This is a summary of links featured on Quantocracy on Thursday, 03/23/2023. To see our most recent links, visit the Quant Mashup. Read on readers!
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Webinar recordings and notebook [Robot Wealth]Towards the end of last year, we ran a couple of free Zoom webinars on: The Basics of Edge Extraction the trader smarts of getting an edge Data Analysis for Traders an interactive research session. Here are the recordings: Basics of Edge Extraction Data analysis for Traders The colab research notebook for the second session can be found here. (To make sense of it youll want to
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Volume and Mean Reversion Part 2 [Alvarez Quant Trading]From the Volume and Mean Reversion post, a reader sent a suggestion to instead use the ratio of 10 day moving average of the Close times Volume divided by the 63-day moving average of the Close times Volume (CV10/63). I had not tried this before and wanted to see how well it would work. First Steps I decided to follow the same path as the previous post. First testing on a very simple mean
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Myth-Busting: The Economy Drives the Stock Market [Finominal]US real GDP growth and US stock market returns were positively correlated since 1900 However, the correlation was not consistent and even turned negative The evidence of this relationship from other countries is mixed INTRODUCTION Switch on Bloomberg TV or CNBC at any time of the day, and there is a good possibility the host will be explaining the daily ups and downs of the stock market as a
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Generative Adversarial Networks: A rivalry that strengthens [Quant Dare]How does ChatGPT work? What is behind deep fake images of celebrities? How do we deal with the lack of data in finance? All these issues have in common the same underlying concept; they are based on generative models. Generative models are algorithms that create new instances of data that mimic the data on which they have been trained. Depending on their task, different generative models are