This is a summary of links featured on Quantocracy on Wednesday, 02/01/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
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Tactical Asset Allocation in January [Allocate Smartly]This is a summary of the January performance of a number of excellent tactical asset allocation strategies. These strategies are sourced from books, academic papers, and other publications. While we dont (yet) include every published TAA model, these strategies are broadly representative of the TAA space. Read more about our backtests or let AllocateSmartly help you follow these strategies in
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How to Apply Machine Learning to Trading [Signal Plot]Recently, I have been interested in applying machine learning to trading. This post contains some of my thoughts regarding a framework for thinking about trading as a machine learning problem, treating trading as a classification or regression problem, and transforming the output of a machine learning model into a trading signal. 1. Introduction to Machine Learning Applications to Trading Machine
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Creating a stock market sentiment Twitter bot with automated image processing [Troy Shu]One of the side projects I worked on in the past handful of months was Mr. Market Feels: a stock market sentiment Twitter bot that used automated image processing to extract and tweet the value of CNN Moneys Fear and Greed Index every day. Motivation There have been attempts to backtest the predictive power of the Fear and Greed Index when buying and selling the overall stock market index
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Non-parametric Estimation [Quant Dare]How can we predict future returns of a series? Many series contain enough information in their own past data to predict the next value, but how much information is useable and which data points are the best for the prediction? Is it enough to use only the most recent data points? How much information can we extract from past data? Once we have answered all these questions we should think about