Quantocracy’s Daily Wrap for 03/13/2019

This is a summary of links featured on Quantocracy on Wednesday, 03/13/2019. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Advances in Financial Machine Learning Package (Update) [Quants Portal]

    First of all we want to thank everyone who has reached out to us with ideas and contributions to our package. Without all of your help, none of this would be possible. We have done a lot of work this week and hope that this update provides you with more insight into both the package for Advances in Financial Machine Learning, as well as the research notebooks which answer the questions at the back
  • How is mean reversion doing? Dead, Shrinking or Doing Just Fine [Alvarez Quant Trading]

    A common question I get from readers is does mean reversion still work? The last time I wrote about this topic was in 2015, a long time ago, in the post The Health of Stock Mean Reversion: Dead, Dying or Doing Just Fine I did not realize it had been so long. Time to look at it again. The Test Date Range: 1/1/2001 to 12/31/2018 Entry: Stock is member of the Russell 3000 Two period RSI
  • Why Taleb’s Antifragile Book is a Fraud [Falkenblog]

    In Nassim Taleb book Antifragile he emphasizes that if you see a fraud and do not say fraud, you are a fraud, I am thus compelled to note that Antifragile is a fraud because its theme is based on intentional misdirection. The most conspicuous and popular examples he presents are also explicitly mentioned as not the essence of antifragility. Indeed, incoherence is Talebs explicit
  • Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis [Alpha Architect]

    R is a programming language that owes its lineage to S, a language designed in its own developers words, to turn ideas into software, quickly and faithfully.(1) Shiny is an interactive web technology that makes it easy to take R models and publish them to the web. Jonathan L. Regenstein, Jr., the director of financial services at RStudio (an integrated development environment for
  • Ranking Quality [Quant Dare]

    The application of Machine Learning for ranking is widely spread. This application of Machine Learning is a little different from the classical ones of classification and regression. In the case of ranking, the interest is not in the accuracy of an estimated value (regression) or the guess about the membership of an element in a cluster or other (classification); rankings care about proper
  • State of Trend Following in February [Au Tra Sy]

    A fairly late and flat report for our State of Trend Following Index. Not the greatest start of the year. Please check below for more details. Detailed Results The figures for the month are: February return: 0.71% YTD return: -6.26% Below is the chart displaying individual system results throughout February: StateTF February And in tabular format: System February Return YTD Return BBO-20 -0.79%

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