DROP

v4.09 12 March 2019

DROP implements libraries for single products (fixed income, FX, and asset backed), portfolios (optimal portfolios, XVA, and Exposure analytics), and numerical support (linear algebra, numerical optimizer, and algorithm support).

DROP is composed of three modules.

• Analytics Core Module => Fixed Income Analytics, Asset Back Analytics, Exposure and Margin Analytics, and XVA Analytics.
• Portfolio Core Module => Portfolio Contruction, along with Execution/Transaction Cost Models.
• Numerical Core Module => Model Validation, Spline Builder, Statistical Learning, Numerical Optimizer, and Algorithm Support Libraries.

Pointers

Repo Structure

Module, Library, and Project Layout

Project	Home	Issues	Library	Module
analytics	README	Git	Fixed Income Analytics	Analytics
assetbacked	README	Git	Fixed Income Analytics	Analytics
dynamics	README	Git	Fixed Income Analytics	Analytics
execution	README	Git	Transaction Cost Analytics	Portfolio
exposure	README	Git	Exposure Analytics	Analytics
feed	README	Git	Algorithm Support	Numerical
function	README	Git	Numerical Optimizer	Numerical
historical	README	Git	Algorithm Support	Numerical
json	README	Git	Algorithm Support	Numerical
learning	README	Git	Statistical Learning	Numerical
market	README	Git	Fixed Income Analytics	Analytics
measure	README	Git	Numerical Optimizer	Numerical
optimization	README	Git	Numerical Optimizer	Numerical
param	README	Git	Fixed Income Analytics	Analytics
portfolio construction	README	Git	Asset Allocation Analytics	Portfolio
pricer	README	Git	Fixed Income Analytics	Analytics
product	README	Git	Fixed Income Analytics	Analytics
quant	README	Git	Numerical Optimizer	Numerical
regression	README	Git	Algorithm Support	Numerical
sequence	README	Git	Statistical Learning	Numerical
service	README	Git	Algorithm Support	Numerical
simm	README	Git	Exposure Analytics	Analytics
spaces	README	Git	Statistical Learning	Numerical
spline	README	Git	Spline Builder	Numerical
state	README	Git	Fixed Income Analytics	Analytics
template	README	Git	Fixed Income Analytics	Analytics
validation	README	Git	Model Validation	Numerical
xva	README	Git	XVA Analytics	Analytics

Installation

Installation is as simple as building a jar and dropping into the classpath. There are no other dependencies.

Samples

Java Samples | Excel Samples | Test Data

Documentation

Javadoc API | DROP Specifications | Release Notes | User guide is a work in progress!

Misc

JUnit Tests | Jacoco Coverage | Jacoco Session | Credit Attributions | Version Specifications

Contact

[email protected]