In the previous chapter, we discussed in detail how to estimate implied volatility with a hypothetic set of input values. To use real-world data to estimate implied volatility, we could define a function with three input variables: ticker, month, and year as follows:
def get_option_data(tickrr,exp_date): x = Options(ticker,'yahoo') puts,calls = x.get_options_data(expiry=exp_date) return puts, calls
To call the function, we enter three values, such as IBM, 2, and 2014, when we plan to retrieve options expired in February, 2014. The code with these three values is shown as follows:
def from pandas.io.data import Options import datetime ticker='IBM' exp_date=datetime.date(2014,2,28) puts, calls =get_option_data(ticker,exp_date) print puts.head() Strike Symbol Last Chg Bid Ask Vol Open Int 0 100 IBM140222P00100000 0.01 0 NaN 0.03 16 16 1 105 IBM140222P00105000 0.04 0 NaN 0.03 10 ...