Finding Patterns: Machine Learning for Automated Trading in Forex and Stocks Part 5

TL;DR

In this video, the percent change function is examined and the main issue is identified as the function not being compatible with decimal numbers.

Transcript

part 5 of machine learning for the use of algorithmic trading where we left of percent change function we have not actually tested it what are the main issues is a is going to be but its not its not a gonna gonna willing to run with a output let me just show you percent change lets do 5 8 it returns a zero what if we did percent change 5 point 0 no... Read More

Key Insights

• 🏆 Testing the percent change function is crucial in algorithmic trading to ensure accurate calculations.
• #️⃣ The main issue with the function is its incompatibility with decimal numbers, resulting in incorrect outputs.
• 🛝 Python's default behavior of rounding division results to whole numbers can cause discrepancies in percent change calculations.
• #️⃣ Working with decimal numbers instead of whole numbers is necessary to achieve accurate percent change results.
• 💄 Accuracy in percent change calculations is essential for making informed decisions in algorithmic trading.
• 🥺 Python's preference for whole numbers simplifies calculations but can lead to inaccuracies in certain scenarios.
• 🗂️ Using decimal numbers when dividing in Python avoids the rounding issue and provides accurate results.

Questions & Answers

Q: What is the purpose of testing the percent change function in algorithmic trading?

Testing the percent change function is important in algorithmic trading as it helps to determine the accuracy of calculations and ensure accurate decision-making based on market trends.

Q: Why does the percent change function return zero when given decimal numbers?

The percent change function returns zero for decimal numbers because Python rounds division results to whole numbers by default. This causes inaccurate calculations and incorrect outputs.

Q: How can we obtain the correct percent change for decimal numbers?

To obtain the correct percent change for decimal numbers, we need to ensure that any division calculations are done using decimal numbers rather than whole numbers.

Q: Why does Python prefer dealing with whole numbers?

Python prefers dealing with whole numbers because it simplifies calculations and makes them easier to understand and work with in most cases. However, in situations like percent change calculations, it can lead to inaccuracies.

Summary & Key Takeaways

• The video discusses testing the percent change function in machine learning for algorithmic trading.

• The main issue is that the function does not return the correct output when dealing with decimal numbers.

• Python typically rounds division results to whole numbers, causing incorrect calculations.