Twitter (TWTR) hits upward sloped trend line and generates bullish signal

After a month-long pullback, Twitter (NYSE: TWTR) has hit an upward sloped trend line that connects the lows from December and March. The stock hit the trend line on June 3 and bounced the next day.

The overbought/oversold indicators had hit oversold levels, but those indicators have now turned higher. The daily stochastic readings made a bullish crossover on June 4 and this could indicate better days in the weeks ahead.

The Tickeron Trend Prediction Engine generated a bullish signal for Twitter on June 3. That signal showed a confidence level of 89% and it calls for a gain of at least 4% in the next month. Previous signals on Twitter have been successful 78% of the time.

Looking at the fundamentals for Twitter, the company has performed extremely well in recent years. The company has seen its earnings grow by 35% per year over the last three years and the EPS jumped 131% in the most recent quarterly report. Sales have increased by an average of 8% per year over the last three years and they were up by 18% in the most recent quarter.

The company’s profit margin is solid at 28.7%, but the return on equity is only 11.2%. That is one of the few areas of concern at this point.

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.