dax-ml/V0.1.0_SETUP_COMPLETE.md

Version 0.1.0 - Project Foundation Complete ✅

Summary

The project foundation for ICT ML Trading System v0.1.0 has been successfully created according to the project structure guide.

What Was Created

✅ Directory Structure

• Complete directory tree matching the project structure
• All required subdirectories for data, models, logs, tests, etc.
• .gitkeep files in empty directories

✅ Project Files

• .gitignore - Comprehensive ignore patterns
• requirements.txt - Production dependencies
• requirements-dev.txt - Development dependencies
• setup.py - Package installation configuration
• pyproject.toml - Modern Python project configuration
• Makefile - Common commands automation
• README.md - Project documentation
• .pre-commit-config.yaml - Pre-commit hooks
• CHANGELOG.md - Version history
• LICENSE - MIT License

✅ Configuration Files

• config/config.yaml - Main application configuration
• config/logging.yaml - Logging setup with JSON and console formatters
• config/detectors.yaml - Pattern detector parameters
• config/models.yaml - ML model hyperparameters
• config/trading.yaml - Trading strategy parameters
• config/alerts.yaml - Alert system configuration
• config/database.yaml - Database connection settings

✅ Core Infrastructure

• src/core/constants.py - Application-wide constants
• src/core/enums.py - Enumerations (PatternType, Grade, SetupType, etc.)
• src/core/exceptions.py - Custom exception hierarchy (7 exception classes)
• src/core/base_classes.py - Abstract base classes (BaseDetector, BaseModel, BaseFeatureEngineering)

✅ Logging System

• src/logging/logger.py - Logger setup and configuration
• src/logging/formatters.py - JSON, Detailed, and Colored formatters
• src/logging/handlers.py - Rotating file handlers and error handlers
• src/logging/filters.py - Sensitive data filter and rate limit filter
• src/logging/decorators.py - @log_execution, @log_exceptions, @log_performance

✅ Configuration Management

• src/config/config_loader.py - Load and merge YAML configs with env vars
• src/config/settings.py - Pydantic dataclasses for type-safe config
• src/config/validators.py - Configuration validation logic

✅ Test Suite

• tests/conftest.py - Pytest fixtures and configuration
• tests/unit/test_core/test_exceptions.py - Exception tests
• tests/unit/test_logging/test_logger.py - Logger tests
• tests/unit/test_config/test_config_loader.py - Config loader tests

✅ Utility Scripts

• scripts/validate_setup.py - Setup validation script

Next Steps

1. Initialize Git Repository (if not done)

git init
git add .
git commit -m "feat(v0.1.0): project foundation with logging and config"
git tag v0.1.0

2. Set Up Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

make install-dev
# Or manually:
pip install -r requirements-dev.txt

4. Create .env File

cp .env.example .env
# Edit .env with your configuration

5. Validate Setup

python scripts/validate_setup.py

6. Run Tests

make test
# Or:
pytest tests/ -v

Validation Checklist

• All directories created
• All configuration files created
• Core infrastructure implemented
• Logging system implemented
• Configuration management implemented
• Initial test suite created
• No linting errors
• Git repository initialized (user needs to do this)
• Dependencies installed (user needs to do this)
• Tests passing (user needs to verify)

Notes

• The logging system uses a singleton pattern to avoid reconfiguration
• Configuration supports environment variable substitution (${VAR} or ${VAR:-default})
• All exceptions include error codes and context for better debugging
• Logging automatically redacts sensitive data (API keys, passwords, etc.)
• Tests use pytest fixtures for clean test isolation

Ready for v0.2.0

The foundation is complete and ready for the next version: v0.2.0 - Data Pipeline