$0.608
+0.012 (+1.96%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
LGPS 相似圖表分析預測
Alphio's LGPS stock price prediction model matches the current LogProstyle Inc tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.61 (+0.34%). The 1-week path is $0.60 (-0.65%). 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 LogProstyle Inc 股票嗎?
Logprostyle Inc. is not a strong buy at this moment due to its current price of 0.61, which is significantly below its 52-week high of 1.60, indicating a 61.88% drop from its peak. Additionally, the RSI of 43.914 suggests the stock is nearing oversold territory, but not quite there yet, indicating potential for further downside. The stock has also seen a year-to-date change of -38.48%, reflecting ongoing struggles in performance. The main risk lies in the high debt-to-equity ratio of 406.41%, which could impact financial stability.
LGPS 股價於 Friday 收在 $0.61,此前上漲 +1.96%
LogProstyle Inc (LGPS) last closed at $0.6078, gaining +1.96%. These facts feed the 1-day and 1-week LGPS price prediction above.
預測驅動因素
LogProstyle Inc(LGPS)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 LGPS 最接近的股票,依相似度排序。
LGPS 季節性分析
Historically the probability of a positive November return for LGPS is 0.00%. July offers the highest probability of a positive month at 34.09%, while February is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
LGPS 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Nov 2026 | $0.92 | $1.08 | $1.21 | +77.91% |
| Dec 2026 | $0.96 | $1.13 | $1.32 | +85.27% |
2026 年,LogProstyle Inc(LGPS)預計區間為 $0.92 至 $1.32。
LogProstyle Inc 2026 年月度預測
2026 年 Nov,LogProstyle Inc 預計均價 $1.08,最低 $0.92,最高 $1.21。
2026 年 Dec,LogProstyle Inc 預計均價 $1.13,最低 $0.96,最高 $1.32。
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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。