This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being expanded over the next few months. The library is structured along three tiers: Foundational methods. Core
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 developme
Note: this is the v3 branch, which is currently in beta. See the docs for v3. If needed the 2.x branch is here, but is in bugfix only mode. pyinfra automates infrastructure using Python. It’s fast and scales from one server to thousands. Great for ad-hoc command execution, service deployment, configuration management and more. Documentation ⇒ Getting Started • Examples • Help & Support • Contribut
Send feedback Stay organized with collections Save and categorize content based on your preferences. Work with stored procedures for Apache Spark This document is intended for data engineers, data scientists, and data analysts to create and call stored procedures for Spark in BigQuery. Using BigQuery, you can create and run Spark stored procedures that are written in Python, Java, and Scala. You c
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