$28.920
-0.512 (-1.77%)Al cierre
Predicciones de precio
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Predicción y previsión de BTU por análisis de gráficos similares
Alphio's BTU stock price prediction model matches the current Peabody Energy Corp tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $28.91 (-0.05%). The 1-week path is $31.94 (+10.45%). 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 Peabody Energy Corp?
Peabody Energy Corp (BTU) is currently a hold. The stock's current price is $28.92, which is near the analyst price target of $27 from UBS and $29 from B. Riley. The RSI is at 72.789, indicating overbought conditions, which suggests caution. Additionally, the company reported a significant net loss of $90.6 million in Q2, which is a major concern for potential investors. The primary risk is the anticipated drop in seaborne thermal coal sales volume to 12.4 to 13 million tons in 2026, reflecting weakening demand.
BTU cerró en $28.92 el Friday, tras bajar -1.77%
Peabody Energy Corp (BTU) last closed at $28.92, losing -1.77%. These facts feed the 1-day and 1-week BTU price prediction above.
Factores de la previsión
Los factores de la previsión de Peabody Energy Corp (BTU) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de BTU, ordenadas por similitud.
Análisis de estacionalidad de BTU
Historically the probability of a positive August return for BTU is 0.00%. October offers the highest probability of a positive month at 100.00%, while July 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.