GA-MSSR：求解RoboTrading的最大Sharpe和Sterling比方法的遗传算法
GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading
摘要：
外汇是全球最大的金融市场，也是波动最大的市场之一。技术分析在外汇市场中起着重要作用，交易算法是利用机器学习技术设计的。大多数文献使用历史价格信息和技术指标进行培训。然而，市场的噪声性质影响了算法的一致性和盈利能力。为了解决这个问题，我们设计了交易规则功能，这些功能来自技术指标和交易规则。优化技术指标的参数以最大限度地提高交易性能。我们还提出了一个新的成本函数，计算风险调整收益率，夏普和英镑比率（SSR），在努力减少的方差和提取的幅度。利用遗传算法最大化夏普与斯特林比率模型（GA-MSSR）设计了一种自动机器人交易策略（RoboTrading）。该实验是在2018年至2019年6个主要货币对的日内数据上进行的。结果一致显示出显着的正回报和交易系统的性能是优越的使用优化的基于规则的功能。使用5分钟AUDUSD货币对获得的最高回报率为每年320%。此外，与基准模型相比，该模型在最大回撤和收益方差等风险因素方面表现最佳。该代码可以在https://github.com/zzzac/rule-based-forextrading-system上访问
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
