$0.920
-0.053 (-5.75%)Al cierre
Predicciones de precio
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Predicción y previsión de IQST por análisis de gráficos similares
Alphio's IQST stock price prediction model matches the current iQSTEL Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.92 (-0.40%). The 1-week path is $0.91 (-1.12%). 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 iQSTEL Inc?
While IQSTEL Inc shows potential for growth with a projected revenue increase of 59% year-over-year and a significant acquisition that could enhance profitability, the current price of $0.98 is still below its 200-day moving average of $2.023, indicating a bearish trend. Additionally, the RSI of 43.53 suggests that the stock is nearing oversold territory but has not yet confirmed a reversal. The primary risk is the company's substantial net losses, with a net income of -2.66 million in Q2 2026, which raises concerns about its financial health.
IQST cerró en $0.92 el Tuesday, tras bajar -5.75%
iQSTEL Inc (IQST) last closed at $0.9201, losing -5.75%. These facts feed the 1-day and 1-week IQST price prediction above.
Factores de la previsión
Los factores de la previsión de iQSTEL Inc (IQST) 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 IQST, ordenadas por similitud.
Análisis de estacionalidad de IQST
Historically the probability of a positive September return for IQST is 42.86%. December offers the highest probability of a positive month at 48.84%, while January 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.