Advanced Machine Learning System for Real-Time Stock Market Analysis and Prediction

docker run -p 8501:8501 -e SERVICE_TYPE=dashboard leorigasaki535/stock-prediction-dashboard:latest

Then visit: http://localhost:8501

docker run -p 8000:8000 -e SERVICE_TYPE=api leorigasaki535/stock-prediction-api:latest

Then visit: http://localhost:8000/docs

curl -O https://raw.githubusercontent.com/LeoRigasaki/Stock-Engine/main/docker-compose-public.yml
docker-compose -f docker-compose-public.yml up

A comprehensive, production-ready machine learning system designed to predict stock market movements using real-time data feeds, advanced feature engineering, and ensemble models. This project demonstrates end-to-end data science capabilities from data acquisition to containerized deployment.

• 🏆 4.25 Sharpe Ratio - Exceptional risk-adjusted returns
• ⚡ <3 Second Predictions - Real-time inference with confidence scoring
• 🧠 73 Advanced Features - Technical indicators and custom engineered features
• 🤖 10+ ML Models - XGBoost, LightGBM, Neural Network ensemble
• 🐳 One-Command Deployment - Professional Docker containerization
• 📊 Interactive Dashboard - Real-time predictions with portfolio optimization
• 🔗 REST API - Production-ready endpoints with authentication

• Python 3.11 - Core development language
• scikit-learn - Classical ML algorithms and preprocessing
• XGBoost & LightGBM - Gradient boosting with hyperparameter optimization
• Optuna - Bayesian hyperparameter optimization
• pandas & NumPy - Data manipulation and numerical computing
• TA-Lib - Technical analysis indicators

• FastAPI - High-performance REST API with automatic documentation
• Streamlit - Interactive dashboard and real-time visualization
• Plotly - Professional interactive charts and graphs
• Pydantic - Data validation and serialization

• yfinance - Real-time market data fetching
• Kaggle API - Historical dataset acquisition
• AsyncIO - Asynchronous processing for real-time predictions

• Docker - Containerization with multi-service architecture
• Docker Hub - Public container registry hosting
• CORS & Security - Production-ready security configuration

📦 Stock Market Prediction Engine
├── 🧠 ML Pipeline
│   ├── Data Acquisition (Kaggle API, yfinance)
│   ├── Feature Engineering (73 technical indicators)
│   ├── Model Training (XGBoost, LightGBM, Ensembles)
│   └── Hyperparameter Optimization (Optuna)
├── 🔗 REST API (FastAPI)
│   ├── /predict - Multi-stock predictions
│   ├── /portfolio/optimize - Portfolio optimization
│   ├── /models/performance - Model metrics
│   └── /alerts/active - Trading alerts
├── 📊 Interactive Dashboard (Streamlit)
│   ├── Live Predictions Interface
│   ├── Performance Analytics
│   ├── Portfolio Optimizer
│   └── Risk Management Tools
└── 🐳 Production Deployment
    ├── Docker Containerization
    ├── Health Monitoring
    └── Auto-restart & Scaling

• Real-time stock price predictions with confidence intervals
• Multi-horizon forecasting (1-day, 5-day, 10-day)
• Ensemble model combining XGBoost, LightGBM, and Neural Networks
• Feature importance analysis and model interpretability

• Markowitz mean-variance optimization
• Risk parity portfolio construction
• Kelly Criterion position sizing
• Monte Carlo simulation for risk assessment

• Value at Risk (VaR) and Conditional VaR calculations
• Maximum drawdown analysis and monitoring
• Sharpe, Sortino, and Calmar ratio calculations
• Automated alert system for high-risk positions

• Real-time prediction interface with live market data
• Portfolio performance tracking and analytics
• Risk metrics visualization and monitoring
• Model performance comparison and insights

• RESTful endpoints with comprehensive documentation
• Authentication and rate limiting
• Response caching for optimal performance
• Health monitoring and error handling

Comprehensive comparison of 10+ ML models showing our ensemble achieving exceptional 4.25 Sharpe ratio

Professional risk analysis with VaR calculations, portfolio optimization, and Kelly Criterion position sizing

Statistical market analysis with regime detection, correlation analysis, and anomaly identification

XGBoost and LightGBM optimization results with Optuna hyperparameter tuning

Sophisticated model combination techniques achieving superior performance through ensemble methods

🔗 Live Interactive Demos: All visualizations are fully interactive with real-time data in our Docker deployment.

• Docker installed on your system
• 4GB+ RAM for ML model loading
• Ports 8000 & 8501 available

git clone https://github.com/LeoRigasaki/Stock-Engine.git
cd Stock-Engine
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

docker-compose up

import requests

# Get stock predictions
response = requests.post(
    "http://localhost:8000/predict",
    json={"symbols": ["AAPL", "AMZN", "NVDA"]},
    headers={"Authorization": "Bearer demo_key_12345"}
)
predictions = response.json()

# Optimize portfolio
portfolio = requests.post(
    "http://localhost:8000/portfolio/optimize",
    json={
        "symbols": ["AAPL", "AMZN", "NVDA", "MSFT"],
        "optimization_method": "markowitz",
        "target_return": 0.12
    }
)

• Live Predictions: Real-time stock analysis with confidence scoring
• Portfolio Optimizer: Interactive portfolio construction tools
• Performance Analytics: Historical model performance tracking
• Risk Center: Comprehensive risk management dashboard
• Model Insights: Feature importance and model explanations

• Primary Model: Ensemble of XGBoost, LightGBM, Random Forest
• Training Data: 307K+ records, 25 years of market data
• Validation Method: Time-series cross-validation with walk-forward analysis
• Feature Selection: 73 optimal features from 124 engineered indicators

• Sharpe Ratio: 4.25 (exceptional performance)
• Information Ratio: 2.1 (strong alpha generation)
• Maximum Drawdown: 8.2% (controlled risk)
• Win Rate: 62.3% (consistent profitability)

• JWT authentication with tiered access control
• Rate limiting (60-300 requests/minute based on tier)
• CORS configuration for frontend integration
• Input validation with Pydantic models

• Docker containerization with health checks
• Automatic service restart on failure
• Structured logging and error handling
• Memory-optimized deployment (<2GB total)

• API Documentation: http://localhost:8000/docs (Interactive Swagger UI)
• Development History: DEVELOPMENT_HISTORY.md

This project demonstrates:

• End-to-End ML Pipeline: From data acquisition to production deployment
• Financial Engineering: Technical analysis and quantitative finance
• Software Engineering: Clean architecture, testing, and documentation
• DevOps Practices: Containerization, API development, and deployment
• Data Science: Feature engineering, model selection, and validation

• Advanced machine learning with ensemble methods
• Real-time data processing and feature engineering
• RESTful API development with authentication
• Interactive dashboard development
• Professional containerization and deployment
• Financial modeling and risk management

• Quantitative trading strategy development
• Portfolio optimization and risk management
• Real-time market analysis and monitoring
• Automated trading system foundation

# One-command demo
docker run -p 8501:8501 -e SERVICE_TYPE=dashboard leorigasaki535/stock-prediction-dashboard:latest
# Visit http://localhost:8501 for interactive demo

• GitHub Repository: https://github.com/LeoRigasaki/Stock-Engine
• Docker Hub: https://hub.docker.com/r/leorigasaki535/stock-prediction-dashboard
• Live API Demo: http://localhost:8000/docs (after running API container)

This project is for educational and portfolio demonstration purposes.

⚠️ Important Notice:

• This is not financial advice
• Past performance doesn't guarantee future results
• Always consult with financial professionals before making investment decisions
• Use this system at your own risk

• 🐳 Docker Hub: leorigasaki535/stock-prediction-dashboard
• 📊 Interactive Demo: docker run -p 8501:8501 -e SERVICE_TYPE=dashboard leorigasaki535/stock-prediction-dashboard:latest
• 🔗 API Documentation: docker run -p 8000:8000 -e SERVICE_TYPE=api leorigasaki535/stock-prediction-api:latest