$65.130
-0.547 (-0.84%)At close
Price Predictions
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QTWO stock price ended at $65.13 on Friday, after losing -0.84%
Q2 Holdings Inc (QTWO) last closed at $65.13, losing -0.84%. These facts feed the 1-day and 1-week QTWO price prediction above.
Analyst Consensus
Q2 Holdings Inc (QTWO): 10 buy, 3 hold, 0 sell. Outlook: Buy.
Sell / Buy
Technical signals summary based on buy and sell indicators. The gauge shows the overall market view.
Forecast Drivers
Q2 Holdings Inc (QTWO) forecast drivers combine similar chart patterns, seasonality, and moving averages.
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bullish
Seasonality analysis for QTWO
Historically the probability of a positive August return for QTWO is 34.92%. November offers the highest probability of a positive month at 51.28%, while February is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
10-year price projections for QTWO
| Month | Min Price | Avg Price | Max Price | Potential ROI |
|---|---|---|---|---|
| Aug 2026 | $103.87 | $105.19 | $114.39 | +61.51% |
| Sep 2026 | $98.71 | $106.74 | $124.88 | +63.89% |
| Oct 2026 | $64.17 | $83.95 | $97.14 | +28.89% |
| Nov 2026 | $93.46 | $105.46 | $126.84 | +61.92% |
| Dec 2026 | $72.40 | $90.99 | $111.89 | +39.70% |
In 2026, Q2 Holdings Inc (QTWO) is projected to range from $64.17 to $126.84.
Q2 Holdings Inc 2026 monthly forecast
In Aug 2026, Q2 Holdings Inc is projected to average $105.19, with a low of $103.87 and high of $114.39.
In Sep 2026, Q2 Holdings Inc is projected to average $106.74, with a low of $98.71 and high of $124.88.
In Oct 2026, Q2 Holdings Inc is projected to average $83.95, with a low of $64.17 and high of $97.14.
In Nov 2026, Q2 Holdings Inc is projected to average $105.46, with a low of $93.46 and high of $126.84.
In Dec 2026, Q2 Holdings Inc is projected to average $90.99, with a low of $72.40 and high of $111.89.
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.