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quantconnect--lean/Algorithm.Python
Jhonathan Abreu 69d2f5ae82
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Futures and Future Options file-based universes (#8480)
* Make FOPs selection universe file-based for backtesting

* Make FOPs option chains universe file based

* Make Future universe selection file-based like option universe

* Make Future universe selection file-based like option universe

* Abstraction cleanup

* Add FuturesChains API to QC algorithm

Also refactor future chain provider to use the new FutureUniverse instead of zip file names

* Update regression algorithms stats

* Refactor QuantBook option and future history to use new universes

* Fix failing tests

* Fix failing tests

* Fix failing tests

* Minor future chains unit test improvement

* Add futures chains DataFrame property

Also, remove IDerivativeSecurity interface from Future

* Add DataFrame property to FuturesChains class

* Add regression algorithms

* Add regression algorithms

* Replace QCAlgorithm.FutureChainProvider usages with new FuturesChain api

* Minor fixes

* Reduce number of universe files in repo

* Minor data fixes

* Regression algorithms updates

* Add implicit conversion from FuturesContract to Symbol

Modified algorithms to use futures contract objects directly instead of accessing their Symbol property.
Removed unnecessary import statements and redundant lines in various files.

* Improve resolution handling for history requests

* Changed _auxiliaryData field to lazily-initialized AuxiliaryData property

* Refactor data handling in BaseChain and TimeSliceFactory

- Added `AddData` method to `BaseChain` for adding market data
- Refactored `TimeSliceFactory` to use `BaseChain.AddData` method

* Remove specific constructors and indexers from Chain classes

Removed public indexers in `BaseChains` for getting or setting `BaseChain` instances by `ticker` or `Symbol`, which were used for Pythonnet compatibility.

* Remove chain cache logic from FuturesChainUniverse

* Refactor class and interface names for clarity

Renamed `FileBasedUniverse` to `BaseChainUniverseData` and
`IFileBasedUniverse` to `IChainUniverseData`.

* Add base class for options and futures contracts

- Introduced `BaseContract` as an abstract base class for contracts, consolidating common properties and methods.
- Removed ISymbolInterface

* Add minor fix for future options tickers parsing

Added tests

* Trigger Build

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* Clean chain provider classes up

* Remove ZipEntryName other classes and unused code

Removed ZipEntryName class and references across various files.
Removed DataQueueFuturesChainUniverseDataCollectionEnumerator and DataQueueOptionChainUniverseDataCollectionEnumerator classes.
Removed OptionChainUniverseSubscriptionEnumeratorFactory class.
Removed unused code for handling OptionChainUniverse and FuturesChainUniverse in FileSystemDataFeed.cs and LiveTradingDataFeed.cs.
Removed several test files related to enumerator factories and universe data collection.

* Minor changes and cleanup

* Trigger Build

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* Refactor FuturesContract data handling

Forward price data from bars and ticks stored in private fields for improved memory usage

* Fix: use universe data for market data in FuturesContract

* Update regression algorithms stats after rebase

Added HSI futures universe files

* Sort configs by internal flag

Internals go first

* Throw from option universe data filters for future options

Future options IV, Open interest and greeks are not supported for future options

* Minor changes

* Improve some regression algorithms

* Minor fix for failing unit tests

* Update FOPs universe file header

Removed greeks and IV columns.
Updated FOPs universe files: removed outdated columns.

* Minor unit test fix

* Trigger Build

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* Minor fix

* Add history provider as constructor argument for chain providers

* Update new regression algorithms data points count

* Minor fix for FakeDataQueue

* Add initialize method to chain providers classes

* Minor changes

* Trigger Build

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* Minor fix

* Minor fix

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* Add logs to ProcessedDataProvider

* Removed test logs

* Minor fix

* Support downloading options and futures universe files from api data provider
2025-03-25 16:22:38 -04:00
..
2020-06-02 12:52:28 -03:00
2020-06-02 12:52:28 -03:00
2015-09-01 22:17:35 -04:00

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

  1. Use the Windows x86-64 MSI Python 3.11.11 installer from python.org or Anaconda for Windows installer.
  2. When asked to select the features to be installed, make sure you select "Add python.exe to Path"
  3. Create PYTHONNET_PYDLL environment variable to the location of your python dll in your installation (e.g. C:\Dev\Python311\python311.dll or C:\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}
  4. Install pandas=2.1.4 and its dependencies.
  5. Install wrapt=1.16.0 module.
  6. Reboot computer to ensure changes are propagated.

macOS

  1. Use the macOS x86-64 package installer from Anaconda and follow "Installing on macOS" instructions from Anaconda documentation page.
  2. Set PYTHONNET_PYDLL environment 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-profile with 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
    
  3. Install pandas=2.1.4 and its dependencies.
  4. Install wrapt=1.16.0 module.

Linux

  1. 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
  1. Create a new Python environment with the needed dependencies
conda create -n qc_lean python=3.11.11 pandas=2.1.4 wrapt=1.16.0
  1. Set PYTHONNET_PYDLL environment 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/environment with 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

Run Python Algorithms

  1. Update the config to run a python algorithm:
    "algorithm-type-name": "BasicTemplateAlgorithm",
    "algorithm-language": "Python",
    "algorithm-location": "../../../Algorithm.Python/BasicTemplateAlgorithm.py",
    
  2. Build LEAN.
  3. 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-packages directory 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