LABS Latest Post

ZeroMQ – Transaction Reporting via MetaTrader (ZMQ-III)

16 February 2018

In this third installment of our ZeroMQ series, we describe how to use ZeroMQ in non-MQL trading strategies to get the following information: Account Information (e.g. equity, margin, balance, etc) Trades at market (live or pending) Historical Trades If you haven’t already, please consider reading the following posts before proceeding further in this article: ZMQ-I: […]

ZeroMQ – Trade Execution in MetaTrader (ZMQ-II)

31 January 2018

This post builds on the contents of the previous article in this series, namely ZeroMQ – How to Interface Python/R with MetaTrader 4. Therein, we proposed a solution to creating trading strategies in ZeroMQ supported programming languages outside the MetaTrader environment, with the latter simply acting as the intermediary to the market. Leveraging ZeroMQ’s convenient […]

Constructing a Currency Portfolio in MetaTrader

24 January 2018

This post describes how to construct a currency portfolio composed of any number of currency pairs (from those available on the Darwinex platform) and allocations, in MetaTrader. A few common use-cases for constructing currency portfolios include: Studying the correlation of a trading strategy’s returns to market volatility. Trading currency strength instead of single pairs themselves. […]

Working with DARWIN Time Series Data in R (MLD-II)

17 January 2018

This post describes how to prepare and analyse OHLC time series objects in R, from DARWIN datasets available publicly on our GitHub profile. Unlike the introductory posts in this series (see below) where we focused on environment configuration and fundamentals, from here on all concepts will be presented in a practical manner, with fully functional code examples […]

Machine Learning on DARWIN Datasets (MLD-I)

8 December 2017

Machine learning in essence, is the research and application of algorithms that help us better understand data. By leveraging statistical learning techniques from the realm of machine learning, practitioners are able to draw meaningful inferences from and turn data into actionable intelligence. Furthermore, the availability of several open source machine learning tools, platforms and libraries […]

Setting up a DARWIN Data Science Environment

30 November 2017

This post describes how to setup a data science environment for DARWIN R&D. Whether you’re a Data Scientist, Quant, Trader, Investor, Researcher, Developer or just someone keen on putting the DARWIN asset class under a scientific microscope, the contents of this post should hopefully give you a sound start. The tools, libraries and datasets referenced herein […]