U.S. Steel hitting upper rail of downward sloped channel

Iconic steel manufacturer U.S. Steel Group (NYSE: X) has been trending lower for over a year now and the stock has rallied in the past month and a half. Unfortunately the stock could be facing some resistance now as it is hitting the upper rail of a trend channel that has defined trading over the past six months.

You can see how the upper rail is formed by the highs in July and November. The lower rail isn’t as clear cut, but if the stock drops anywhere near the lower rail, it will take the price down below the $16 level.

The Tickeron AI Trend Prediction tool generated a bearish signal on the stock on February 5. That prediction showed a confidence level of 72% and previous predictions on U.S. Steel have been accurate 86% of the time. The signal calls for a decline of at least 4% over the coming month.

U.S. Steel’s fundamentals are all over the place at this time. Earnings growth has been flat over the last few years, but the most recent quarterly report showed a jump of 139%. The company has a good return on equity at 25.6%, but the profit margin is a paltry 2.1%.

Given the downward trend, the confusing fundamentals, and the bearish signal, it would be hard to bet on U.S. Steel going up at this time.

Contributor

Harry Richardson|Expert

Harry Richardson — Algorithmic Trader & Strategy Developer Harry is an algorithmic trader specializing in impulse and breakout trading strategies across cryptocurrency and equity markets. With more than 10 years of experience in developing automated trading systems, he focuses on building structured algorithms designed to capture momentum while maintaining strict risk control. His approach combines quantitative analysis, real-market execution, and continuous performance monitoring. Vitalii prioritizes risk management, drawdown control, and strategy stability over short-term optimization, ensuring algorithms are adaptable to changing market conditions. He has developed and tested hundreds of automated strategies, working extensively with live trading environments, forward testing, and portfolio-level algorithm management. His work centers on transforming trading ideas into fully operational, scalable automated systems.