$48.930
-1.551 (-3.17%)收盘时
价格预测
在 App 中打开以解锁更长周期的预测。
BP 相似图表分析预测
Alphio's BP stock price prediction model matches the current BP PLC tape against historical breakout patterns, then publishes 1-day and 1-week targets first. The 1-day print is $49.94 (+0.30%). The 1-week path is $47.97 (-3.67%). 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.
该买入 BP PLC 股票吗?
BP PLC appears to be a good buy right now due to its current price of $51.32, which is below the average analyst price target of $53 to $57. The company's strong financial performance is highlighted by a gross margin of 20.58% in the latest quarter and a forward P/E ratio of 9.84, suggesting it is undervalued compared to its earnings potential. However, the main risk is the high RSI of 83.239, indicating that the stock may be overbought in the short term, which could lead to a price correction.
BP 股价于 Tuesday 收于 $48.93,此前下跌 -3.17%
BP PLC (BP) last closed at $49.7949982, gaining +1.77%. These facts feed the 1-day and 1-week BP price prediction above.
预测驱动因素
BP PLC(BP)的预测驱动因素综合了相似的图表形态、季节性与移动平均线。
Similar patterns
Bullish
Seasonality
Bullish
SMA 20
Bullish
SMA 200
Bullish
相似图表形态
近期走势与 BP 最接近的股票,按相似度排序。
BP 季节性分析
Historically the probability of a positive October return for BP is 65.15%. March offers the highest probability of a positive month at 100.00%, while November is the weakest seasonal window. Alphio blends this calendar with technical signals and similar chart pattern matching before it writes the forecast.
BP 十年价格预测
| 月份 | 最低价 | 平均价 | 最高价 | 潜在回报率 |
|---|---|---|---|---|
| Oct 2026 | $24.30 | $24.46 | $26.30 | -50.88% |
| Nov 2026 | $28.67 | $30.96 | $32.46 | -37.83% |
| Dec 2026 | $23.87 | $27.29 | $28.21 | -45.20% |
2026 年,BP PLC(BP)预计区间为 $23.87 至 $32.46。
BP PLC 2026 年月度预测
2026 年 Oct,BP PLC 预计均价 $24.46,最低 $24.30,最高 $26.30。
2026 年 Nov,BP PLC 预计均价 $30.96,最低 $28.67,最高 $32.46。
2026 年 Dec,BP PLC 预计均价 $27.29,最低 $23.87,最高 $28.21。
本页仅供研究参考,不构成投资建议。模型可能出错。过往表现不代表未来结果。