What does this research mean for the field?

Adding an AI-based predictive decision layer to selectively activate algorithmic trading strategies improves risk-adjusted returns and reduces drawdowns compared to continuous trading, with tree-based models performing best for major forex pairs and LSTMs for gold. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

February 18, 2026Enterprise Information Systems1 citations

View Full Paper

An AI-based hyperautomation framework for algorithmic trading: forecasting automated trading strategy profitability from market data

CLChun‐Chia LeeNational Taiwan Normal University KHKuo‐Lun HsiaoNational Taichung University of Science and Technology PTPo-Chun TsaiNational Taichung University of Science and Technology

Key Points

Key points are not available for this paper at this time.

Abstract

An “always-on” Expert Advisor can trade even in poor market conditions. This study adds a simple decision layer that turns the EA on or off each week. Using weekly data, AI models predict whether the EA is likely to make money next week and activate it only when the outlook is positive. Logistic Regression, XGBoost, and LightGBM are compared with LSTM-based models. Tests on 2020–2025 data for five instruments and MT5 backtests show higher Sharpe Ratio and Profit Factor, with lower maximum drawdown, than keeping the EA always on. Tree models work best for major FX; LSTMs suit gold.

Ask AI

Helpful

Bookmark

View Full Paper

Ask AI

Helpful

Bookmark

View Full Paper

An AI-based hyperautomation framework for algorithmic trading: forecasting automated trading strategy profitability from market data

Key Points

Abstract

Cite This Study

Also Consider

Also Consider