Lean

Open source alternative to QuantConnect and MultiCharts

22.2K stars
Apache-2.0
Active this week
Last commit today

Last verified Oct 23, 2026

Build algorithmic trading strategies with backtesting, optimization, and live trading across nine asset classes under Apache 2.0.

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Read the full breakdownWho it is for, how it works, trade-offs, the quick start and the FAQ

who it's for

Who Lean is for#

Quant developers backtesting systematic strategies

Lean provides a survivorship-bias-free backtesting engine with accurate accounting for corporate actions and position-level P&L. Developers write strategies in Python or C#, run them against historical data with realistic fill and slippage models, and compare results across parameter sets using the optimization runner.

Skip if:

Skip if you need a no-code backtesting tool with a graphical interface. Lean requires writing strategy code in Python or C# and is not suitable for traders who prefer drag-and-drop strategy builders.

Proprietary trading firms migrating off commercial platforms

Firms running on MultiCharts or similar commercial platforms can migrate strategies to Lean and eliminate recurring license costs. The Algorithm Framework's modular architecture makes it practical to port existing logic component by component. Over 300 hedge funds use Lean in production as evidence of institutional viability.

Skip if:

Skip if your current platform's specific order types or data adapters have no Lean equivalent. Migrating execution logic for exotic derivatives or niche exchanges may require building a custom brokerage model.

Academic researchers studying market microstructure

Lean's open execution stack, custom data import, and Jupyter Lab integration make it suitable for research on market microstructure, algorithmic market impact, and strategy simulation. Custom data sources can be fed directly into the engine, and the full execution trace is inspectable at the source level.

Skip if:

Skip if your research requires only statistical analysis of historical price series without execution simulation. A pandas-based Python workflow is simpler for that scope.

Hedge fund engineers building proprietary execution infrastructure

Lean's Apache 2.0 license, production track record ($100B per month notional volume), and Algorithm Framework make it a credible foundation for a proprietary fund's execution layer. The plug-in architecture means the engine can be extended for specific clearing, reporting, or risk management requirements.

Skip if:

Skip if your fund requires a fully managed execution platform with vendor support SLAs. Lean is self-operated, and operational incidents require in-house engineering to diagnose and resolve.

the problem

The problem it solves#

Building and testing algorithmic trading strategies on commercial platforms means paying recurring license fees for software whose execution logic you cannot inspect or modify. Platforms like MultiCharts charge per-version or per-seat licenses, and closed-source execution engines make it impossible to verify exactly how orders are filled, how slippage is calculated, or how corporate actions are handled during a backtest. When a strategy produces unexpected results, the inability to read and modify the engine becomes a real debugging problem.

For quant teams moving toward production, vendor lock-in adds another cost. A strategy built against a proprietary brokerage API or data format is expensive to migrate. Cloud-based platforms remove infrastructure overhead but replace it with per-month subscription costs and data sovereignty concerns for funds with compliance requirements. Self-hosting was always the answer for serious funds, but the complexity of building a production-grade backtesting and execution engine from scratch put it out of reach for most teams.

how Lean solves it

How it solves it#

Nine-asset-class trading from one portfolio

Lean trades equities, forex, options, futures, future options, index options, crypto, and CFDs from a single portfolio. All nine asset classes share the same margin, buying power, and profit-and-loss accounting, so multi-asset strategies run without stitching together separate execution environments.

Survivorship-bias-free backtesting

The engine automatically accounts for splits, dividends, corporate actions, listings, delistings, and mergers during a backtest. Without point-in-time handling of these events, backtests that hold stocks through delistings produce inflated results. Lean handles all of it automatically using historical corporate action data.

100+ built-in technical indicators

Over 100 technical indicators ship with the engine, pre-tested and applicable to any data source. They work across all supported asset classes and timeframes, can be chained as inputs to other indicators, and require no external library.

Pluggable execution and risk models

Every component of the execution stack is replaceable: slippage, fill, fee, and brokerage models are plug-in points. You can implement the exact margin and fill behavior of your target broker or test against idealized assumptions. Risk models run passively or actively hedge exposed positions.

Python and C# strategy support

Strategies run in Python or C#. Python is the preferred language for machine learning integrations; C# is used when execution speed matters. The LEAN CLI sets up a Jupyter Lab environment for interactive research and connects to VSCode for local debugging with full autocomplete.

Algorithm Framework for modular strategy design

The Algorithm Framework separates universe selection, alpha generation, portfolio construction, execution, and risk management into distinct, swappable modules. QuantConnect ships pre-built modules (Equal Weighting, Mean-Variance, Black-Litterman portfolios) that can be combined without writing them from scratch, accelerating strategy development.

strengths · trade-offs

Strengths and trade-offs#

Strengths

Apache 2.0 license with no commercial restrictionThe Apache 2.0 license allows running Lean commercially, modifying the engine, and distributing changes without releasing source code. No royalties, no per-seat licensing, no vendor approval needed for production use. Over 300 hedge funds currently run Lean-powered strategies in live trading.
Battle-tested in production at scaleThe engine has run 375,000 live algorithms since 2015 and currently processes over $100B per month in notional trading volume. It is maintained by QuantConnect with contributions from 180+ engineers and has continuous integration with regression tests visible on GitHub.
Full engine transparency for debuggingEvery model is readable and modifiable source code. When a backtest produces unexpected fills or P&L, you can trace the execution path through the engine. Commercial platforms offer no equivalent access; subtle bugs in fill modeling are only discoverable when the code is open.
Cross-platform deployment on Windows, macOS, and LinuxLean runs on all three major operating systems via the LEAN CLI and Docker, or by compiling the .NET solution directly. MultiCharts, the primary commercial alternative in systematic trading, is Windows-only. Python strategy development and Jupyter research work identically across platforms.

Trade-offs

-Engine development requires C# and .NET toolingThe engine is written in C#, so extending the core or debugging execution internals requires the .NET 10 SDK and C# familiarity. Python-only developers using the LEAN CLI can write strategies without this, but the CLI abstracts the engine rather than exposing it. Quants who need to modify execution logic will need to learn the C# codebase.
-285 open issues across a broad feature matrixWith 285 open GitHub issues against a multi-asset, multi-broker feature matrix, edge cases in specific broker integrations or asset class combinations may surface in production. The community forum is active, but production-critical deployments should expect to investigate issues in the engine source directly.
-Historical data must be sourced and formatted separatelySelf-hosting Lean means you supply your own historical data in LEAN-compatible format. QuantConnect's cloud platform includes curated data from 40+ providers; the self-hosted path requires contracts with data vendors and an ETL step to convert data into LEAN's format before backtesting.

versus alternatives

Lean vs alternatives#

Lean vs QuantConnect

Lean is the open source engine that powers QuantConnect's cloud platform. Choosing between them is a build-vs-buy decision on data and infrastructure.

Feature	Lean (self-hosted)	QuantConnect (cloud)
License	Apache 2.0	Proprietary SaaS
Cost	Free (you supply infrastructure)	Monthly subscription
Historical data	You source and format it	40+ providers included
Source access	Full engine code	Engine runs cloud-side
Infrastructure	Self-managed	Fully managed

Self-hosting Lean is the right choice when your fund has data sovereignty or compliance requirements that prohibit cloud processing, when you need to modify the engine itself, or when your data contracts are already in place and you only need execution infrastructure. QuantConnect's cloud is the better fit when you want curated data from 40+ providers without an ETL step, no infrastructure management, and access to the community marketplace of strategies. Strategies written for QuantConnect's cloud run on a self-hosted Lean instance with the same code, so migrating later is practical.

Lean vs MultiCharts

MultiCharts is a commercial algorithmic trading platform with per-version license fees and no access to the underlying execution engine source code.

Feature	Lean	MultiCharts
License	Apache 2.0	Commercial (per-version)
Source access	Full	None
Asset classes	9 (equities through crypto)	Primarily equities and futures
Language support	Python and C#	PowerLanguage and C#
Operating systems	Windows, macOS, Linux	Windows only
Cost	Free to self-host	Per-version license fee

Lean trades more asset classes, runs on all three major operating systems, and costs nothing in license fees. For Python-oriented quant teams, Lean's native Python support and Jupyter Lab integration are concrete advantages MultiCharts does not offer. MultiCharts is worth considering for traders already invested in its PowerLanguage ecosystem, its charting interface for mixed discretionary and systematic workflows, or teams that want a GUI-first strategy builder rather than a code-first engine.

install · quick start

Quick start#

bash

Install the LEAN CLI with pip, then run strategies locally using Docker.
```bash
pip install lean
lean backtest
```

tech stack · detected from GitHub

What it's built on#

Languages: C#Python

frequently asked

FAQ#

Is Lean free to use commercially?

Yes. Lean is licensed under Apache 2.0, which allows commercial use, modification, and distribution without royalties or per-seat fees. Over 300 hedge funds use it in production trading. The license does not require publishing your strategy code or sharing modifications.

How does self-hosted Lean differ from the QuantConnect cloud platform?

Lean is the open source engine that powers QuantConnect's cloud platform. Self-hosting Lean runs the same engine without the cloud subscription cost. The trade-off is that you supply your own historical data and infrastructure; QuantConnect's cloud includes curated data from 40+ providers and no infrastructure management. Strategies written for the cloud run on a self-hosted instance with the same code.

What asset classes does Lean support?

Lean supports equities, forex, options, futures, future options, indexes, index options, crypto, and CFDs. All nine asset classes can be traded simultaneously from a single portfolio with unified margin and P&L accounting. Corporate actions, delistings, and splits are handled automatically using point-in-time data.

How do I install and run Lean locally?

The quickest path is the LEAN CLI: run pip install lean to install it, then lean backtest to run a strategy locally using Docker, or lean live to deploy a live algorithm. For direct engine development, clone the repository and build with dotnet build QuantConnect.Lean.sln. The CLI path requires only pip and Docker; the build path requires the .NET 10 SDK.

Does Lean support Python strategies?

Yes. Lean runs Python strategies natively alongside C#. Python is the preferred language for machine learning integrations, while C# is used when execution speed matters. The LEAN CLI sets up a Jupyter Lab environment for interactive Python research. A full Python setup guide is in the Algorithm.Python project within the repository.

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Find open source alternatives to paid software

Lean vs QuantConnect

Lean is the open source engine that powers QuantConnect's cloud platform. Choosing between them is a build-vs-buy decision on data and infrastructure.

Feature	Lean (self-hosted)	QuantConnect (cloud)
License	Apache 2.0	Proprietary SaaS
Cost	Free (you supply infrastructure)	Monthly subscription
Historical data	You source and format it	40+ providers included
Source access	Full engine code	Engine runs cloud-side
Infrastructure	Self-managed	Fully managed

Lean vs MultiCharts

MultiCharts is a commercial algorithmic trading platform with per-version license fees and no access to the underlying execution engine source code.

Feature	Lean	MultiCharts
License	Apache 2.0	Commercial (per-version)
Source access	Full	None
Asset classes	9 (equities through crypto)	Primarily equities and futures
Language support	Python and C#	PowerLanguage and C#
Operating systems	Windows, macOS, Linux	Windows only
Cost	Free to self-host	Per-version license fee

Who Lean is for#

Quant developers backtesting systematic strategies

Skip if:

Proprietary trading firms migrating off commercial platforms

Skip if:

Academic researchers studying market microstructure

Skip if:

Hedge fund engineers building proprietary execution infrastructure

Skip if:

The problem it solves#

How it solves it#

Nine-asset-class trading from one portfolio

Survivorship-bias-free backtesting

100+ built-in technical indicators

Pluggable execution and risk models

Python and C# strategy support

Algorithm Framework for modular strategy design

Strengths and trade-offs#

Strengths

Trade-offs

Lean vs alternatives#

Lean vs QuantConnect

Lean vs MultiCharts

Quick start#

What it's built on#

FAQ#

Similar open-source tools#

Open source tools in these categories#

Finance & Fintech

Developer Tools

Data & Analytics

Find open source alternatives to paid software

Who Lean is for#

Quant developers backtesting systematic strategies

Skip if:

Proprietary trading firms migrating off commercial platforms

Skip if:

Academic researchers studying market microstructure

Skip if:

Hedge fund engineers building proprietary execution infrastructure

Skip if:

The problem it solves#

How it solves it#

Nine-asset-class trading from one portfolio

Survivorship-bias-free backtesting

100+ built-in technical indicators

Pluggable execution and risk models

Python and C# strategy support

Algorithm Framework for modular strategy design

Strengths and trade-offs#

Strengths

Trade-offs

Lean vs alternatives#

Lean vs QuantConnect

Lean vs MultiCharts

Quick start#

What it's built on#

FAQ#

Similar open-source tools#

Open source tools in these categories#

Finance & Fintech

Developer Tools

Data & Analytics

Find open source alternatives to paid software