LABS Latest Post

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“) […]

DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory

14 April 2017

In 1952 [1], the great Harry Markowitz published a paper on portfolio selection that essentially set the stage for modern portfolio theory in a mathematical context. For those not familiar with this Nobel Prize winning economist [2], he devised a methodology whereby investors could mathematically evaluate the proportion of total available capital to allocate, to […]

Why Quantitative? – Serious Algorithmic Trading Series (Part 2)

20 March 2017

In case you haven’t already, you may wish to read the first two posts of the Serious Algorithmic Trading Series, here and here. Many individual traders will often hear the term “Quant” and may visualize either: A sharply dressed City trader working in a hedge fund, sat in front of his Bloomberg or Thomson Reuters […]

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