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Capital Asset Pricing for Advanced Traders in Perpetual Futures
| Section | Key Points |
|---|---|
| Definition | Fitted value = model’s predicted output (price, return, volatility) |
| Importance | Validates models, improves risk control, aids forecasting & portfolio optimization |
| Applications | Risk management, backtesting, forecasting, portfolio optimization |
| Method 1 | Linear regression: simple, interpretable, but too linear & sensitive |
| Method 2 | Machine learning: captures complexity, scalable, but costly & less interpretable |
| Comparison | Regression = simple use; ML = advanced institutional systems |
| Implementation Steps | Data prep → Model selection → Backtesting → Live integration |
| Best Practices | Combine models, avoid overfitting, align with horizon, optimize speed, monitor |
| Case Study 1 | Hedge fund used XGBoost, cut drawdowns by 18% |
| Case Study 2 | Retail app added regression, user engagement +25% |
| Challenges | Data quality, computational costs, interpretability, regulation |
| Future Trends | AI adaptive models, cloud scaling, explainable AI, alt-data integration |
| FAQ 1 | Calculate via regression or ML (XGBoost, LSTM) |
| FAQ 2 | Apply in risk, backtesting, forecasting, portfolio tools |
| FAQ 3 | Risks: overfitting, false confidence, latency |
| FAQ 4 | Improves prediction accuracy, risk exposure, profits |
| Conclusion | Hybrid approach = regression for clarity + ML for power |