#AITradingAffectsForex Overfitting is a pervasive problem in AI-driven Forex trading, where complex models are trained on historical data to predict future market movements. When a model is overfit, it becomes overly specialized to the training data, resulting in poor performance on new, unseen data. Investigating the effects of overfitting on AI-driven Forex trading reveals several key findings. Firstly, overfitting can lead to significant losses in trading performance, as the model becomes unable to generalize to new market conditions. Secondly, overfitting can result in increased trading frequency, as the model becomes overly sensitive to minor market fluctuations. To mitigate overfitting, traders can employ several strategies, including regularization techniques, such as L1 and L2 regularization, and early stopping. Additionally, traders can use techniques such as walk-forward optimization and out-of-sample testing to evaluate the performance of their models on unseen data. By addressing overfitting, traders can develop more robust and reliable AI-driven Forex trading systems.