MetaTrader Expert Advisors: The Set & Forget Myth [EAS-II]

MetaTrader EAs: The “Set & Forget” Myth [EAS-II]

19 February 2018

This is the second post in the MetaTrader Expert Advisor [EAS] series we’ve begun recently. In case you missed it, here’s a link to the first post: Commercial Expert Advisors: everything that glitters is not gold. If you’re wondering what “set & forget” means, it’s a common catch phrase used widely by many commercial MetaTrader […]

Machine Learning on DARWIN Datasets

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 […]

$DWC 1-Minute Differenced Series

Mean Reversion Tests on DARWIN $DWC

9 July 2017

In a previous post – Quantitative Modeling for Algorithmic Traders – we discussed the importance of Expectation, Variance, Standard Deviation, Covariance and Correlation. In this post we’ll discuss how those concepts can be applied to DARWIN assets. As a practical example, we will employ a series of statistical tests to assess if DARWIN $DWC is […]

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 […]

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