Custom benchmark (python)

To set a custom benchmark in QuantConnect, you can use the self.SetBenchmark() function with a lambda function. However, the lambda function should accept a datetime parameter and return a number.

For your specific case, you want to dollar-cost-average into SPY every month. This would involve buying a fixed dollar amount of SPY every month, regardless of its price. This is more of a strategy than a benchmark. Benchmarks are typically a static reference point like a constant value or a security's price.

However, you can create a custom data series that represents the value of this strategy and use it as a benchmark. Here's a rough idea of how you might implement this:

class DollarCostAveragingBenchmark:
    def __init__(self, symbol, monthly_investment):
        self.symbol = symbol
        self.monthly_investment = monthly_investment
        self.last_investment_time = None
        self.total_investment = 0
        self.total_shares = 0

    def update(self, time, price):
        if self.last_investment_time is None or time.month != self.last_investment_time.month:
            self.total_shares += self.monthly_investment / price
            self.total_investment += self.monthly_investment
            self.last_investment_time = time

    def value(self):
        return self.total_shares * self.Benchmark.Evaluate(self.Time)

# In your algorithm:
self.dca_benchmark = DollarCostAveragingBenchmark("SPY", 1000)
self.SetBenchmark(self.dca_benchmark.value)

In the Update method, we check if it's a new month and if so, we invest a fixed amount into SPY. In the value method, we return the total value of our investment.

Please note that this is a simplified example and might not work out of the box. You would need to call the update method at the appropriate times in your algorithm, and handle cases where the price data for SPY is not available. Also, this example does not take into account transaction costs or slippage.

For more information on setting benchmarks in QuantConnect, you can refer to the following forum discussions:

• Set Benchmark SPY
• Warning on Setbenchmark
• How to set benchmark when using custom data

this error occured
During the algorithm initialization, the following exception has occurred: cannot convert object to target type  at Initialize    self.SetBenchmark(self.dca_benchmark.value) in main.py: line 59 cannot convert object to target type

The error message "cannot convert object to target type" suggests that the self.SetBenchmark() method is expecting a different type of argument than what is being provided. In QuantConnect, the SetBenchmark() method expects either a string representing the symbol of a security or a function that takes a datetime and returns a float.

In your case, self.dca_benchmark.value is a method, not a value. You should pass the method itself, not call it. Here's how you can do it:

self.SetBenchmark(self.dca_benchmark.value)

If self.dca_benchmark.value is a method that takes no arguments and returns a float, this should work. However, if self.dca_benchmark.value is a property or a field, you need to create a function that returns this value and pass that to SetBenchmark. Here's how you can do it:

self.SetBenchmark(lambda: self.dca_benchmark.value)

This creates an anonymous function (lambda function) that returns the value of self.dca_benchmark.value and sets it as the benchmark.

Please note that the benchmark function should return a float representing the benchmark's value at a specific time. If self.dca_benchmark.value does not represent a time-series value, it cannot be used as a benchmark.