Energy of Minas Gerais Co DRC

Predicción y previsión de precio de Energy of Minas Gerais Co DRC (CIG.C)

$3.150

+0.017 (+0.53%)Al cierre

Predicciones de precio

1-Day Price Prediction
-3.56%
1-Week Prediction
-0.31%
1-Month Prediction
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2026 Prediction
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2030 Prediction
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Ábrelo en la app para desbloquear previsiones a más largo plazo.

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Predicción y previsión de CIG.C por análisis de gráficos similares

Alphio's CIG.C stock price prediction model matches the current Energy of Minas Gerais Co DRC tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $2.89 (-3.56%). The 1-week path is $2.99 (-0.31%). 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 Energy of Minas Gerais Co DRC?

CEMIG appears to be a good buy right now due to its low forward P/E of 11.64 and a strong year-to-date change of +32.95%.

CIG.C cerró en $3.15 el Tuesday, tras subir +0.53%

Energy of Minas Gerais Co DRC (CIG.C) last closed at $3, losing -4.76%. These facts feed the 1-day and 1-week CIG.C price prediction above.

Factores de la previsión

Los factores de la previsión de Energy of Minas Gerais Co DRC (CIG.C) combinan patrones gráficos similares, estacionalidad y medias móviles.

Similar patterns

Positive

Bullish

Seasonality

Positive

Bullish

SMA 20

Negative

Bearish

SMA 200

Negative

Bearish

Patrones gráficos similares

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

Análisis de estacionalidad de CIG.C

Historically the probability of a positive October return for CIG.C is 100.00%. September offers the highest probability of a positive month at 100.00%, while January is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.

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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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