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di4bl00000's Projects

abu icon abu

阿布量化交易系统(股票,期权,期货,比特币,机器学习) 基于python的开源量化交易,量化投资架构

darwinexlabs icon darwinexlabs

Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex (www.darwinex.com)

easytrader icon easytrader

提供券银河/华泰客户端/雪球的基金、股票自动程序化交易以及自动打新,支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件

elitequant icon elitequant

A list of online resources for quantitative modeling, trading, portfolio management

elitequant_cpp icon elitequant_cpp

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.

elitequant_excel icon elitequant_excel

EliteQuant Excel for Quantitative Modeling, Trading, and Portfolio Management. It enables you to create quantitative financial models in Excel spreadsheet, in the same way how financial professionals such as traders, quants, and portfolio managers do their day to day work. You are able to create pricing tools for products across all asset classes such as interest rate or FX, and from plain vanilla to exotic instruments. You are also able to backtest and live trade from Excel, with the so-called RTD, or real-time data support.

elitequant_matlab icon elitequant_matlab

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.

elitequant_python icon elitequant_python

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.

elitequant_r icon elitequant_r

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.

finrl icon finrl

FinRL: The first open-source project for financial reinforcement learning. Please star. 🔥

ib-zmq icon ib-zmq

Interactive Brokers API to ZeroMQ proxy

mlfinlab icon mlfinlab

Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning

openalgo icon openalgo

:chart: openAlgo is a public repository for various work product relavant to algorithms and the high frequency low latency electronic trading space with a bias toward market microstructure as well as exchange traded futures and options.

openfe icon openfe

OpenFE: automated feature generation with expert-level performance

pyzmq icon pyzmq

PyZMQ: Python bindings for zeromq

quantcode icon quantcode

分享量化投资相关的论文,代码和代码复现。nutquant

quanttrading icon quanttrading

A real-time quantitative trading/backtesting platform in C#, supporting IB (full brokerage) and Google Finance (quote only). It adds R support through R.NET.

rib icon rib

An R implementation of Interactive Brokers API

tiny-cpp-perf-stats icon tiny-cpp-perf-stats

Project intends to provide an easy framework for collecting and analyzing performance counters

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