# Run a Monte Carlo simulation on Deadline Cloud
<a name="examples-jb-monte-carlo"></a>

The [monte\_carlo\_simulation](https://github.com/aws-deadline/deadline-cloud-samples/tree/mainline/job_bundles/monte_carlo_simulation) job bundle prices a portfolio of autocallable structured notes using Monte Carlo simulation with QuantLib's Heston stochastic volatility model. The job is a Deadline Cloud port of the [Pricing Financial Derivatives with AWS Batch](https://ec2spotworkshops.com/monte-carlo-with-batch.html) workshop.

The bundle defines a two-step pipeline:

1. `PricePositions` — One task per portfolio position. Tasks are grouped into chunks that calibrate the Heston model once and price all positions in the chunk, amortizing calibration cost.

1. `AggregateResults` — Collects per-position results into a portfolio summary.

The bundle uses the Open Job Description [TASK\_CHUNKING](https://github.com/OpenJobDescription/openjd-specifications/blob/mainline/rfcs/0001-task-chunking.md) extension for load balancing. The scheduler starts by dispatching individual positions, observes how long they take, and then automatically grows the chunk size to match a target runtime. Fast positions are grouped into larger chunks; slow positions stay in small chunks to keep work spread across the fleet.

To run this bundle, you need a queue with a conda queue environment that includes the `conda-forge` channel for `quantlib-python`. When you deploy the [starter farm template](https://github.com/aws-deadline/deadline-cloud-samples/tree/mainline/cloudformation/farm_templates/starter_farm), set the `ProdCondaChannels` parameter to `deadline-cloud conda-forge`.

Submit the bundle with the GUI submitter:

```
deadline bundle gui-submit monte_carlo_simulation/
```

Or submit a quick test with fewer positions:

```
deadline bundle submit monte_carlo_simulation/ \
  -p PositionRange="0-1" -p NumPaths=100
```