$0.140
-0.003 (-2.09%)Al cierre
Predicciones de precio
Ábrelo en la app para desbloquear previsiones a más largo plazo.
Predicción y previsión de KNRX por análisis de gráficos similares
Alphio's KNRX stock price prediction model matches the current Knorex Ltd tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.14 (-0.64%). The 1-week path is $0.14 (-1.77%). 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 Knorex Ltd?
KNRX is not a good buy right now due to its significant financial struggles and compliance issues. The current price is $0.14, which is near its 52-week low of $0.13, and the stock has experienced a staggering year-to-date change of -89.98%. Additionally, the RSI is at 31.51, indicating that the stock is oversold but lacks positive momentum for recovery. The company has failed to file its annual report on time, which poses a risk of delisting, further undermining investor confidence.
KNRX cerró en $0.14 el Friday, tras bajar -2.09%
Knorex Ltd (KNRX) last closed at $0.1403, losing -2.09%. These facts feed the 1-day and 1-week KNRX price prediction above.
Factores de la previsión
Los factores de la previsión de Knorex Ltd (KNRX) 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 KNRX, ordenadas por similitud.
Análisis de estacionalidad de KNRX
Historically the probability of a positive November return for KNRX is 0.00%. January offers the highest probability of a positive month at 60.00%, 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.