Providing Custom data in OptionPricingModels

Based on the information provided in the extracts, it seems that you cannot directly modify the available option price models when running in the QuantConnect Cloud. This includes providing custom data for the Risk-free Rate and Dividend yield to the Black Scholes option pricing model.

However, it is mentioned that you can duplicate the function and use custom estimators offline with personal data. This would likely involve modifying the LEAN engine, which is the underlying technology powering QuantConnect.

Here are the default estimators used by all models in the LEAN engine:

private static IQLUnderlyingVolatilityEstimator _underlyingVolEstimator = new ConstantQLUnderlyingVolatilityEstimator();   
private static IQLRiskFreeRateEstimator _riskFreeRateEstimator = new ConstantQLRiskFreeRateEstimator();   
private static IQLDividendYieldEstimator _dividendYieldEstimator = new ConstantQLDividendYieldEstimator();

You can find more information about these estimators and the Black Scholes model in the QuantConnect LEAN GitHub repository.

Please note that this approach requires a good understanding of both the LEAN engine and the financial models you are working with. If you are not comfortable with this, you might want to consider other approaches to incorporate the Risk-free Rate and Dividend yield into your strategy.

is there a way to work with Python? I am using Python to write the algorithm.

Yes, QuantConnect does support Python. You can use Python to write your algorithms on the platform. The Python integration in QuantConnect allows you to make use of popular Python libraries and the API has been designed to be intuitive for Python programmers.

Here is a simple example of a Python algorithm in QuantConnect:

class HistorySpike(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2018, 7, 2)
        self.SetEndDate(2018, 7, 2)
        self.SetCash(25000)
        self.AddEquity("SPY", Resolution.Minute)
        slices = self.History(TimeSpan.FromDays(5))
        if not slices.empty:
            bars = slices.loc["SPY"]
            close = bars["close"]
            self.Log("...")

You can find more examples of Python algorithms in the QuantConnect LEAN GitHub repository.

If you encounter any issues or need further assistance, you can reach out to the QuantConnect support team at support@quantconnect.com.