GS Quant comes with a
timeseries package which provides a number of functions for dealing with
analytics of financial timeseries. This is based on the pandas series
object to provide extensions which are specific to analyzing asset prices or other observable market
data. These functions are used by our strategists to analyze and backtest trading strategies.
Below is an overview of the various timeseries modules within GS Quant. Some are simple wrappers around the equivalent pandas or NumPy function to provide consistent interface and documentation. Many are Goldman Sachs' implementations of useful financial analysis functions which we use to quantify the performance of different strategies.
|Algebra||Basic numerical and algebraic operations, including addition, division, multiplication and other functions on timeseries|
|Analysis||Functions used to analyze properties of timeseries, including lagging, differencing, autocorrelation, co-integration and other related operations|
|Datetime||Date and time manipulation for timeseries, including date or time shifting, calendar operations, curve alignment and interpolation operations|
|Econometrics||Standard economic and time series analytics operations, including returns, drawdowns, volatility and other numerical operations which are generally finance-oriented|
|Statistics||Basic statistical operations, including probability and distribution analysis (generally not finance-specific routines)|
|Technicals||Technical analysis functions including moving averages, volatility indicators, and and other numerical operations for analyzing statistical properties of trading activity|
Import timeseries package or individual modules to access functionality:
import gs_quant.timeseries as ts x = ts.generate_series(1000) # Generate random timeseries with 1000 observations vol = ts.volatility(x, 22) # Compute realized volatility vol.tail() # Show last few values
2021-12-20 12.898025 2021-12-21 12.927230 2021-12-22 12.929520 2021-12-23 13.987033 2021-12-24 14.048165 dtype: float64
In addition to the standard contribution guidelines for GS Quant, we request that all timeseries function have 100% test coverage and full mathematical documentation using Latex. This helps ensure consumers of these functions can understand the exact mathematical definition and usage semantics.
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