Guides

Code-first walkthroughs of the API, with real request/response pairs you can reproduce — and the conventions and pitfalls spelled out. Fixed income and risk first; that is what this desk is for.

Fixed income

Price a fixed-rate bond with a REST API

Clean/dirty price, accrued interest, YTM, duration, convexity and BPV from one POST — flat yield in, the full analytics sheet out.

Fixed income

Bond duration, convexity and BPV over HTTP

The full analytics sheet of a fixed-rate bond in one POST — clean/dirty, accrued, YTM — and why day counts and settlement dates are where spreadsheets go wrong.

Fixed income

Clean vs dirty price and accrued interest

Dirty = clean + accrued: the same bond priced on a coupon date and mid-period, side by side, with the act/act accrual arithmetic shown.

Fixed income

DV01 / BPV of a bond, with a worked example

What the bpv field means, how it ties to modified duration, and how to scale it to a real position without losing precision to rounding.

Curves

Bootstrap a yield curve from deposits and swaps

Market quotes in, discount factors and continuously-compounded zero rates at every pillar out — conventions and interpolation written down.

Curves

Discount factor vs zero rate: convert between them

DF(t) = exp(−z·t) and back — the two views of the same curve, verified in pure Python against the API's own pillars.

Curves

Price a bond on a bootstrapped zero curve

The desk workflow in two calls: bootstrap the swap curve, feed the pillars into the bond pricer, read off the curve-implied YTM.

Risk

Value-at-Risk and Expected Shortfall in one call

Historical and parametric VaR side by side, portfolio mode with a positions × returns matrix, and a reproducible numpy example with byte-identical output.

Risk

Expected Shortfall: the average loss beyond VaR

Why ES is always the bigger number, why Basel FRTB standardized on it, and why the parametric-normal column deserves suspicion on fat-tailed data.

Risk

Historical vs parametric VaR: when to use each

Empirical percentile vs fitted normal — a seeded example where the parametric number comes out higher, and why the gap is a distribution diagnostic, not a rule.

Risk

Portfolio VaR from positions and asset returns

Matrix mode: T×N returns plus market values in, VaR and Expected Shortfall in currency out — correlations captured by P&L aggregation, no correlation matrix input.

Options

Price an option in 5 lines of Python

European and American pricing with full greeks, implied volatility, and the unit conventions (theta/day, vega per point) that usually bite first.

Options

Back out implied volatility from a market price

The volatility that reproduces an observed option price, by numerical inversion — with the no-arbitrage bounds and the 422s that guard them.

Choosing your stack

QuantLib as a service: hosted API vs self-hosting, and where ORE fits

An honest comparison of self-hosted QuantLib, self-hosted ORE and a hosted API — by cost, coverage, setup and who should pick which. Including when not to use Quant-Box.