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Tutorials

Financial Measures

GS Quant allows for access to more complex market models and associated measures. These are functions which allow more intuitive slicing of various market-model based datasets. Examples of this would include:

MeasureDescription
Implied VolatilityHistorical implied volatility curve for different strikes and tenors
Normalized SkewDifference in volatility between out-of-the-money and in-the-money option (Put - call ) / ATM
Term StructureForward looking term structures of volatility or forward levels at a given point in time

Skew

info

Note

Examples require an initialized GsSession and data subscription. Please refer to Sessions for details

The following example shows how to chart historical skew level for SPX:

from datetime import date
from gs_quant.data import DataContext
from gs_quant.markets.securities import SecurityMaster, AssetIdentifier, ExchangeCode
import matplotlib.pyplot as plt
import gs_quant.timeseries as ts

data_ctx = DataContext(start=date(2018, 1, 1), end=date(2018, 12, 31))  # Create a data context covering 2018
spx = SecurityMaster.get_asset('SPX', AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE) # Lookup S&P 500 Index via the Security Master

with data_ctx:                                                          # Use the data context we setup
    skew = ts.skew(spx, '1m', ts.SkewReference.DELTA, 25)               # Get 25 delta skew
    skew.plot(title='SPX 25 Delta Skew')

plt.show()                                                              # Plot output

Should produce something like this:


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