Video Tutorial | How to Interface Python/R Algorithmic Trading Strategies with MetaTrader 4|2021

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This webinar follows on from our blog post (

Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees:

** 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:

Risk Disclosure:

Associated GitHub repo:

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.

Interested in deepening your algorithmic trading knowlegde?

If you consider yourself a talented trader, we’d love to have your strategy listed on our Exchange, enabling you to earn performance fees on investor profits.

Check it out here:

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Darwinex relies almost exclusively on organic growth, primarily through recommendation via informative content.

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With seemingly small actions such as:
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More than €2M in performance fees paid to traders to date!

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