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Prerequisites for Pattern Pursuit Challenge

Technical Skills Requirements

Programming Skills

  • Strong proficiency in Python programming (Python 3.9+)
  • Experience with Jupyter notebooks and JupyterLab environment
  • Familiarity with git version control
  • Understanding of software design patterns and object-oriented programming
  • Basic command line and shell scripting knowledge

Data Science & Machine Learning

  • Strong understanding of numerical computing with NumPy and Pandas
  • Experience with machine learning frameworks (scikit-learn)
  • Deep learning fundamentals and experience with PyTorch/TensorFlow
  • Understanding of data preprocessing and feature engineering
  • Knowledge of model evaluation metrics and validation techniques

Financial Markets Knowledge

  • Understanding of financial market concepts and terminology
  • Basic knowledge of technical analysis
  • Familiarity with trading strategies and portfolio management
  • Understanding of market risks and trading costs
  • Knowledge of S&P 500 market structure

Development Environment

Required Software

  • Python 3.9 or higher
  • Git 2.x or higher
  • Docker Desktop
  • JupyterLab
  • Code editor (VS Code recommended)

Python Packages

# Core Data Science
numpy>=1.21.0
pandas>=1.3.0
scipy>=1.7.0

# Machine Learning
scikit-learn>=1.0.0
pytorch>=1.9.0  # or tensorflow>=2.6.0
keras>=2.6.0

# Financial Analysis
yfinance>=0.1.63
pandas-ta>=0.3.0
financialanalysis>=0.0.4

# Data Visualization
matplotlib>=3.4.0
seaborn>=0.11.0
plotly>=5.1.0

# Development Tools
jupyter>=1.0.0
jupyterlab>=3.0.0
black>=21.5b2
flake8>=3.9.0
pytest>=6.2.0

# API Development
fastapi>=0.68.0
uvicorn>=0.15.0

# Production Tools
docker>=5.0.0
prometheus-client>=0.11.0

System Requirements

  • Minimum 16GB RAM (32GB recommended)
  • 100GB available disk space
  • Modern multi-core CPU
  • GPU recommended for deep learning models
  • Stable internet connection for data downloads

Data Requirements

Market Data Access

  • Access to daily S&P 500 stock data (minimum 5 years historical)
  • Real-time market data feed (for production phase)
  • Market sentiment data sources
  • Alternative data sources (optional)

Storage Infrastructure

  • Local storage for development datasets
  • Database system for production (PostgreSQL recommended)
  • Data backup solution

Infrastructure Access

Development Environment

  • GitHub account
  • Docker Hub account
  • Cloud platform access (AWS/GCP/Azure) for production deployment
  • CI/CD platform access (optional)

Monitoring Tools

  • Metrics collection system (Prometheus)
  • Logging infrastructure (ELK Stack recommended)
  • Alert management system

Time Commitment

  • 3-4 hours daily for implementation
  • Additional time for research and documentation
  • Consistent 30-day availability

Additional Considerations

Documentation

  • Knowledge of Markdown for documentation
  • Understanding of API documentation standards
  • Familiarity with technical writing

Best Practices

  • Understanding of code quality standards
  • Knowledge of testing methodologies
  • Familiarity with agile development practices

Security

  • Basic understanding of security best practices
  • Knowledge of API security
  • Understanding of data privacy considerations

Optional but Recommended

Skills

  • Experience with system architecture design
  • Knowledge of microservices architecture
  • Understanding of DevOps practices

Tools

  • Containerization orchestration (Kubernetes)
  • CI/CD pipelines (Jenkins/GitHub Actions)
  • Cloud services (AWS/GCP/Azure)

Getting Ready

  1. Install all required software and packages
  2. Set up development environment
  3. Configure version control
  4. Prepare data access and storage
  5. Review project documentation
  6. Set up monitoring tools