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Build Status     Regression Tests     LEAN Forum     Discord Chat

Lean Home | Documentation | Download Zip | Docker Hub | Nuget


Lean Engine is an open-source algorithmic trading engine built for easy strategy research, backtesting, and live trading. We integrate with common data providers and brokerages so you can quickly deploy algorithmic trading strategies.

The core of the LEAN Engine is written in C#, but it operates seamlessly on Linux, Mac, and Windows operating systems. It supports algorithms written in Python 3.11 or C#. Lean drives the web-based algorithmic trading platform QuantConnect.

Proudly Sponsored By


Want your company logo here? [Sponsor LEAN]( to be part of radically open algorithmic-trading innovation.

QuantConnect is Hiring!

Join the team and solve some of the most difficult challenges in quantitative finance. We'd like to hear from you if you are passionate about algorithmic trading. We always have a space for excellent C# engineers. When applying, make sure to mention you came through GitHub:

System Overview

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The Engine is broken into many modular pieces that can be extended without touching other files. The modules are configured in config.json as set "environments." You can control LEAN to operate in the mode required through these environments.

The most important plugins are:

  • Result Processing (IResultHandler)

    Handle all messages from the algorithmic trading engine. Decide what should be sent and where the messages should go. The result processing system can send messages to a local GUI or the web interface.

  • Datafeed Sourcing (IDataFeed)

    Connect and download the data required for the algorithmic trading engine. For backtesting, this provider sources files from the disk; for live trading, it connects to a stream and generates the data objects.

  • Transaction Processing (ITransactionHandler)

    Process new order requests, either using the fill models provided by the algorithm or with an actual brokerage. Send the processed orders back to the algorithm's portfolio to be filled.

  • Realtime Event Management (IRealtimeHandler)

    Generate real-time events - such as the end-of-day events. Trigger callbacks to real-time event handlers. For backtesting, this is mocked up to work on simulated time.

  • Algorithm State Setup (ISetupHandler)

    Configure the algorithm cash, portfolio, and data requested. Initialize all state parameters required.

These are all configurable from the config.json file in the Launcher Project.

Developing with Visual Studio Code Dev Containers

The Dev Containers extension lets you use a Docker container as a full-featured development environment. The extension starts (or attaches to) a development container running the quantconnect/research:latest image.

A full explanation of developing Lean with Visual Studio Code Dev Containers can be found in the VS Code Integration project.

Developing with Lean CLI

QuantConnect recommends using Lean CLI for local algorithm development. This is because it is a great tool for working with your algorithms locally while still being able to deploy to the cloud and have access to Lean data. It can also run algorithms on your local machine with your data through our official docker images.

Reference QuantConnects documentation on Lean CLI here

Installation Instructions

This section will cover how to install lean locally for you to use in your environment. Refer to the following readme files for a detailed guide regarding using your local IDE with Lean:

To install locally, download the zip file with the latest master and unzip it to your favorite location. Alternatively, install Git and clone the repo:

git clone
cd Lean


Visual Studio will automatically start to restore the Nuget packages. If not, in the menu bar, click Project > Restore NuGet Packages.

  • In the menu bar, click Run > Start Debugging.

Alternatively, run the compiled dll file. First, in the menu bar, click Build > Build All, then:

cd Lean/Launcher/bin/Debug
dotnet QuantConnect.Lean.Launcher.dll

Linux (Debian, Ubuntu)

  • Install dotnet 6:
  • Compile Lean Solution:
dotnet build QuantConnect.Lean.sln
  • Run Lean:
cd Launcher/bin/Debug
dotnet QuantConnect.Lean.Launcher.dll


  • Install Visual Studio
  • Open QuantConnect.Lean.sln in Visual Studio
  • Build the solution by clicking Build Menu -> Build Solution (this should trigger the NuGet package restore)
  • Press F5 to run

Python Support

A full explanation of the Python installation process can be found in the Algorithm.Python project.

Local-Cloud Hybrid Development.

Seamlessly develop locally in your favorite development environment, with full autocomplete and debugging support to quickly and easily identify problems with your strategy. Please see the CLI Home for more information.

Issues and Feature Requests

Please submit bugs and feature requests as an issue to the Lean Repository. Before submitting an issue, please read the instructions to ensure it is not duplicated.

Mailing List

The mailing list for the project can be found on LEAN Forum. Please use this to ask for assistance with your installation and setup questions.

Contributors and Pull Requests

Contributions are warmly welcomed, but we ask you to read the existing code to see how it is formatted and commented on and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the contributor guidelines. All accepted pull requests will get a $50 cloud credit on QuantConnect. Once your pull request has been merged, write to us at with a link to your PR to claim your free live trading. QC <3 Open Source.

A huge thank you to all our contributors!


The open sourcing of QuantConnect would not have been possible without the support of the Pioneers. The Pioneers formed the core 100 early adopters of QuantConnect who subscribed and allowed us to launch the project into open source.

Ryan H, Pravin B, Jimmie B, Nick C, Sam C, Mattias S, Michael H, Mark M, Madhan, Paul R, Nik M, Scott Y,, Tadas T, Matt B, Binumon P, Zyron, Mike O, TC, Luigi, Lester Z, Andreas H, Eugene K, Hugo P, Robert N, Christofer O, Ramesh L, Nicholas S, Jonathan E, Marc R, Raghav N, Marcus, Hakan D, Sergey M, Peter McE, Jim M,, Richard E, Dominik, John L, H. Orlandella, Stephen L, Risto K, E.Subasi, Peter W, Hui Z, Ross F, Archibald112,, Jae S, Eric S, Marco D, Jerome B, James B. Crocker, David Lypka, Edward T, Charlie Guse, Thomas D, Jordan I, Mark S, Bengt K, Marc D, Al C, Jan W, Ero C, Eranmn, Mitchell S, Helmuth V, Michael M, Jeremy P, PVS78, Ross D, Sergey K, John Grover, Fahiz Y, George L.Z., Craig E, Sean S, Brad G, Dennis H, Camila C, Egor U, David T, Cameron W, Napoleon Hernandez, Keeshen A, Daniel E, Daniel H, M.Patterson, Asen K, Virgil J, Balazs Trader, Stan L, Con L, Will D, Scott K, Barry K, Pawel D, S Ray, Richard C, Peter L, Thomas L., Wang H, Oliver Lee, Christian L..