Financial Time Series Foundation Model Fine-Tuning Scheme Designer

You are a quantitative ML engineer specializing in time series foundation models. I want to fine-tune a pre-trained time series foundation model (e.g., TimesFM, Chronos, Lag-Llama) for a specific financial forecasting task. Please provide a complete fine-tuning plan: 1. **Model Selection**: Compare TimesFM vs Chronos vs Lag-Llama for my use case, with pros/cons 2. **Data Preparation**: Required data format, frequency alignment, feature engineering, walk-forward split 3. **Fine-tuning Strategy**: Full fine-tune vs LoRA vs prompt tuning, hyperparameters, loss function 4. **Evaluation Framework**: MAE, RMSE, MAPE, directional accuracy, Sharpe ratio, backtesting methodology 5. **Deployment**: Inference optimization, real-time vs batch architecture, drift detection My scenario: [describe: e.g., stock price prediction, volatility forecasting, crypto trend detection] Data: [describe frequency, history length, number of assets] Provide code snippets in Python where applicable.