onerandompotato

I agree with your opposition to the comment above, but I feel like the condescension towards silicon valley, and non-government employees is not a way to start a healthy dialogue. Like you are not going to convince anybody by calling 80+% of people here (private sector employees) "a joke".

I agree that randos on twitter are a bad source of information.

If there was a way to directly formulate every parameter of the black Scholes formula you would be correct. The problem that you run into is how to calculate volatility itself? Without the volatility value, your algorithm cannot trade on it.

Using history of volatility is insufficient, because volatility is a forward looking measure. Just because the stock was volatile in the past does not mean it will be in the future, and vice versa. There are even more nuances with this, as volatility is a smile (or a surface), not a singular number https://en.wikipedia.org/wiki/Volatility_smile.

TLDR Trading in volatility is a very complicated topic. However, volatility is a useful parameter, and black Scholes is typically used to deduce the forward looking volatility from option price, in addition to volatility -> option price.

I mean, the paper uses data from the National Cattlemen's Beef Association™, which is a beef lobbying group. The third author of the paper is also from the NBCA, and the paper was funded by the Beef Checkoff™, which a beef marketing program. I have my reservations about the accuracy of this paper.

Wouldn't Julia be that language? Its fast, supports static typing, and is built from the ground up for machine learning/ data science. It also has a built in REPL.