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.
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()
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)
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.unitPrice