GS Quant — Goldman Sachs Quantitative Finance Toolkit
Goldman Sachs's open-source Python library for derivatives pricing, risk analytics, and systematic trading.
GS Quant — Goldman Sachs Quantitative Finance Toolkit
gs-quant is Goldman Sachs's open-source Python toolkit for quantitative finance, supporting derivatives pricing, risk analytics, backtesting, and portfolio construction. Built on top of GS's internal quant infrastructure and open-sourced for the community under Apache-2.0.
Key features
- Derivatives pricing: options, swaps, swaptions, FX, rates, equities
- Risk analytics: delta, gamma, vega, theta; scenario analysis and stress tests
- Data access via Marquee API (GS market data; API key required for live data)
- Portfolio construction and optimization with risk constraints
- Backtesting engine with transaction costs
Quick start
pip install gs-quant
from gs_quant.instrument import EqOption
from gs_quant.common import OptionType, OptionStyle
from gs_quant.session import GsSession
GsSession.use() # uses env var GS_CLIENT_ID / GS_CLIENT_SECRET
option = EqOption(".SPX", expiration_date="3m", strike_price="ATMF",
option_type=OptionType.Call, option_style=OptionStyle.European)
option.resolve()
print(option.price())
npx ai-supply add gs-quant-goldman-sachs
Curated mirror of the open-source gs-quant (Apache-2.0). Get it from the source.
Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.
Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).
The same gate an agent runs before installing (POST /api/v1/trust/gs-quant-goldman-sachs/check). Click a policy:
Consume GS Quant — Goldman Sachs Quantitative Finance Toolkit programmatically. Authenticate with an API key or session — see Authorize an agent.
# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/gs-quant-goldman-sachs
# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/gs-quant-goldman-sachs/check \
-H "Content-Type: application/json" \
-d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'
# CLI
npx ai-supply add gs-quant-goldman-sachs
# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/gs-quant-goldman-sachs/install \
-H "Authorization: Bearer $AIM_KEY"
# MCP tool
install_listing({ "slug": "gs-quant-goldman-sachs" })OpenAPI spec →Curated mirror — latest upstream source. See the repository for tagged releases.