Financial Analysis - Build a ChatGPT Pairs Trading Bot - Correcting the Z-Score Computation

The video tutorial focuses on fixing errors in code related to Z score calculations for trading. It discusses the importance of using rolling mean and standard deviation instead of future data, highlights incorrect code logic, and demonstrates how ChatGPT responds to corrections. The tutorial emphasizes the need for critical evaluation of AI-generated code and concludes with a discussion on trading signal logic and lessons learned.

Why did the instructor choose to fix errors before running the code?

• To improve code readability

• To avoid unnecessary debugging

• Because the errors were already known

• To save time during execution

What was the main issue with using future data for Z score calculation?

• It required more memory

• It caused syntax errors

• It was not applicable in live trading

• It made the code run slower

What alternative solution was suggested for Z score calculation?

• Using a different programming language

• Using exponential smoothing

• Using a dynamic window size

• Using a fixed mean and standard deviation

What was incorrect about ChatGPT's initial implementation?

• It ignored the rolling mean

• It used a different dataset

• It used the spread's mean and standard deviation for assets

• It did not calculate the spread

How did ChatGPT respond to the correction?

• By explaining why it was correct

• By apologizing and updating the code

• By ignoring the feedback

• By providing a different solution

What lesson can be learned from ChatGPT's response to feedback?

• Ignore feedback if you are confident

• Feedback is not important

• Accept corrections and learn from them

• Always defend your initial solution

What change did ChatGPT make regarding the trading signals?

• It used a fixed threshold of -1 and +1

• It removed the trading signals

• It used a threshold of -2 and +2

• It made the signals dynamic

Why might using a static threshold for trading signals be problematic?

• It ignores the dynamic nature of the market

• It requires more computational power

• It is harder to implement

• It makes the signals too complex

What is the main advantage of using Bollinger Bands over static thresholds?

• They are easier to calculate

• They adapt to market changes

• They require less data

• They are more accurate

What is the Z score essentially a measure of?

• The absolute value of a time series

• The standardized value of a time series

• The maximum value of a time series

• The average value of a time series