Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time


Utility library for caching and analysis of estimates from Monte Carlo simulations.


Monte Carlo simulations generate random samples based on some theoretical probabilistic model. A numerical fact of the theoretical model can be estimated from the random samples generated.

montecarloop provides features for two challenges with Monte Carlo estimation:

  1. long computation times
  2. accuracy of estimates

Mitigating long computation times

Monte Carlo simulations often make an extreme trade-off between estimation accuracy vs computation time. montecarloop can help make this trade-off less painful, especially when doing exploratory data analysis of slow Monte Carlo estimates within Python notebooks.

montecarloop can cache running statistics of Monte Carlo estimates into a file. A slow long running script can run Monte Carlo calculations independent of Python notebooks which can read and process preliminary estimates without being blocked.

Evaluation of estimator accuracy

After running a large nubmer of independent Monte carlo simulations, montecarloop will calculate statistics on the estimates, such as the standard deviation of the estimates. Furthermore, if montecarloop is provided a function which calculated the theoretical expected value of estimate, a p-value from a t-test will be performed. By running simulations for long periods of time, subtle statistical biases can potentially be found.

Quick Start

python3 -m pip install git+

Checkout the example notebook and script at

Columns of data table returned by Dealer.summary()

  • stat: the name of the statistic returned by monte carlo simulation estimator
  • num: number of monte carlo estimates calculated
  • mean: the average value of the num estimates calculated
  • std_dev: the standard deviation of the num estimates calculated
  • std_err: the estimated standard deviation of an average of num random estimates
  • @...: parameters passed to the monte carlo simulation estimator

if Dealer.summary(theoretical_calc) called with a parameter

  • null_hypo: the null hypothesis estimate returned by theoretical_calc
  • pvalue: the p-value from a two-tailed t-test


Monte Carlo Simulation Utility







No releases published


No packages published