$0.419
-0.298 (-71.12%)收盤時
價格預測
在 App 中開啟以解鎖更長週期的預測。
UOKA 相似圖表分析預測
Alphio's UOKA stock price prediction model matches the current MDJM Ltd tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $0.41 (-3.12%). The 1-week path is $0.39 (-5.69%). 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 MDJM Ltd 股票嗎？
MDJM Ltd (UOKA) is not a strong buy at this time for a beginner investor with a long-term strategy. The technical indicators suggest a bearish trend with oversold conditions, but no significant positive catalysts or trading signals are present to justify immediate action. The lack of financial data, valuation metrics, and significant trading trends further supports a cautious approach.
UOKA 股價於 Thursday 收在 $0.42，此前下跌 -71.12%
MDJM Ltd (UOKA) last closed at $0.4188, losing -71.12%. These facts feed the 1-day and 1-week UOKA price prediction above.
預測驅動因素
MDJM Ltd（UOKA）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
Bearish
Seasonality
Bearish
SMA 20
Bearish
SMA 200
Bearish
相似圖表形態
近期走勢與 UOKA 最接近的股票，依相似度排序。
UOKA 季節性分析
Historically the probability of a positive September return for UOKA is 0.00%. January offers the highest probability of a positive month at 8.20%, while March is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
UOKA 十年價格預測
| 月份 | 最低價 | 平均價 | 最高價 | 潛在報酬率 |
|---|---|---|---|---|
| Sep 2026 | $2.63 | $3.06 | $3.09 | +631.72% |
| Oct 2026 | $2.86 | $3.03 | $3.21 | +622.95% |
| Nov 2026 | $2.70 | $3.18 | $3.56 | +658.29% |
| Dec 2026 | $2.48 | $2.93 | $3.29 | +599.87% |
2026 年，MDJM Ltd（UOKA）預計區間為 $2.48 至 $3.56。
MDJM Ltd 2026 年月度預測
2026 年 Sep，MDJM Ltd 預計均價 $3.06，最低 $2.63，最高 $3.09。
2026 年 Oct，MDJM Ltd 預計均價 $3.03，最低 $2.86，最高 $3.21。
2026 年 Nov，MDJM Ltd 預計均價 $3.18，最低 $2.70，最高 $3.56。
2026 年 Dec，MDJM Ltd 預計均價 $2.93，最低 $2.48，最高 $3.29。
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。