Quantopian Theory to Practice: Implied Volatility

To enhance the code with history calls, you can use the History method in QuantConnect. This method provides historical data on a particular asset. Here is an example of how to use it in Python:

volumes = self.History(self.stock , MA_S, Resolution.Daily)['volume'].unstack(level = 0).dropna()

This code gets the historical volume data for a stock at a daily resolution.

For calculating the Implied Volatility Rank (IVR), you can use the ImpliedVolatility indicator in QuantConnect. Here is an example of how to use it in Python:

_iv = IV(_option, _mirrorOption)

This code calculates the implied volatility of an option using the Black-Scholes Model.

To speed up your code, consider using a RollingWindow to avoid the daily History calls. Here is an example of how to use it in Python:

window = self.symbolData[symbol].window

This code creates a RollingWindow for a particular symbol.

Remember, you can also set up a warm-up period for your algorithm to access historical data. If you don't want to run certain code in the warm-up period, you can use the following statement:

if self.IsWarmingUp: return

Sources:

• Forum > Discussion > 11252 > My-first-quantconnect-indicator-the-percentage-volume-oscillator Part 4
• Docs > V2 > Writing-algorithms > Indicators > Supported-indicators > Implied Volatility Part 1
• Forum > Discussion > 2484 > Creating-rolling-windows-for-multiple-stocks > Create local variable to readability
• Forum > Discussion > 5549 > Is-there-an-api-for-iv-rank Part 2

What a great contribution to the community. Thanks for this. Will play around with it and share any noteworthy developments here.