GS Quant

The complete solution for quantitative finance

GS Quant is a Python toolkit for quantitative finance, created on top of one of the world’s most powerful risk transfer platforms. Designed to accelerate development of quantitative trading strategies and risk management solutions, crafted over 25 years of experience navigating global markets.


Designed by our quants

Created by our quants, for our quants. Our analytics tools are trusted daily by over a thousand quantitative developers at Goldman Sachs to manage our global trading business.

Comprehensive and cross-asset

Our business covers all asset classes. Our financial toolkit is designed from the ground up to be a complete solution for all markets, delivered through an intuitive interface.

Proven by markets

Leverage models and datasets which have been tested and refined through decades of experience at the center of global derivatives markets. Use what we use.

Comprehensive Solutions

Data Analytics

Proprietary data and timeseries analytics

Seamless access to a comprehensive range of intraday and end-of day datasets, natively in python. GS Quant provides a complete data platform and timeseries analytics package, designed around proven market models to make integration seamless.

from gs_quant.data import Dataset

# Dataset for equity implied volatility
vol_dataset = Dataset(Dataset.GS.EDRVOL_PERCENT_SHORT)

# Get S&P 500 1m at-the-money-forward volatility
vol_data = vol_dataset.get_data(
    ticker='SPX',
    tenor='1m',
    strikeReference='forward',
    relativeStrike=1
)

# Show last few values
vol_data.tail()

Pricing and Risk

Powerful derivative pricing models

GS Quant provides access to our proprietary derivatives pricing models and market dynamics. Compute prices and sensitivities through our risk engines, and apply complex shocks and scenarios. Fully documented instrument and risk coverage makes structuring and portfolio analytics painless.

from gs_quant.instrument import IRSwap, Currency, PayReceive
from gs_quant.risk import IRDelta

# Create 10y dollar swap
swap = IRSwap(PayReceive.Pay, "10y", Currency.USD)

# Use historical market
with(PricingContext(market_data_as_of=date(2019,4,1))):

    # Compute derivative price
    price = swap.price()

    # Calculate rates delta
    delta = swap.calc(IRDelta)

Trading

Seamless execution

Full connectivity to our streaming prices and quoting engines. Request dealable prices and execute trades to automate workflows and scale your business. Native python quote streams, seamless order submission, and trade execution through a simple interface.

from gs_quant.session import GsSession, Environment
from gs_quant.markets.securities import AssetClass
from gs_quant.api.gs.trades import GsTradesApi
from datetime import date

# Authenticate to our trading APIs
GsSession.use(client_id, secret)

# List recent trades
trades = GsTradesApi.get_trades(AssetClass.FX, date(2019,1,1), date.today())

# Get unit price
price = trades[0].unitPrice