论文标题
GA-MSSR：遗传算法最大化夏普和固定比方法的机器人法
GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading
论文作者
论文摘要
外汇是世界上最大的金融市场，也是最动荡的市场之一。技术分析在外汇市场中起着重要的作用，并且使用机器学习技术设计了交易算法。大多数文献使用历史价格信息和技术指标进行培训。但是，市场的嘈杂性影响算法的一致性和盈利能力。为了解决这个问题，我们设计了从技术指标和交易规则中得出的交易规则功能。技术指标的参数被优化以最大程度地提高交易绩效。我们还提出了一种新型的成本函数，该功能计算了风险调整后的回报，Sharpe和Sterling比率（SSR），以减少差异和下降的幅度。自动机器人交易（Robotrading）策略是通过提出的遗传算法最大化Sharpe和Sterling Hatio模型（GA-MSSR）模型设计的。该实验是根据2018年至2019年的6个主要货币对的日内数据进行的。结果始终显示出明显的正回报，并且使用基于规则的优化功能，交易系统的性能优越。使用5分钟的AUDUSD货币对每年获得的最高回报率为320％。此外，与基准模型相比，提议的模型在风险因素上达到了最佳性能，包括最大的缩减和差异。可以从https://github.com/zzzac/rule-lase-forextrading-system访问代码
Foreign exchange is the largest financial market in the world, and it is also one of the most volatile markets. Technical analysis plays an important role in the forex market and trading algorithms are designed utilizing machine learning techniques. Most literature used historical price information and technical indicators for training. However, the noisy nature of the market affects the consistency and profitability of the algorithms. To address this problem, we designed trading rule features that are derived from technical indicators and trading rules. The parameters of technical indicators are optimized to maximize trading performance. We also proposed a novel cost function that computes the risk-adjusted return, Sharpe and Sterling Ratio (SSR), in an effort to reduce the variance and the magnitude of drawdowns. An automatic robotic trading (RoboTrading) strategy is designed with the proposed Genetic Algorithm Maximizing Sharpe and Sterling Ratio model (GA-MSSR) model. The experiment was conducted on intraday data of 6 major currency pairs from 2018 to 2019. The results consistently showed significant positive returns and the performance of the trading system is superior using the optimized rule-based features. The highest return obtained was 320% annually using 5-minute AUDUSD currency pair. Besides, the proposed model achieves the best performance on risk factors, including maximum drawdowns and variance in return, comparing to benchmark models. The code can be accessed at https://github.com/zzzac/rule-based-forextrading-system
