Deep Reinforcement Learning Based Trading Application at JP Morgan Chase

https://medium.com/@ranko.mosic/reinforcement-learning-based-trading-application-at-jp-morgan-chase-f829b8ec54f2

FT released a story today about the new application that will optimize JP Morgan Chase trade execution ( Business Insider article on the same topic for readers that do not have FT subscription ). The intent is to reduce market impact and provide best trade execution results for large orders.

It is a complex application with many moving parts:

 

Its core is an RL algorithm that learns to perform the best action ( choose optimal price, duration and order size ) based on market conditions. It is not clear if it is Sarsa ( On-Policy TD Control) or Q-learning (Off-Policy Temporal Difference Control Algorithm ) as both algorithms are present in JP Morgan slides:

 

Sarsa

 

Q-learning

State consists of price series, expected spread cost, fill probability, size placed, as well as elapsed time, %progress, etc. Rewards are immediate rewards ( price spread ) and terminal ( end of episode ) rewards like completion, order duration and market penalties ( obviously those are negative rewards that punish the agent along these dimensions ).

 

Actions are memorized as weights of a Deep Neural Network — function approximation via NN is used since state, action space is too big to be handled in tabular form. We assume stochastic gradient descent is used for both feed forward and backprop operation operation ( hence Deep designation ):

 

JP Morgan is convinced this is the very first real time trading AI/ML application on Wall Street. We are assuming this is not true i.e. there are surely other players operating in this space as RL implementation to order execution is known for quite a while now ( Kearns and Nevmyvaka 2006 ).

The latest LOXM developmentswill be presented at QuantMinds Conference in Lisbon (May of 2018).

Instinet is also using Q-learning, probably for the same purpose ( market impact reduction ).

[转]Deep Reinforcement Learning Based Trading Application at JP Morgan Chase的更多相关文章

  1. 【资料总结】| Deep Reinforcement Learning 深度强化学习

    在机器学习中,我们经常会分类为有监督学习和无监督学习,但是尝尝会忽略一个重要的分支,强化学习.有监督学习和无监督学习非常好去区分,学习的目标,有无标签等都是区分标准.如果说监督学习的目标是预测,那么强 ...

  2. (转) Deep Reinforcement Learning: Playing a Racing Game

    Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playi ...

  3. (转) Deep Reinforcement Learning: Pong from Pixels

    Andrej Karpathy blog About Hacker's guide to Neural Networks Deep Reinforcement Learning: Pong from ...

  4. (转) Playing FPS games with deep reinforcement learning

    Playing FPS games with deep reinforcement learning 博文转自:https://blog.acolyer.org/2016/11/23/playing- ...

  5. (zhuan) Deep Reinforcement Learning Papers

    Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. Th ...

  6. 论文笔记之：Asynchronous Methods for Deep Reinforcement Learning

    Asynchronous Methods for Deep Reinforcement Learning ICML 2016 深度强化学习最近被人发现貌似不太稳定,有人提出很多改善的方法,这些方法有很 ...

  7. [DQN] What is Deep Reinforcement Learning

    已经成为DL中专门的一派,高大上的样子 Intro: MIT 6.S191 Lecture 6: Deep Reinforcement Learning Course: CS 294: Deep Re ...

  8. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach

    Learning how to Active Learn: A Deep Reinforcement Learning Approach 2018-03-11 12:56:04 1. Introduc ...

  9. 18 Issues in Current Deep Reinforcement Learning from ZhiHu

    深度强化学习的18个关键问题 from: https://zhuanlan.zhihu.com/p/32153603 85 人赞了该文章 深度强化学习的问题在哪里?未来怎么走?哪些方面可以突破? 这两 ...

随机推荐

  1. 深入理解php内核

    目录 第一部分 基本原理 第一章 准备工作和背景知识 第一节 环境搭建 第二节 源码布局及阅读方法 第三节 常用代码 第四节 小结 第二章 用户代码的执行 第一节 PHP生命周期 第二节 从SAPI开 ...

  2. ssh -vT git@github.com get “ No such file or directory” 错误

    在"Are you sure you want to continue connnecting"的时候回复y,不要打空格跳过 参考:http://stackoverflow.com ...

  3. Shelld5的使用

    Shelld的连接7步   ·   · huhu_k: 想和你相遇.

  4. Liunx下安装jdk7

    一. 查询系统中是否有安装过jdk: #  rpm -qa | grep java 查询到有安装,使用命令卸载: #  rpm -e --nodeps java-1.6.0-openjdk-1.6.0 ...

  5. MSSQL数据库后台进程（线程）

    与Oracle数据库类似,微软数据库产品MSSQL也有诸多后台进程来保证数据库系统的高效正常运转,由于MSSQL采用的是线程模型,应该叫做后台线程,为了大家易于理解,我们在暂且称之为后台进程,下面我们 ...

  6. svn分支使用 SVN trunk(主线) branch(分支) tag(标记) 用法详解和详细操作步骤

    比较好的介绍了分支的创建和使用. 转:http://blog.csdn.net/vbirdbest/article/details/51122637

  7. Test22455

  8. jquery select使用

    <select class="selector"></select> 1) 设置选中值为pxx的选项 $('.selector').val('pxx'); ...

  9. 在项目中使用 SCSS

    背景概述 1. CSS预处理器 css预处理器定义了一种新的编程语言,编译后成正常的CSS文件.为CSS增加一些编程的特性,无需考虑浏览器的兼容问题,让CSS更加简洁,适应性更强,可读性更佳,更易于代 ...

  10. maven scope和项目发布需要注意的地方

    Maven Scope的使用: http://www.cnblogs.com/wangyonghao/p/5976055.html servlet-api和jsp-api等jar包,一般由servle ...