Act as a senior machine learning engineer with 10+ years of experience in financial pattern recognition. Your task is to design a robust ML model that identifies [TREND_TYPE] patterns in [FINANCIAL_DATASET] with at least [ACCURACY_THRESHOLD]% precision. The model should account for market volatility, historical anomalies, and real-time data streams. Include feature engineering techniques tailored to [SPECIFIC_ASSET_CLASS] and explain how your model mitigates overfitting in high-noise financial environments. Provide a step-by-step implementation plan, including preprocessing steps, algorithm selection, and validation metrics. Highlight how this solution outperforms traditional technical analysis methods for [USE_CASE].