论文标题
使用多层Perceptron的中挥发性资产的股票交易系统
A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron
论文作者
论文摘要
股票市场预测是一个有希望的利润的利润丰厚的领域，但并非没有困难，而对于某些人来说，甚至可能是失败的原因。金融市场本质上是复杂的，非线性的和混乱的，这意味着准确地预测其一部分资产的价格变得非常复杂。在本文中，我们提出了一个股票交易系统，其主要核心是前馈深度神经网络（DNN），以预测纽约股票交易所（NYSE）股票市场上Abercrombie＆Fitch Co.（ANF）发行的股票的接下来30天的价格。 我们详细阐述的系统计算了最有效的技术指标，将其应用于DNNS计算的预测，用于产生交易。结果表明，诸如夏普，排序和Calmar比率分别为2.194、3.340和12.403的预期率诸如预期比率为2.112％。作为验证，我们在系统中采用了一个回溯模拟模块，该模块将交易映射到由ANF资产上公开市场的最后30天组成的实际测试数据。总体而言，结果有望在一个月的100美元预算中，在一个月内将总利润系数为3.2％。这是可能的，因为系统通过选择最有效，最有效的交易来减少交易数量，从而节省了佣金和滑倒成本。
Stock market forecasting is a lucrative field of interest with promising profits but not without its difficulties and for some people could be even causes of failure. Financial markets by their nature are complex, non-linear and chaotic, which implies that accurately predicting the prices of assets that are part of it becomes very complicated. In this paper we propose a stock trading system having as main core the feed-forward deep neural networks (DNN) to predict the price for the next 30 days of open market, of the shares issued by Abercrombie & Fitch Co. (ANF) in the stock market of the New York Stock Exchange (NYSE). The system we have elaborated calculates the most effective technical indicator, applying it to the predictions computed by the DNNs, for generating trades. The results showed an increase in values such as Expectancy Ratio of 2.112% of profitable trades with Sharpe, Sortino, and Calmar Ratios of 2.194, 3.340, and 12.403 respectively. As a verification, we adopted a backtracking simulation module in our system, which maps trades to actual test data consisting of the last 30 days of open market on the ANF asset. Overall, the results were promising bringing a total profit factor of 3.2% in just one month from a very modest budget of $100. This was possible because the system reduced the number of trades by choosing the most effective and efficient trades, saving on commissions and slippage costs.
