Customize Universal Selection

The code provided by Dharmesh has several issues that need to be addressed:

1. Import Errors: The modules parameters, Logger, and UniverseSelection cannot be found. Ensure these modules are correctly defined and available in your project.

2. Attribute Errors: In main.py, the methods SetStartDate and SetCash should be renamed to set_start_date and set_cash to follow Python's naming conventions for methods in QuantConnect.

3. Indentation Error: In UniverseSelection.py, the method CoarseSelectionFunction has an indentation issue. Ensure that the method definition is properly aligned with the other methods in the class.

Here's how you can correct these issues:

main.py

from AlgorithmImports import *
import parameters as parameters
from Logger import Logger
from UniverseSelection import UniverseSelection

class DailyBreakOut(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2023, 1, 1)
        self.set_cash(parameters.INITIAL_CASH)
        self.logger = Logger(self)
        self.add_universe_selection(UniverseSelection(self, self.logger)) 

    def on_data(self, data):
        # Your on_data logic here
        pass

UniverseSelection.py

class UniverseSelection(UniverseSelectionModel):
    def __init__(self, algorithm: QCAlgorithm, logger: Logger):
        super().__init__()
        self.algorithm = algorithm
        self.logger = logger
        self.logger.log("Starting UniverseSelection initialization...")
        try:
            self.history = {}
            self.moving_averages_200 = {}
            self.moving_averages_20 = {}
            self.previous_close = {}
            self.atr = {}
            self.volatility = {}
            self.avg_volatility = {}
            self.logger.log("Finished UniverseSelection initialization.")
        except BaseException as e:
            self.logger.log(f"Error during UniverseSelection initialization: {str(e)}")
            raise e

    def create_universes(self) -> List[Universe]:
        # This method must return one or more universes
        return [self.algorithm.AddUniverse(self.coarse_selection_function)]

    def coarse_selection_function(self, coarse):
        selected: List[str] = []
        for stock in coarse:
            if stock.Price > params.PRICE_THRESHOLD and stock.DollarVolume > params.DOLLAR_VOLUME_THRESHOLD:
                selected.append(stock.Symbol)
                # Initialize the historical data if not already done
                if stock.Symbol not in self.history:
                    self.initialize_indicators(stock.Symbol)
        return selected

Make sure that the parameters, Logger, and UniverseSelection modules are correctly defined and available in your project.

Sources: Stock Selection Strategy Based On Fundamental Factors, Momentum And State Of Market Filters, Custom Data Universe Regression Algorithm, Asset Growth Effect, January Effect In Stocks