Great thing about SLV is you usually get 0DTE 3X per week. Thats more my speed. GL to you though OP.

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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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OP can you explain what software you did for this and how?

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I actually saw the PCT chart the other morning. Lit up on a in “top of daily range” scan I have. I’ll ride with you OP https://preview.redd.it/7u0aem7p3h8g1.jpeg?width=328&format=pjpg&auto=webp&s=a33fdf525e066d198c1c53c3950e1f59ed399717

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Today OP learns about overfitting

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Beautiful OP

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Holy shit this may be the most retarded shit I’ve seen since full porting Nana’s inheritance into Intel. Which means it’s gonna work. Godspeed, OP.

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Think about this for a second. It's already too small of a sample size to begin with, but the OP concludes that by decreasing the sample size further post 2022, it's more proof that his findings are real. Check out this article to increase your ability to see through BS [https://www.statology.org/correlation-does-not-imply-causation-examples/](https://www.statology.org/correlation-does-not-imply-causation-examples/)

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What a shit post. I applaud that regards exist in such force that this post is a real thing. Rather than learning about the market, or even just…going outside, OP spent time to build an A+ quality shit post complete with graphs and everything. Very entertaining. I feel bad for your family, obviously. But very entertaining.

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OP will be going back to the cooking range to flip some lobsters

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