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Quantocracy’s Daily Wrap for 06/12/2018

This is a summary of links featured on Quantocracy on Tuesday, 06/12/2018. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Fun with the Cryptocompare API [Robot Wealth]

    Cryptocompare is a platform providing data and insights on pretty much everything in the crypto-sphere, from market data for cryptocurrencies to comparisons of the various crytpo-exchanges, to recommendations for where to spend your crypto assets. The user-experience is quite pleasant, as you can see from the screenshot of their real-time coin comparison table: cryptcurrency prices As nice as the
  • Labeling and Meta-Labeling Returns for ML Prediction [Black Arbs]

    This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado. In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period
  • Estimating the Hurst Exponent Using R/S Range [Flare 9x]

    In this post we will estimate the Hurst exponent using the R/S method. The Hurst exponent determines the long range memory of a time series (more below). If a series has no memory ie if each point in time is independent from previous points in time then its said to be more of a random process. Examples of random processes are markov processes, Brownian motion and white noise. A series which trends

Filed Under: Daily Wraps

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