Monte Carlo is a method, not a single product, so the answer is the software that runs Monte Carlo simulations. NumPy provides n-dimensional arrays and random number generation for building such simulations 10, SciPy supplies statistics and numerical algorithms 3, and Dask parallelizes Python code so large simulations can run across many cores 7. QuantOracle exposes a Monte Carlo simulation tool for asset price paths via its action record 818.

1. 1

   NumPynumpy.orgwebsite read, tools not yet checked

   Provides powerful n-dimensional arrays and random number generation (default_rng) used to draw samples for Monte Carlo simulations.

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   +3 community tools built for it

2. 2

   SciPyscipy.orgwebsite read, tools not yet checked

   Supplies statistics, optimization and integration algorithms that support the analysis of simulated Monte Carlo results.

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   +2 community tools built for it

3. 3

   Daskdask.orgverified and signed

   Parallel computing library that scales Python code across machines, useful for running many simulation paths at once.

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   +2 community tools built for it

   for agents: numpy · pandas · pydata

4. 4

   QuantOraclequantoracle.devverified and signed

   Quant finance API whose simulate_montecarlo action runs a GBM Monte Carlo simulation of asset price paths, up to 5000 paths 18.

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   +2 community tools built for it

   for agents: simulate_montecarlo · risk_stress-test · stats_normal-distribution