Extremely high probabilities on bullish signal for chip ETF
The chip sector has been one of the top performing industries since the Christmas low, but it suffered a setback last week when Intel reported earnings. The company beat its EPS and revenue estimates, but it dialed back its guidance for 2019 as a whole. The guidance, if it is accurate, would mean a year over year revenue decline for the first time since 2015.
Intel dropped 8.99% on April 26 and the VanEck Vectors Semiconductor ETF (NYSE: SMH) fell 1.22% on the same day. Looking at the daily chart for the SMH, it has seen a trend channel form over the last few months and the price is still in the channel after dropping the last few sessions.
The upper rail is formed by the highs from late February and April 24. The lower rail connects the lows from early and late March. The lower rail is currently sitting just under $112.50 and rising. The 50-day moving average is just below the lower rail.
We see that the SMH was overbought up until the last few days and the two days of selling pressure moved the indicators below the threshold.
If you think the SMH is going to drop a lot more, you might want to reconsider. The Tickeron AI Trend Prediction tool generated a bullish signal for the SMH on April 26 and it calls for a rally of at least 4% over the next month. What was especially impressive was the signal showed a confidence level of 90%. Just as impressive is the fact that 94% of previous predictions on the SMH have been successful.
Contributor
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.
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