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.

翻译：本文介绍了一种基于先进人工智能的算法交易系统的实现，该系统专为外汇市场高频环境下的欧元-美元货币对设计。方法论的核心在于整合一套全面的输入特征集：包括从欧元区和美国收集的关键基础宏观经济变量（例如国内生产总值和失业率），以及一整套技术变量（涵盖指标、振荡器、斐波那契水平和价格背离）。通过标准机器学习指标量化预测精度，并利用历史数据进行回测模拟以评估交易盈利能力和风险，从而评估所得算法的性能。研究最后通过比较分析，确定哪一类输入特征——基础变量或技术变量——在生成盈利交易信号方面提供更强且更可靠的预测能力。