$1.450
-0.128 (-8.81%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
GP 相似圖表分析預測
Alphio's GP stock price prediction model matches the current GreenPower Motor Company Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $1.42 (-2.27%). The 1-week path is $1.41 (-2.60%). 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 GreenPower Motor Company Inc 股票嗎?
Currently, GreenPower Motor Company is not a good buy. The stock is trading at $1.45, down 63.29% year-to-date, and has a low RSI of 30.83, indicating oversold conditions. However, the forward P/E ratio of 51.28 suggests that investors have high expectations for future growth, which may not be justified given the recent performance.
GP 股價於 Friday 收在 $1.45,此前下跌 -8.81%
GreenPower Motor Company Inc (GP) last closed at $1.45, losing -8.81%. These facts feed the 1-day and 1-week GP price prediction above.
預測驅動因素
GreenPower Motor Company Inc(GP)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
Bearish
SMA 20
Bearish
SMA 200
Bearish
相似圖表形態
近期走勢與 GP 最接近的股票,依相似度排序。
GP 季節性分析
Historically the probability of a positive October return for GP is 0.00%. January offers the highest probability of a positive month at 60.66%, while October is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
GP 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Oct 2026 | $0.35 | $0.39 | $0.47 | -73.33% |
| Nov 2026 | $0.33 | $0.47 | $0.54 | -67.86% |
| Dec 2026 | $0.31 | $0.38 | $0.51 | -73.48% |
2026 年,GreenPower Motor Company Inc(GP)預計區間為 $0.31 至 $0.54。
GreenPower Motor Company Inc 2026 年月度預測
2026 年 Oct,GreenPower Motor Company Inc 預計均價 $0.39,最低 $0.35,最高 $0.47。
2026 年 Nov,GreenPower Motor Company Inc 預計均價 $0.47,最低 $0.33,最高 $0.54。
2026 年 Dec,GreenPower Motor Company Inc 預計均價 $0.38,最低 $0.31,最高 $0.51。
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。