“Runtime Error: Please register to receive data for symbol ' P' using the AddSecurity() function.

Hi David,

The error message you're seeing is typically due to trying to use a symbol that hasn't been added to your algorithm using the AddSecurity() or AddEquity() function. In your case, it seems like you're trying to use a symbol 'W5373A5QVCRP' that hasn't been added.

In your CoarseSelectionFunction, you're creating a Simple Moving Average (SMA) indicator for each coarse object. However, you're not adding these securities to your algorithm. You should add these securities using the AddEquity() function before creating the SMA indicator.

Here's how you can modify your CoarseSelectionFunction:

def CoarseSelectionFunction(self, coarse):
    if self.reb != 1:
        return self.symbols
    for cf in coarse:
        if cf.Symbol not in self.averages:
            self.AddEquity(cf.Symbol.Value, Resolution.Daily)  # Add this line
            self.averages[cf.Symbol] = self.SMA(cf.Symbol, 200)
        self.averages[cf.Symbol].Update(cf.EndTime, cf.AdjustedPrice)
    ...

This will ensure that each symbol you're trying to create an SMA for is added to your algorithm.

Also, please note that you have two OnData methods in your code. Python doesn't support method overloading like C#, so the second OnData method will overwrite the first one. You should remove or rename one of them.

I hope this helps! Let me know if you have any other questions.

Sources:

• QuantConnect Documentation
• QuantConnect Forum Discussion

Hi Mia,

Thanks for the quick response! In that case since I am trying to find the SMAs for only the equities being coarse/fine selected is there a method in which I could add all available equities with data, such as self.AddEquity([], Resolution.Daily)? Or is this not possible as it would be too much data to backtest on the cloud?

Hi David,

Adding all available equities is not feasible due to the sheer volume of data. It would be computationally expensive and could exceed the memory limit, causing your algorithm to fail.

Instead, you can use the Universe Selection feature, which is designed to handle large amounts of data. With Universe Selection, you can filter down the universe of all available equities to a manageable size based on your specific criteria.

In your case, you're already using the CoarseSelectionFunction to filter down the universe. You can continue to use this approach to select the equities you're interested in and calculate the SMA for those equities.

Remember, you can add the equities to your algorithm in the CoarseSelectionFunction as I suggested in the previous response. This way, you're only adding the equities that pass your coarse selection criteria, which should be a manageable number.

Here's the modified CoarseSelectionFunction again for reference:

def CoarseSelectionFunction(self, coarse):
    if self.reb != 1:
        return self.symbols
    for cf in coarse:
        if cf.Symbol not in self.averages:
            self.AddEquity(cf.Symbol.Value, Resolution.Daily)  # Add this line
            self.averages[cf.Symbol] = self.SMA(cf.Symbol, 200)
        self.averages[cf.Symbol].Update(cf.EndTime, cf.AdjustedPrice)
    ...

I hope this helps! Let me know if you have any other questions.

Sources:

• QuantConnect Documentation
• QuantConnect Forum Discussion

Thank you again, that works out. The quantconnect indicator universes documentation really helped too.

I'm glad to hear that you found the solution helpful and the QuantConnect Indicator Universes documentation useful. If you have any more questions or run into any other issues, feel free to ask. Happy coding!