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 […]

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 […]

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 […]

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