AI-Based Stock Market Prediction System: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Develop an AI-based system to predict stock market trends and assist investors in making informed decisions.
Scope of the system: Applicable for individual investors, financial analysts, and trading firms.
2. Requirements Analysis
Functional Requirements:
· - Analyze historical stock data and trends.
· - Provide predictions for stock prices or market indices.
· - Generate insights such as buy/sell signals based on predictions.
· - Allow user registration and portfolio tracking.
Non-Functional Requirements:
· - High accuracy and reliability of predictions.
· - Scalable to handle large datasets and multiple users.
· - Secure handling of sensitive financial data.
3. System Design
Architecture:
· - Modular design with components for data collection, preprocessing, modeling, and user interaction.
· - Use of AI/ML models for prediction and analysis.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of user interactions and data flow from input to output.
· - Level 1: Detailed processes for data preprocessing, prediction modeling, and user notifications.
Database Design:
· - Tables: Users, Historical Data, Predictions, Portfolios.
4. Technology Stack
Frontend:
· - Web or mobile app using React.js, Angular, or Flutter for user interaction.
Backend:
· - Python (Flask/Django) or Node.js for API development.
· - AI/ML Frameworks: TensorFlow, Keras, PyTorch, or Scikit-learn for predictive modeling.
Database:
· - SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase).
Cloud Services:
· - AWS, Google Cloud, or Azure for hosting and data storage.
5. Implementation
Data Collection:
· - Gather historical stock market data from sources such as Yahoo Finance or Alpha Vantage APIs.
· - Include data preprocessing steps to clean and normalize data.
Model Development:
· - Train machine learning models using time series analysis techniques (e.g., LSTMs, ARIMA).
· - Evaluate models for accuracy, precision, and recall.
Frontend Development:
· - Design interfaces for displaying market trends, predictions, and user portfolios.
· - Implement interactive charts and graphs for data visualization.
Backend Development:
· - Develop APIs for data retrieval, prediction, and user management.
· - Implement secure data storage for user portfolios and predictions.
Integration:
· - Connect frontend, backend, and prediction models for seamless operation.
6. Security
Encrypt data during storage and transmission using protocols like TLS.
Implement authentication and authorization mechanisms for secure access.
Ensure compliance with financial data regulations.
7. Testing
Unit Testing: Validate individual components like prediction models and APIs.
Integration Testing: Ensure smooth communication between frontend, backend, and AI models.
System Testing: Test the system with real-world market data.
Performance Testing: Evaluate system scalability and response times under heavy loads.
8. Deployment
Deploy the system on cloud platforms for reliability and scalability.
Set up monitoring tools for performance tracking and anomaly detection.
Provide user support and training for effective system use.
9. Maintenance and Updates
Regularly update prediction models with new data for improved accuracy.
Monitor system logs and user feedback to identify and resolve issues.
Incorporate advancements in AI and market analysis techniques for system enhancements.
10. Appendix
Glossary of terms.
References and additional resources.