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Market & alternative data★ 8 GitHub starsPython⏱ No commits in over six months

Descriptive random draws with market-like return statistics

What it is

A Python generator of random sequences that aim to carry fat tails, volatility clustering, and heavier-than-Gaussian scale, without forecasting prices. You fit it on a series of closes, or start from a frozen preset, and it emits centered log-returns that can be cumulated into a synthetic path. Researchers who compare Monte Carlo draws with a plain Gaussian baseline can install the package and rerun the bundled frozen benchmark. The project treats the output as descriptive rather than predictive, and its own comparison marks volatility clustering as the weak match to real returns, so this is a simulation aid rather than a market data feed.

At a glance

Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.

Best for	Professional quants
Used for	Data analysis, Strategy research
Markets	Multi-market
Stack	Python
Learning curve	Moderate learning curve
Practical value	Low practical value
Cost	Free and open source
Hardware	No GPU needed
Maintenance	No commits in over six months

GitHub stars, last 30 days

Daily snapshots since 2026-09-28 (up to 30 days): −1 over the period, now 8. Gaps mean no snapshot was taken that day.

In the author's words

Contingency Random Number Generator that produces random numbers with real financial market statistical signatures (fat tails, volatility clustering, kurtosis). Matches 86% of real market m

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