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.

What was the main reason the teacher decided to fix the errors before running the code?

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Explain the significance of using a rolling mean and standard deviation in the Z score calculation.

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What was the error identified in the calculation of the Z score for the assets?

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How did the teacher suggest correcting the Z score calculation?

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What lesson did the teacher emphasize regarding accepting corrections?

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How does the teacher suggest handling errors in coding and statistical analysis?

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What was the teacher's final assessment of the corrected code after the changes were made?

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Discuss the implications of using a static threshold (-1 and +1) instead of dynamic Bollinger Bands.

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What is the relationship between Z scores and standard deviations in the context of trading signals?

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Why is it important to critically evaluate the output generated by AI tools like ChatGPT?

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