Resources

Explore our technical insights, research, andtraining materials across high-performance computing, AI infrastructure,quantitative finance, and trading systems. Whether you're deepening expertiseor discovering how we work, these resources are here to support your goals.

Whitepaper

Smart Asset Allocation using Deep Learning

We explore how deep learning and evolutionary algorithms, can be employed to create a global and flexible asset allocation model.

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Predict Default Probabilities with Deep Learning

This white paper explores how default probabilities can be accurately predicted with deep learning, using a variety of economic data.

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Optimise Trading Strategies with Machine Learning

This white paper describes how machine learning, in particular random forests, can be utilised to automatically find the best algorithmic trading strategy

Case Study

Major Bookmaker Run Live Sport Betting Simulation

This case study highlights how a major bookmaker used Xcelerit to implement a live in game sports betting simulation on GPUs.

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Financial Trading with Deep Learning

This white paper describes a deep learning method to detect patterns in financial charts to aid trading decisions or to build algorithmic trading strategies.

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Event-Driven Market Prediction with Deep Learning

This white paper uses deep learning to predict the market effects of economic events such as earnings announcements or changes in regulations.

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Smart Index Replication with Deep Learning

This white paper describes a method for smart index replication using a deep learning auto-encoder, for cost-efficient performance prediction of a large index.

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Efficient Implementations for Solvency II

This white paper analyses typical implementations and highlights opportunities for code modernisation and acceleration.

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Algorithmic Trading Strategies with Deep Learning

A deep learning method (DBN) to predict financial time series and consequently build efficient algorithmic trading strategies, trained on CPU and GPU.

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A Guide to Deep Learning in Quantitative Finance

We explore machine learning in quantitative finance, with applications in algorithmic trading, risk management, economic studies, asset allocation, etc.

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A Guide to Fast Reverse Time Migration (RTM)

Leverage modern CPU features to achieve large performance gains for RTM. It further details how to tap into the compute power of GPUs and Xeon Phi.

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Accelerated Kirchhoff Time Migration (KTM)

Leverage modern CPU features to achieve large performance gains for KTM. It further details how to tap into the compute power of GPUs and Xeon Phi.

Case Study

Trinity College Use Xcelerit & GPUs for Machine Learning

This case study shows how tremendous speedups were achieved for a machine learning wireless network simulation using GPU accelerators with Xcelerit.

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Efficient Reservoir Simulation

This white paper gives an overview of the state-of-the-art in reservoir modelling and simulation and outlines where the computational complexities lie

Video

In this video, Dongsheng Lu, Head of Quantitative Research at BNP Mellon give an introduction to CCR, explaining how CVA algorithms are typically implemented

Video

Risk-Aware OTC Pricing

In this video, risk trainer Justin Clarke outlines the forces driving changes in banking practice.

Video

Integrating AD in Large Quant Libraries

This video gives practical guidelines on how to cope with the complexities involved when integrating AD into large quant libraries

Video

Automatic Algorithmic Differentiation

The two main approaches for automatic AD are covered in this video: source code transformation and operator overloading

Video

Accelerating CVA Computations

In this video, risk trainer Justin Clarke explain how CVA algorithms are typically implemented, and discuss how the increasing complexity consume vast computing resources.

Video

AD Theory and Manual Implementation

This video gives an introduction into algorithmic differentiation: theory, manual implementation, live example

Case Study

Global Investment Bank Turns to Xcelerit for XVA

A tier 1 bank implemented a centralised XVA system with near real-time calculation capabilities, including sensitivities.

Case Study

Global Dealer Chooses Xcelerit for a Risk Web App

Xcelerit helped a tier one bank implement a state-of-the-art web application for risk analytics, replacing a legacy Excel interface with an Angular front-end, microservices, Docker container technology, and RESTful APIs.

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White paper: AAD for Model Calibration

The paper exposes the benefits of using AAD for financial model calibration using practical examples, and shows the costs-benefits trade-off of AAD vs. bumping

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Solving the FRTB P&L Attribution Test Puzzle

This paper explains the FRTB IMA P&L attribution test, highlights the practical challenges involved, and gives recommendations on how to overcome these.

Case Study

Tier one Bank Turns to Xcelerit for MVA with SIMM

The client computes MVA using ISDA's SIMM, implemented using Algorithmic Differentiation (AAD)

Case Study

Major Financial Institution Turns to Xcelerit for FRTB-IMA

A tier 1 bank implemented a fast FRTB-IMA market risk system

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Fast XVA Stress Testing Calculation

This white paper introduces XVA stress testing, and illustrates how to perform fast calculations through software optimisation

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Fast Sensitivities for FRTB SA-CVA (Capital)

The paper gives an overview of the proposed FRTB SA-CVA framework, discusses the approaches for computing CVA sensitivities, and shows how AAD can be applied

Case Study

Global Dealer Choose Xcelerit to Boost CVA Sensitivities Calculation

The tier one bank implemented fast CVA sensitivity calculation using Algorithmic Differentiation (AAD) with Xcelerit

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Fast Portfolio Expected Shortfall for FRTB-IMA

This white paper introduces FRTB-IMA, and illustrates how to perform fast Expect Shortfall (ES) calculations through software optimisation

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Fast KVA Calculation

This white paper introduces KVA, and illustrates how to perform fast calculations through software optimisation

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Fast Greeks: Algorithmic Differentiation Made Easy

This paper zooms on Adjoint Algorithmic Differentiation (AAD) as an efficient and robust alternative to compute sensitivities vs. bump-and-revalue

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Efficient XVA Sensitivities Computation with AAD

This paper introduces the use of sensitivities for managing CVA / XVA, details the complexities involved, and introduces AAD as a solution

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Fast Credit Exposure Metrics Calculation

This white paper introduces the different Credit Exposure measures, and illustrates how to boost their calculations through software optimisation

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Whitepaper: Efficient XVA Calculations

This white paper gives an overview of the different XVA adjustments, shows how they are typically computed, and outlines where the computational challenges lie.

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Coping with the Tsunami of Compute Load

This paper reviews the complexities in credit exposure calculations, FRTB SBA, FRTB IMA, FRTB SA-CVA, SIMM, and XVA calculations

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Boosting FRTB SBA Sensitivities (Market Risk)

This paper introduces FRTB SBA, discusses its definition of the delta and vega sensitivities, and illustrates how AAD can be used to compute them.

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A Guide to Accelerated Sensitivities for SIMM

This paper introduces the SIMM method, illustrates how AAD can be applied for computing the sensitivities, and explains dynamic SIMM for MVA simulations.

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A Guide to xVA Algorithmic Optimisation

This white paper describes major algorithmic optimisations for XVA and illustrates the possible computational savings.

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Accelerating Back-testing of Algorithmic Trading Strategies

This white paper surveys methods for back-testing trading strategies and highlights opportunities for acceleration.