$0.991
-0.137 (-13.82%)Al cierre
Predicciones de precio
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Predicción y previsión de AIIO por análisis de gráficos similares
Alphio's AIIO stock price prediction model matches the current Robo.ai Inc. tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.97 (+0.57%). The 1-week path is $0.96 (-0.78%). 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 Robo.ai Inc.?
Robo.ai Inc is not a good buy right now due to its extremely low RSI of 13.529, indicating oversold conditions, and a staggering year-to-date change of -83.15%. The company has reported a significant projected revenue of $600 million for 2026, but the negative trends in stock performance and financial metrics, such as a forward P/E of 0 and a gross margin of only 0.33%, suggest substantial risks. The main risk is the declining equity, with total equity reported at -69,215,000 USD in Q2 2025, indicating potential financial instability.
AIIO cerró en $0.99 el Wednesday, tras bajar -13.82%
Robo.ai Inc. (AIIO) last closed at $0.968, losing -2.33%. These facts feed the 1-day and 1-week AIIO price prediction above.
Factores de la previsión
Los factores de la previsión de Robo.ai Inc. (AIIO) 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 AIIO, ordenadas por similitud.
Análisis de estacionalidad de AIIO
Historically the probability of a positive October return for AIIO is 35.29%. September offers the highest probability of a positive month at 45.24%, 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.