How To Unit Test QuantumAlgorithm Python

It seems you're interested in using quantconnect-stubs for unit testing QuantConnect algorithms in a local Python environment. Here's a detailed guide on how to set it up and run your tests effectively:

Step-by-Step Setup

1. Create a Virtual Environment:

   • Create a new virtual environment in your project directory:

     python -m venv venv
     

2. Activate the Virtual Environment:

   • On Unix or MacOS, use:

     source venv/bin/activate
     

   • On Windows, use:

     .\venv\Scripts\activate
     

3. Install Required Packages:

   • Install quantconnect-stubs and pytest (or any other testing framework you prefer):

     pip install quantconnect-stubs pytest
     

4. Writing Tests:

   • Use quantconnect-stubs to simulate QuantConnect API classes and methods.
   • Here's a basic example to illustrate writing a test for a QuantConnect algorithm:

import pytest
from quantconnect_stubs import QCAlgorithm
from clr import AddReference
AddReference("QuantConnect.algorithm")
AddReference("QuantConnect.logging")
from QuantConnect import Resolution
from QuantConnect.algorithm import QCAlgorithm as QCAlgo
class ExampleAlgorithm(QCAlgo):
    def initialize(self):
        self.set_start_date(2020, 1, 1)
        self.set_end_date(2020, 1, 31)
        self.set_cash(100000)
        self.symbol = self.add_equity("SPY", Resolution.DAILY).Symbol
    def on_data(self, data):
        if not self.portfolio[self.symbol].invested:
            self.set_holdings(self.symbol, 1)
# Test cases
def test_initialize():
    algo = ExampleAlgorithm()
    algo.initialize()
    assert algo.portfolio.Cash == 100000
    assert algo.symbol is not None
def test_on_data():
    algo = ExampleAlgorithm()
    algo.initialize()
    algo.on_data(None)  # Simulate on_data call
    assert algo.portfolio[algo.symbol].invested

1. Running Tests:
   • Run your unit tests using your preferred testing framework like pytest:

     pytest
     

Reviewing API References

If you're looking to better understand how certain methods and classes in QuantConnect work, here's a quick overview of some relevant ones:

• warm_up_indicator: This method facilitates warming up an indicator with historical data.
• on_data: The core method that gets called with new data slices, pivotal in algorithm operation.
• on_end_of_algorithm: Handy for asserting final states or cleaning up resources at the algorithm's end.

This setup allows you to test parts of your code that interact with the QuantConnect API, facilitating development and debugging in a local environment before deploying onto QuantConnect's platform.

Sources: Getting Started - QuantConnect.com, Getting Started - QuantConnect.com, Documentation Home - QuantConnect.com, Documentation Home - QuantConnect.com, Documentation Home - QuantConnect.com

I suppose I was wrong, this stubs are just interfaces for IDEs but cannot be used for unit test

I hope someone from Quantconnect can explain how you suppose to do unit test.
Decoupling form Lean is almost impossible, I cannot create a test double for each class, from the simple Symbol to QCAlgorithm creation of Indicators or RollingWindow

forget if you want to do a Functional unit test (Kent Beck's style, classical TDD style)