quantconnect/lean.datasource.nasdaqdatalink

The Lean Data Source SDK is a template that helps data vendors feed their custom datasets into QuantConnect, a platform where people build and run trading algorithms. If you have a financial or alternative dataset (say, weather data or shipping records) and you want algorithmic traders to use it, this repo gives you the starting point to make that data compatible with QuantConnect's system. At a practical level, the repo provides a sample project structure with code for defining how your data looks and scripts for downloading and converting it into the format QuantConnect expects. You fork the repository, customize it for your specific dataset, and then build and test everything locally. The conversion scripts can be written in Python or even bash, so there's some flexibility in how you prepare your data. The audience here is specifically data vendors, companies or individuals who maintain a dataset they want to sell or distribute through the QuantConnect Data Market. A concrete example might be a firm that tracks satellite imagery of retail parking lots and wants to offer that as a signal for trading algorithms. Once a vendor finishes their integration, they contact QuantConnect support, who then lists the dataset in their marketplace. QuantConnect sets a high bar for inclusion. Vendors need at least a year of historical point-in-time data, reliable API uptime, consistent delivery schedules, survivorship-bias-free data, and solid ticker mapping using identifiers like ISIN or FIGI. The README emphasizes that raw, well-defined data is preferred over derived signals, since traders build systems on top of it and need stability. The project is built in C# targeting .NET 5, and the scripts run inside QuantConnect's own research environment. This means vendors are working within a fairly prescribed setup, which reduces guesswork but also requires comfort with that specific ecosystem.