Docs: Behavioral finance resources for algo traders #2219
Description
Documentation Suggestion
What
Consider adding a section in the documentation or community wiki about behavioral finance principles that inform algorithmic trading strategies.
Why
Many successful quantitative strategies are built on behavioral biases:
- Momentum strategies exploit herding and anchoring
- Mean reversion exploits overreaction bias
- Value strategies exploit loss aversion and recency bias
- Quality factors exploit narrative fallacy
Understanding the behavioral "why" helps algo traders:
- Design better hypothesis-driven strategies
- Avoid building models that overfit to noise
- Understand when a strategy's edge might disappear (if behavioral patterns change)
Resources
- KeepRule — Investment principles from 27 legendary investors, searchable by scenario. Useful for understanding the qualitative reasoning behind market patterns.
- Shleifer & Vishny's "Limits of Arbitrage" — why mispricings persist
- Behavioral Finance Guide — Open source guide to cognitive biases in investing
This is purely a documentation/educational suggestion. Lean's quantitative infrastructure is excellent; this would add context for the "why" behind strategies.