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Project Structure Documentation

Overview

The Pattern Pursuit Challenge project follows a modular structure designed to support incremental development over 30 days, from basic model implementation to production deployment.

Directory Structure

pattern-pursuit/
├── README.md                    # Project overview and getting started
├── LICENSE                      # Project license
├── setup.py                     # Project installation configuration
├── requirements.txt             # Project dependencies
├── .env.example                 # Environment variable templates
├── .gitignore                  # Git ignore patterns
│
├── core/                        # Core package directory
│   ├── setup.py                # Package setup configuration
│   ├── requirements.txt        # Package-specific dependencies
│   ├── dlcore/                 # Main package source
│   │   ├── __init__.py
│   │   ├── models/            # Model implementations
│   │   ├── data/              # Data processing modules
│   │   ├── evaluation/        # Evaluation metrics and tools
│   │   └── utils/             # Utility functions
│   └── tests/                  # Package tests
│
├── challenges/                  # Daily challenge implementations
│   ├── templates/              # Templates for daily work
│   └── day_XX/                 # Day-specific directories
│       ├── notebook.ipynb      # Implementation notebook
│       ├── technical_report.md # Technical documentation
│       ├── summary_report.md   # Executive summary
│       └── README.md          # Day overview
│
├── infrastructure/             # Infrastructure configuration
│   ├── docker/                # Docker configurations
│   │   ├── development/       # Development environment
│   │   └── production/        # Production environment
│   ├── k8s/                   # Kubernetes manifests
│   ├── airflow/               # Airflow DAGs and config
│   └── monitoring/            # Monitoring configuration
│
├── data/                       # Data directory
│   ├── raw/                   # Raw market data
│   ├── processed/             # Processed features
│   └── README.md              # Data documentation
│
├── docs/                       # Project documentation
│   ├── getting_started.md     # Setup guide
│   ├── infrastructure/        # Infrastructure docs
│   ├── development/           # Development guides
│   ├── api/                   # API documentation
│   └── phases/                # Phase-specific docs
│
├── notebooks/                  # Research and analysis notebooks
│   ├── research/              # Research experiments
│   └── analysis/              # Data analysis
│
├── scripts/                    # Utility scripts
│   ├── setup/                 # Setup scripts
│   ├── data/                  # Data processing scripts
│   └── deployment/            # Deployment scripts
│
└── api/                       # API implementation
    ├── routes/                # API endpoints
    ├── models/                # API models
    └── tests/                 # API tests

Key Components

Core Package (core/)

The dlcore package contains reusable components:

  • models/: Trading model implementations
    • Base model classes
    • Model architectures
    • Training utilities
  • data/: Data processing functionality
    • Data loaders
    • Feature engineering
    • Data validation
  • evaluation/: Evaluation framework
    • Performance metrics
    • Backtesting tools
    • Risk metrics
  • utils/: Utility functions
    • Helper functions
    • Common calculations
    • System utilities

Daily Challenges (challenges/)

Each day's implementation includes:

  • Jupyter notebook with code
  • Technical documentation
  • Executive summary
  • Day-specific data and models
  • Progress tracking

Infrastructure (infrastructure/)

Configuration for all services:

  • Docker containers
  • Kubernetes deployments
  • Service configurations
  • Monitoring setup

Documentation (docs/)

Comprehensive project documentation:

  • Setup guides
  • Architecture documentation
  • API documentation
  • Development guidelines
  • Phase documentation

Data Management (data/)

Organized data storage:

  • Raw market data
  • Processed features
  • Model predictions
  • Evaluation results

API Implementation (api/)

REST API for model serving:

  • Route definitions
  • Request/response models
  • Authentication
  • Testing

Development Workflow

Local Development

  1. Clone repository
  2. Install dependencies
  3. Set up infrastructure
  4. Start development services

Daily Implementation

  1. Create new day directory
  2. Implement solution
  3. Document findings
  4. Update core package
  5. Run tests
  6. Commit changes

Production Deployment

  1. Build containers
  2. Deploy to Kubernetes
  3. Configure monitoring
  4. Enable API endpoints

Version Control

Branch Structure

  • main: Production-ready code
  • development: Integration branch
  • feature/*: Feature branches
  • day-XX: Daily challenge branches

Commit Guidelines

  • Conventional commits format
  • Descriptive messages
  • Reference issues/tasks

Documentation Standards

Code Documentation

  • Google style docstrings
  • Inline comments for complexity
  • README files for components

Technical Documentation

  • Markdown format
  • Clear structure
  • Code examples
  • Diagrams when needed

Quality Assurance

Testing

  • Unit tests for core package
  • Integration tests for API
  • Performance tests
  • Documentation tests

Code Quality

  • Linting (flake8)
  • Formatting (black)
  • Type checking (mypy)
  • Code review process