Lean README

The project's README from github.com/QuantConnect/Lean. As one markdown file: README.md.

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Build Status     Regression Tests     LEAN Forum     Discord Chat

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

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LEAN is an event-driven, professional-caliber algorithmic trading platform built with a passion for elegant engineering and deep quant concept modeling. Out-of-the-box alternative data and live-trading support.

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modular-header LEAN is modular in design, with each component pluggable and customizable. It ships with models for all major plug-in points.

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QuantConnect Lean CLI is a command-line interface tool for interacting with the Lean algorithmic trading engine, which is an open-source platform for backtesting and live trading algorithms in multiple financial markets. It allows developers to manage projects, run backtests, deploy live algorithms, and perform various other tasks related to algorithmic trading directly from the terminal. The CLI simplifies the workflow by automating tasks, enabling seamless integration with cloud services, and facilitating collaboration with the QuantConnect community. It's designed for quant developers who need a powerful and flexible tool to streamline their trading strategies. Please watch the instructions videos to learn more.

Installation

pip install lean

Commands

Create a new project containing starter code

lean project-create

Run a local Jupyter Lab environment using Docker

lean research

Backtest a project locally using Docker

lean backtest

Optimize a project locally using Docker

lean optimize

Start live trading a project locally using Docker

lean live

Download the LEAN CLI Cheat Sheet for the full list of commands.

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diagram

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This section will cover how to install lean locally for you to use in your environment. For most users we strongly recommend the LEAN CLI which is prebuilt and runs on all platforms. 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 https://github.com/QuantConnect/Lean.git
cd Lean

macOS

NOTE: Visual Studio for Mac has been discontinued, use Visual Studio Code instead

Linux (Debian, Ubuntu)

dotnet build QuantConnect.Lean.sln

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

Windows

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 [email protected] with a link to your PR to claim your free live trading. QC <3 Open Source.

A huge thank you to all our contributors!


Acknowledgements

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, BinaryExecutor.com, 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, INTJCapital.com, Richard E, Dominik, John L, H. Orlandella, Stephen L, Risto K, E.Subasi, Peter W, Hui Z, Ross F, Archibald112, MooMooForex.com, 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..

Lean (QuantConnect/Lean)

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Lean
这是什么东西？LEAN 是 QuantConnect 的开源事件驱动算法交易引擎，用 C# 编写，同时支持 Python。你可以用它来构建、回测和实盘交易策略，并且开箱即支持另类数据。
这是要安装点击使用的应用，还是要写代码调用的框架？它是一个用来写代码的框架：你用 C# 或 Python 编写策略，由 LEAN 引擎来运行。它的设计是模块化的——每个组件都可以替换——你可以通过 LEAN CLI、Docker 或自己的 IDE 来驱动它。
我怎么把它跑起来？对大多数用户，README 推荐使用预构建的 LEAN CLI，它在所有平台上都能运行。如果想从源码构建，先安装 dotnet 10 SDK 并克隆或解压仓库，然后在 Windows 上用 Visual Studio（按 F5），在 macOS 上用 VS Code，在 Linux 上直接用 dotnet 命令。
如果我的策略用 Python 写，还需要装 .NET SDK 吗？需要——引擎本身是基于 .NET 的 C# 程序，所以必须安装 dotnet 10 SDK 才能构建和运行它（Windows 上用 Visual Studio，macOS 上用 VS Code 加 C# Dev Kit）。你的策略仍然可以用 Python 编写。
安装上有什么坑需要提前知道？主要的一个在 macOS 上：Visual Studio for Mac 已经停止维护了，请改用 Visual Studio Code 并安装 C# Dev Kit 扩展。其余的关键就是先装好 dotnet 10，才能开始构建。
它能在我的 Mac 上运行吗，还是只支持 Windows？它可以在 Windows、macOS 和 Linux 上运行（明确点名了 Debian 和 Ubuntu），LEAN CLI 也为所有平台提供了预构建版本。如果你更喜欢用容器，Docker Hub 上也发布了 Docker 镜像。
它能下真实的交易单，还是只能做回测？它开箱即支持实盘交易，不只是回测，另类数据也是内置的。另外，被合并的 pull request 还能获得 50 美元的 QuantConnect 云端额度，外加免费的实盘交易。
引擎内部有哪些东西可以自定义？基本上整个技术栈都可以：LEAN 是模块化的，每个组件都能替换，而且所有主要的插件点都自带默认模型，所以你可以换成自己的实现。
我必须用 QuantConnect 云端吗，还是可以完全在本地运行？不需要云端——引擎基于 Apache-2.0 开源，可以在本地运行。README 还介绍了一种本地与云端混合的模式，你可以在本地开发，并享受完整的自动补全和调试支持。
What are similar projects?StockSharp, bytex, nautilus_trader, awesome-quant, quant-trading, bytex, Vibe-Trading, awesome-systematic-trading
Which topics is it in?algorithmic-trading-engine, forex, trading-platform, trading-strategies, engine, market-data, backtesting, trading-bot, quant, c-sharp, quantitative-finance, algorithm
Creator
QuantConnect
Size
574 MB
Stars
22K
Updated
CommitOct 16Releasev2.4.0.1Aug 08
Age
11y
Commits · 90 Days