Displaying all Patterns Recognized: Machine Learning for Algorithmic Trading in Forex and Stocks

TL;DR

This video explains how to generate and display similar patterns on the same chart for stocks and forex trading, using machine learning and pattern recognition techniques.

Transcript

you hello everybody and welcome to the 12th machine learning and pattern recognition for stocks and forex trading where we left off we were generating a similar pattern on the chart and now what I would like to do is kind of mesh them all together if you remember you know if it had maybe five similar patterns we would have to run cycle through five... Read More

Key Insights

• 💹 Combining similar patterns on a chart helps visualize multiple patterns simultaneously.
• 💱 Adjusting the similarity threshold can change the number of patterns considered similar and displayed.
• 🫥 Assigning a distinct color and line thickness to the original pattern improves its visibility in the combined display.
• 🗯️ The right similarity threshold is crucial in ensuring the desired level of similarity in the patterns.
• 🥺 Changing the similarity threshold can lead to either more or fewer patterns being identified as similar.
• 👻 Combining similar patterns allows for easier pattern recognition and analysis.
• 🎮 The process demonstrated in the video is a step towards backtesting and analyzing the outcomes of the patterns.

Questions & Answers

Q: How do you combine similar patterns on a chart using machine learning?

To combine similar patterns, you need to set the starting point of the data and adjust the similarity threshold. The patterns that meet the criteria are added to a plot pattern array, which is then plotted on the same chart.

Q: What happens if the similarity threshold is increased or decreased?

Increasing the similarity threshold leads to fewer patterns being considered as similar. Conversely, decreasing the threshold results in more patterns being included in the plot pattern array. The right threshold value depends on the desired level of similarity.

Q: How can you identify the original pattern in the combined display?

To distinguish the original pattern from the rest, you can assign a specific color and line thickness to it. This ensures that the original pattern stands out among the plotted patterns.

Q: How does adjusting the similarity threshold affect the number of lines displayed?

Changing the similarity threshold can significantly impact the number of lines displayed. A higher threshold reduces the number of similar patterns, resulting in fewer lines. Conversely, a lower threshold increases the number of lines.

Summary & Key Takeaways

• The video demonstrates how to combine similar patterns on a chart into one display, instead of running through multiple charts individually.

• The process involves setting the starting point of the data and adjusting the similarity threshold.

• The patterns that match the criteria are added to a plot pattern array, which is then plotted on the chart along with the original pattern.