Skip to main content

Goldman Sachs Quant

Project description

GS Quant

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.

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.

In order to access the APIs you will need a client id and secret. These are available to institutional clients of Goldman Sachs. Please speak to your sales coverage or Marquee Sales for further information.

Please refer to Goldman Sachs Developer for additional information.

Requirements

  • Python 3.9 or greater
  • Access to PIP package manager

Installation

pip install gs-quant

Examples

You can find examples, guides and tutorials in the respective folders on Goldman Sachs Developer.

Help

Please reach out to gs-quant@gs.com with any questions, comments or feedback.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gs_quant-1.6.35.tar.gz (943.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gs_quant-1.6.35-py3-none-any.whl (1.2 MB view details)

Uploaded Python 3

File details

Details for the file gs_quant-1.6.35.tar.gz.

File metadata

  • Download URL: gs_quant-1.6.35.tar.gz
  • Upload date:
  • Size: 943.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.20

File hashes

Hashes for gs_quant-1.6.35.tar.gz
Algorithm Hash digest
SHA256 b489ee6413d4ca74e9f81b31a5c19c9ffec0abfc799c1a47f230377ee0e18d4a
MD5 9bb5a5c0c5423bb5bbf8a84d6fc32afd
BLAKE2b-256 2deb64a10042fc4e807fd2f2c4a46ad5e130c506de1b71206f6a28b9c2f0b5bd

See more details on using hashes here.

File details

Details for the file gs_quant-1.6.35-py3-none-any.whl.

File metadata

  • Download URL: gs_quant-1.6.35-py3-none-any.whl
  • Upload date:
  • Size: 1.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.20

File hashes

Hashes for gs_quant-1.6.35-py3-none-any.whl
Algorithm Hash digest
SHA256 00a537ee448a6a9f37021ccec0a01a5248c564b96170a4c3a7bd406603da4b76
MD5 8df99e2af23e8bd22fe2a9c615d49ee3
BLAKE2b-256 02556b237042c8ec56ab9610ca63b081ce9c9953b15614be2fcd52afd8524cf3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page