u/NecessarySpread2592

Getting good data is a big hurdle for retail investors. Reliable return histories are often locked behind thousand dollar a year subscriptions. But you can get a lot for free.

I put together a small return dataset covering developed-market stocks, sovereign bonds, interest rates, and currencies.

The goal is to consolidate the kinds of return series that are useful for testing global asset allocation strategies, especially those involving foreign equity, sovereign bonds, currency hedging, and excess returns.

The dataset includes 50+ years of coverage across several files. All available for free. Check it out!

https://github.com/birjusuketupatel/ReturnDataFiles/tree/main

Note: Reposting bc the mods removed post on original subreddit.

Getting good data is a big hurdle for retail investors. Reliable return histories are often locked behind thousand dollar a year subscriptions. But you can get a lot for free.

Below is a small return dataset covering developed-market stocks, sovereign bonds, interest rates, and currencies.

The goal is to consolidate the kinds of return series that are useful for testing global asset allocation strategies, especially those involving foreign equity, sovereign bonds, and currency hedging.

Data is pulled from reliable sources like the Fed, the OECD, and the World Bank.

The dataset includes 50+ years of coverage across several files. All available for free on GitHub. Check it out!

https://github.com/birjusuketupatel/ReturnDataFiles/tree/main

Getting good data is a big hurdle for retail investors. Reliable return histories are often locked behind thousand dollar a year subscriptions. But you can get a lot for free.

I put together a small return dataset covering developed-market stocks, sovereign bonds, interest rates, and currencies.

The goal is to consolidate the kinds of return series that are useful for testing global asset allocation strategies, especially those involving foreign equity, sovereign bonds, currency hedging, and excess returns.

The dataset includes 50+ years of coverage across several files. All available for free. Check it out!

https://github.com/birjusuketupatel/ReturnDataFiles/tree/main

I watched 3Blue1Brown’s linear algebra lecture series and was inspired to dive deeper into it. For me, the most natural way to understand the subject was to conceptualize it as a generalization of 2D geometry to higher dimensions.

For instance, the formula for the dot product can be found via the law of cosines. Or the determinant is the signed volume of the parallelotope spanned by the column vectors of the matrix.

But back when I was taught matrices in high school, all this geometric intuition was missing. They introduced matrices as a way to represent data. The determinant was taught as just a complex formula we had to memorize, as was matrix multiplication. And we learned how to solve linear equations with Cramer’s rule, which computationally is an incredibly inefficient way to solve systems compared to LU decomposition so it isn’t even clear to the student why they should use matrices at all. For an example, check out this chapter on matrices from a McGraw-Hill Algebra 2 book (https://www.nlpanthers.org/Downloads/chap047.pdf).

I understand high schools must focus on computation so they can test students. But algorithms like Gram-Schmidt have a clear geometric meaning but are never taught in high schools.

So why is high school linear algebra taught like that?