Hey I have a data science background. This is good and entertaining fluff, but if you know what you're looking at, there are important things missing and some things that look good but don't make sense. I give him 4.8/5 for making it look important. For example: He has two time series (S&P500 and the bonds) and he compares them to many possible offsets. This is called cross-correlation analysis. It's a real thing, but it's also notorious for overfitting the data and showing spurious relationships if you misuse it like OP dies here. When you test many different offsets, you increase the probability of finding a high correlation *somewhere*, purely out of random chance. This is kind of like flipping a coin and getting heads 10 times in a row; it's impressive if you only flipped the coin 10 times, but much less exciting if you flipped it 10 million times. You were bound to get a 10-head steak at some point. An overfit predictor is one that performs very well on the historical data used to find it, but poorly on new, unseen data. If you select the single best lag based purely on the highest R-value from your historical test (precisely what OP did here), you risk overfitting to random noise that exists in the sample, but isn't truly predictive. And that's almost surely been done here and the validation should have been on showing that the model isn't overfit. To validate a model like that you wouldn't back-test (what OP does). Some things you could do are split the data into in and out of sample (e.g. make the model based on only the first X days in the series, and then judge it based on its ability to predict the data after day X). You should/could take steps to remove seasonality or trends within the time series first (which we already damn well know the stock market is seasonal, so him using untransformed values is most definitely increasing his calculated correlation). It would also be good to do bootstrapping to check statistical significance, instead of just p value. But it is very entertaining. OP probably also has a data background, to be knowing what to do to specifically torture the data this way.

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https://preview.redd.it/17v1pahavh8g1.jpeg?width=1018&format=pjpg&auto=webp&s=192449324441677045fcb120d952237971f1a136

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https://preview.redd.it/ekmp45smuh8g1.jpeg?width=1440&format=pjpg&auto=webp&s=771620f06b8aa28f0be426c21f60eb72d0eed487 Im right there with you buddy.

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It’s basing, it will get back to the 20s once/if the DOE announces a partnership. The DOE themselves called dysprosium “the single most critical element for emerging clean energy technologies” back in 2015. Take note, this was before it was evident the China supply restrictions were a geopolitical reality, and also doesn’t account for the fact it’s critical for defence industries and fuel injectors of all kinds and nuclear control rods. https://preview.redd.it/1wk55aeyth8g1.jpeg?width=960&format=pjpg&auto=webp&s=824de8de738256f48c3882a8b8d075da17ccc7ea

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If we don’t watch, China will pull their own “OPEC” thing like we had done with oil in the 70’s.  

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