* 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>
QuantConnect Python Algorithm Project
This document contains information regarding how to use Python with the Lean engine, this includes how to use Python Autocomplete, setting up Lean for Python algorithms, PythonNet compilation for devs, and what imports to use to replicate the web IDE experience in your local development.
Local Python Autocomplete
To enable autocomplete for your local Python IDE, install the quantconnect-stubs package from PyPI using the following command:
pip install quantconnect-stubs
To update your autocomplete to the latest version, you can run the following command:
pip install --upgrade quantconnect-stubs
Copy and paste the imports found here to the top of your project file to enable autocomplete.
In addition, you can use Skylight to automatically sync local changes to the cloud.
Setup Lean Locally with Python
Before setting up python support, follow the installation instructions to get LEAN running C# algorithms on your machine.
Installing Python 3.11:
Next we must prepare a Python installation for Lean to use. Follow the instructions for your OS.
Windows
- Use the Windows x86-64 MSI Python 3.11.11 installer from python.org or Anaconda for Windows installer.
- When asked to select the features to be installed, make sure you select "Add python.exe to Path"
- Create
PYTHONNET_PYDLLenvironment variable to the location of your python dll in your installation (e.g.C:\Dev\Python311\python311.dllorC:\Anaconda3\python311.dll):- Right mouse button on My Computer. Click Properties.
- Click Advanced System Settings -> Environment Variables -> System Variables
- Click New.
- Name:
PYTHONNET_PYDLL - Value:
{python dll location}
- Name:
- Install pandas=2.2.3 and its dependencies.
- Install wrapt=1.16.0 module.
- Reboot computer to ensure changes are propagated.
macOS
- Use the macOS x86-64 package installer from Anaconda and follow "Installing on macOS" instructions from Anaconda documentation page.
- Set
PYTHONNET_PYDLLenvironment variable to the location of your python dll in your installation directory (e.g./Users/{your_user_name}/anaconda3/lib/libpython3.11.dylib):- Open
~/.bash-profilewith a text editor of your choice. - Add a new line to the file containing
export PYTHONNET_PYDLL="/{your}/{path}/{here}/libpython3.11.dylib"- Save your changes, and either restart your terminal or execute
source ~/.bash-profile - Open
- Install pandas=2.2.3 and its dependencies.
- Install wrapt=1.16.0 module.
Linux
- Install Python using miniconda by following these commands; by default, miniconda is installed in the users home directory (
$HOME):
export PATH="$HOME/miniconda3/bin:$PATH"
wget https://cdn.quantconnect.com/miniconda/Miniconda3-py311_24.9.2-0-Linux-x86_64.sh
bash Miniconda3-py311_24.9.2-0-Linux-x86_64.sh -b -p /opt/miniconda3
rm -rf Miniconda3-py311_24.9.2-0-Linux-x86_64.sh
- Create a new Python environment with the needed dependencies
conda create -n qc_lean python=3.11.11 pandas=2.2.3 wrapt=1.16.0
- Set
PYTHONNET_PYDLLenvironment variable to location of your python dll in your installation directory (e.g./home/{your_user_name}/miniconda3/envs/qc_lean/lib/libpython3.11.so):- Open
/etc/environmentwith a text editor of your choice. - Add a new line to the file containing
PYTHONNET_PYDLL="/home/{your_user_name}/miniconda3/envs/qc_lean/lib/libpython3.11.so"- Save your changes, and logout or reboot to reflect these changes
- Open
Run Python Algorithms
- Update the config to run a python algorithm:
"algorithm-type-name": "BasicTemplateAlgorithm", "algorithm-language": "Python", "algorithm-location": "../../../Algorithm.Python/BasicTemplateAlgorithm.py", - Build LEAN.
- Run LEAN. You should see the same result of the C# algorithm you tested earlier.
Python.NET development - Python.Runtime.dll compilation
LEAN users do not need to compile Python.Runtime.dll. The information below is targeted to developers who wish to improve it. Download QuantConnect/pythonnet github clone or downloading the zip. If downloading the zip - unzip to a local pathway.
Note: QuantConnect's version of pythonnet is an enhanced version of pythonnet with added support for System.Decimal and System.DateTime.
Below are some examples of build commands that create a suitable Python.Runtime.dll.
msbuild pythonnet.sln /nologo /v:quiet /t:Clean;Rebuild
OR
dotnet build pythonnet.sln
Python Autocomplete Imports
Adding from AlgorithmImports import * to the top of your Python file is enough to enable autocomplete and import the required types for the algorithm at runtime.
Known Issues
- Python can sometimes have issues when paired with our quantconnect stubs package on Windows. This issue can cause modules not to be found because
site-packagesdirectory is not present in the python path. If you have the required modules installed and are seeing errors about them not being found, please try the following steps:- remove stubs -> pip uninstall quantconnect-stubs
- reinstall stubs -> pip install quantconnect-stubs