Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

  • 📰 hackernoon
  • ⏱ Reading Time:
  • 25 sec. here
  • 2 min. at publisher
  • 📊 Quality Score:
  • News: 13%
  • Publisher: 51%

México Noticias Noticias

México Últimas Noticias,México Titulares

FinRL is an open-source framework for quantitative traders, simplifying DRL strategy development with customizable, reproducible, and beginner-friendly tools.

Authors: Xiao-Yang Liu, Hongyang Yang, Columbia University ; Jiechao Gao, University of Virginia ; Christina Dan Wang , New York University Shanghai . Table of Links Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.3 Agent Layer 3.4 Environment Layer 3.

1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.2 Application Layer 3.3 Agent Layer 3.3 Agent Layer 3.4 Environment Layer 3.4 Environment Layer 3.

 

Gracias por tu comentario. Tu comentario será publicado después de ser revisado.
Hemos resumido esta noticia para que puedas leerla rápidamente. Si estás interesado en la noticia, puedes leer el texto completo aquí. Leer más:

 /  🏆 532. in MX

México Últimas Noticias, México Titulares