Get Implied Volatility from historical data.

Hello Andy,

Currently, it's not possible to directly access historical implied volatility data in QuantConnect. This is confirmed in a forum discussion where a user asked a similar question and a QuantConnect team member responded that it's not currently possible to access implied volatility from GetOptionHistory in research. However, they are working on implementing it.

In the meantime, you can calculate the Implied Volatility by solving the Black-Scholes Model pricing equation based on the option data available. There is a guide on how to do this in the Introduction to Options tutorial.

Here is a Python code example from the tutorial:

def bsmprice(self, option, d1, d2):
    S = self.Securities[option.Underlying].Price
    K = option.Strike
    r = self.rf
    q = self.q
    T = (option.Expiry - self.Time).days / 365
    v = option.ImpliedVolatility
    return S * math.exp(-q * T) * self.norm_cdf(d1) - K * math.exp(-r * T) * self.norm_cdf(d2)

def impliedvol(self, option, market_price):
    a = 0
    b = 1
    tol = 1e-5
    x = (a + b) / 2
    while (b - a) / 2 > tol:
        option.update(self.Time, self.Securities[option.Underlying].Price)
        f = self.bsmprice(option, x) - market_price
        if f == 0:
            break
        elif f < 0:
            a = x
        else:
            b = x
        x = (a + b) / 2
    return x

In this code, bsmprice is a method to calculate option price and impliedvol is a method to solve implied volatility using a bisection approach.