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A deep learning based stock trading model with 2-D CNN trend detection

机译：基于深度学习的二维CNN趋势检测股票交易模型

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The success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account.
机译：卷积神经网络在计算机视觉领域的成功吸引了来自其他领域的许多研究人员的关注。财务预测是其中积极使用神经网络的研究领域之一。在本文中，我们提出了一种使用CNN预测股票价格走势的新方法。为了避免市场剧烈波动并最大程度地提高利润，ETF被用作主要金融资产。我们从金融时间序列数据中提取常用的趋势指标和动量指标，并将其用作我们的功能。采用滑动窗口方法，我们通过拍摄每天在窗口范围内的快照来生成图像。然后，我们执行每日预测，即用于预测ETF价格的回归和用于预测第二天价格变动的分类，可以对其进行修改以估计每周或每月趋势。为了增加图像数量，我们使用了许多ETF。最后，我们通过进行票据交易并计算最终资本来评估我们的方法。我们还将我们的方法的性能与常用的经典交易策略进行比较。我们的结果表明，考虑到交易成本的现实价值，我们可以以72％的准确度预测第二天的价格，并以初始资本的5：1结束。

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