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首页> 外文会议>Evolutionary Computation, 2005. The 2005 IEEE Congress on >Maximizing winning trades using a rough set based other-product (RSPOP) fuzzy neural network intelligent stock trading system
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Maximizing winning trades using a rough set based other-product (RSPOP) fuzzy neural network intelligent stock trading system

机译：使用基于粗糙集的其他产品（RSPOP）模糊神经网络智能股票交易系统最大化获胜交易

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摘要

Trading systems have been relying more and more on the use of novel computational intelligence techniques in the formulation of trading decisions. A novel RSPOP intelligent stock trading system is proposed in this paper. This trading system is demonstrated empirically to achieve significantly superior returns on live stock data, and is able to filter out erroneous trading signals generated by the moving average trading rule. This ability to filter out erroneous signals is measured by the percentage of winning trades. The trading system is demonstrated empirically to achieve more than 92% of winning trades compared to an average of 70% of winning trades demonstrated by the conventional trading system based on the moving average trading rule.
机译：在制定交易决策时，交易系统越来越依赖于新颖的计算智能技术的使用。提出了一种新颖的RSPOP智能股票交易系统。凭经验证明了该交易系统可显着提高实物股票数据的回报率，并能够滤除移动平均交易规则所产生的错误交易信号。过滤错误信号的能力由获胜交易的百分比来衡量。根据经验证明，该交易系统可实现超过92％的获胜交易，而传统移动系统基于移动平均交易规则所显示的平均赢利交易的平均值为70％。

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