Hello JP,

It's great to hear that you're interested in using QuantConnect for your backtesting needs. Based on the information provided in the extracts, it seems you're looking to use the Fractal Adaptive Moving Average (FRAMA) as a filter in your universe selection.

While the extracts don't provide a direct example of using FRAMA in universe selection, they do provide some guidance on how to use indicators and universe selection in general.

In QuantConnect, you can create and update indicators like FRAMA in your algorithm. This is usually done in the Initialize method. You can also use the CoarseSelectionFunction and FineSelectionFunction to filter your universe of stocks.

Here's a basic structure of how you might set up your algorithm (note: this is a pseudo-code and might not run as-is):

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2018, 1, 1)
        self.SetEndDate(2018, 2, 1)
        self.SetCash(10000)
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.UniverseSettings.Resolution = Resolution.Daily
        self.symbols = []
        # set up FRAMA indicator here
    def OnData(self, data):
        # update FRAMA indicator here
        pass
    def CoarseSelectionFunction(self, coarse):
        # filter symbols based on FRAMA indicator here
        pass
    def FineSelectionFunction(self, fine):
        # further filter symbols if needed
        pass
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For more detailed examples and guidance, I recommend checking out the QuantConnect documentation, Boot Camp, and example algorithms on GitHub.

I hope this helps you get started with your project. Happy coding!

Sources:

• Forum Discussion 8652
• Forum Discussion 6560
• Forum Discussion 8016
• Forum Discussion 10281
• Forum Discussion 808
• Forum Discussion 5587
• Forum Discussion 4599