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Introducing DarwinexLabs – Prop Investing DARWINs

19 June 2017

Introducing DarwinexLabs We’re sending our Quant team on a new mission – and what better way to start than a new name? Introducing DarwinexLabs! What mission? DarwinexLabs’ next mission is to openly beat the market leveraging the DARWIN data-set. Why, and why now? Re-loaded introduces a new visual interface, but the bigger changes are under the hood. All diagnostic […]

Introducing DWC, the hedging DARWIN

18 June 2017

Introducing DWC, the hedging DARWIN DWC, the hedging DARWIN is DarwinexLabs’ first creation. It’s already listed at the Exchange. This post lays out: The rationale for a hedging DARWIN? Lessons learnt in the development process How DWC revenues will be shared with the community As you’ll gather, actively leveraging community data is a major strategic milestone […]

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 […]

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 […]