Predicting Euro to Dollar Exchange Rates using Machine Learning

In the fast-moving world of forex trading, accurately predicting currency trends can significantly impact investment decisions. The EUR/USD currency pair, one of the most actively traded pairs globally, experiences fluctuations driven by various market factors, including economic reports, geopolitical developments, and market sentiment. Traders, ranging from experienced professionals to individual investors, constantly seek strategies to gain a competitive advantage. This paper explores the practical use of machine learning to predict trends in the EUR/USD market. By leveraging historical market data, we extract key technical indicators such as Average True Range, Relative Strength Index, and Simple Moving Averages widely used tools in technical analysis. These features are fed into a K-Nearest Neighbors algorithm to predict whether the market will trend upwards, downwards, or remain uncertain in the near term. This approach is particularly valuable as it combines traditional technical analysis with the advanced capabilities of machine learning, providing traders with a more data-driven method to guide their decisions. In addition, our model incorporates moving averages and trend slopes to refine its predictions, mimicking how traders analyze market behavior over time. We employ a time-series split validation technique to simulate real-world market conditions and ensure the model’s robustness in handling unseen data. The results demonstrate promising levels of accuracy, offering a valuable tool for traders looking to enhance their forecasting abilities and make better-informed decisions in the ever-evolving forex market. Further, this method highlights the potential of machine learning in adapting to dynamic market conditions and evolving alongside the trading landscape