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$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 ..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 […]

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 […]

DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory

5 June 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 […]

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 […]