VadePy

Interest rate analytics at compiled speed

vade is a production-grade Python library for curve construction, instrument pricing, and risk analytics — with all numerical computation running in native Rust.

Get Started View Quick Start

<1mscurve calibration

8module coverage

Dual/Dual2automatic diff

Built for production quant workflows

Everything you need for rates analytics

From curve construction to exotic instrument pricing and risk — vade handles it in native Rust speed.

Rust-Powered Core

All computation — curve interpolation, AD propagation, solver calibration — runs in compiled Rust via PyO3. No Python interpreter overhead in the inner loops.

Automatic Differentiation

Dual and Dual2 types propagate first- and second-order derivatives through every operation. Delta and gamma risk fall out naturally — no finite differences.

Multi-Curve Calibration

Jacobian-based Solver calibrates discount, forward, and spread curves simultaneously to FRAs, IRS, OIS, bonds, CDS, and more.

Wide Instrument Coverage

IRS, FRA, ZCS, OIS, bonds, callable bonds, CDS, XCS, deposits, caps, floors, NDFs — modelled in pure Rust, exposed to Python with full type stubs.

Curve Flexibility

DiscountCurve, LineCurve, composite and spread curves. Interpolation methods: log-linear, linear on rates, flat-forward, Nelson-Siegel, NSS, Smith-Wilson.

Production Performance

9-instrument SOFR curve calibration in under 1ms. Cache-friendly Rust Vec<f64> layouts, compiled AD arithmetic, and zero GIL contention for numerical work.

Quick start

From zero to calibrated curve in minutes

Build a multi-node SOFR discount curve, calibrate it to live market instruments, price swaps, and compute delta risk — all with idiomatic Python backed by compiled Rust.

Automatic differentiation — no finite differences

Sub-millisecond calibration on modern hardware

Full IDE support via PyO3 type stubs

View Full Guide

quick_start.py

import datetime
from vade import DiscountCurve, IRS, Solver

# Build a SOFR discount curve
nodes = {
    datetime.date(2025, 6, 16): 1.0,
    datetime.date(2026, 6, 16): 1.0,   # 1Y
    datetime.date(2027, 6, 16): 1.0,   # 2Y
    datetime.date(2030, 6, 16): 1.0,   # 5Y
    datetime.date(2035, 6, 16): 1.0,   # 10Y
}

curve = DiscountCurve(nodes, interpolation="log_linear")

# Calibrate to market instruments
irs_2y = IRS(effective=datetime.date(2025, 6, 16),
             termination="2Y", fixed_rate=3.85)

solver = Solver(curves=[curve], instruments=[
    (irs_2y, 3.85, 0, "2Y_IRS", "USD"),
])
result = solver.iterate()
assert result.converged  # True ✓

# Compute delta risk via automatic differentiation
delta = solver.delta(irs_2y, result)