Data-driven stock quant investment with Python Part 2

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The fundamental reason why I started giving lectures,
This is exactly what I wanted to convey to my students!

1. Backtesting & Data Analysis
   • (N:M ratio for two specific stocks) vs (all-in on one stock) which is better?
   • Correlation between All-Weather and VAA Strategy Portfolio Returns
2. Rebalancing period
   • (Rebalancing at the end of the month) vs (Rebalancing at the beginning of the month)
   • Stocks:Bonds:Gold = 6:3:1 strategy --> One month, six months, or one year rebalancing?
   • I can't tell if the backtest in the paper waited until the end of the month and the beginning of the month and then switched to defensive assets at the time of rebalancing, or if it switched to defensive assets immediately when the canary asset sign changed 😭😭
3. Tweaking the strategy
   • What happens if you ride the wave of an all-weather strategy?
   • Which of the following is better for stock assets in All Weather?
     1. Developed country stocks (VT)
     2. US stocks (VTI) / developed market stocks (VEA) / emerging market stocks (VWO)
   • I want to include a certain percentage of virtual currency in my static asset allocation. Is that okay?
   • Would it be okay to have a 50/50 ratio of stocks for a specific strategy?
   • What are the returns and MDD data when purchasing only when the canary asset group is + during the DAA strategy?

The previous lectures were merely a preparatory course to properly take this lecture.
The core and final content of the 'Python + Stock Quantitative Investment' curriculum!

Programming is not something to be studied and practiced.
It is a tool for efficiently solving real-world problems.

1. [Python Basics] Satisfies one of the two conditions below.
   • Completed Python course, accessible to anyone, from liberal arts students to non-majors.
   • Or you know 'all' about the content below.
     • Any for statement can be converted to a while statement.
     • I am familiar with the concepts of libraries and modules.
     • I am well aware of the difference between classes and objects.
     • You can distinguish between situations where you should use the list data type and when you should use the dictionary data type.
     • Have experience using jupyter lab or notebook.
2. [Pandas Proficiency with Financial Data] Meets one of the following two conditions:
   • Completed Part 1 of Data-Driven Quantitative Stock Investing with Python
   • Or you know 'all' about the content below.
     • I know the difference and relationship between DataFrame and Series.
     • When performing arithmetic operations (add, divide, etc.) between DataFrames or Series, I understand the index and column alignment.
     • I have experience performing simple data preprocessing on my own using Pandas. (e.g., I improved code using a for loop by converting it to Pandas.)
     • I know the difference between concat(), join(), and merge().
3. [Basic Math Knowledge] I have **simple** experience learning the following (not required)
   • Basic probability/statistics, including the meaning of random variables, expected value (E[X]), and variance (Var[X]).
   • The meaning of independence in probability/statistics
   • The meaning and relationship between log and exponential, and the characteristics of log (how to add logs with the same base)