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

The video tutorial discusses using ChatGPT to identify and correct errors in code, focusing on calculating portfolio returns. It highlights the importance of basing returns on actual asset returns rather than Z scores. The tutorial explains the process of calculating returns, including cumulative returns, and discusses trading simulations using daily data. The video also reflects on the capabilities of ChatGPT in correcting code based on natural language input, emphasizing the technological advancements it represents.

What was the initial oversight in the code related to portfolio returns?

• Using incorrect asset positions

• Using the wrong data type

• Ignoring the Z scores

• Not computing returns at all

What was the main error in computing returns initially?

• Using a weighted average of normalized prices

• Using the maximum asset price

• Using the median of asset prices

• Using a simple average

How does the corrected method calculate the returns of each asset?

• By using the absolute change

• By using the logarithmic change

• By using the exponential change

• By using the percent change method

Why is it important to consider actual returns rather than deviations from rolling means?

• To ensure accuracy in portfolio performance

• To simplify calculations

• To reduce computational time

• To avoid using complex algorithms

What is the assumption made in trading simulations regarding trading frequency?

• Trading multiple times a day

• Trading once per month

• Trading once per week

• Trading once per day

What price is typically used in trading simulations to compute profits?

• Midday price

• Opening price

• Average price

• Close price

What is the significance of using the close price in trading simulations?

• It is the most volatile price

• It is the average price of the day

• It represents the highest price of the day

• It accounts for dividends and is an approximation