NASDAQ 100 Historical Weights and Fundamental Data Study

Based on the provided extracts, it seems like 'Harrison' is trying to use the ETF constituents filter to select the constituents of the QQQ ETF. However, the provided code in the ETFConstituentsFilter function is not correct. The function should return a list of symbols sorted by weight, but the current implementation is returning a list of all symbols for each constituent.

Here is the corrected code:

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

This code sorts the constituents by weight in descending order and returns the symbols of the sorted constituents.

Regarding the fundamental data, the extracts provided do not contain information on how to retrieve ROIC, earnings revision ratio, and the current 5Yr. Avg. EPS estimate. However, QuantConnect does provide a way to select stocks based on corporate fundamental data powered by Morningstar®. This data includes approximately 5,000 tickers with 900 properties each. You can use a fundamental universe to select stocks based on this data.

Unfortunately, without more specific extracts or code related to the fundamental data 'Harrison' is trying to retrieve, I can't provide a more detailed answer. I would recommend 'Harrison' to refer to the QuantConnect Documentation for more information on how to use fundamental data in QuantConnect.

Here are the two fixes I tried to force the algo to only debug once per month, I have commented both out as they are both causing errors when I try to run them. Any thoughts?

Based on the provided code, it seems like the issue might be with the indentation in the ETFConstituentsFilter method. The return statement is inside the for loop, which means it will return on the first iteration, effectively making the loop useless. Here's the corrected code:

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

As for the second part of your question, to pull fundamental data on each symbol, you can use the FineFundamental object. However, the specific data you're looking for (ROIC, earnings revision ratio, and the current 5Yr. Avg. EPS estimate) might not be directly available. You might need to calculate these values yourself using the available data in the FineFundamental object.

To schedule the function to run at the start of each month, you can use the Schedule function in the Initialize method. Here's an example:

def Initialize(self):
    # ... other initialization code ...
    self.Schedule.On(self.DateRules.MonthStart("QQQ"), self.TimeRules.AfterMarketOpen("QQQ"), self.Rebalance)

In this example, Rebalance is a method that you would define to handle the rebalancing of your portfolio. This method will be called at the start of each month, right after the market opens.

Please note that the code snippets provided are based on the information available in the extracts and might need to be adjusted to fit your specific algorithm.