Discussing the article: "Data Science and ML (Part 41): Forex and Stock Markets Pattern Detection using YOLOv8"
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Check out the new article: Data Science and ML (Part 41): Forex and Stock Markets Pattern Detection using YOLOv8.
Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.
Pattern detection in financial markets is a challenging task in machine learning and AI. As effortlessly as it seems to us human beings, it requires some work for a machine to be able to detect and interpret these patterns, simply because unlike the tabular two-dimensional (tabular) data we often use in trading, pattern detection extends to two-dimensional image data which is usually stored in formats types such as .png, .jpg, etc.
There is a huge number of traders with strategies that depend on price action and specific chart patterns in the markets, such as:
When it comes to programming, patterns such as candlestick patterns and indicator reactions, which can be identified even without sophisticated lines of code, the chart patterns mentioned above are quite complex.
It will take sophisticated, well-written, and optimized code to be able to detect even a simple pattern such as the W bottom, so why not use AI to help us with this boring task?
Author: Omega J Msigwa