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Data

Data Contexts

DataContexts are objects which provide a set of parameters which describe how to query data. These can be used to provide a common context which can be reused for a number of different data access or manipulation functions. The GS Quant market data apis use DataContext to determine how to query data (e.g. what date range to use) if no specific parameters are provided.

Creating a DataContext

Creating a DataContext is straightforward given a start and end date:

from gs_quant.data import DataContext
from datetime import date

data_ctx = DataContext(start=date(2018, 1, 1), end=date(2018, 12, 31))       # Create data context

This example creates a context covering 2018.

Usage

DataContexts can be used within a scoped block to provide a common reference for subsequent operations. Similar to Sessions, there are two ways to use a DataContext:

  • Set as global
  • Use within a block

Global Context

Any request which is not scoped will use the global data context automatically. The global context can be accessed through current:

current = DataContext.current     # Get current data context
DataContext.current = data_ctx    # Set current data context

Scoped Context

DataContext objects can be used within blocks. This allows the same data query parameters shared by multiple requests within a defined scope.

data_ctx_2017 = DataContext(start=date(2017, 1, 1), end=date(2017, 12, 31))       # Create 2017 data context
data_ctx_2018 = DataContext(start=date(2018, 1, 1), end=date(2018, 12, 31))       # Create 2018 data context

with data_ctx_2017:

    # query data for 2017

with data_ctx_2018:

    # query data for 2018

Example

The following example shows how to access historical implied volatility level for SPX using a DataContext:

info

Note

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

from datetime import date
from gs_quant.data import DataContext
from gs_quant.markets.securities import SecurityMaster, AssetIdentifier, ExchangeCode
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 Security Master

with data_ctx:                                                              # Use the data context we setup
    vol = ts.implied_volatility(spx, '1m', ts.VolReference.DELTA_CALL, 25)  # Get 25 delta call implied volatility

vol.tail()

Output:

Out[1]:
2021-12-20 26.108257
2021-12-21 21.794968
2021-12-22 22.398788
2021-12-23 20.985507
2021-12-24 19.574263
dtype: float64

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