System.OutOfMemoryException

To address the System.OutOfMemoryException and improve the CoarseSelectionFunction, you need to make the following changes:

1. Correct Attribute Names: The attributes Price, DollarVolume, and Symbol should be lowercase. Use stock.price, stock.dollar_volume, and stock.symbol.

2. Define Parameters: Ensure that parameters.PRICE_THRESHOLD and parameters.DOLLAR_VOLUME_THRESHOLD are defined and accessible within the function. You might need to define these parameters in your class or pass them as arguments.

Here's the corrected version of your function:

def CoarseSelectionFunction(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
    self.logger.log("CoarseSelectionFunction Started.")
    selected: List[Symbol] = []
    for stock in coarse:
        if stock.price > self.price_threshold and stock.dollar_volume > self.dollar_volume_threshold:
            selected.append(stock.symbol)
            # Initialize the historical data if not already done
            # if stock.symbol not in self.history:
            #     self.InitializeIndicators(stock.symbol)
    self.logger.log(f"CoarseSelectionFunction Selected {len(selected)} stocks.")      
    return selected

Additional Recommendations:

• Optimize Memory Usage: Consider filtering the stocks further before adding them to the selected list to reduce memory usage.
• Batch Processing: If possible, process stocks in smaller batches to avoid loading too much data into memory at once.
• Monitor Memory Usage: Use logging to track memory usage and identify any operations that consume excessive memory.

Sources: Legacy Fundamental Universes - QuantConnect.com, Stock Selection Strategy Based On Fundamental Factors, Fundamental Factor Long Short Strategy, Momentum Short Term Reversal Strategy, Coarse Fine Fundamental Combo Algorithm

1. How to do batch process to avoid to load too many data into memory. write sample code
2. How to track memory usage, write sample code 

Hi Dharmesh

1. Do several rounds of backtest with splitted selections (like each algorithm selects the tickers start with “A”/"B"/…)
2. Your cloud algorithm will not be able to handle this, but you can take a look at the sidebar's performance.

It is recommended to upgrade your backtest node if you have a large universe to work with.

Best
Louis