Looking for help for basic stuff

To ensure your algorithm's main logic executes just once a day, you should use QuantConnect's Schedule method to trigger specific events at desired times, rather than relying on the OnData method which processes data streams often (as in minute or second resolution). Here's how you can adjust your algorithm to accomplish your goals:

1. Schedule a Daily Event: Utilize the scheduling feature to run your code at a specific time each day. This can be achieved by setting self.Schedule in your Initialize method.

2. Daily Task Logic: Put your daily logic in a separate method that will be triggered by the scheduled event.

3. Order Management: Within the scheduled daily task function, manage new order placements or the renewal of existing orders.

Below is an adjusted version of your code using scheduled events:

from AlgorithmImports import *

class SquareYellowDolphin(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2023, 8, 21)
        self.set_end_date(2023, 10, 21)
        self.set_cash(100000)
        self.universe_settings.resolution = Resolution.DAILY

        # Select from US market equities
        self.add_universe(self.coarse_selection_function)

        # Schedule the daily task
        symbol = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)

        # Run daily routine every day 10 minutes after market open
        self.schedule.on(self.date_rules.every_day(symbol), 
                         self.time_rules.after_market_open(symbol, 10), 
                         self.daily_task)

    def coarse_selection_function(self, coarse):
        sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)
        return [x.Symbol for x in sorted_by_dollar_volume[:100]]

    def daily_task(self):
        self.debug(f"Daily task executed at [{self.Time}]")

        # Implement your logic to select stocks and manage orders here

        # Example pseudocode for making orders
        # selected_stocks = self.select_investment_candidates()
        # for stock in selected_stocks:
        #    if not self.portfolio[stock].invested:
        #        self.set_holdings(stock, 0.1)  # Example position size

        # Renew or modify existing orders if needed

Key Points

• Scheduled Events: Utilize self.schedule.on() for the scheduled events. This ensures the specified method runs at precise times, such as after the market opens.
• Daily Logic: Replace real logic in daily_task() with your actual investment criteria and order management.
• Universe Selection: Adjust the universe filter as per your requirements using coarse and fine selection functions. The example sorts by dollar volume from available stocks.
• Market Symbol: Ensure the symbol used for scheduling exists to avoid runtime errors.

This approach allows you to decouple the stream processing OnData() from your main daily logic, relying instead on a more deterministic schedule.

Sources: Scheduled Events - QuantConnect.com, Scheduled Events Algorithm, Key Concepts - QuantConnect.com, Scheduled Queuing Algorithm, Dual Thrust Trading Algorithm

Thank you for answering! And another quick question: how to configure universe to US market stocks? To use “US Equity Security Master” dataset in coarse selection?

Thanks!

Hi Evgeni

The US Equity Security Master is only for data normalization factoring and an event notice. It is not available for universe selection. Instead, you may consider combining the Upcoming Splits dataset with fundamental selection. You can find how to chain universes here.

Best
Louis