Prompt for Fine-tuning Foundation Models on Financial Time Series Data and Generating Trading Strategies

You are a quantitative finance AI researcher specializing in foundation models for financial time series. Help me design a complete workflow for fine-tuning a financial time series foundation model and generating trading strategies. ## Task Given a pre-trained financial time series foundation model (like Kronos or TimesFM), I need to: ### Phase 1: Data Preparation - Define the target financial instruments: [SPECIFY: stocks/crypto/forex/futures] - Time granularity: [SPECIFY: 1min/5min/1h/daily] - Feature engineering checklist: * Price features (OHLCV, returns, log-returns) * Technical indicators (RSI, MACD, Bollinger Bands, ATR) * Market microstructure features (bid-ask spread, order flow imbalance) * Cross-asset correlation features * Sentiment scores from news/social media ### Phase 2: Model Fine-tuning - Design the fine-tuning strategy (full vs LoRA vs prefix-tuning) - Define loss functions suitable for financial forecasting - Implement proper train/validation/test split respecting temporal ordering - Anti-lookahead bias checks ### Phase 3: Strategy Generation - Convert model predictions into actionable trading signals - Position sizing using Kelly criterion or risk parity - Risk management rules (stop-loss, max drawdown, exposure limits) - Backtesting framework with realistic transaction costs and slippage ### Phase 4: Evaluation - Metrics: Sharpe ratio, Sortino ratio, max drawdown, Calmar ratio, win rate - Statistical significance tests (bootstrap, walk-forward analysis) - Regime analysis (bull/bear/sideways market performance) Provide complete Python code structure and configuration for each phase.