$27.110
-0.228 (-0.84%)Al cierre
Predicciones de precio
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Predicción y previsión de SLDE por análisis de gráficos similares
Alphio's SLDE stock price prediction model matches the current Slide Insurance Holdings Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $27.32 (+0.77%). The 1-week path is $26.90 (-0.79%). 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 Slide Insurance Holdings Inc?
Slide Insurance Holdings Inc is a good buy right now due to its current price of $27.07, which is below the analyst price target of $28, indicating potential upside. The company has demonstrated strong financial performance with a 47.9% year-over-year revenue increase in Q2 2026, reaching $386.8 million, and a net income increase of 92.4% to $134.9 million. However, the main risk is the high RSI of 86.709, suggesting the stock may be overbought in the short term.
SLDE cerró en $27.11 el Wednesday, tras bajar -0.84%
Slide Insurance Holdings Inc (SLDE) last closed at $27.11, losing -0.84%. These facts feed the 1-day and 1-week SLDE price prediction above.
Factores de la previsión
Los factores de la previsión de Slide Insurance Holdings Inc (SLDE) combinan patrones gráficos similares, estacionalidad y medias móviles.
Similar patterns
Bullish
Seasonality
Bearish
SMA 20
Bullish
SMA 200
Bullish
Patrones gráficos similares
Acciones cuya trayectoria reciente más se parece a la de SLDE, ordenadas por similitud.
Análisis de estacionalidad de SLDE
Historically the probability of a positive October return for SLDE is 39.47%. July offers the highest probability of a positive month at 100.00%, while November 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.