Discussing the article: "Integrating Computer Vision into Trading (Part 1): Creating Basic Functions"

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Check out the new article: Integrating Computer Vision into Trading (Part 1): Creating Basic Functions.

The EURUSD forecasting system with the use of computer vision and deep learning. Learn how convolutional neural networks can recognize complex price patterns in the foreign exchange market and predict exchange rate movements with up to 54% accuracy. The article shares the methodology for creating an algorithm that uses artificial intelligence technologies for visual analysis of charts instead of traditional technical indicators. The author demonstrates the process of transforming price data into "images”, their processing by a neural network, and a unique opportunity to peer into the "consciousness" of AI through activation maps and attention heatmaps. Practical Python code using the MetaTrader 5 library allows readers to reproduce the system and apply it in their own trading.

Have you ever wondered what a neural network feels when it looks at the EURUSD market? How does it perceive every spike in volatility, every trend reversal, every elusive pattern formation?

Imagine a computer that does not just mindlessly apply pre-programmed rules, but truely sees the market — capturing subtle nuances of price movements that are invisible to the human eye. Artificial intelligence that looks at the EURUSD chart the way an experienced captain looks at the ocean horizon, sensing an approaching storm long before the first signs of bad weather occur.

Today, I invite you on a journey to the cutting edge of financial technology, where computer vision meets stock market analytics. We shall create a system that does not simply analyze the market — it understands it visually, recognizing complex price patterns as naturally as you recognize a friend's face in a crowd.

This feature creates a real map of the model's consciousness, showing which areas of the chart it pays most attention to when making a decision. Red zones of increased attention often coincide with key levels and reversal points, confirming that our model has indeed learned to identify significant price formations.


Author: Yevgeniy Koshtenko

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