$1.360
-0.102 (-7.48%)Al cierre
Predicciones de precio
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Predicción y previsión de SGLY por análisis de gráficos similares
Alphio's SGLY stock price prediction model matches the current Singularity Future Technology Ltd tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $1.82 (+31.86%). The 1-week path is $2.93 (+112.47%). 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 Singularity Future Technology Ltd?
Given the current price of 1.47 and an RSI of 18.39, which indicates the stock is heavily oversold, it may seem like a potential buy. However, the stock has seen a staggering decline of 90.19% over the past year, and the latest financials show a net income loss of 9,471,102 in Q1 2026, raising significant concerns about its viability. The primary risk is the 42.80% drop over the last 5 days, indicating a lack of investor confidence and potential volatility.
SGLY cerró en $1.36 el Monday, tras bajar -7.48%
Singularity Future Technology Ltd (SGLY) last closed at $1.38, gaining +1.47%. These facts feed the 1-day and 1-week SGLY price prediction above.
Factores de la previsión
Los factores de la previsión de Singularity Future Technology Ltd (SGLY) 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 SGLY, ordenadas por similitud.
Análisis de estacionalidad de SGLY
Historically the probability of a positive September return for SGLY is 4.76%. January offers the highest probability of a positive month at 55.74%, while March 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.