$0.431
-0.016 (-3.80%)Al cierre
Predicciones de precio
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Predicción y previsión de BQ por análisis de gráficos similares
Alphio's BQ stock price prediction model matches the current Boqii Holding Ltd tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.43 (-0.83%). The 1-week path is $0.43 (-0.59%). 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 Boqii Holding Ltd?
Boqii Holding Ltd is not a good buy right now due to its significant downward momentum and negative financial indicators. The current price is $0.43, which is close to its 52-week low of $0.42, indicating a lack of upward price support. Additionally, the stock has a very low RSI of 7.216, suggesting it is heavily oversold, but this could also indicate continued bearish sentiment. The forward P/E ratio is 0, reflecting uncertainty about future earnings, and the stock has dropped 78.68% year-to-date, highlighting severe performance issues.
BQ cerró en $0.43 el Friday, tras bajar -3.80%
Boqii Holding Ltd (BQ) last closed at $0.4307, losing -3.80%. These facts feed the 1-day and 1-week BQ price prediction above.
Factores de la previsión
Los factores de la previsión de Boqii Holding Ltd (BQ) 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 BQ, ordenadas por similitud.
Análisis de estacionalidad de BQ
Historically the probability of a positive November return for BQ is 27.27%. October offers the highest probability of a positive month at 50.00%, while March is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
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Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.