Warmup is needed for Live deployment ?

In live trading, the SetStartDate, SetEndDate, and SetCash methods are ignored. These methods are typically used for backtesting to define the date range and initial cash for the backtest.

The SetWarmUp method is used to warm up your algorithm and its indicators for trading. It simulates winding back the clock from the time you deploy the algorithm. In a backtest, this is the StartDate of your algorithm. In live trading, it's the current date.

So, for live trading, you don't need to set the start date, end date, and cash. However, you may want to keep the SetWarmUp method if you need to warm up your indicators or other parts of your algorithm.

Here is how you can set up the warm-up period in your algorithm:

self.SetWarmUp(timedelta(days=5))

This will wind back time 5 days from the start of your algorithm.

Sources:

• QuantConnect Forum Discussion
• QuantConnect Documentation

I am using Resolution of Second for all my stocks so during the live deployment , SetWarmUp(timedelta(days=50)) takes forever and timeout and got timed out so I removed the of the SetWarmUp. So Resolution is Second, so how does setWarmUp for 50days get data ? Does it get data for each seconds for 50days ?

 def initialize_buyable_assets(self):        
        for item in self.items:
            symbol = item["Symbol"]            
            if symbol and symbol.strip():                
                self.buyable_assets[symbol] = self.algorithm.AddEquity(symbol, Resolution.Second).Symbol

The SetWarmUp method in QuantConnect is used to initialize your algorithm with historical data. When you set a warm-up period of 50 days with a resolution of seconds, the algorithm tries to load and process every second of data for the past 50 days for each of your securities. This can be a very large amount of data and can cause the algorithm to take a long time to start or even time out.

If you're using a high-resolution data like seconds, it's recommended to use a smaller warm-up period or use a lower resolution for the warm-up period. For example, you could warm up with minute or hour resolution data instead.

Here's how you can set a warm-up period with a specific resolution:

self.SetWarmUp(50, Resolution.Daily)

This will warm up your algorithm with 50 days of daily data, regardless of the resolution of your data subscriptions.

However, if you're using indicators that require second resolution data, warming up with daily data might not be sufficient. In this case, you might need to manually warm up your indicators or use a different approach.

Sources:

• QuantConnect Documentation
• QuantConnect Forum Discussion
• QuantConnect Forum Discussion

Indeed ,I need daily data for the indicator so I can use Daily Resolution for the WarmUp and for trading , I can use Second Resolution.

Yes, you can certainly do that. You can use the SetWarmUp method with a daily resolution to warm up your indicators, and then use second resolution data for your trading decisions.

Here's how you can set a warm-up period with a specific resolution:

self.SetWarmUp(50, Resolution.Daily)

This will warm up your algorithm with 50 days of daily data, regardless of the resolution of your data subscriptions.

Then, in your initialize_buyable_assets method, you can subscribe to second resolution data for your securities:

def initialize_buyable_assets(self):        
    for item in self.items:
        symbol = item["Symbol"]            
        if symbol and symbol.strip():                
            self.buyable_assets[symbol] = self.algorithm.AddEquity(symbol, Resolution.Second).Symbol

This way, your algorithm will use second resolution data for trading decisions, but your indicators will be warmed up with daily data.

Sources:

• QuantConnect Documentation
• QuantConnect Forum Discussion
• QuantConnect Forum Discussion