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quantconnect--lean/Algorithm.Python
Ricardo Andrés Marino Rojas 9e51f10b77
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Add Feature Signal Exports feature (#7145)
* Add Collective2SignalExportClass
Add SignalExportTarget interface

* Collective2SignalExport test working

Add SignalExportManager
Add draft of CrunchDAOSignalExport

* Modify SignalExportManager

Instantiate SignalExportManager in QCAlgorithm constructor
Draft of CrunchDAOSignalExport

* Improve SignalExportManager

- Add regression tests SignalExportDemonstrationAlgorithm in C# and
  Python

* Improve SignalExportDemonstrationAlgorithm

Address requested changes in Collective2SignalExport, SignalExportManger and SignalExportTargetTests.cs

* Add CrunchDAOSignalExport.cs

Add CrunchDAOSignalExport unit tests in SignalExportTargetTests.cs

* Add NumeraiSignalExport.cs

Modify SignalExportDemonstrationAlgorithm.cs to test NumeraiSignalExport
Add unit test in SignalExportTargetTests to test NumeraiSignalExport

* Address required changes

- Add BaseSignalExport.cs
- Add SignalExportParameters.cs
- Add PortfolioSignalExportDemonstrationAlgorithm.cs/py
- Improve Error handling in SignalExport provider classes
- Collective2SignalExport now gets the correct number of shares for each holding
- SignalExportManager now computes the correct holding percentage of each portfolio target
- SignalExportManager now takes into account if the algorithm is in live mode
- Demonstration algorithms now are more simple

* Address last required changes

- PortfolioSignalExportDemonstrationAlgorithm.cs/py now inherits from SignalExportDemonstrationAlgorithm.cs/py
- Add more unit tests to assert SignalExportManager gets the correct percentage quantity for each holding
- Change Collective2SignalExport, CrunchDAOSignalExport and NumeraiSignalExport Send() method to return true if there was no error while sending the signals and false otherwise
- Nit changes

* Remove exceptions thrown

- Add more unit tests and more test cases
- Enhance BaseSignalExport.Dispose() method

* Add Collective2SignalExportClass
Add SignalExportTarget interface

* Collective2SignalExport test working

Add SignalExportManager
Add draft of CrunchDAOSignalExport

* Modify SignalExportManager

Instantiate SignalExportManager in QCAlgorithm constructor
Draft of CrunchDAOSignalExport

* Improve SignalExportManager

- Add regression tests SignalExportDemonstrationAlgorithm in C# and
  Python

* Improve SignalExportDemonstrationAlgorithm

Address requested changes in Collective2SignalExport, SignalExportManger and SignalExportTargetTests.cs

* Add CrunchDAOSignalExport.cs

Add CrunchDAOSignalExport unit tests in SignalExportTargetTests.cs

* Add NumeraiSignalExport.cs

Modify SignalExportDemonstrationAlgorithm.cs to test NumeraiSignalExport
Add unit test in SignalExportTargetTests to test NumeraiSignalExport

* Address required changes

- Add BaseSignalExport.cs
- Add SignalExportParameters.cs
- Add PortfolioSignalExportDemonstrationAlgorithm.cs/py
- Improve Error handling in SignalExport provider classes
- Collective2SignalExport now gets the correct number of shares for each holding
- SignalExportManager now computes the correct holding percentage of each portfolio target
- SignalExportManager now takes into account if the algorithm is in live mode
- Demonstration algorithms now are more simple

* Address last required changes

- PortfolioSignalExportDemonstrationAlgorithm.cs/py now inherits from SignalExportDemonstrationAlgorithm.cs/py
- Add more unit tests to assert SignalExportManager gets the correct percentage quantity for each holding
- Change Collective2SignalExport, CrunchDAOSignalExport and NumeraiSignalExport Send() method to return true if there was no error while sending the signals and false otherwise
- Nit changes

* Remove exceptions thrown

- Add more unit tests and more test cases
- Enhance BaseSignalExport.Dispose() method

* Fix failing regression tests

* Fix failing unit tests

* Nit changes

* Nit change

* Nit change

* Fix failing unit tests

* Changes required

- Break regression algos `SignalExportDemonstrationAlgorithm.cs/py` nad `PortfolioSignalExportDemonstrationAlgorithm.cs/`y` into three ones, one for each signal export provider
- Change SignalExportManager constructor to receive current algorithm as a parameter
- Fix bug in `SignalExportManager.GetPortfolioTargets()`, now it computes the correct percentage for each holding
- Make `BaseSignalExport.DefaultAllowedSecurityTypes` overrdible
- Handle case were `Collective2SignalExport.ConvertPercentageToQuantity()` returns null
- Clean unnecessary code in `Collective2SignalExport()`, `CrunchDAOSignalExport()` and `NumeraiSignalExport()`

* Nit change

* Nit change

* Minor tweaks after review

* Remove indexes from signal exports

* Required changes
- Change EMA indicators period from 200, 300 to 10,100 in regression algorithms
- Remove Indices from regression algorithms
- Add more XML documentation to regression algorithms
- Change `Log.Error` to `_algorithm.Error` in Signal export providers. Besides, fix error message format
- Change default value for `platformId` parameter in `Collective2SignalExport.cs` constructor
- Solve small bugs in SignalExportProvider when verificating the amount of porfolio targets is greater than zero and each portfolio target is allowed
- Handle case when `PortfolioTarget.Percent()` returns null in `Collective2SignalExport.ConvertPercentageToQuantity()`
- Handle error format message from Collective2 API
- Check every ticker signal is between 0 and 1 (inclusive) in `CrunchDAOSignalExport.cs`
- Modifiy `NumeraiSignalExport.cs` constructor to take into account filename given in the arguments
- Fix small bug with the return value of `ConvertTargetsToNumerai()` method in `NumeraiSignalExport.cs`
- Modify `SignalExportManager.cs` to return true when the algorithm being ran is not in live mode
- Remove indices from CrunchDAO unit tests

* Enhance ´CrunchDAOSignalExport.cs´ implementation

---------

Co-authored-by: Martin-Molinero <martin@quantconnect.com>
2023-04-11 17:45:07 -03:00
..
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 3.8:

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 3.8.13 installer from python.org or Anaconda for Windows installer. "Anaconda 5.2" installs 3.5.2 by default, after installation of Anaconda you will need to upgrade python to make it work as expected: conda install -y python=3.8.13
  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\Python38\python38.dll or C:\Anaconda3\python38.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=1.4.3 and its dependencies.
  5. Install wrapt=1.14.1 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/libpython3.8.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}/libpython3.8.dylib"
    
    • Save your changes, and either restart your terminal or execute
    source ~/.bash-profile
    
  3. Install pandas=1.4.3 and its dependencies.
  4. Install wrapt=1.14.1 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/Miniconda3-4.5.12-Linux-x86_64.sh
bash Miniconda3-4.5.12-Linux-x86_64.sh -b
rm -rf Miniconda3-4.5.12-Linux-x86_64.sh
conda update -y python conda pip
  1. Create a new Python environment with the needed dependencies
conda create -n qc_lean python=3.8.13 pandas=1.4.3 wrapt=1.14.1
  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/libpython3.8.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/libpython3.8.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