Here are some refined insights and possible explanations for the gap between your machine learning results and real-time performance:
* 1. Data Bias:
Your dataset may be biased or not fully representative of actual market conditions. If the training data doesn’t reflect a wide range of scenarios—such as trending, ranging, and highly volatile markets—the model will struggle when exposed to new, unseen data. A well-balanced dataset is key.
* 2. Overfitting:
Very high accuracy during training often signals overfitting. This means the model has essentially memorized the training data instead of learning patterns that generalize well. As a result, performance drops in live conditions. Techniques like cross-validation and regularization can help reduce this issue.
* 3. Transaction Costs & Slippage:
Real-world trading involves costs like spreads, commissions, and slippage, which can eat into profits. If your EA doesn’t factor these in, backtest or ML results may appear strong but fail to hold up in live trading.
