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 contextThis 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 contextScoped 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 2018Example
The following example shows how to access historical implied volatility level for SPX using a
DataContext:
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: float64Related Content
Datasets
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