Serve Robotics Inc

Serve Robotics Inc(SERV)價格預測

$2.880

-0.169 (-5.88%)收盤時

價格預測

1-Day Price Prediction
+0.01%
1-Week Prediction
-2.12%
1-Month Prediction
—
2026 Prediction
—
2030 Prediction
—

在 App 中開啟以解鎖更長週期的預測。

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SERV 相似圖表分析預測

Alphio's SERV stock price prediction model matches the current Serve Robotics Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $2.78 (+0.01%). The 1-week path is $2.72 (-2.12%). 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.

該買進 Serve Robotics Inc 股票嗎?

Given the current price of $3.23, the RSI at 13.018 indicates the stock is heavily oversold, but the significant 72.70% year-to-date decline and ongoing net losses, with a net income of -64127000 USD in Q2 2026, suggest that this is not a good buy right now. The main risk is the ongoing insider selling, which has increased by 133.99% over the last month, indicating a lack of confidence from those closest to the company.

SERV 股價於 Thursday 收在 $2.88,此前下跌 -5.88%

Serve Robotics Inc (SERV) last closed at $2.78, losing -3.47%. These facts feed the 1-day and 1-week SERV price prediction above.

分析師一致預期

Serve Robotics Inc(SERV):買進 5、持有 2、賣出 1。展望:Buy。

Technical signals summary based on buy and sell indicators. The gauge shows the overall market view.

Strong sellSellNeutralBuyStrong buy
Strong buy
Sell
1
Buy
5

預測驅動因素

Serve Robotics Inc(SERV)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

Positive

Bullish

Seasonality

Negative

Bearish

SMA 20

Negative

Bearish

SMA 200

Negative

Bearish

相似圖表形態

近期走勢與 SERV 最接近的股票,依相似度排序。

SERV 季節性分析

Historically the probability of a positive October return for SERV is 11.11%. January offers the highest probability of a positive month at 90.00%, while March is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.

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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。

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