Built an AI-powered Descending Triangle pattern detector for crypto - here's how the pattern logic works

Hey IH community 👋

Been building ChartScout.io - an AI/ML-based chart pattern detection tool for crypto traders. One of the core patterns we detect is the Descending Triangle, and I wanted to share a full breakdown of how it works, both from a pattern education angle and a detection logic perspective.

What is a Descending Triangle?

It's one of the most recognized patterns in crypto technical analysis, formed by:

→ A flat horizontal support line (price repeatedly bouncing at the same zone)

→ A descending resistance line (progressively lower highs)

→ Both trendlines converging toward an apex as price gets compressed

Our ML model identifies this pattern by validating trendline touches, measuring maturity (how far along the pattern is toward its apex), and assigning a confidence score based on how cleanly the structure forms.

Full breakdown here:

🔗 https://chartscout.io/descending-triangle-pattern-crypto

The article covers:

→ Pattern structure and formation logic

→ How it differs from Symmetrical & Ascending Triangles

→ Real crypto chart examples detected by ChartScout

→ How confidence and maturity scoring works in our detection engine

For fellow builders: If you're working on anything in the fintech, trading tools, or ML pattern recognition space, would love to swap notes. The trendline validation logic was genuinely one of the trickier engineering problems we solved — happy to discuss it in the comments.

What's your approach to building educational content around a technical tool? Do you lead with the product or the education first?