* Wait for first session bar before filling equity market orders at open EquityFillModel.MarketFill could fill a market order placed right after market open using data from the previous trading date, because the first bar of the current session has not been emitted yet. ShouldWaitForFreshData only covered hour/daily resolutions, so minute/second orders filled on stale prices. Add IsWithinFirstResolutionSpanAfterMarketOpen: when the order time is within the lowest subscribed resolution span after the open and the price is stale, wait for the first bar instead of filling on the previous date's price. * Share opening-bar stale-fill wait across fill models Move IsWithinFirstResolutionSpanAfterMarketOpen to the base FillModel and add a ShouldWaitForFreshDataOnStale sibling helper that combines it with the existing coarse-resolution ShouldWaitForFreshData check. The base FillModel, FutureFillModel and EquityFillModel market fills now share this single wait decision at their stale-data guards. ShouldWaitForFreshData is intentionally left untouched at its GetMarketFillPrice call site, which uses it to choose the bar open vs current price and is not gated by staleness, so fill prices for finer resolutions are unchanged. The opening-bar helper is guarded against always-open markets, which have no session open to wait for. * Add regression algorithm for stale fill at market open Reproduces the opening-bar stale fill issue: a market order placed one second after the open while subscribed to minute resolution. Without the fix the order fills on the previous trading date's stale price; the algorithm asserts in OnOrderEvent that a fill never happens within the first minute after the open, so it errors without the fix and passes with it. Uses SPY minute data over 2013-10-07 to 2013-10-11, which is available in the repository Data folder. * Add unit tests for stale fill wait at market open Cover the opening-bar stale fill scenario directly at the fill model level: a market order placed within the first bar after the session open, while only the previous session's stale bar is available, must wait instead of filling on the stale price, and fills once the first session bar arrives. EquityFillModel also asserts the boundary (orders past the first bar still fill on stale data), and FutureFillModel covers the shared base helper from the future path. * Generalize stale market-order fill wait to any time of day Replace the market-open-specific wait with a generic check: a market order that would be filled on stale data waits for fresh data when the latest available data is more than one subscribed resolution bar behind the current time. This no longer considers the market open explicitly; it covers the opening bar (the first session bar has not been emitted yet) and any intraday data gap larger than the resolution. ShouldWaitForFreshDataOnStale now takes the latest data end time and the current time instead of the order time, and is shared by FillModel, FutureFillModel and EquityFillModel. Coarse resolutions (hour/daily) still always wait; tick never waits. Internal configurations are included when sizing the resolution bar. EquityFillModel's best-effort price helpers now report the stale data end time so the gap can be measured. Tests: EquityFillModelTests and FutureFillModelTests cover the market-open and mid-session stale cases (wait then fill on fresh data) plus the within-one-bar boundary (fill on stale). The regression algorithm is generalized to assert no fill happens on data staler than the resolution, with orders at the open and mid-session. Pre-existing plumbing/data-selection tests that used degenerate timestamps were given fresh timestamps so they still exercise their original intent. * Add sample data and adjust regression algorithms for stale-fill wait Add minute/daily sample data so market orders that now wait for fresh data can fill (ES futures gap days, TWX/GOOG equities and options, SPXW weeklies, GC futures/options copy for 2020-01-06). Adjust a few regression algorithms to the deferred-fill behavior: cap orders in the extended-market continuous future test, ignore daily-resolution SPY in the automatic-seed data checks, and refresh OptionAssignmentStatistics expected constants. * Update regression expected statistics for stale-fill wait Regenerate ExpectedStatistics, DataPoints and AlgorithmHistoryDataPoints for the regression algorithms affected by the wait-for-fresh-data fill change and the added sample data: futures/options fill-timing shifts, ES data-point count increases, and GOOG 2015-12-28 outcome changes. * Trim SPXW sample data to expiries within filter window The two SPXW algorithms filter with Expiration(0,7), so contracts expiring more than a week out are never subscribed. Drop those far-dated expiries from the 2021-01-06/08 minute files (760KB->108KB and 776KB->108KB on the quote files). Fills, DataPoints and statistics are unchanged; both regression tests still pass. * Trim ES minute and GOOG option sample data to order-fill minimum The ES minute gap-day files source no order fills (daily-resolution algos fill from es_daily); keep only the front contract used for execution and drop the unused back-month contracts. Trim the GOOG 2015-12-28 option file (no fill depends on it) to the morning chain window. Regenerate the back-month futures statistics affected by the dropped back-month bars. Full CSharp regression suite passes (722/722). * Use SMA gap threshold in BasicTemplateContinuousFuture for C#/Python parity At a fast/slow SMA cross the two averages can coincide to within rounding noise, where the C# (decimal) and Python (double) comparisons disagree, producing different orders between languages. Require a minimum gap before acting on a cross so both languages stay in lockstep, and update the shared expected statistics accordingly. * Mirror order cap in Python algorithm and update future history counts Apply the same pre-2013-11-12/3-order cap to the Python BasicTemplateContinuousFutureWithExtendedMarket algorithm for C#/Python parity, and update the QuantBook future-history expected counts to reflect the added ES sample data. * Use SMA gap threshold in BasicTemplateContinuousFutureWithExtendedMarket for C#/Python parity This algorithm had the same fast/slow SMA cross divergence already fixed in BasicTemplateContinuousFutureAlgorithm (ad8fc33): at the 2013-10-29 cross the two averages coincide to within rounding noise (C# decimal diff -1e-25, Python double diff exactly 0.0), so the raw `_fast > _slow` / `_fast < _slow` comparisons disagree between languages. C# fired a liquidate+rebuild that Python skipped, producing 5 orders in C# vs 3 in Python. Require a minimum 0.001 gap before acting on a cross so both languages stay in lockstep, and regenerate the shared expected statistics (Total Orders 5 -> 3). * Document SMA cross threshold as a C#/Python parity workaround Add a short note before the fast/slow SMA comparisons in both continuous-future template algorithms clarifying that the minimum-gap threshold exists only so the C# and Python versions take the exact same trades on the limited sample data in the repository, where decimal vs double rounding can disagree at a cross. * Fetch subscription configs once per equity market fill MarketFill resolved the subscription configs twice per fill: once via the best-effort price helpers (GetSubscribedTypes) and again via ShouldWaitForFreshDataOnStale. Fetch them once and thread them through both paths via optional parameters, leaving existing callers unchanged. * Measure stale-fill wait against order submission time ShouldWaitForFreshDataOnStale compared the latest data end time against the security current time. Compare against the order submission time instead so the decision to wait for fresh data reflects how stale the data is relative to when the order was placed. Realign the stale-price warning fill test accordingly. * Fix stale market data in SendingNewOrderFromOnOrderEvent test The market price tick was timestamped a day before the order submission time, so under the order-time staleness check the market orders waited for fresh data instead of filling. Use a reference time with the tick one minute before the order so the data is fresh and the orders fill. * Centralize internal-inclusive subscription config lookup in fill models ShouldWaitForFreshDataOnStale re-resolved the subscription configs through the ShouldWaitForFreshData call it makes first, and GetMarketFillPrice did the same. Thread the already-fetched configs through ShouldWaitForFreshData and GetMarketFillPrice so each market fill resolves them at most once. Add a GetSubscriptionDataConfigs(Security) helper on the base FillModel that returns the internal-inclusive configs, and route every fill-model call site through it to remove the duplicated lookup and repeated comment. * Avoid list allocation in ShouldWaitForFreshData Replace the Where(...).ToList() + All(...) with a single foreach over the subscription configs, short-circuiting on the first non-coarse resolution.
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