Installation

QuantSupport can be used as a Rust crate, a Python package or an Excel add-in. Choose the interface that fits your application. The Python package includes a compiled Rust extension; the Excel add-in is distributed as a Windows XLL.

Rust applications

QuantSupport uses the Rust 2021 edition. A stable toolchain installed through rustup supplies the compiler and Cargo. Add the published crate to an application with:

cargo add quantsupport

A local application can instead use a path dependency during development:

[dependencies]
quantsupport = { path = "../quantsupport" }

The optional plot feature enables plotting support; it is not necessary for core pricing. Applications loading JSON add their own serde_json dependency. The library owns serializable financial types, while the application owns file locations and I/O.

For the source checkout:

git clone https://github.com/jmelo11/quantsupport
cd quantsupport
cargo build -p quantsupport
cargo run -p valuation-swaps

The workspace includes the library, Rust examples, benchmarks, and both binding packages. Run cargo test -p quantsupport to check the native library and its doctests. Contributor checks also include Clippy and documentation builds.

Python and Excel

Install the published Python package with:

python -m pip install quantsupport

For a manual download, the Python packages page provides folder ZIPs and wheels by operating system, architecture and Python version. Folder ZIPs contain only QuantSupport; install dependencies separately with python -m pip install pandas.

The package supports Python 3.9 and later and includes pandas reporting. Optional typing tools are available with quantsupport[typing]. To use local source changes, build the extension using the binding development instructions.

Excel requires 64-bit Windows desktop Excel. Installing the add-in provides the released XLL and platform-specific steps. Excel for macOS and the browser cannot load it.

The first valuation establishes a numerical reference in Rust. The Python and Excel chapters explain how their execution and reporting models differ while using the same native financial objects.