$0.343
+0.012 (+3.47%)Al cierre
Predicciones de precio
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Predicción y previsión de QCLS por análisis de gráficos similares
Alphio's QCLS stock price prediction model matches the current Q/C Technologies Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.35 (+0.94%). The 1-week path is $0.34 (-0.84%). 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 Q/C Technologies Inc?
Q/C Technologies Inc (QCLS) is not a good buy right now due to its extremely low RSI of 16.116, indicating oversold conditions, and a staggering year-to-date change of -91.90%. The company is facing significant financial challenges, with a trailing twelve-month net loss of approximately $23.4 million, which raises concerns about its viability in the near future. The main risk is the ongoing decline in stock price, which has dropped nearly 96% year-to-date, reflecting a lack of investor confidence and market interest.
QCLS cerró en $0.34 el Friday, tras subir +3.47%
Q/C Technologies Inc (QCLS) last closed at $0.3427, gaining +3.47%. These facts feed the 1-day and 1-week QCLS price prediction above.
Factores de la previsión
Los factores de la previsión de Q/C Technologies Inc (QCLS) 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 QCLS, ordenadas por similitud.
Análisis de estacionalidad de QCLS
Historically the probability of a positive November return for QCLS is 0.00%. January offers the highest probability of a positive month at 80.00%, while April 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.