This is a summary of links featured on Quantocracy on Tuesday, 06/14/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
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Differential equation that models a support and resistance strategy [Tulip Quant]Support and resistance indicators are widely used in technical analysis. What I tried to do is to model this strategy using a suitable differential equation, in order to test it with historical data. Support-resistance_binary_options A simple model could be the following: S'(t)=\gamma_t \displaystyle{\min_{s\in[0, N]} }\left ( |S'(t-s)|^\alpha + |S(t-s)-S(t)|^\beta \right ) Where S(t)
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Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R [Quant Start]In the previous article on cointegration in R we simulated two non-stationary time series that formed a cointegrated pair under a specific linear combination. We made use of the statistical Augmented Dickey-Fuller, Phillips-Perron and Phillips-Ouliaris tests for the presence of unit roots and cointegration. A problem with the ADF test is that it does not provide us with the necessary ? regression
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The Brutal Math of a 60/40 Portfolio [EconomPic]Think only a bear market can keep returns of a 60/40 near 0%… think again. Given the huge opportunity cost of allocating to cash or bonds at current yield levels, even generally optimistic return assumptions for stocks are enough to keep portfolio level returns near 0% real. The goal of this post is to set the stage for a future post where I hope to share potential solutions that may improve
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Trading strategy for the S&P 500 index based on ARIMA models [Tulip Quant]ARIMA models are a family of models for time series that are used to forecast future behaviour. It can be (and it is) used in finance, and in particular in trading. In this post I will try to show a specific use for a trading strategy based on these models, and it will be applied to the S&P 500 index. ARIMA models are denoted by ARIMA(p,d,q), where: p is the order of the autorregresive factor.
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Mini-Meucci : Applying The Checklist – Steps 6-7 [Return and Risk]Today we'll be visiting 2 sites along Via Meucci, Evaluation and Attribution. Evaluation We need some way to measure the goodness of the ex-ante portfolio across the scenarios from the Aggregation step, and for this Meucci introduces the concept of a Satisfaction index. Given the distribution of the ex-ante performance we can compute the satisfaction index (or its opposite, the risk index)