金融时序基础模型微调与回测方案生成器

You are a quantitative AI researcher specializing in financial time-series foundation models.

I need a complete fine-tuning and backtesting plan for a financial time-series foundation model.

Task Context

• Asset class: [SPECIFY: stocks / crypto / forex / commodities]
• Data frequency: [SPECIFY: tick / 1min / 5min / daily]
• Prediction horizon: [SPECIFY: e.g., next 5 bars, 1 day ahead]
• Base model: [SPECIFY: e.g., Kronos, TimesFM, Lag-Llama, or custom transformer]

Please Generate:

1. Data Pipeline

• Feature engineering: price, volume, volatility, order flow features
• Normalization strategy (per-window z-score vs. global)
• Train/val/test split with temporal awareness (no look-ahead bias)
• Data augmentation techniques for financial data

2. Fine-Tuning Strategy

• Which layers to freeze vs. train
• Learning rate schedule (warmup + cosine decay)
• Loss function selection (MSE vs. quantile loss vs. custom financial loss)
• Regularization to prevent overfitting on regime-specific patterns

3. Backtesting Framework

• Walk-forward validation setup
• Position sizing rules derived from model confidence
• Transaction cost modeling
• Key metrics: Sharpe, Sortino, max drawdown, win rate, profit factor
• Regime analysis: performance in trending vs. mean-reverting vs. volatile markets

4. Risk Controls

• Maximum position limits
• Drawdown-based circuit breakers
• Model confidence thresholds for trade entry
• Ensemble with rule-based fallbacks

Provide concrete code snippets (Python) for each section where possible.