Martin-Molinero
27de93f78f
* Market orders wait for fresh data instead of filling on stale prices A market order would previously fill immediately on the most recent available data even when that data was older than StalePriceTimeSpan (default one hour), only attaching a warning. This is unrealistic for a coarse resolution asset (hour/daily) where the latest bar is the stale previous close when the order is placed mid-bar or via an intraday scheduled event. The default fill models (FillModel, EquityFillModel, FutureFillModel) now wait for fresh data instead of filling on a stale price, but only for hour and daily resolutions; the order fills when the next bar closes. For minute/second/tick subscriptions the previous behavior is kept (fill on the stale price with a warning), since stale data there is a genuine gap rather than a bar still forming. Adds HourResolutionMarketOrderStalePriceRegressionAlgorithm, updates the FillOutsideHours daily expectation, and regenerates statistics for the hour/daily algorithms whose fills change. FutureOptionDaily buys and liquidates a day apart now (a same-day buy + liquidate cannot fill on daily data once stale fills are disabled). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Normalize and correct StalePriceTimeSpan XML docs The interface and class docs now match and reflect the actual behavior: the wait-for-fresh-data only applies to hour/daily resolutions, while minute/second/tick subscriptions still fill on stale data with a warning. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Fill resting market orders at the bar open instead of the close A hour/daily market order that was resting before the current bar opened (it predates the bar - placed after the previous close or while waiting for fresh data) now fills at the bar open, the price when trading resumed (like a MarketOnOpen), instead of the bar close. Orders placed during the bar still fill at the current/close price, so intraday mid-bar fills are unchanged. Equity fills are unchanged (resting equity orders are already converted to MarketOnOpen by QCAlgorithm.MarketOrder). Adds the shared FillModel.GetMarketFillPrice helper used by the base FillModel and FutureFillModel, a unit test, and regenerates statistics for the affected daily/hour futures, index and crypto regression algorithms (order counts unchanged, only fill prices). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Add regression algorithm asserting resting market orders fill at the bar open RestingMarketOrderFillsAtBarOpenRegressionAlgorithm buys a daily future on the bar that delivers it (fills at that bar's close) and submits a liquidation while the market is closed (overnight pulse, no fresh bar). The liquidation rests and fills on a later bar at the bar open, not its close - asserting the new GetMarketFillPrice behavior. The in-bar buy is asserted to fill at the close, for contrast. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Carry the bar start time on Prices instead of re-reading the cache Add Prices.Time (the bar start, mirroring BaseData.Time/EndTime), populated from the source bar/tick in every GetPrices path. GetMarketFillPrice now uses prices.Time directly instead of a second asset.Cache.GetData() lookup. Behavior is unchanged (prices.Time equals the previously read cache time). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Add regression algorithm asserting in-session hour orders fill at the latest close HourMarketOrderFillsAtBarCloseRegressionAlgorithm submits an hour resolution market order mid-bar (via an intraday scheduled event) while the market is open, using the default one hour StalePriceTimeSpan. It asserts the order fills immediately at the latest available bar's close - not waiting and not at the bar open - since the latest bar is within the stale window. Guards the resting-order open-fill behavior against affecting ordinary in-session fills. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Regenerate custom fill model algorithm statistics for the open-fill change CustomModelsAlgorithm and CustomPartialFillModelAlgorithm subscribe SPY at hour resolution and their custom fill models delegate to base.MarketFill, so resting orders now fill at the bar open. Regenerate their statistics (C#/Python) and the inline expected statistics of the PEP8StyleCustomModelsWork test. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Lean Home | Documentation | Download Zip | Docker Hub | Nuget
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.
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.
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
- Install Visual Studio Code for Mac
- Install the C# Dev Kit extension
- Install dotnet 9 SDK:
- To build the solution, either:
- choose Run Task > build from the Panel task dropdown, or
- from the command line run
dotnet build
- To run the solution, either:
- choose Run and Debug from the Activity Bar, then click Launch, or
- click F5, or
- from the command line run
cd Launcher/bin/Debug dotnet QuantConnect.Lean.Launcher.dll
Linux (Debian, Ubuntu)
- Install dotnet 9:
- Compile Lean Solution:
dotnet build QuantConnect.Lean.sln
- Run Lean:
cd Launcher/bin/Debug
dotnet QuantConnect.Lean.Launcher.dll
Windows
- Install Visual Studio
- Open
QuantConnect.Lean.slnin Visual Studio - Build the solution by clicking Build Menu -> Build Solution (this should trigger the NuGet package restore)
- Press
F5to 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 support@quantconnect.com 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..