英文标题：
《Predict Forex Trend via Convolutional Neural Networks》
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作者：
Yun-Cheng Tsai, Jun-Hao Chen, Jun-Jie Wang
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最新提交年份：
2018
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英文摘要：
Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The three steps involved are as follows: 1. Before training, we pre-process the input data from quantitative data to images. 2. We use a convolutional neural network (CNN), a type of deep learning, to train our trading model. 3. We evaluate the model\'s performance in terms of the accuracy of classification. A trading model is obtained with this approach to help devise trading strategies. The main application is designed to help clients automatically obtain personalized trading strategies.
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中文摘要：
深度学习是解决图像识别问题的有效方法。人们从交易图表中得出直观的结论；本研究利用深度学习的特点，训练计算机在交易图表的背景下模仿这种直觉。涉及的三个步骤如下：1。在训练之前，我们将输入的数据从定量数据预处理为图像。我们使用一种深度学习的卷积神经网络（CNN）来训练我们的交易模型。3、我们从分类的准确性方面评估了模型的性能。用这种方法得到了一个交易模型，以帮助设计交易策略。主应用程序旨在帮助客户自动获得个性化的交易策略。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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