Custom Pricing Model

Yes, it is possible to create a custom pricing model for Indian Indexes in QuantConnect. However, modifying the available option price models when running in QC Cloud is currently not possible as per the forum discussion.

For creating a custom pricing model, you can use the LEAN engine which works in any timezone with any data. You can submit a brokerage implementation to the QuantConnect's GitHub and they will add support for it in the QuantConnect.com interface.

For adding custom dividend yield and risk-free rate data, you can use the ConstantQLDividendYieldEstimator and ConstantQLRiskFreeRateEstimator classes respectively. However, these classes currently return 0 always, meaning any attempt at using the crucial IV is made completely irrelevant.

Here is an example of how you can use these classes:

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

Please note that this code is in C# and you might need to convert it to Python if you are using Python for your project.

For more information, you can refer to the QuantConnect documentation and the QuantConnect forum for discussions related to your query.

ConstantQLRiskFreeRateEstimator (uses default interest_rate.csv) which is fed interest rate data but in India we have data based on our indexes … so how we should store that data and Similarly for Dividend yield we are having complete historical data but I can't find the correct lean format to store that data in data-folders for backtesting my strategies.

Or I need to create my own data reader and custom greeks calculator using our dividend yields data.

Yes, you can create your own data reader and use custom data for your specific needs in QuantConnect. You can import your custom data for dividend yields and risk-free rates and use them in your algorithm.

To import custom data, you need to create a class that extends the BaseData class and override the GetSource and Reader methods. Here is an example in C#:

public class Nifty : BaseData
{
 public decimal Open;
 public decimal High;
 public decimal Low;
 public decimal Close;

 public Nifty()
 {
 }

 public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
 {
 var url = "http://cdn.quantconnect.com.s3.us-east-1.amazonaws.com/uploads/CNXNIFTY.csv";
 return new SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile);
 }

 public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
 {
 var index = new Nifty();
 index.Symbol = config.Symbol;
 }

You can store your custom data in a CSV file and upload it to a cloud storage provider like Dropbox or GitHub. Then, you can use the URL of the file in the GetSource method.

For more information on how to use custom data in QuantConnect, you can refer to the QuantConnect documentation and the QuantConnect forum.