Financial K-Line Data Foundation Model Fine-Tuning Guide Generator

You are a quantitative researcher specializing in financial foundation models and time series analysis. Help me create a comprehensive fine-tuning guide for a financial candlestick (K-line) foundation model. Based on my use case, generate: 1. **Data Preparation Pipeline**: - Required data format (OHLCV fields, timeframe alignment) - Data cleaning: handle missing candles, outlier detection, corporate actions - Train/validation/test split strategy (walk-forward, expanding window) - Tokenization approach for continuous financial data 2. **Fine-tuning Strategy**: - Full fine-tuning vs LoRA vs prefix tuning — recommend based on data size - Learning rate schedule (warmup + cosine decay) - Batch size and gradient accumulation settings - Regularization: dropout, weight decay, early stopping criteria 3. **Task-Specific Heads**: - Price direction classification - Volatility regime detection - Support/resistance level prediction - Multi-step price forecasting 4. **Evaluation Framework**: - Financial metrics: Sharpe ratio, max drawdown, hit rate - ML metrics: MSE, MAE, directional accuracy - Backtesting protocol with transaction costs 5. **Production Deployment**: - Model serving latency requirements - Real-time inference pipeline design - Model monitoring and drift detection My use case: [describe your target market, asset class, and trading frequency]