$10.390
0.000 (0.00%)Al cierre
Predicciones de precio
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Predicción y previsión de SOCA por análisis de gráficos similares
Alphio's SOCA stock price prediction model matches the current Solarius Capital Acquisition Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $10.60 (+2.05%). The 1-week path is $10.46 (+0.64%). 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.
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Solarius Capital Acquisition Corp (SOCA) is currently priced at $10.37, which is near its 52-week high of $10.44, indicating limited upside potential. The RSI is at 43.6, suggesting it is neither overbought nor oversold, which means it may not be the best time to buy. Additionally, the forward P/E is 0, indicating uncertainty about future earnings. The main risk is the current ratio of 2.42, which, while above 1, shows that the company has a relatively low liquidity position compared to its liabilities.
SOCA cerró en $10.39 el Thursday, tras subir 0.00%
Solarius Capital Acquisition Corp (SOCA) last closed at $10.3900003, gaining +0.00%. These facts feed the 1-day and 1-week SOCA price prediction above.
Factores de la previsión
Los factores de la previsión de Solarius Capital Acquisition Corp (SOCA) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de SOCA, ordenadas por similitud.
Análisis de estacionalidad de SOCA
Historically the probability of a positive November return for SOCA is 100.00%. March offers the highest probability of a positive month at 100.00%, while September 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.