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Quantocracy’s Daily Wrap for 03/21/2017

This is a summary of links featured on Quantocracy on Tuesday, 03/21/2017. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Intro to Expectation-Maximization, K-Means, Gaussian Mixture Models with Python, Sklearn [Black Arbs]

    Post Outline Part 1 Recap Part 2 Goals Jupyter (IPython) Notebook References part 1 recap In part 1 of this series we got a feel for Markov Models, Hidden Markov Models, and their applications. We went through the process of using a hidden Markov model to solve a toy problem involving a pet dog. We concluded the article by going through a high level quant finance application of Gaussian mixture
  • Cryptocurrency Time-Series for N-CryptoAsset Portfolio Analysis in Python [Quant at Risk]

    Welcome to a brand new era of financial assets the crypto-assets. The impossible became possible. Yes, now you can trade cryptocurrencies: money that have been created in a virtual world with a physical impact onto our everyday cash-in-the-bank reality. The grande picture is still enigmatic for majority of us. Not too many have even heard of cryptocurrencies different than bitcoin (BTC).

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