How-To Guides

Step-by-step guides for implementing and optimizing MatLogica AADC in your projects

Practical Implementation Guides

Our how-to guides walk you through common implementation scenarios, from accelerating legacy quant libraries to building new high-performance applications with AADC.

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.

For: Quant DeveloperC++ DeveloperRisk Teams using QuantLib

Steps Overview

  1. Review existing QuantLib code
  2. Apply automated integration scripts
  3. Test and validate results
  4. Deploy accelerated models

Expected Results

  • Minimal code changes required (<1%)
  • Luigi Ballabio (QuantLib founder) endorsement
  • Production-ready approach proven
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AADC

Accelerate Python Models with AADC

For: Quantitative DevelopersQuant AnalystsRisk ManagersIT ArchitectsTechnical LeadersCOOs and CTOs

Using Claude AI to Accelerate Python Models 345× with AADC

A practical guide to using Claude AI for AADC integration: accelerate your existing Python pricing models 345× with minimal code changes — in under half a day.

For: Quantitative DevelopersQuant AnalystsRisk ManagersIT ArchitectsTechnical LeadersCOOs and CTOs

Expected Results

  • Arithmetic average Asian option under GBM
  • Monte Carlo simulation with Greeks (Delta, Rho, Vega)
  • Pure Python: 367 lines, ~4 minutes for 10 trades
  • Python + AADC: 391 lines, 0.7 seconds for 10 trades
  • 100 trades with 16 threads: 1.9 seconds
intermediate

Step-by-Step Guide to Accelerating Legacy Quant Libraries

Transform ORE/QuantLib into a LiveRisk service: 1M FX trades priced in 0.4 seconds (100x faster) with complete delta risk. Black box approach - no C++ refactoring needed.

For: Quantitative DevelopersQuant Library OwnersRisk ManagersIT ArchitectsTechnical Leaders

Steps Overview

  1. Identify input points (market data entry)
  2. Identify output points (NPV extraction)
  3. Hook with markAsInput() and markAsOutput()
  4. Record computational graph (pre-market)
  5. Deploy kernel for sub-second updates

Expected Results

  • 1M FX trades: 98s → 0.4s (245x faster NPV)
  • NPV + all delta risks in <1 second
  • One-time recording: 350s pre-market
  • Hook at CSVLoader::loadFile (input)
  • Hook at ReportWriter::writeNpv (output)
Read Guide
AADCAADC Toolkit

Related Resources

Explore more documentation and examples

Accelerate Python Models 345× with AADC

Prototype in Python, achieve C++ performance. Use AADC to accelerate your existing Python pricing models with minimal code changes — no rewrites required.

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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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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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Client Results Benchmarks

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