RTB Digital Inc

Predicción y previsión de precio de RTB Digital Inc (RTB)

$12.420

-0.969 (-7.80%)Al cierre

Predicciones de precio

1-Day Price Prediction
-1.15%
1-Week Prediction
+2.52%
1-Month Prediction
2026 Prediction
2030 Prediction

Ábrelo en la app para desbloquear previsiones a más largo plazo.

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Predicción y previsión de RTB por análisis de gráficos similares

Alphio's RTB stock price prediction model matches the current RTB Digital Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $12.28 (-1.15%). The 1-week path is $12.73 (+2.52%). 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 RTB Digital Inc?

Currently, Ryvyl Inc. (RTB) is not a strong buy. The stock is trading at $12.99, which is down 4.42% over the past five days and has a low RSI of 35.595, indicating it may be oversold. Additionally, the forward P/E ratio is 0, suggesting uncertainty about future earnings. The gross margin is relatively strong at 37.41%, but the company has reported significant net losses, with a net income of -9,583,000 USD in the latest quarter. The main risk is the high debt-to-equity ratio of 4.98%, which could impact financial stability.

RTB cerró en $12.42 el Tuesday, tras bajar -7.80%

RTB Digital Inc (RTB) last closed at $12.42, losing -7.80%. These facts feed the 1-day and 1-week RTB price prediction above.

Factores de la previsión

Los factores de la previsión de RTB Digital Inc (RTB) combinan patrones gráficos similares, estacionalidad y medias móviles.

Similar patterns

Positive

Bullish

Seasonality

Positive

Bullish

SMA 20

Negative

Bearish

SMA 200

Positive

Bullish

Patrones gráficos similares

Acciones cuya trayectoria reciente más se parece a la de RTB, ordenadas por similitud.

Análisis de estacionalidad de RTB

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Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.

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