Financial Anomaly Detection AWS-Powered Stock Market Analysis with Real-time Alerts
A comprehensive machine learning system that combines LSTM neural networks, AWS cloud services, and real-time data processing to detect stock market anomalies and provide intelligent trading insights.
System Capabilities
LSTM Neural Networks
Advanced LSTM models with batch normalization and dropout layers for accurate stock price prediction and anomaly detection.
Real-time Monitoring
Continuous monitoring of stock movements with instant alerts for potential anomalies and trading opportunities.
LLM Integration
LLaMA-2 integration for intelligent question generation and natural language processing of financial data.
AWS Architecture
Scalable serverless architecture using SageMaker, Lambda, SQS, and EventBridge for reliable processing.
Custom Model Training
Upload your own datasets and train custom models with automated pipeline and progress tracking.
Smart Notifications
Personalized alert system with multiple frequencies and intelligent threshold management.
AWS Services Architecture
User Interface
Console dashboard for model management, data upload, and real-time monitoring.
API Gateway
RESTful API endpoints with authentication, rate limiting, and request transformation.
Lambda Functions
Serverless computation for user requests, authentication, and service coordination.
SageMaker
ML model training, deployment, and inference with automatic scaling and monitoring.
RDS Database
Stores users, jobs, models, subscriptions, and notifications with ACID compliance.
RESTful API Design
Technical Implementation
Security & Authentication
JWT-based authentication with password hashing, role-based access control, and secure API endpoints.
Data Processing
Advanced preprocessing pipeline for financial data with feature engineering and time series alignment.
Containerization
Docker-based deployment with multi-stage builds for consistent environments and optimization.
Job Processing
Asynchronous job processing with SQS queues, status tracking, and comprehensive error handling.
Database Schema
Users
- userid (Primary Key)
- username
- password_hash
- created_at
Jobs
- jobid (Primary Key)
- userid (Foreign Key)
- status
- original_data_file
- results_file_key
Models
- modelid (Primary Key)
- title
- version
- endpoint
- privacy
Subscriptions
- subscriptionid (Primary Key)
- userid (Foreign Key)
- modelid (Foreign Key)
- frequency
- active
Challenges & Future Improvements
Key Challenges
The primary challenge was deploying trained models using SageMaker endpoints. The complexity of model serialization, endpoint configuration, and real-time inference optimization required significant troubleshooting and iterative development.
Planned Enhancements
- Add auto-scaling to Live models on SageMaker
- Enhanced error handling and user feedback
- Advanced visualization dashboard
- Advanced anomaly detection algorithms
- Mobile application for monitoring
Interested in this project?
Feel free to reach out to discuss collaboration opportunities or ask any questions.