How To Interface Python - R Algorithmic Trading Strategies With MetaTrader 4

Darwinex UnCut

In late 2017, we embarked on a series of algorithmic trading tutorials that fast became a movement of its own. What started off as a Pythonic solution for those not using MetaTrader's built-in MQL programming language to be able to access Darwinex liquidity via Python, became an open source project rich in code, examples and even multi-OS Docker images packaged and distributed by passionate algorithmic and quant community groups! Three years on in 2020, we still see the same enthusiasm and passion for algorithmic R&D among users of the DWX ZeroMQ Connector project as we first observed in 2017 when it was launched, the first of its kind addressing a complex need with a free, open source solution. Here is the podcast version of the VERY first webinar where the project was introduced in its bare bones state.. and from there on developed into what it is today. Enjoy! -- In this presentation, we demonstrate how to build a communications bridge between trading strategies written in non-MQL programming languages (e.g. Python, R, Java, C++, etc), using ZeroMQ. ZeroMQ is a distributed messaging and concurrency framework, using which traders can create sophisticated distributed trading architecture otherwise difficult to implement in MQL. It also therefore allows traders to send instructions to MetaTrader 4 from outside the platform, and leverage the features of non-MQL environments (e.g. Machine Learning toolkits in Python/R) to create more sophisticated trading strategies for execution via MetaTrader 4. Get the latest updates to the DWX-ZeroMQ-Connector project, troubleshoot your applications, give and get help from fellow algorithmic traders and more, over at the Darwinex Collective Slack Workspace: https://join.slack.com/t/darwinex-collective/shared_invite/enQtNjg4MjA0ODUzODkyLWFiZWZlMDZjNGVmOGE2ZDBiZGI4ZWUxNjM5YTU0MjZkMTQ2NGZjNGIyN2QxZDY4NjUyZmVlNmU3N2E2NGE1Mjk