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 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 […]

Quantitative Modeling for Algorithmic Traders – Primer

3 May 2017

Quantitative Modeling techniques enable traders to mathematically identify, what makes data “tick” – no pun intended 🙂 . They rely heavily on the following core attributes of any sample data under study: Expectation – The mean or average value of the sample Variance – The observed spread of the sample Standard Deviation – The observed […]

Hidden Markov Models & Regime Change: DARWINs vs. S&P500

24 April 2017

In this post, we will employ a statistical time series approach using Hidden Markov Models (HMM), to firstly obtain visual evidence of regime change in the S&P500. We will then compare the index’ performance to a DARWIN Portfolio, between June 2014 and March 2017. Detecting significant, unforeseen changes in underlying market conditions (termed “market regimes“) […]

Les CFD sont des instruments complexes et présentent un risque élevé de perdre de l'argent rapidement en raison de l'effet de levier. 77 % des comptes d’investisseurs privés perdent de l’argent lors de l’échange de CFD avec ce fournisseur. Vous devriez être conscient du fonctionnement des CFD et savoir si vous pouvez vous permettre de prendre le risque élevé de perdre votre argent.