Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 1
cBot
版本 1.0, Jan 2026
Windows 版、Mac 版, Mobile, Web
4.0
评价:4
2
盈利系数
8%
最大回撤
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 2
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 3
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 4
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 5
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 6
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 7
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 8
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 9
Trading product for Temporal_Volatility_Alpha_forPropFirm cBot Indices EURUSD, image 10
10M
交易量
10.64K
盈利点数
22
销售
1.2K
免费安装

前五份副本将以 出售 44 的价格出售。

之后将 涨价78。 于2025年12月开发

1. 执行摘要

时间波动阿尔法 是一种量化均值回归算法,旨在捕捉EURUSD货币对的短期无效性。该策略基于核心前提,即价格偏离统计规范(由波动率包络线定义)时,若发生在特定的高流动性时间窗口内,存在高概率的反转机会。

2. 操作逻辑与方法论

该算法利用多层过滤过程以筛选高质量入场点:

  • 时间门控(时间过滤器): 交易严格限制在高成交量的市场重叠时段(例如伦敦/纽约交易时段)。这种“时间分段”过滤掉了低流动性噪音和非交易时段不可预测的点差扩大。
  • 波动率回归: 入场基于标准差极值触发(24周期布林带,2倍偏差)。系统识别超买/超卖状态,价格统计上可能回归均值。
  • 动量确认: RSI(相对强弱指数)过滤器防止逆势交易,确保仅在动量减弱时入场。

3. 风险管理架构

系统通过严格的风险框架优先保证资本保全:

  • 正期望值: 策略采用固定的 1:2 风险回报比(止损约25点/止盈约50点)。这确保即使胜率低至35%,算法仍保持盈利。
  • 主动交易防御: 一种 波动率调整的移动止损 在持仓盈利9点后立即激活。该“保本防御”机制迅速消除风险,确保“免费交易”并保护浮动权益。

4. 绩效指标(挑战阶段)

在为期7个月的评估阶段,策略表现出卓越的统计稳健性:

  • 净投资回报率: 策略实现了总计 73%的净投资回报率,显著超出资金要求的标准利润目标。
  • 利润因子: 算法保持了较高的 3的利润因子,表明每承担1美元的风险,系统产生了4美元的毛利润。
  • 胜率与一致性: 系统维持了 69%的胜率(81笔交易中56笔获胜)。结合策略固定的1:2风险回报比,这一高命中率产生了高度稳定且呈抛物线形的权益曲线,停滞极少。

5. 高级风险比率

  • 夏普比率(> 3): 表明“优秀”的风险调整回报,确认策略在极低波动性下产生高利润。
  • 卡尔玛比率(> 5): 以约120%的年化速度和估计低于5%的最大回撤,该比率证明策略对深度亏损具有高度抵抗力。
  • 索提诺比率(> 4): 确认“下行波动性”几乎不存在;重大连败的风险在统计上可忽略不计。

该策略已成功通过资金公司挑战。

交易概览
4.0
评价:4
5
25 %
4
50 %
3
25 %
2
0 %
1
0 %
客户评价
March 26, 2026
So far, optimization and execution are very promising. Low-frequency trades combined with a lot of patience give this strategy a real edge, which I am willing to test long-term. After a couple of weeks, only one trade has fired, but it was a successful one. So far, I am happy with this bot.
March 9, 2026
The strategy presents a clean mean-reversion concept using Bollinger Bands, RSI filtering and time-segmented trading windows. However the long-term backtest shows negative performance and optimization logs reveal extremely unrealistic stop-loss values, suggesting a potential parameter overflow or optimization bug. The strategy concept is sound but the current implementation appears unstable.
February 3, 2026
January 26, 2026
Indices
EURUSD
RSI
Bollinger
cTrader Store 提供的所有产品,包括交易机器人、指标和插件,均由第三方开发者提供,仅供信息参考和技术访问之用。cTrader Store 并非经纪商,不提供投资建议、个人推荐或任何未来业绩保证。

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