This is a summary of links featured on Quantocracy on Saturday, 02/20/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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A simple statistical edge in SPY [Trading with Python]I've recently read a great post by the turinginance blog on how to be a quant. In short, it describes a scientific approach to developing trading strategies. For me personally, observing data, thinking with models and forming hypothesis is a second nature, as it should be for any good engineer. In this post I'm going to illustrate this approach by explicitly going through a number of
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Technologies Screening -III- [Algorythmn Trader]In my previous post, I introduced the messaging topic. Now its time to talk about what I found on the message framework universe. To get a overview about past and upcoming topics, please have look here: Content++. There were several message frameworks I was come across and played around. The first was of course WCF comes as part of .Net and C#. This is a very convenient way to handle all the
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Chasing Returns and Avoiding “Spaghetti against the Wall Fund Companies” [Alpha Architect]Psychology research suggests that when we make predictions, we suffer from representative bias, and mistakenly overweight observations that fit a particular narrative, and fail to consider base rate probabilities. For example, if we flip a coin 5 times and it shows up H, H, H, H, H, we may assume that Hs is more likely, even though the probability is still 50/50. Consider a more tangible
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Using Heavy-Tailed Distributions with TASI: Student t Distribution [Bayan Analytics]In this post, I continue trying to fit the daily log returns of TASI index using heavy-tailed distributions. In the previous post, I used Pareto distribution to model TASI indexs left tail. In this post, I use Student t distribution. Recently, Student t distribution has been used widely by financial engineers as models for heavy-tailed distribution such as the distribution of financial