$0.171
-0.008 (-4.42%)Al cierre
Predicciones de precio
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Predicción y previsión de OPTT por análisis de gráficos similares
Alphio's OPTT stock price prediction model matches the current Ocean Power Technologies Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.17 (-1.77%). The 1-week path is $0.17 (+1.01%). 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 Ocean Power Technologies Inc?
Ocean Power Technologies Inc (OPTT) is not a good buy right now due to its significant financial losses and declining performance metrics. The current price is $0.171, with a year-to-date change of -45.83% and a one-year change of -66.91%. The company's gross margin has turned negative, reaching -305.42% in the latest quarter, indicating severe operational challenges. Additionally, the RSI is at 27.814, suggesting the stock is oversold but lacks immediate recovery signals.
OPTT cerró en $0.17 el Friday, tras bajar -4.42%
Ocean Power Technologies Inc (OPTT) last closed at $0.171, losing -4.42%. These facts feed the 1-day and 1-week OPTT price prediction above.
Factores de la previsión
Los factores de la previsión de Ocean Power Technologies Inc (OPTT) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de OPTT, ordenadas por similitud.
Análisis de estacionalidad de OPTT
Historically the probability of a positive August return for OPTT is 0.00%. February offers the highest probability of a positive month at 43.10%, while August 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.