$0.836
+0.037 (+4.46%)Al cierre
Predicciones de precio
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Predicción y previsión de APRE por análisis de gráficos similares
Alphio's APRE stock price prediction model matches the current Aprea Therapeutics Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.82 (-1.62%). The 1-week path is $0.84 (+0.86%). 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 Aprea Therapeutics Inc?
Aprea Therapeutics appears to be a good buy right now due to a recent 5-day price increase of 30.97%, suggesting strong momentum. Additionally, the company has a solid cash position of $46.5 million, which is expected to sustain operations until Q1 2028, reducing financial risk. However, the main risk is the negative earnings trend, with a recent EPS of -0.07, indicating ongoing losses. Overall, the combination of positive price movement and financial stability supports a buy recommendation.
APRE cerró en $0.84 el Tuesday, tras subir +4.46%
Aprea Therapeutics Inc (APRE) last closed at $0.8357, gaining +4.46%. These facts feed the 1-day and 1-week APRE price prediction above.
Factores de la previsión
Los factores de la previsión de Aprea Therapeutics Inc (APRE) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de APRE, ordenadas por similitud.
Análisis de estacionalidad de APRE
Historically the probability of a positive September return for APRE is 0.00%. January offers the highest probability of a positive month at 80.33%, 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.