$1.160
-0.010 (-0.85%)Al cierre
Predicciones de precio
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Predicción y previsión de MGX por análisis de gráficos similares
Alphio's MGX stock price prediction model matches the current Metagenomi Therapeutics Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $1.16 (-0.23%). The 1-week path is $1.22 (+4.85%). 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 Metagenomi Therapeutics Inc?
Metagenomi Inc is not a good buy right now due to significant financial losses and negative performance trends. The current price is $1.19, with a year-to-date change of -28.74% and a 1-year change of -34.62%. Additionally, the RSI is at 35.391, indicating potential oversold conditions but also reflecting weak momentum. The main risk is the ongoing net losses, with the latest reported net income at -$27,482,000 for Q2 2026, which raises concerns about the company's financial stability.
MGX cerró en $1.16 el Wednesday, tras bajar -0.85%
Metagenomi Therapeutics Inc (MGX) last closed at $1.16, losing -0.85%. These facts feed the 1-day and 1-week MGX price prediction above.
Factores de la previsión
Los factores de la previsión de Metagenomi Therapeutics Inc (MGX) 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 MGX, ordenadas por similitud.
Análisis de estacionalidad de MGX
Historically the probability of a positive September return for MGX is 0.00%. March offers the highest probability of a positive month at 31.75%, while May 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.