Code Examples & Repositories

Reference implementations, code samples, and GitHub repositories

Get Started with Code

Explore our reference implementations, code examples, and GitHub repositories. These resources provide working code to help you integrate AADC into your projects.

code

XVA Reference Implementation with AAD

Reference implementation for path-wise sensitivity calculations and XVA metrics computation.

What You'll Learn

  • Compositional approach to financial contracts
  • Smooth approximations for discontinuous payoffs
  • Regression-based continuation value estimation
  • Framework ready for AAD integration

Use Cases

  • Path-wise sensitivity calculations
  • Optimal exercise estimation for callable products
  • XVA metrics computation via regression

Impact

  • Full AAD-enabled XVA and American Monte Carlo
  • Reusable production-grade patterns
View on GitHub
AADC
code

Accelerate QuantLib 6-100x with Minimal Changes

Step-by-step guide to accelerating QuantLib and ORE with AADC integration requiring less than 1% code changes.

What You'll Learn

  • Integration steps required
  • Performance gains achievable
  • AAD enablement process
  • Minimal code changes needed (<1%)

Use Cases

  • QuantLib Acceleration
  • Open-Source Integration
  • XVA
  • ORE

Impact

  • 6-100x speedup for QuantLib achieved
  • Works with 20+ years of existing models
  • Enables AAD capabilities for free
  • 245x ORE acceleration demonstrated
View on GitHub
AADC
code

Browser-Based Live Environment with AADC and QuantLib

Browser-based live Jupyter-style environment pre-loaded with AADC and QuantLib for instant experiments.

What You'll Learn

  • Zero-install playground
  • Immediate prototyping

Use Cases

  • Hands-on testing
  • QuantLib integration
  • Zero-install experimentation

Impact

  • Instant access to AADC + QuantLib environment
View on GitHub
AADC

Documentation

Get started with AADC integration

AADC Integration Guide

Get started with AADC in 3 simple steps: see code examples, timelines, and best practices for rapid deployment. No templates required, minimal code changes, and results in weeks not months.

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How AADC Works

AADC extracts a computational graph (DAG) from existing analytics at runtime and compiles it to optimized machine code, achieving 100-1000× for Python and 6-100× for C++ with automatic derivatives.

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Live Demos Research Papers

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