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DarwinexLabs liste son second DARWIN: $DWF

6 March 2018

Dans cet article, nous présenterons le deuxième DARWIN du laboratoire Darwinex (DarwinexLabs): $DWF Dans le but d’obtenir une plus grande quantité de Dividendes Darwiniens à distribuer via DarwinIA aux meilleurs traders du mois, DarwinexLabs poursuit son travail de recherche et de création de stratégies de trading, en utilisant au mieux les données de notre communauté de […]

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 […]

Hedging DARWIN Portfolio Risk with $DWC

18 July 2017

In this blog post, we’ll discuss how DARWIN Investors can diversify away some of the excess risk posed to their portfolios by Loss Aversion, a common and well-researched phenomenon in behavioural finance. In particular, we’ll discuss why it makes sense to include DARWIN $DWC in a portfolio that’s partially or entirely composed of loss averse […]

$DWC – A Real Time Sentiment Index & Security

5 July 2017

Fundamental and Technical trading indicators have long been used as a proxy for market sentiment. But by definition, these indicators have always lagged the movements they’ve been used to forecast. With the advent of “Big Data”, social data too has joined the ranks, e.g. Twitter, Facebook, LinkedIn, with various attempts being made to harness any […]

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 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 […]