Enhancing Forex Forecasting Accuracy: The Impact of Hybrid Variable Sets in Cognitive Algorithmic Trading Systems

TL;DR

This study develops an AI-based forex trading system for EUR-USD, integrating macroeconomic and technical variables to improve prediction accuracy and profitability in high-frequency trading environments.

Contribution

It introduces a hybrid feature set combining fundamental and technical data, and evaluates their impact on forex prediction performance using machine learning and backtesting.

Findings

Fundamental variables improve predictive accuracy more than technical indicators.

Hybrid models outperform single-feature models in profitability.

Technical features contribute significantly to short-term trading signals.

Abstract

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.