# External Dependencies

## Overview
This document describes all external dependencies used in the orderflow backtest system, their purposes, versions, and justifications for inclusion.

## Production Dependencies

### Core Framework Dependencies

#### Dash (^2.18.2)
- **Purpose**: Web application framework for interactive visualizations
- **Usage**: Real-time chart rendering and user interface
- **Justification**: Mature Python-based framework with excellent Plotly integration
- **Key Features**: Reactive components, built-in server, callback system

#### Dash Bootstrap Components (^1.6.0)
- **Purpose**: Bootstrap CSS framework integration for Dash
- **Usage**: Responsive layout grid and modern UI styling
- **Justification**: Provides professional appearance with minimal custom CSS

#### Plotly (^5.24.1)
- **Purpose**: Interactive charting and visualization library
- **Usage**: OHLC candlesticks, volume bars, depth charts, OBI metrics
- **Justification**: Industry standard for financial data visualization
- **Key Features**: WebGL acceleration, zooming/panning, dark themes

### Data Processing Dependencies

#### Pandas (^2.2.3)
- **Purpose**: Data manipulation and analysis library
- **Usage**: Minimal usage for data structure conversions in visualization
- **Justification**: Standard tool for financial data handling
- **Note**: Usage kept minimal to maintain performance

#### Typer (^0.13.1)
- **Purpose**: Modern CLI framework
- **Usage**: Command-line argument parsing and help generation
- **Justification**: Type-safe, auto-generated help, better UX than argparse
- **Key Features**: Type hints integration, automatic validation

### Data Storage Dependencies

#### SQLite3 (Built-in)
- **Purpose**: Database connectivity for historical data
- **Usage**: Read-only access to orderbook and trade data
- **Justification**: Built into Python, no external dependencies, excellent performance
- **Configuration**: Optimized with immutable mode and mmap

## Development and Testing Dependencies

#### Pytest (^8.3.4)
- **Purpose**: Testing framework
- **Usage**: Unit tests, integration tests, test discovery
- **Justification**: Standard Python testing tool with excellent plugin ecosystem

#### Coverage (^7.6.9)
- **Purpose**: Code coverage measurement
- **Usage**: Test coverage reporting and quality metrics
- **Justification**: Essential for maintaining code quality

## Build and Package Management

#### UV (Package Manager)
- **Purpose**: Fast Python package manager and task runner
- **Usage**: Dependency management, virtual environments, script execution
- **Justification**: Significantly faster than pip/poetry, better lock file format
- **Commands**: `uv sync`, `uv run`, `uv add`

## Python Standard Library Usage

### Core Libraries
- **sqlite3**: Database connectivity
- **json**: JSON serialization for IPC
- **pathlib**: Modern file path handling
- **subprocess**: Process management for visualization
- **logging**: Structured logging throughout application
- **datetime**: Date/time parsing and manipulation
- **dataclasses**: Structured data types
- **typing**: Type annotations and hints
- **tempfile**: Atomic file operations
- **ast**: Safe evaluation of Python literals

### Performance Libraries
- **itertools**: Efficient iteration patterns
- **functools**: Function decoration and caching
- **collections**: Specialized data structures

## Dependency Justifications

### Why Dash Over Alternatives?
- **vs. Streamlit**: Better real-time updates, more control over layout
- **vs. Flask + Custom JS**: Integrated Plotly support, faster development
- **vs. Jupyter**: Better for production deployment, process isolation

### Why SQLite Over Alternatives?
- **vs. PostgreSQL**: No server setup required, excellent read performance
- **vs. Parquet**: Better for time-series queries, built-in indexing
- **vs. CSV**: Proper data types, much faster queries, atomic transactions

### Why UV Over Poetry/Pip?
- **vs. Poetry**: Significantly faster dependency resolution and installation
- **vs. Pip**: Better dependency locking, integrated task runner
- **vs. Pipenv**: More active development, better performance

## Version Pinning Strategy

### Patch Version Pinning
- Core dependencies (Dash, Plotly) pinned to patch versions
- Prevents breaking changes while allowing security updates

### Range Pinning
- Development tools use caret (^) ranges for flexibility
- Testing tools can update more freely

### Lock File Management
- `uv.lock` ensures reproducible builds across environments
- Regular updates scheduled monthly for security patches

## Security Considerations

### Dependency Scanning
- Regular audit of dependencies for known vulnerabilities
- Automated updates for security patches
- Minimal dependency tree to reduce attack surface

### Data Isolation
- Read-only database access prevents data modification
- No external network connections required for core functionality
- All file operations contained within project directory

## Performance Impact

### Bundle Size
- Core runtime: ~50MB with all dependencies
- Dash frontend: Additional ~10MB for JavaScript assets
- SQLite: Zero overhead (built-in)

### Startup Time
- Cold start: ~2-3 seconds for full application
- UV virtual environment activation: ~100ms
- Database connection: ~50ms per file

### Memory Usage
- Base application: ~100MB
- Per 1000 OHLC bars: ~5MB additional
- Plotly charts: ~20MB for complex visualizations

## Maintenance Schedule

### Monthly
- Security update review and application
- Dependency version bump evaluation

### Quarterly
- Major version update consideration
- Performance impact assessment
- Alternative technology evaluation

### Annually
- Complete dependency audit
- Technology stack review
- Migration planning for deprecated packages