融合宏观与技术指标，提升欧元美元汇率预测精度。
Enhancing Forex Forecasting Accuracy: The Impact of Hybrid Variable Sets in Cognitive Algorithmic Trading Systems
- 结合欧元区与美国的宏观经济数据及技术指标构建输入特征
- 回测显示该系统在历史数据上具备较高盈利与风险控制能力
- 验证了混合变量集比单一类型变量更具预测优势
本文提出一种面向欧元-美元（EUR-USD）货币对的高频率外汇市场人工智能算法交易系统。方法上整合了来自欧元区和美国的关键基本面宏观经济变量（如国内生产总值、失业率）以及全面的技术分析变量（包括指标、震荡指标、斐波那契水平和价格背离）。通过标准机器学习评估指标量化预测准确性，并基于历史数据进行回测模拟以评估交易盈利性与风险表现。研究最终通过对比分析，确定哪类输入特征——基本面或技术面——在生成可盈利交易信号方面具有更强且更可靠的预测能力。
原文摘要 · Abstract (English)
This paper presents the implementation of an advanced artificial intelligence-based algorithmic trading system specifically designed for the EUR-USD pair within the high-frequency environment of the Forex market. The methodological approach centers on integrating a holistic set of input features: key fundamental macroeconomic variables (for example, Gross Domestic Product and Unemployment Rate) collected from both the Euro Zone and the United States, alongside a comprehensive suite of technical variables (including indicators, oscillators, Fibonacci levels, and price divergences). The performance of the resulting algorithm is evaluated using standard machine learning metrics to quantify predictive accuracy and backtesting simulations across historical data to assess trading profitability and risk. The study concludes with a comparative analysis to determine which class of input features, fundamental or technical, provides greater and more reliable predictive capacity for generating profitable trading signals.
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