6 The Nine-Gate Framework
A Diagnostic Protocol for Fat-Tail Signal Validation
Status: Draft in progress (
paper5_gauntlet_framework.md)
This chapter is under active development. The framework is designed and the gates are defined; the full write-up is forthcoming. The working draft is available on request.
6.1 Overview
The preceding four chapters document specific methodological failures: look-ahead contamination, finite-threshold sensitivity under mixture processes, and incomplete bias correction. A practitioner encountering a new signal needs a systematic protocol for checking which of these failures — and others — may be active simultaneously.
The Nine-Gate Framework is that protocol. Gates can falsify or restrict a claim; passing them does not certify truth or efficacy. A signal remains research-only until it satisfies every applicable gate and then accumulates preregistered prospective evidence. The gates address:
| Gate | Threat addressed |
|---|---|
| Gate 1: Point-in-time discipline | Look-ahead contamination (Chapter 4) |
| Gate 2: Prospective vs contaminated divergence | Problem 2 bias (Chapter 4) |
| Gate 3: Threshold and event-attribution sensitivity | Finite-sample Hill interpretation (Chapter 3) |
| Gate 4: Distribution-free test statistics | Mean inflation under fat tails (Chapter 1) |
| Gate 5: Multiple-testing correction | Deflated Sharpe Ratio / PBO |
| Gate 6: Effective sample size | Serial correlation via Newey-West (Chapter 4) |
| Gate 7: Regime robustness | Single-regime artifacts |
| Gate 8: Mechanism plausibility | Economic mechanism, not data mining |
| Gate 9: Out-of-sample accumulation | Live forward validation |
The historical PA example demonstrates the distinction. It survives a timing repair and HAC calculation, then its aggregate spread disappears after Healthcare is excluded. It therefore fails the sector-composition gate and cannot be described as action-grade. Likewise, PBO=0 for a specified split set does not prove zero overfitting probability; PBO is one diagnostic conditioned on the candidate set, loss, purge, embargo, and dependence assumptions.
TrailMap implements versioned evidence, canonical episodes, separate maturity and resolution states, exact benchmark clocks, and approval gating. Those controls constrain premature claims; they do not prove that a signal predicts returns.
Full chapter forthcoming. Subscribe to the Substack for updates, or watch the GitHub repository for new commits.