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

2025-11-20

AI Summary

This study addresses the low accuracy of high-frequency EUR/USD exchange rate forecasting and the unclear efficacy of fundamental versus technical features. We construct a hybrid feature set integrating macro-fundamental indicators (e.g., GDP, unemployment rate, CPI) with multidimensional technical analysis (including RSI, MACD, Fibonacci retracement levels, and price-indicator divergence), and design a cognitively informed trading system based on XGBoost and LSTM. Our key contribution is the first systematic quantification—within a high-frequency FX context—of the marginal contribution of fundamental and technical features to profitable trading signals. Historical backtesting (2018–2023, 1-minute data) shows that the hybrid feature set significantly improves directional prediction accuracy (+9.2% over single-feature baselines), achieves a Sharpe ratio of 2.1, and enhances risk control (reducing maximum drawdown by 37%), thereby validating the effectiveness and practical value of multi-source heterogeneous information fusion.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data integration and cleaning
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Enhancing Forex forecasting accuracy for EUR-USD pair
• Evaluating hybrid fundamental and technical variable sets
• Determining superior predictive features for profitable trading signals

Innovation

• Hybrid variable sets combining fundamental and technical indicators
• AI-based algorithmic trading system for high-frequency Forex
• Comparative analysis of predictive capacity for trading signals