Nate’s Project Details

Source: Notion | Last edited: 2025-03-20 | ID: 1bb2d2dc-3ef...

---

Project Details: AI-Driven Portfolio Optimization System Using XGBoost (Q3-Q4 2024)

Technological Uncertainty

During Q3-Q4 2024, our team faced several complex technical challenges in developing an advanced portfolio optimization system:

• XGBoost Model Performance and Stability
• Challenge: Achieving consistent prediction accuracy across different market regimes while handling high-dimensional financial data
• Existing Solutions: Traditional XGBoost implementations struggled with feature importance stability and overfitting in financial markets
• Technical Requirements: Model accuracy >65% across different market conditions while maintaining interpretability
• Feature Engineering Complexity
• Challenge: Creating robust financial features that capture market dynamics without introducing look-ahead bias
• Existing Solutions: Standard technical indicators and fundamental factors showed insufficient predictive power
• Technical Requirements: Feature sets that maintain predictive power across market regimes while being computationally efficient

Systematic Investigation

Q3 2024 Experiments

• Iteration 1: Advanced Feature Selection Framework
• Hypothesis: Implementing a dynamic feature importance evaluation system would improve model stability
• Method: Developed rolling window-based feature importance analysis with time decay
• Results:
• 25% improvement in feature stability
• Reduced dimensionality by 40%
• New challenge: Computational overhead in real-time feature selection
• Iteration 2: Custom Loss Function Development
• Hypothesis: Portfolio-specific loss function would better capture investment objectives
• Method: Implemented custom loss function incorporating Sharpe ratio and maximum drawdown
• Results:
• 15% improvement in risk-adjusted returns
• Reduced maximum drawdown by 20%
• New challenge: Optimization convergence issues

Q4 2024 Experiments

• Iteration 3: Ensemble Architecture
• Hypothesis: Multiple specialized XGBoost models would better handle different market regimes
• Method: Developed regime-switching ensemble system with adaptive weights
• Results:
• Achieved 70% prediction accuracy in volatile markets
• Reduced model variance by 35%
• Iteration 4: Portfolio Constraint Integration
• Hypothesis: Direct integration of portfolio constraints into model training would improve real-world applicability
• Method: Implemented custom split finding algorithm with constraint awareness
• Results:
• Successfully maintained position limits while optimizing
• Reduced rebalancing costs by 25%

Technical Advancements

• Novel feature importance stability framework for financial time series
• Custom XGBoost loss function incorporating multiple portfolio metrics
• Adaptive ensemble architecture for different market regimes
• Constraint-aware tree splitting algorithm for portfolio optimization

Metrics and Validation

• Backtesting results across different market conditions (2010-2024)
• Out-of-sample performance metrics including Sharpe ratio, maximum drawdown, and turnover
• Computational efficiency measurements for real-time deployment
• Model stability metrics across different market regimes