Automated Tick Data Collection & Storage with R, MetaTrader and a VPS.

Automated Tick Data Collection & Storage with R, MetaTrader, and a VPS

13 April 2018

Let’s continue where we left off in our last post on tick data collection. If you missed it, it’s important that you familiarise yourself with its contents first. Here’s the link again: https://blog.darwinex.com/download-tick-data-metatrader/ Therein, we promised a follow-up post that would discuss an approach for retail traders to automate tick data collection with the R […]

Setting up a DARWIN Data Science Environment in Windows, Linux & MacOS

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 […]

ZeroMQ - Distributed Trading Infrastructure

ZeroMQ – How To Interface Python/R with MetaTrader 4

27 August 2017

In this post, we present a technique employing ZeroMQ (an Open Source, Asynchronous Messaging Library and Concurrency Framework) for building a basic – but easily extensible – high performance bridge between external (non-MQL) programming languages and MetaTrader 4.   Reasons for writing this post: Lack of comprehensive, publicly available literature about this topic on the […]

LVQ and Machine Learning for Algorithmic Traders – Part 3

17 June 2017

In the last two posts, LVQ and Machine Learning for Algorithmic Traders – Part 1, and LVQ and Machine Learning for Algorithmic Traders – Part 2, we demonstrated how to use: Linear Vector Quantization Correlation testing ..to determine the relevance/importance of and correlation between strategy parameters respectively. Yet another technique we can use to estimate […]

LVQ and Machine Learning for Algorithmic Traders – Part 2

14 June 2017

    In LVQ and Machine Learning for Algorithmic Traders – Part 1, we discussed and demonstrated a technique (Linear Vector Quantization) to decipher the relevance and relative importance of each feature variable in the dataset under study. In doing so, algorithmic traders would be able to isolate which of a dataset’s features (read: strategy […]

LVQ and Machine Learning for Algorithmic Traders – Part 1

8 June 2017

Algorithmic traders across all spectra of asset classes, often face a rather daunting challenge. What are the best inputs for an algorithmic trading strategy’s parameter space? Different algorithmic trading strategies (whether manual or automated) will each have their own unique set of parameters that govern their behaviour. Granted.. Genetic and Walk-Forward Optimization will help algorithmic […]