GS Quant is a Python toolkit for quantitative finance, which provides access to derivatives pricing and risk capabilities through the Goldman Sachs developer APIs, as well as standalone packages for financial analytics.
It is created and maintained by quantitative developers (quants) at Goldman Sachs to enable the development of trading strategies and analysis of derivative products. GS Quant can be used to facilitate derivative structuring, trading, and risk management, or as a set of statistical packages for data analytics applications.
See also Getting Started notebook in the gs_quant folder or package.
- Python 3.6 or greater
- Access to PIP package manager
You can verify your Python version with the command python --version.
Any Python-ready IDE will work. However, most of our team uses PyCharm.
pip install gs-quant
pip install gs-quant[internal] --user
The following example generates a random timeseries and computes 1-month (22 day) rolling realized volatility:
import gs_quant.timeseries as ts x = ts.generate_series(1000) # Generate random timeseries with 1000 observations vol = ts.volatility(x, Window(22, 0)) # Compute realized volatility using a window of 22 and a ramp up value of 0 vol.tail() # Show last few values
Out: 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
Clients of Goldman Sachs have access to a wide array of data through our developer APIs (please contact your sales coverage for details):
from gs_quant.session import Environment, GsSession from gs_quant.data import Dataset, Fields from datetime import date with GsSession.get(Environment.PROD, <client_id>, <client_secret>, scopes=('read_product_data')): basket_ds = Dataset(Dataset.GS.CB) start_date = date(2007,1,1) vip_px = basket_ds.get_data_series(Fields.CLOSE_PRICE, start=start_date, ticker='GSTHHVIP') vip_px.tail()
Entitled users can also access pricing and risk engines programmatically:
from gs_quant.instrument import IRSwap from gs_quant.common import Currency, PayReceive import gs_quant.risk as risk with GsSession.get(Environment.PROD, <client_id>, <client_secret>, scopes=('read_product_data','run_analytics')): # price an interest rate swap and compute its bucketed delta irs = IRSwap(PayReceive.Pay, "5y", Currency.USD, fixed_rate=0.0275) pv = irs.price() ir_delta = irs.calc(risk.IRDelta)
Contributions are encouraged! Please see CONTRIBUTING.MD for more details
If you need any help or have feedback, please email us at: email@example.com