FinancePy
Python library for pricing derivatives and measuring their risk
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3,138 0 copies · 7 days GPL-3.0updated 2 Sep '26
Overview
FinancePy is a Python library for pricing and risk-managing options, futures, bonds, credit, FX, and rates derivatives, compiled via Numba.
- Forstudents, academics, and quants who want to price a derivative and inspect the underlying model code, not just call a black box.
- NeedsPython; pip install financepy, with market data (curves, vol surfaces) supplied by you.
- Runsa pure-Python library (market/models/products/utils folders) that Numba-compiles models on first import, then caches them for later imports.
- Limitit is currently in beta, and the first wildcard import can take several seconds while Numba compiles the models.
Vibe it
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Source: https://github.com/domokane/FinancePy — clone it and read its README and examples before writing anything; its documented setup beats what you remember. Stack: a pure-Python library split into market (curves, vol surfaces), models (pricing math), products (bonds, credit, equity, FX, rates), and utils (dates, day counts); Numba-compiled on first import. Needs: Python; market data (curves, vol surfaces) you build or supply yourself — FinancePy prices from data you pass in, it does not fetch any. Start: `pip install financepy`, then work through one of the 90-plus example notebooks — start with the Quick Start Guide (docs/QUICKSTART.md) — before pricing my own instrument. Limit: it is currently in beta, and the first wildcard import takes several seconds while Numba compiles the models; later imports are fast from the cache. Done means: you show me the price (and, where the notebook covers it, the risk/Greeks) for one instrument from my own inputs, and the notebook or script you used. Before you start, ask me: which instrument type I want to price (bond, option, credit, FX, or rates product) and what market data I have for it.
