$0.170
+0.006 (+3.66%)Al cierre
Predicciones de precio
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Predicción y previsión de RUBI por análisis de gráficos similares
Alphio's RUBI stock price prediction model matches the current Rubico Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.15 (-1.14%). The 1-week path is $0.16 (+3.00%). 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 Rubico Inc?
Rubico Inc. is not a good buy right now. The stock has experienced a staggering 99.99% year-to-date change and a 56.52% decline over the past five days, indicating severe downward momentum. Additionally, the RSI is at an extremely low 16.82, suggesting that the stock is oversold but may not recover soon due to ongoing negative sentiment surrounding its reverse stock splits and strategic shifts. The main risk is the company's current liabilities of $11,839,000, which exceed its current assets of $13,442,000, indicating potential liquidity issues.
RUBI cerró en $0.17 el Friday, tras subir +3.66%
Rubico Inc (RUBI) last closed at $0.151, losing -11.18%. These facts feed the 1-day and 1-week RUBI price prediction above.
Factores de la previsión
Los factores de la previsión de Rubico Inc (RUBI) 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 RUBI, ordenadas por similitud.
Análisis de estacionalidad de RUBI
Historically the probability of a positive October return for RUBI is 0.00%. January offers the highest probability of a positive month at 5.00%, while February 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.