Features
Step-by-step strategy building from scratch
Covers ETF selection and portfolio allocation methods
Teaches rebalancing, stop-loss, and take-profit rules
Performance evaluation from four key perspectives
Overfitting prevention techniques (walk-forward, cross-validation)
Live trading execution guidance
Strategy monitoring, diagnostics, and iteration
Introduction to factor research
All content available as open-source markdown
Companion Jupyter notebooks in separate repo
Community-driven feedback via GitHub issues
WeChat reader group for direct support
Dual license (CC BY-NC-SA for content, MIT for code)
Supports GitBook and VitePress static site publishing
Reverse-engineer causal mechanisms of AI models
Reveal internal structure and hidden representations
Detect performative chain-of-thought in LLMs
Identify confounders and debug model behavior
Validate whether models learned real clinical understanding
Trace unstable behaviors to brittle internal features
Reduce hallucinations via features as rewards
Accelerate materials discovery with self-correcting search
Control training precisely with less data and off-target effects
Support for LLMs, life sciences, and robotics/vision models
Harvest activations from trillion-parameter models
SOC 2 Type II certified security and compliance
Analyze latent policy structure in robotics models
Interpret genomic models like Evo 2
Discover novel biomarkers via model reverse-engineering
