How do you find these stocks so early

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I also did that with TSLA…. and UPST (although I sold at a price higher than now, so that makes me feel better) not making the same mistake with RKLB and PL

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If you think a company that literally got awarded 800M Friday from the first of many potential HUGE contracts AND is going to make another 200M from supplying the OTHER winners is going to tank I encourage you to short it. This wasn't even their launch business. They aren't even close to being a household name. So go ahead and short it.

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You know what, I’ve been thinking of selling some shares of my space bag which includes common names here to take some profit after a couple folds increase. I feel so tempted to sell not to mention your case. 

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Shhh don't engage a troll that's missing out on gains and picking a fight with everyone loving RKLB. Instead, buy more calls so when you have money, you can fuck his girlfriend for the price of half a RKLB stock.

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Don’t think so- have you seen the recent news with Oklo and plutonium?

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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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It broke out of like a 14 year base so gonna be less sellers at these levels + shortages + dollar is losing value its got more room to run.

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I expected that and expect that, on both counts. Per usual, want to get rich on a particular stock?  Especially obscure, thinly held stocks (not that UUUU is, but how many people are searching about this stock on Saturday night?) So, want to get rich on a particular stock?  Get to it before the news hits.  Yeah, I’m talking about *insider* knowledge.  Be that through a political connection or the guy in the office who works on the printers.

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Love how you call it a port, so pro and sexy

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