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investment

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packandsell
packandsell commented Jul 20, 2019

Expected Behavior

Is there an elegant and simple way to visualize a few trading models plotted on 1 chart? In fact a lot of projects like mine are centered around comparing various models (and even groups of models), so that would be handy to have a sugary way around this process, so:

  1. individual models with notations on one chart
  2. a group of models trading on the same account each ha
ragrok
ragrok commented Sep 24, 2019

本人是业余手动交易者,以交易美股为主,主业是Java后台开发,把楼主的开源项目下了下来,一通操作总算把项目运行起来了。之后看了楼主的项目框架整合和设计,原生的Netty + Disruptor, 整个项目的设计是很厉害的,本来最近打算学习Python去使用vn.py做量化,比较了下,Java其实对高频交易的支撑是非常优秀的,老虎还有盈透都有Java Api放出来,完全不用愁外部的交易接口。以下是我的问题整理:

  1. 现在我的主要问题是,没有使用文档,看着空空的前端页面不知道去干什么,自己去折腾需要太多的时间,所以希望能增加说明文档。
  2. 解决Idea无法及时获取静态文件的问题
    ![图片](https://user-images.githubusercontent.com/16792321/65488983-ccb38800-dedc-11e9-8226-19a8af

Python quantitative trading and investment platform; Python3 based multi-threading, concurrent high-frequency trading platform that provides consistent backtest and live trading solutions. It follows modern design patterns such as event-driven, server/client architect, and loosely-coupled robust distributed system. It follows the same structure and performance metrix as other EliteQuant product line, which makes it easier to share with traders using other languages.

  • Updated Jan 7, 2019
  • Python

C/C++ 11 High frequency quantitative trading platform. It follows modern design patterns such as event-driven, server/client architect, dependency injection and loosely-coupled robust distributed system. It is self-contained and can be used out of box. At the same time, it serves as server side for other EliteQuant projects.

  • Updated May 25, 2018
  • C

Python-based portfolio / stock widget which sources data from Yahoo Finance and calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice (constructed with wxPython)

  • Updated Apr 9, 2019
  • Python

R quantitative trading and investment platform. EliteQuant_R is R based multi-threading, concurrent high-frequency trading platform that provides consistent backtest and live trading solutions. It follows modern design patterns such as event-driven, server/client architect, and loosely-coupled robust distributed system. It follows the same structure and performance metrix as other EliteQuant product line, which makes it easier to share with traders using other languages.

  • Updated Jan 18, 2018
  • R

Matlab quantitative trading and investment platform. EliteQuant_Matlab is Matlab based multi-threading, concurrent high-frequency trading platform that provides consistent backtest and live trading solutions. It follows modern design patterns such as event-driven, server/client architect, and loosely-coupled robust distributed system. It follows the same structure and performance metrix as other EliteQuant product line, which makes it easier to share with traders using other languages.

  • Updated Jan 29, 2018
  • MATLAB

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