$36.390
+0.426 (+1.17%)Al cierre
Predicciones de precio
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Predicción y previsión de VIA por análisis de gráficos similares
Alphio's VIA stock price prediction model matches the current Via Renewables Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $35.61 (-2.13%). The 1-week path is $36.11 (-0.76%). 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 Via Renewables Inc?
Via Renewables Inc is a good buy right now, trading at $36.75 with a strong recent performance reflected in a 5-day change of +15.10% and a year-to-date change of +35.21%. The RSI is at 81.154, indicating strong momentum, although it is approaching overbought territory. The forward P/E ratio is high at 67.1141, suggesting that investors are expecting significant growth in the future. The main risk is the operating loss of $23.6 million reported in Q1, which highlights ongoing profitability challenges.
VIA cerró en $36.39 el Wednesday, tras subir +1.17%
Via Renewables Inc (VIA) last closed at $36.3899994, gaining +1.17%. These facts feed the 1-day and 1-week VIA price prediction above.
Factores de la previsión
Los factores de la previsión de Via Renewables Inc (VIA) 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 VIA, ordenadas por similitud.
Análisis de estacionalidad de VIA
Historically the probability of a positive October return for VIA is 97.67%. October offers the highest probability of a positive month at 97.67%, 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.