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Development Guide

This section provides guidance for developers working on the RWA calculator.

Overview

The development guide covers:

Development Setup

Prerequisites

  • Python 3.13+
  • uv package manager (recommended)
  • Git

Setup Steps

# Clone the repository
git clone https://github.com/OpenAfterHours/rwa_calculator.git
cd rwa_calculator

# Install with development dependencies
uv sync --all-extras

# Verify installation
uv run pytest

IDE Configuration

VS Code: 

{
  "python.defaultInterpreterPath": ".venv/bin/python",
  "python.formatting.provider": "none",
  "[python]": {
    "editor.defaultFormatter": "charliermarsh.ruff",
    "editor.formatOnSave": true
  }
}

PyCharm: 1. Open project directory 2. Configure interpreter to use .venv 3. Mark src as Sources Root 4. Enable Ruff integration

Development Workflow

TDD Approach

The project follows Test-Driven Development:

  1. Write acceptance test - Define expected behavior
  2. Write unit tests - Test component behavior
  3. Implement - Write code to pass tests
  4. Refactor - Improve code while tests pass

Branch Strategy

master
  └── feature/feature-name
  └── fix/bug-description
  └── tests/test-description

Commit Convention

type: short description

- Detail 1
- Detail 2

Co-Authored-By: Name <email>

Types: feat, fix, docs, test, refactor, chore

Project Structure

src/rwa_calc/
├── config/           # Configuration (FX rates)
├── contracts/        # Interfaces and data contracts
│   ├── bundles.py   # Data transfer objects
│   ├── config.py    # Configuration classes
│   ├── errors.py    # Error types
│   ├── protocols.py # Component interfaces
│   └── validation.py
├── data/            # Schemas and lookup tables
│   ├── schemas.py   # Polars schemas
│   └── tables/      # Regulatory tables
├── domain/          # Core domain
│   └── enums.py     # Enumerations
└── engine/          # Calculation engine
    ├── pipeline.py  # Orchestration
    ├── loader.py    # Data loading
    ├── hierarchy.py # Hierarchy resolution
    ├── classifier.py # Classification
    ├── ccf.py       # Credit conversion factors
    ├── aggregator.py # Aggregation
    ├── crm/         # Credit risk mitigation
    ├── sa/          # Standardised approach
    ├── irb/         # IRB approach
    └── slotting/    # Slotting approach

Key Development Principles

1. LazyFrame Operations

Always use Polars LazyFrames:

# Good
result = df.with_columns(
    rwa=pl.col("ead") * pl.col("risk_weight")
)

# Bad - row iteration
for row in df.iter_rows():
    rwa = row["ead"] * row["risk_weight"]

2. Protocol-Based Design

Implement protocols for new components:

from rwa_calc.contracts.protocols import CalculatorProtocol

class MyCalculator:
    def calculate(
        self,
        exposures: pl.LazyFrame,
        config: CalculationConfig,
    ) -> ResultBundle:
        # Implementation
        ...

3. Immutable Data

Use frozen dataclasses for data contracts:

from dataclasses import dataclass

@dataclass(frozen=True)
class MyBundle:
    data: pl.LazyFrame
    metadata: dict

4. Error Accumulation

Collect errors instead of raising:

errors = []
for exposure in exposures:
    if not valid(exposure):
        errors.append(CalculationError(
            exposure_id=exposure.id,
            message="Invalid exposure"
        ))

return Result(data=data, errors=errors)

Running Tasks

Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=src/rwa_calc

# Run specific test file
uv run pytest tests/unit/test_pipeline.py

# Run specific test
uv run pytest tests/unit/test_pipeline.py::test_crr_calculation

Linting

# Check code style
uv run ruff check src tests

# Fix automatically
uv run ruff check --fix src tests

# Format code
uv run ruff format src tests

Type Checking

# Run mypy
uv run mypy src

Documentation

# Serve documentation locally
uv run zensical serve

# Build documentation
uv run zensical build

Next Steps