GBP (British Pound)
- And more...