$5.805
+0.035 (+0.61%)Al cierre
Predicciones de precio
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Predicción y previsión de STTK por análisis de gráficos similares
Alphio's STTK stock price prediction model matches the current Shattuck Labs Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $5.76 (-0.85%). The 1-week path is $5.88 (+1.37%). 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 Shattuck Labs Inc?
Shattuck Labs Inc is a good buy right now due to its promising drug candidate SL-325, which is currently in Phase 2b clinical testing and has the potential to capture a significant share of a $20 billion market. The current price is $5.49, significantly lower than analyst price targets of $17 to $19, indicating substantial upside potential. Additionally, the stock has shown a year-to-date increase of 47.98% and a one-year change of 166.50%, reflecting strong investor interest and confidence in the company's future.
STTK cerró en $5.80 el Wednesday, tras subir +0.61%
Shattuck Labs Inc (STTK) last closed at $5.8049998, gaining +0.61%. These facts feed the 1-day and 1-week STTK price prediction above.
Factores de la previsión
Los factores de la previsión de Shattuck Labs Inc (STTK) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bearish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de STTK, ordenadas por similitud.
Análisis de estacionalidad de STTK
Historically the probability of a positive October return for STTK is 65.15%. February offers the highest probability of a positive month at 89.66%, while August 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.