A Hybrid Pattern Extraction and Confidence-Weighted Volume Framework for Machine Learning-Based Forex Price Forecasting
Keywords:
Feature Engineering, Binary-Coded Patterns, Algorithmic trading, Forex Price Forecasting, Risk ManagementAbstract
Since its inception, the Forex market has attracted various market players, including central banks, financial institutions, and even academic researchers, due to its high volatility and potential profitability following the technical analysis saying that "history tends to repeat itself", RWBCPE captures the historical patterns to anticipate future changes (up or down) within certain possible boundaries to avoid signals that lack confidence thresholds. RWBCPE underwent testing on the EUR/USD currency pair in the context of three different simple machine learning models: Random Forests, Support Vector Machines, and Long Short-Term Memory networks to demonstrate its strength and efficiency, even using simple, conventional machine learning and deep learning models. The framework achieved a higher accuracy than the traditional technical indicators, such as RSI and MACD. To evaluate the practical feasibility of the framework, we conducted a 25-year training set from 2000 to 2025 and a 1-year backtest from 2025 to 2026 with an initial balance of $10,000. The RWBCPE-driven strategy achieved 65.96% accuracy and 67.29% precision, growing the account balance to $34,844.75. The system also recorded a profit ratio of 2.17 and a maximum drawdown of 4.6%, demonstrating strong predictive performance and low-risk trading through confidence-weighted position size.
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Copyright (c) 2025 Maysam Yazdanpanahi; Morteza Zahedi, Mohammad Mehdi Hosseini (Author)
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