awesome-quant

No, it looks more like a Rust equivalent of libraries like ffn (financial functions for python) or many of the other ones listed here https://github.com/wilsonfreitas/awesome-quant Using rust to do exploratory analysis in python seems like a misguided idea. But using rust to productize models that have performance and accuracy sensitivities, the things that C/C++ is still used for, indeed sounds like a good idea. Most of the python libraries used in finance, like numpy/pandas, call out to C for performance reasons; the libraries are essentially python bindings + syntax to C functions. It would be interesting to think about replacing that backend with rust.

The S&P500 is a backtesting reference benchmark. You can compare portfolio performance through drawdown periods with backtesting tools. Macroeconomic drawdowns are good times to have cash for acquisitions; instead of free government cheese. "Tear Sheets: Definition and Examples in Finance, Vs. Prospectus" https://www.investopedia.com/terms/t/tearsheets.asp : > While tear sheets date back to the old days when stockbrokers would rip individual pages out of the S&P summary book and send them to current or potential clients, most information is extracted online today. Therefore, any concise representation of a company's business fundamentals could be considered a tear sheet. From https://news.ycombinator.com/item?id=24428206#24429801 : > pyfolio.tears.create_interesting_times_tear_sheet measures algorithmic trading algorithm performance duri