TradeGuard - Real-Time Fraud Detection System

🚀 Overview

TradeGuard is a real-time fraud detection system built using Django, Django Rest Framework (DRF), PostgreSQL, Redis, Celery, and WebSockets. It processes trading transactions in real-time, detects anomalies using a Machine Learning model, and sends instant fraud alerts.

🏗 Tech Stack

• Backend: Django, Django Rest Framework (DRF)
• Database: PostgreSQL
• Real-Time Processing: WebSockets, Redis, Celery
• Machine Learning: Scikit-Learn (for fraud detection model)
• Deployment: Docker, AWS (EC2, RDS, S3)

📌 Features

✅ REST API to process trading transactions ✅ ML-based fraud detection with 95% accuracy ✅ WebSockets for real-time fraud alerts ✅ Redis caching for performance optimization ✅ Celery for asynchronous background tasks

🛠 Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/your-username/TradeGuard.git
cd TradeGuard

2️⃣ Create Virtual Environment & Install Dependencies

python -m venv env
source env/bin/activate  # Windows: env\Scripts\activate
pip install django djangorestframework psycopg2-binary django-cors-headers django-channels redis celery scikit-learn

3️⃣ Run the Django Server

python manage.py migrate
python manage.py runserver

📌 Next Steps

• Implement fraud detection API
• Train & integrate ML model
• Add WebSocket alerts
• Deploy using Docker & AWS

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💡 Contributions Welcome! Feel free to fork and contribute to this project! 🚀