$3.955
-0.274 (-6.94%)Al cierre
Predicciones de precio
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Predicción y previsión de ANTX por análisis de gráficos similares
Alphio's ANTX stock price prediction model matches the current AN2 Therapeutics Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $3.94 (-0.26%). The 1-week path is $3.93 (-0.75%). 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 AN2 Therapeutics Inc?
ANTX is a good buy right now due to its recent FDA clearance for the epetraborole drug, which is expected to drive future growth. The current price is $4.35, significantly below the analyst target of $9, suggesting a potential upside of over 100%. Additionally, the stock has shown a strong year-to-date change of +291.89%, indicating robust momentum. However, a risk to consider is the company's ongoing net losses, which reached $10.03 million in Q1 2026, reflecting the need for continued investment in R&D.
ANTX cerró en $3.95 el Thursday, tras bajar -6.94%
AN2 Therapeutics Inc (ANTX) last closed at $3.9549999, losing -6.94%. These facts feed the 1-day and 1-week ANTX price prediction above.
Factores de la previsión
Los factores de la previsión de AN2 Therapeutics Inc (ANTX) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de ANTX, ordenadas por similitud.
Análisis de estacionalidad de ANTX
Historically the probability of a positive November return for ANTX is 9.30%. March offers the highest probability of a positive month at 47.62%, while December 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.