$2.770
-0.151 (-5.46%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de QTI por análisis de gráficos similares
Alphio's QTI stock price prediction model matches the current QT Imaging Holdings Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $2.70 (-2.45%). The 1-week path is $2.74 (-0.95%). Longer windows use the same pattern set plus seasonality; the 1-month and multi-year forecasts sit behind unlock because they include precise min / avg / max bands.
¿Debería comprar acciones de QT Imaging Holdings Inc?
QT Imaging Holdings Inc appears to be a good buy right now due to its recent 103% revenue growth in Q2 2026, bringing total revenue to $7.4 million, and a current price of $3.09, which is below its historical SMA of $4.982. The company also received FDA approval for enhanced product capabilities, which could drive future sales. However, the main risk is the high debt-to-equity ratio of 680.85%, indicating potential financial instability.
QTI cerró en $2.77 el Tuesday, tras bajar -5.46%
QT Imaging Holdings Inc (QTI) last closed at $2.77, gaining +0.00%. These facts feed the 1-day and 1-week QTI price prediction above.
Factores de la previsión
Los factores de la previsión de QT Imaging Holdings Inc (QTI) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de QTI, ordenadas por similitud.
Análisis de estacionalidad de QTI
Historically the probability of a positive September return for QTI is 0.00%. April offers the highest probability of a positive month at 48.84%, while June is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.