$2.205
-0.005 (-0.23%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de HIND por análisis de gráficos similares
Alphio's HIND stock price prediction model matches the current Vyome Holdings Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $2.17 (-1.55%). The 1-week path is $2.18 (-1.18%). 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 Vyome Holdings Inc?
Vyome Holdings Inc is a good buy right now due to its recent revenue performance and analyst optimism. The company reported a Q1 revenue of $31.59 million, showing stable growth despite challenges, and has a significant analyst price target of $8, indicating a potential upside of over 260% from the current price of $2.21. However, the main risk lies in the company's net loss of approximately $963,000 for Q1 2026, which highlights ongoing financial pressures.
HIND cerró en $2.21 el Friday, tras bajar -0.23%
Vyome Holdings Inc (HIND) last closed at $2.205, losing -0.23%. These facts feed the 1-day and 1-week HIND price prediction above.
Factores de la previsión
Los factores de la previsión de Vyome Holdings Inc (HIND) 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 HIND, ordenadas por similitud.
Análisis de estacionalidad de HIND
Historically the probability of a positive August return for HIND is 0.00%. January offers the highest probability of a positive month at 0.00%, while January 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.