This is a summary of links featured on Quantocracy on Thursday, 01/21/2021. To see our most recent links, visit the Quant Mashup. Read on readers!
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How to Analyze Volume Profiles With Python (h/t @PyQuantNews) [Minh Nguyen]When trading in markets such as equities or currencies it is important to identify value areas to inform our trading decisions. One way to do this is by looking at the volume profile. In this post, we explore quantitative methods for examining the distribution of volume over a period of time. More specifically, well be using Python and statistical and signal processing tools in SciPys suite
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Trend-Following Filters Part 2/2 [Alpha Architect]Part 1 of this analysis, which is available here, examines filters modeled on second-order processes from a digital signal processing (DSP) perspective to illustrate their properties and limitations. To briefly recap, a time series based on a second-order process consists of a mean a and a linear trend b which is contaminated with random normally distributed noise (t) where (t) ~ N(0, 2):
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Copula for Pairs Trading: A Detailed, But Practical Introduction [Hudson and Thames]Suppose that you encountered a promising pair of stocks that move closely together, the spread zig-zagged around 0 like some fine needle stitching that sure looks like a nice candidate for mean-reversion bets. Whats more, you find out that the two stocks prices for the past 2 years are all nicely normally distributed. Great! You can avoid some hairy analysis for now. Therefore you fit them