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-1.6.7.tar.gz (908.5 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-1.6.7-py3-none-any.whl (1.1 MB view details)

Uploaded Python 3

File details

Details for the file gs_quant-1.6.7.tar.gz.

File metadata

  • Download URL: gs_quant-1.6.7.tar.gz
  • Upload date:
  • Size: 908.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.20

File hashes

Hashes for gs_quant-1.6.7.tar.gz
Algorithm Hash digest
SHA256 8dd12da57039362c0bcec7e81778a8440fedaede9eaef14360020d8fd7b376f3
MD5 933eefe40a60368ee65f8742dd710ca4
BLAKE2b-256 1556403959e55e1e33cdb5d665d85cc2803ad278a3acd04b36d89d6e298051fc

See more details on using hashes here.

File details

Details for the file gs_quant-1.6.7-py3-none-any.whl.

File metadata

  • Download URL: gs_quant-1.6.7-py3-none-any.whl
  • Upload date:
  • Size: 1.1 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-1.6.7-py3-none-any.whl
Algorithm Hash digest
SHA256 0bfc2d7e9056bffc34a42ccac6df9ee2c20b5441f08fea38e3b981c378a383e4
MD5 5413b640d9576bff27c5a4d46ebd484c
BLAKE2b-256 a81ad50edd7e9ab0eb75a6ba6671f610eea16eb279a9c41aaa72c02328121d52

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