$37.400
-1.201 (-3.21%)Al cierre
Predicciones de precio
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Predicción y previsión de OR por análisis de gráficos similares
Alphio's OR stock price prediction model matches the current OR Royalties Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $37.01 (-1.05%). The 1-week path is $36.16 (-3.32%). 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 OR Royalties Inc?
Osisko Gold Royalties Ltd is a good buy right now due to its strong financial performance, evidenced by a Q2 revenue of $97.82 million, which is a 62.0% year-over-year increase, and a gross margin of 86.39% in Q1 2026. Additionally, the stock has shown positive momentum with a 25.67% increase over the last 20 days and a current RSI of 61.62, indicating bullish sentiment. However, the main risk is the forward P/E ratio of 30.49, which suggests that the stock may be overvalued if earnings do not meet expectations in the coming quarters.
OR cerró en $37.40 el Friday, tras bajar -3.21%
OR Royalties Inc (OR) last closed at $37.4, losing -3.21%. These facts feed the 1-day and 1-week OR price prediction above.
Factores de la previsión
Los factores de la previsión de OR Royalties Inc (OR) 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 OR, ordenadas por similitud.
Análisis de estacionalidad de OR
Historically the probability of a positive August return for OR is 79.37%. April 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.