$12.630
+0.466 (+3.69%)Al cierre
Predicciones de precio
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Predicción y previsión de EQX por análisis de gráficos similares
Alphio's EQX stock price prediction model matches the current Equinox Gold Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $13.15 (+0.21%). The 1-week path is $13.26 (+1.06%). 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 Equinox Gold Corp?
Equinox Gold Corp (EQX) is a good buy right now due to its recent strong earnings performance, with a reported Q2 EPS of $0.28, exceeding expectations by $0.11, and a significant revenue increase of 169.3% year-over-year to $769.8 million. Additionally, the stock's current price of $12.99 is below several analysts' price targets, including a high of $35, indicating substantial upside potential. However, a key risk is the 5-day price change of -3.99%, suggesting recent volatility that investors should monitor closely.
EQX cerró en $12.63 el Wednesday, tras subir +3.69%
Equinox Gold Corp (EQX) last closed at $13.12, gaining +3.88%. These facts feed the 1-day and 1-week EQX price prediction above.
Factores de la previsión
Los factores de la previsión de Equinox Gold Corp (EQX) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de EQX, ordenadas por similitud.
Análisis de estacionalidad de EQX
Historically the probability of a positive September return for EQX is 93.18%. October offers the highest probability of a positive month at 100.00%, while January 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.