Home / Other tools
StochVolModels
Pricing and Monte Carlo for stochastic volatility models
What it is
A Python library for pricing analytics and Monte Carlo simulation under stochastic volatility models, including the log-normal SV model and the Heston model. It provides tools to value options and simulate paths under these models for research. Quant researchers, financial engineers and students of derivatives pricing are the intended users. It focuses on a small family of models, so other volatility dynamics would need separate implementation.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Strategy research, Data analysis |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Steep learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Commits today |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): +1 over the period, now 239. Gaps mean no snapshot was taken that day.
Pricing analytics and Monte Carlo simulation for stochastic volatility models, including the log-normal SV model and the Heston model.
Similar tools
Jupyter notebooks companion to Machine Learning for Trading
Portfolio analytics and performance tear sheets for quants
scikit-learn compatible Python library for portfolio optimization
CVXPY-based portfolio optimization and strategic asset allocation
Goldman Sachs Python toolkit for derivatives and risk
Portfolio and risk analytics, a maintained fork of pyfolio