Accessing ETFConstituentsFilter constituents list

Hmm, I don't know enough to give you a confident answer yet. However, I can provide some general guidance based on the information extracts.

In QuantConnect, you can use multiple universe selection models in your algorithm. You can add these universe selection models in the Initialize method of your algorithm. Here is an example of how to add an ETF constituents universe:

def Initialize(self) -> None:
    symbol = Symbol.Create("SPY", SecurityType.Equity, Market.USA)
    universe = ETFConstituentsUniverseSelectionModel(symbol, self.UniverseSettings, self.ETFConstituentsFilter)
    self.AddUniverseSelection(universe)

In this example, ETFConstituentsFilter is a method that filters the constituents based on their weight:

def ETFConstituentsFilter(self, constituents: List[ETFConstituentData]) -> List[Symbol]:
    selected = sorted([c for c in constituents if c.Weight],
    key=lambda c: c.Weight, reverse=True)[:10]
    return [c.Symbol for c in selected]

You can add another universe selection model in a similar way. Just make sure to define a separate filter method for it.

As for accessing self.ETFConstituentsFilter(constituents) in the OnSecuritiesChanged method, it's not clear from the extracts what the specific issue is. However, OnSecuritiesChanged is typically used to handle events when securities are added or removed from your universe. You might want to use this method to update your portfolio allocations when your universe changes.

For more detailed guidance, I recommend checking out the QuantConnect documentation and tutorials. You can also ask for help in the QuantConnect forum.

Renata you should attach the code

Hi Renata  While reviewing your problem statement, I've devised a simple blueprint. 

It's essential to highlight that SWAN holdings consist predominantly of government notes, comprising 90%. This poses a challenge as these constituents are not stocks, making them incompatible with the platform.SWAN had some stock holdings during the 2019 period for a very short time. 

The problem statement handles multiple ETF/Universes. Each universe has to be handled individually to get the top 10 tickers as per the desired filtration. Attached is the backtest with SPY and QQQ where SPY gets 3% of the portfolio and QQQ gets the remaining. I have used a simple and basic logic for creating positions and rebalancing. You can tweak these logics accordingly. 

Looking forward to your feedback and ideas!