This is a summary of links featured on Quantocracy on Wednesday, 06/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!
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Several Key PerformanceAnalytics Functions From R Now In Python [QuantStrat TradeR]So, thanks to my former boss, and head of direct indexing at BNY Mellon, Vijay Vaidyanathan, and his Coursera course, along with the usual assistance from chatGPT (I officially see it as a pseudo programming language), I have some more software for the Python community now released to my github. As wordpress now makes it very difficult to paste formatted code, Ill be linking more often to
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Negative Hypergeometric Distribution and USDT [Quant at Risk]In crypto market, stablecoins are meant to maintain their constant value with respect to the underlying currency. At least in theory. The problem begins with an idea of stablecoins value to be stable or being stabilised over time. Different backup mechanisms are at work. For example, Tether tokens are called stablecoins because they offer price stability as they are pegged at 1-to-1 to a fiat
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Introduction to Matching Pursuit Algorithm with Stochastic Dictionaries [Quant at Risk]There is a huge number of ways how one can transform financial times-series in order to discover new information about changing price dynamics. We talk here about certain transformation that takes price time-series (or return-series) and transforms it into a new domain. Every solid textbook on Time-Series Analysis lists ample examples. 1. Fourier Transform Interestingly, there is little to few