Why Quant Research Should Be Open
The secrecy myth
Most quant secrecy protects capacity-constrained execution details, not ideas. The ideas themselves — factor construction, regime detection, validation methodology — improve dramatically when exposed to peer review.
The execution edge — low-latency infrastructure, market microstructure insights, capacity constraints — that’s worth protecting. But the research methodology? That only gets better in the open.
Where the line sits
The useful split is between method and execution. Method — factor construction, validation design, the data you used and the choices you made — costs you nothing to publish and gets better when other people can attack it. Execution is the part worth keeping quiet about.
- Method: the construction, the data sources, the parameter choices, and the results that did not work
- Validation: walk-forward design, out-of-sample splits, regime-aware testing
- Tooling: the scripts and pipelines that produced the numbers
- Execution: low-latency infrastructure, order routing, capacity — this is the part that stays private
That is the standard we hold writing to here, and the standard we would ask of anything we published.
The benefits of openness
- Faster iteration: Peer review catches mistakes you miss
- Better methodology: Collective scrutiny improves validation standards
- Community building: Open research attracts collaborators, not competitors
- Reproducibility: Others can verify your results, building trust
Join us
If you spend your weekends reading papers, you already belong here. Come build with us.
Related Reading
- Five Backtesting Pitfalls That Fake Your Sharpe — open methodology for validation