gs-quant-overview

name	gs-quant-overview
description	Quick-start guide for gs_quant: session setup with GsSession, constructing portfolios, resolving instruments. Start here for any coding task that uses the gs_quant library

gs_quant Quick Start

1. Creating a Session with GsSession.use

All API communication in gs_quant flows through an authenticated GsSession. Before making any pricing or data calls you must initialise a session with GsSession.use().

OAuth2 (Application Credentials)

from gs_quant.session import GsSession, Environment

GsSession.use(
    environment_or_domain=Environment.PROD,
    client_id='my_client_id',
    client_secret='my_client_secret',
    scopes=('run_analytics',),
)

• environment_or_domain — Environment.PROD (default), Environment.QA, or Environment.DEV. You can also pass a raw URL string.
• client_id / client_secret — OAuth2 application credentials. When both are provided the library creates an OAuth2Session.
• scopes — Optional iterable of GsSession.Scopes values. Usually when pricing trade you will need run_analytics.

Kerberos / SSO (Internal GS)

If no client_id is supplied, the library will attempt Kerberos or pass-through authentication automatically: This is for internal GS users only and requires appropriate network access and installation of gs-quant-internal.

GsSession.use(Environment.PROD)

Using as a Context Manager

with GsSession.get(Environment.PROD, client_id='...', client_secret='...') as session:
    # session is active inside this block
    ...

Verifying the Session

GsSession.current  # the currently active GsSession instance

---

2. Resolving an Instrument

When you construct an instrument you typically only specify a subset of its parameters. Resolving fills in all remaining fields by sending the instrument to the GS pricing service.

from gs_quant.instrument import IRSwap

swap = IRSwap('Pay', '10y', 'USD')

# Before resolve: swap.fixed_rate is None
swap.resolve()
# After resolve: swap.fixed_rate is now populated with the current par rate

print(swap.fixed_rate)  # e.g. 0.0345

What resolve() Does

1. Sends the instrument to the GS analytics service along with the current PricingContext.
2. The service computes any missing parameters — par rate, premium, forward points, etc.
3. By default (in_place=True), the instrument is updated in place. Pass in_place=False to receive a new resolved copy.

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3. Combining Instruments in Portfolios

The Portfolio class groups instruments for pricing, resolution, and analysis as a single unit.

Creating a Portfolio

from gs_quant.instrument import IRSwap, IRSwaption
from gs_quant.markets.portfolio import Portfolio

swap = IRSwap('Pay', '10y', 'USD', name='USD 10y Payer')
swaption = IRSwaption('Receive', '10y', 'EUR', expiration_date='1y', name='EUR 1y10y Receiver')

portfolio = Portfolio([swap, swaption], name='My Portfolio')

From a dictionary (keys become instrument names):

portfolio = Portfolio(
    {
        'USD 10y Payer': IRSwap('Pay', '10y', 'USD'),
        'EUR 5y Receiver': IRSwap('Receive', '5y', 'EUR'),
    }
)

Nesting Portfolios

usd_book = Portfolio([IRSwap('Pay', '5y', 'USD'), IRSwap('Receive', '10y', 'USD')], name='USD Book')
eur_book = Portfolio([IRSwap('Pay', '5y', 'EUR')], name='EUR Book')
master = Portfolio([usd_book, eur_book], name='Master Book')

Portfolio Operations

portfolio.append(IRSwap('Pay', '2y', 'GBP'))  # add instruments
first = portfolio[0]  # access by index
usd_swap = portfolio['USD 10y Payer']  # access by name
len(portfolio)  # top-level count
portfolio.all_instruments  # all instruments across nested portfolios

Resolving and Pricing

from gs_quant.risk import DollarPrice, IRDelta

portfolio.resolve()  # resolves all instruments in place
prices = portfolio.calc(DollarPrice)  # single risk measure
results = portfolio.calc([DollarPrice, IRDelta])  # multiple risk measures

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See Also

For detailed guidance on specific topics, see these focused files.

• instruments.md — Constructing all instrument types (IRSwap, XCcy swaps, FXOption, EqOption, etc.) and FX pitfalls
• pricing.md — Pricing instruments and portfolios in gs_quant: PricingContext, HistoricalPricingContext for historical pricing over date ranges, and LiveMarket for real-time pricing. Covers pricing dates, market data location, batch mode, and async patterns.
• datasets.md — Accessing market and reference data via the gs_quant Dataset class: get_data, get_data_series, get_data_last, get_coverage, uploading data, batching large queries. Covers TREOD for equities, FXIVOL_STANDARD, symbol dimensions, and common pitfalls.
• backtesting.md — Guide to the gs_quant backtesting framework: Strategy, triggers, actions, GenericEngine, EquityVolEngine, transaction costs, and result extraction.
• measure.md — writing custom measures with @plot_measure and @risk_measure decorators