Pricing Options with the Black-Scholes Model [Short]

Recently, I have been learning about optimal options pricing under various sets of assumptions. The introduction to risk-neutral options pricing is the Black-Scholes model, which assumes you have an arbitrage free market with at least one risk-free asset and one risky asset. Additionally you assume that the log-price of the risky asset follows a Wiener process. i.e. Geometric Brownian motion.

When a perfectly good source exists there is no purpose for me to fully repeat the information. See codearmo for an implementation of options pricing under the Black-Scholes Model in Python.

Black Scholes Model Python
Learn Options Trading Using Python

I am motivated to learn about options pricing because I believe that the stock/derivative markets offer ample opportunity to learn about statistics in a often adversarial environment. Additionally, I wanted some project to get used to writing notes in Obsidian for.

Resources I used for learning about options pricing
Black–Scholes model - Wikipedia
What Is the Black-Scholes Model?
The Black-Scholes model is a mathematical equation used for pricing options contracts and other derivatives, using time and other variables.
Black Scholes Model Python
Learn Options Trading Using Python
Geometric Brownian motion - Wikipedia
Itô calculus - Wikipedia

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