$3.060
+0.125 (+4.08%)Al cierre
Predicciones de precio
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Predicción y previsión de AEC por análisis de gráficos similares
Alphio's AEC stock price prediction model matches the current Anfield Energy Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $3.05 (-0.70%). The 1-week path is $3.02 (-1.66%). 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 Anfield Energy Inc?
Anfield Energy Inc (AEC) is currently not a good buy due to its poor financial performance and negative trends. The stock is trading at a low price of 3.29, down 44.24% year-to-date and 69.76% over the past year. Additionally, the RSI is at 34.5, indicating it is nearing oversold territory, but the lack of revenue and a forward P/E of 0 suggest ongoing financial instability. The main risk is the high net income loss of -8979018 CAD in the most recent quarter, which raises concerns about the company's sustainability.
AEC cerró en $3.06 el Thursday, tras subir +4.08%
Anfield Energy Inc (AEC) last closed at $3.07, gaining +0.33%. These facts feed the 1-day and 1-week AEC price prediction above.
Factores de la previsión
Los factores de la previsión de Anfield Energy Inc (AEC) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de AEC, ordenadas por similitud.
Análisis de estacionalidad de AEC
Historically the probability of a positive October return for AEC is 65.71%. February offers the highest probability of a positive month at 100.00%, while May 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.