This is a summary of links featured on Quantocracy on Saturday, 08/10/2019. To see our most recent links, visit the Quant Mashup. Read on readers!
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How and why I got 75Gb of free foreign exchange Tick data (h/t @PyQuantNews) [Detlev Kerkovius]Towards the end of completing my masters in data science, I started picturing myself doing clever things with machine learning and automated trading. If like me, you have run into the how do I get historical free tick data connundrum, then this post is for you. I have structured my post in three sections: Some background for context. Storytime How to fail and then succeed. Putting it all
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Does Meta-Labeling Add to Signal Efficacy? [Hudson and Thames]Successful and long-lasting quantitative research programs require a solid foundation that includes procurement and curation of data, creation of building blocks for feature engineering, state of the art methodologies, and backtesting. In this project we create a open-source python package (mlfinlab) that is based on the work of Dr. Marcos Lopez de Prado in his book Advances in Financial
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The power of R for trading (part 1) [SR SV]R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders. Even with limited coding skills R outclasses Excel spreadsheets and boosts information efficiency. First, like Excel, the R environment is built around data structures, albeit far more