What is adaptive-wfo-epoch?
Adaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs. Source: terrylica/cc-skills.
Adaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs.
Quickly install adaptive-wfo-epoch AI skill to your development environment via command line
Source: terrylica/cc-skills.
Machine-readable reference for adaptive epoch selection within Walk-Forward Optimization (WFO). Optimizes training epochs per-fold using Walk-Forward Efficiency (WFE) as the objective.
| Walk-Forward Efficiency | Pardo (1992, 2008) | WFE = OOSReturn / ISReturn as robustness metric | | Deflated Sharpe Ratio | Bailey & López de Prado (2014) | Adjusts for multiple testing | | Pareto-Optimal HP Selection | Bischl et al. (2023) | Multi-objective hyperparameter optimization |
| Warm-Starting | Nomura & Ono (2021) | Transfer knowledge between optimization runs |
Adaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs. Source: terrylica/cc-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/terrylica/cc-skills --skill adaptive-wfo-epochAdaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs. Source: terrylica/cc-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/terrylica/cc-skills --skill adaptive-wfo-epoch Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/terrylica/cc-skills