Return per ticket

QuantConnect does not provide a built-in feature to track the return per individual symbol in a strategy. However, you can implement this functionality programmatically in your algorithm.

One way to do this is by creating a dictionary to store the symbol and its corresponding return. You can update the return for each symbol whenever a trade for that symbol is executed. Here's a simplified example of how you can do this in Python:

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.symbols = ["AAPL", "GOOG", "IBM"]
        self.returns = { symbol : 0 for symbol in self.symbols }
        for symbol in self.symbols:
            self.AddEquity(symbol, Resolution.Daily)

    def OnData(self, data):
        for symbol in self.symbols:
            if self.Portfolio[symbol].Invested:
                self.returns[symbol] = (self.Portfolio[symbol].Price - self.Portfolio[symbol].AveragePrice) / self.Portfolio[symbol].AveragePrice

In this example, self.returns is a dictionary that stores the return for each symbol. The return for a symbol is calculated as the difference between the current price and the average price at which the symbol was bought, divided by the average price. This gives the return as a percentage.

Please note that this is a simplified example and the actual implementation may be more complex depending on your specific needs. For example, you might want to take into account dividends, splits, and commissions, or you might want to calculate the return based on a specific benchmark.

You can refer to the QuantConnect documentation and forum discussions for more information on how to implement this in your algorithm.