lecture-python-intro

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Comments by @longye-tian:

Code

• [x] Change global variables to default_param (i.e. default_μ).
• [x] Distinguish the function name in the exercise solution and the one in the main text.

Content

• [x] "we write this statement as" - > "We write this statement as"
• [x] Capitalize only the first bullet point below: "This is the Monte Carlo method, which runs as follows:"
• [x] change another lecture to myst doc link
• [x] "Pricing a european call option under risk neutrality" -> "Pricing a European call option under risk neutrality"
• [x] "Risk-Neutral Pricing" -> "Risk-neutral pricing"
• [x] "$S_0$ is normally distributed" -> "$S_0$ is log-normally distributed"

Comments by @Jiarui-ZH:

Content

• [ ] 18.2.2 - look at adding an animation to demonstrate Monte Carlo (similar to Wikipedia page)
• [ ] Add explanation for number produced by code above section 18.3
• [ ] In 18.3 explain what a European call option is (move definition towards top of section?)
• [ ] Add explanatory text at the end of Section 18.3 (to explain number better) -- perhaps relate it to parameters and see how it varies with discount factor, expected payoff goes up (for example)
• [ ] what does { mean after 18.4.1 equation? (independent over time sequence)
• [ ] In section 18.4.1 and 18.4.3, it would be a good idea to break down the equations in the section and talk about what are the implications behind parameters such as ξ
• [ ] Update figures to use mystnb metadata for Figure numbering and titling
• [ ] Backstory to Figure above 18.4.6 (why are there two plots?)
• [ ] Change section title for 18.4.6 to Computing the option price
• [ ] Get more intuiting between prices and parameters

@Jiarui-ZH would you be able to take on your comments and setup a PR when you have time?