This is a summary of links featured on Quantocracy on Tuesday, 11/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!
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Day 13: Backtest I [OSM]Unlucky 13! Or contrarian indicator? Theres really nothing so heartwarming as magical thinking. Whatever the case, on Day 12 we iterated through the 320 different model and train step iterations to settle on 10 potential candidates. Today, we look at the best performing candidate and discuss the process to see if the forecasts produce a viable trading strategy. As we noted before, we could have
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Lognormal Distribution: Neither Thin- nor Fat-Tailed [Quant at Risk]In probability and statistics, distributions are often classified as either thin-tailed or fat-tailed, a distinction that reflects the likelihood of extreme deviations from the mean. The lognormal distribution, however, defies this binary classification. It possesses characteristics that make it neither fully thin-tailed, as in the case of the Gaussian, nor entirely fat-tailed, like
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Day 12: Iteration [OSM]In Day 11, we presented an initial iteration of train/forecast steps to see if one combination performs better than another. Our metric of choice was root mean-squared error (RMSE)1 which is frequently used to compare model performance in machine learning circles. The advantage of RMSE is that it is in the same units as the forecast variable. The drawback is that it is tough to interpret on its