$2.530
-0.124 (-4.89%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
CURI 相似圖表分析預測
Alphio's CURI stock price prediction model matches the current CuriosityStream Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $2.57 (+1.61%). The 1-week path is $2.50 (-1.04%). 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.
該買進 CuriosityStream Inc 股票嗎?
CuriosityStream Inc. is a good buy right now due to its current price of $2.54, which is significantly below its 52-week high of $5.10, indicating potential for recovery. The company has shown a remarkable gross margin improvement to 72.84% in Q2 2026, coupled with a substantial year-over-year revenue growth of 22%, which positions it well for future profitability. The main risk is its high P/E ratio of 6403.56, which may indicate overvaluation if growth does not materialize as expected.
CURI 股價於 Thursday 收在 $2.53,此前下跌 -4.89%
CuriosityStream Inc (CURI) last closed at $2.53, losing -4.89%. These facts feed the 1-day and 1-week CURI price prediction above.
預測驅動因素
CuriosityStream Inc(CURI)的預測驅動因素綜合了相似的圖表形態、季節性與移動平均線。
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bearish
SMA 200
Bearish
相似圖表形態
近期走勢與 CURI 最接近的股票,依相似度排序。
CURI 季節性分析
Historically the probability of a positive November return for CURI is 90.70%. December offers the highest probability of a positive month at 100.00%, while February is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
CURI 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Nov 2026 | $6.10 | $6.85 | $7.45 | +170.58% |
| Dec 2026 | $6.26 | $6.28 | $6.93 | +148.02% |
2026 年,CuriosityStream Inc(CURI)預計區間為 $6.10 至 $7.45。
CuriosityStream Inc 2026 年月度預測
2026 年 Nov,CuriosityStream Inc 預計均價 $6.85,最低 $6.10,最高 $7.45。
2026 年 Dec,CuriosityStream Inc 預計均價 $6.28,最低 $6.26,最高 $6.93。
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本頁僅供研究參考,不構成投資建議。模型可能出錯。過往表現不代表未來結果。