{"id":19553636,"url":"https://github.com/quantconnect/lean.datasource.quiverquantcongresstrading","last_synced_at":"2025-10-19T04:40:21.785Z","repository":{"id":38080373,"uuid":"379076880","full_name":"QuantConnect/Lean.DataSource.QuiverQuantCongressTrading","owner":"QuantConnect","description":"Quiver Quantitative Congress Trading Data Alternative 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LeanDataSdk\n\n[![Build Status](https://github.com/QuantConnect/LeanDataSdk/workflows/Build%20%26%20Test/badge.svg)](https://github.com/QuantConnect/LeanDataSdk/actions?query=workflow%3A%22Build%20%26%20Test%22)\n\n### Getting started\n\n#### Introduction\n\nThe Lean Data SDK is a cross-platform template repository for developing custom data types for Lean.\nThese data types will be consumed by [QuantConnect](https://www.quantconnect.com/) trading algorithms and research environment, locally or in the cloud.\n\nIt is composed by example .Net solution for the data type and converter scripts.\n\n#### Prerequisites\n\nThe solution targets dotnet 5, for installation instructions please follow [dotnet download](https://dotnet.microsoft.com/download).\n\nThe data downloader and converter script can be developed in different ways: Python script, Python jupyter notebook or even a bash script.\n- The python script should be compatible with python 3.6.8\n- Bash script will run on Ubuntu Bionic\n\nSpecifically, the enviroment where these scripts will be run is [quantconnect/research](https://hub.docker.com/repository/docker/quantconnect/research) based on [quantconnect/lean:foundation](https://hub.docker.com/repository/docker/quantconnect/lean).\n\n#### Installation\n\nThis repository should be forked by each new data provider.\n\nOnce it is cloned locally, should be able to successfully build the solution, run all tests and execute the conveter scripts.\n\n#### Usage\n\n- Once the repository is forked, the existing example implementation should be adjusted to create a new data type for a particular data set.\n\t- The Assembly name and data type have to be changed since they should be unique.\n- Converter and downloader scripts should be developed following the [examples in this repository](https://github.com/QuantConnect/LeanDataSdk/tree/master/DataConverterScript). These script should be provided to `QuantConnect` as well as the fork repository at a particual commit.\n\n### User guide\n\nTODO:\n\n### Tutorials\n\n#### Create Data Type\n\n##### Introduction\n\nThis tutorial we will create a new custom C# data type that will allow Lean algorithms or research environment to consume a particular data set.\n\n##### New Lean Data Type\n\nIn [Lean](https://github.com/QuantConnect/Lean) each data type inherits from [BaseData](https://github.com/QuantConnect/Lean/blob/master/Common/Data/BaseData.cs), overrides a set of methods and incoporates any specific property this data set has.\nThe `DataLibrary` project holds an example custom data type [MyCustomDataType](https://github.com/QuantConnect/LeanDataSdk/blob/master/DataLibrary/MyCustomDataType.cs).\n\n- `GetSource()` method returns an instance of `SubscriptionDataSource` which will tell Lean from where should it source data for a particular given date, ticker, and configuration.\n- `Reader()` method should return a new instance of this data type for a given line of data\n- `Clone()` Clones the data\n- `RequiresMapping()` Indicates whether the data source is tied to an underlying symbol and requires that corporate events be applied to it as well, such as renames and delistings\n- `IsSparseData()` Indicates whether the data is sparse. If true, we disable logging for missing files\n- `ToString()` converts the instance to string format\n- `DefaultResolution()` gets the default resolution for this data and security type if the user provided none\n- `SupportedResolutions()` gets the supported resolution for this data and security type\n- `DataTimeZone()` specifies the data time zone for this data type\n\n##### Tests\n\nIt will be a requisite that each data type has a json and protobuf round trip serialization and deserialization, as well as a clone unit test. Examples provided at [MyCustomDataTypeTests](https://github.com/QuantConnect/LeanDataSdk/blob/master/Tests/MyCustomDataTypeTests.cs)\n\nThe only adjusment `MyCustomDataTypeTests` test suite requires for a new data type should be the `CreateNewInstance()` method. Which should returned a fully initialized data point.\n\n#### Create Algorithm\n\n##### Introduction\n\nCreating an example `QCAlgorithm` will allow quants to understand how to consume a data set and what value could it provide to their trading strategy.\n\n##### Developing Algorithm\n\nA [sample](https://github.com/QuantConnect/LeanDataSdk/blob/master/Tests/CustomDataAlgorithm.cs) algorithm is provided in this repository for the defined custom data type.\n\n- `Initialize()` Specifies the **data** and resolution required, as well as the cash and start-end dates for the algorithm. This is where the custom data should be added.\n- `OnData(Slice slice)` is the primary entry point for the algorithm. Each new data point will be pumped through it. This should be where the custom data is retrieved from the `slice` object and used.\n\n#### Create Data Converters\n\n##### Introduction\n\nData converter scripts will be in charge of fetching new data and processing it into a format that Lean and the [new data type](https://github.com/QuantConnect/LeanDataSdk#create-data-type) will be able to read.\n\nTODO:\n\n##### Python Notebook\n##### Python Script\n##### Bash Script\n\n### Api reference\n\nTODO:","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantconnect%2Flean.datasource.quiverquantcongresstrading","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantconnect%2Flean.datasource.quiverquantcongresstrading","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantconnect%2Flean.datasource.quiverquantcongresstrading/lists"}