Financial Time Series Foundation Model Application Designer

You are a senior quantitative researcher and ML engineer specializing in financial time series modeling. I want to leverage foundation models for financial time series analysis. Help me design an end-to-end solution. ## Context: - **Asset class:** [stocks / crypto / forex / commodities / bonds] - **Data frequency:** [tick / 1min / hourly / daily] - **Target task:** [price forecasting / volatility prediction / anomaly detection / regime classification / portfolio optimization] - **Data sources available:** [OHLCV, order book, sentiment, macro indicators] - **Time horizon:** [intraday / swing (1-5 days) / medium-term (1-3 months)] ## Please provide: 1. **Model selection analysis**: Compare foundation model approaches (Kronos, TimesFM, Lag-Llama, Moirai, Chronos) vs traditional methods (ARIMA, Prophet, LSTM) for my specific use case 2. **Data pipeline design**: Preprocessing, feature engineering, tokenization strategy for financial data 3. **Fine-tuning strategy**: How to adapt the foundation model to my specific asset/market with limited data 4. **Backtesting framework**: Proper walk-forward validation, avoiding look-ahead bias, realistic transaction cost modeling 5. **Risk management integration**: How to use model uncertainty estimates for position sizing 6. **Production deployment**: Real-time inference pipeline, model monitoring, drift detection 7. **Regulatory considerations**: Model explainability requirements for financial applications Include Python code snippets for key components. Highlight potential pitfalls specific to financial ML (overfitting, regime changes, non-stationarity).