---
title: "Rendering Data with CSharp"
description: "This page explains how to create a script to download and process your dataset with C# for QuantConnect distribution."
canonical: https://www.quantconnect.com/docs/v2/lean-engine/contributions/datasets/rendering-data/rendering-data-with-csharp
markdown: https://www.quantconnect.com/docs/v2/lean-engine/contributions/datasets/rendering-data/rendering-data-with-csharp.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/06%20LEAN%20Engine/02%20Contributions/01%20Datasets/04%20Rendering%20Data/02%20Rendering%20Data%20with%20CSharp
modified: 2026-10-30
index: https://www.quantconnect.com/llms.txt
---

# Rendering Data with CSharp

[Documentation](https://www.quantconnect.com/docs/v2.md) > [LEAN Engine](https://www.quantconnect.com/docs/v2/lean-engine.md) > Contributions > Datasets > Rendering Data > Rendering Data with CSharp

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

This page explains how to create a script to download and process your dataset with C# for QuantConnect distribution.

## Using Processing Framework

During this part of the contribution process, you need to edit the `Lean.DataSource.<vendorNameDatasetName> / DataProcessing / Program.cs` file so it transforms and moves your raw data into the format and location the [GetSource methods](https://www.quantconnect.com/docs/v2/lean-engine/contributions/datasets/defining-data-models.md) expect. The program should save all the data history to the `output` directory in your machine's root directory (for example, `C: / output`) and it should save a sample of the data history to the `Lean.DataSource.<vendorNameDatasetName> / output` directory.

Follow these steps to set up the downloading and processing script for your dataset:

1. Change the structure of the `Lean.DataSource.<vendorNameDatasetName> / output` directory to match the path structure you defined in the `get_source` methods (for example, `output / alternative / xyzairline / ticketsales`).
2. In the `Lean.DataSource.<vendorNameDatasetName> / DataProcessing / Program.cs` file, add some code to time how long it takes to process the entire dataset and how long it takes to update the dataset with one day's worth of data.
   > You need this information for when you provide the [dataset documentation](https://www.quantconnect.com/docs/v2/lean-engine/contributions/datasets/data-documentation.md). We need to know how long it takes to process your dataset so we can schedule its processing job.
3. In the processing file, load the raw data from your source.
   You can fetch data from any of the following sources:
   | Source | Considerations |
   | --- | --- |
   | Local Files | It can help to first copy the data into location. |
   | Remote API | Stay within the rate limits. You can use the rate gate class. |
   You should load and process the data period by period. Use the date range provided to the script to process the specific dates provided.
4. If your dataset is for universe selection data and it's at a higher frequency than hour resolution, resample your data to hourly or daily resolution.
5. If any of the following statements are true, skip the rest of the steps in this tutorial:
- Your dataset is not related to Equities.
- Your dataset is related to Equities and already includes the point-in-time tickers.

If your dataset is related to Equities and your dataset doesn't account for ticker changes, the rest of the steps help you to adjust the tickers over the historical data so they are point-in-time.

- If you don't have the [US Equity Security Master dataset](https://www.quantconnect.com/datasets/quantconnect-us-equity-security-master/cli), [contact us](https://www.quantconnect.com/contact).
- In the `Lean.DataSource.<vendorNameDatasetName> / DataProcessing / Program.cs` file, create and initialize a map file provider.
- Create a security identifier.
- In a terminal, compile the data processing project to generate the `process.exe` executable file.

  ```
  $ dotnet build .\DataProcessing\DataProcessing.csproj
  ```

After you finish compiling the `Program.cs` file, run the `process.exe` file to populate the `Lean.DataSource.<vendorNameDatasetName> / output` directory and the `output` directory in your machine's root directory.

> Note: The pull request you make at the end must contain sample data so we can review it and run the demonstration algorithms.

## CSharp Processor Examples

The following examples are rendering datasets with C# processing:

- [Lean.DataSource.BinanceFundingRate](https://github.com/QuantConnect/Lean.DataSource.BinanceFundingRate/blob/master/DataProcessing/BinanceFundingRateDownloader.cs)
- [Lean.DataSource.CoinGecko](https://github.com/QuantConnect/Lean.DataSource.CoinGecko/blob/master/DataProcessing/CoinGeckoDataDownloader.cs)
- [Lean.DataSource.CryptoCoarseFundamentalUniverse](https://github.com/QuantConnect/Lean.DataSource.CryptoCoarseFundamentalUniverse/blob/master/DataProcessing/CryptoCoarseFundamentalUniverseDataDownloader.cs)
- [Lean.DataSource.QuiverInsiderTrading](https://github.com/QuantConnect/Lean.DataSource.QuiverInsiderTrading/blob/master/DataProcessing/QuiverInsiderTradingDataDownloader.cs)
- [Lean.DataSource.VIXCentral](https://github.com/QuantConnect/Lean.DataSource.VIXCentral/blob/master/DataProcessing/VIXContangoProcessor.cs)