英文标题：
《Evolving intraday foreign exchange trading strategies utilizing multiple
  instruments price series》
---
作者：
Simone Cirillo, Stefan Lloyd, Peter Nordin
---
最新提交年份：
2014
---
英文摘要：
  We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system\'s principal features are the evolution of free-form strategies which do not rely on any prior models and the utilization of price series from multiple instruments as input data. This latter feature constitutes an innovation with respect to previous works documented in literature. In this article we utilize Open, High, Low, Close bar data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY currency pairs. We will test the implementation analyzing the in-sample and out-of-sample performance of strategies for trading the USD.JPY obtained across multiple algorithm runs. We will also evaluate the differences between strategies selected according to two different criteria: one relies on the fitness obtained on the training set only, the second one makes use of an additional validation dataset. Strategy activity and trade accuracy are remarkably stable between in and out of sample results. From a profitability aspect, the two criteria both result in strategies successful on out-of-sample data but exhibiting different characteristics. The overall best performing out-of-sample strategy achieves a yearly return of 19%.
---
中文摘要：
我们提出了一种生成外汇交易策略的遗传编程架构。该系统的主要特点是不依赖任何先验模型的自由形式策略的演变，以及利用多个工具的价格序列作为输入数据。后一个特征构成了对文献中记录的以前作品的创新。在本文中，我们以5分钟的频率为AUD使用开放、高、低、闭合条数据。美元，欧元。美元，英镑。美元和美元。日元货币对。我们将通过分析美元交易策略的样本内和样本外绩效来测试实现。通过多次算法运行获得JPY。我们还将评估根据两个不同标准选择的策略之间的差异：一个仅依赖于在训练集上获得的适应度，另一个利用额外的验证数据集。在样本内和样本外结果之间，策略活动和交易准确性非常稳定。从盈利能力的角度来看，这两个标准都会导致策略在样本外数据上成功，但表现出不同的特征。整体表现最佳的样本外策略实现了19%的年回报率。
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
--

---
PDF下载：
-->