Quant Integrity Gate

The description states that Quant Integrity Gate consists of two modules:

1. Integrity Gate: Inspects trade CSVs for:
   • Exact duplicate rows;
   • Economically equivalent trades;
   • Repeated source events;
   • Leakage across train/validation/test splits;
   • Distortion in performance metrics.

It generates row-level evidence, portable HTML reports, JSON evidence, and cleaned canonical CSVs.

1. Setup Lifecycle Gate: Evaluates synthetic candidate summaries across:
   • Exploration;
   • Out-of-sample review;
   • Forward-paper confirmation.

Nine deterministic controls assess sample sufficiency, fold stability, regime concentration, performance retention, drawdown expansion, profit-factor stability, and cost drift.

Candidates receive one of three statuses: READY_FOR_PAPER_REVIEW, REVIEW, or REJECT. Progression is sequential — if a phase fails, later phases are marked BLOCKED but diagnostic evidence remains visible.

The tool exports artifacts in HTML, JSON, and CSV formats with consistent metadata (analysis_id, analyzed_at, policy identifier).

Evidence strength Self-reported only. No revenue, customers, or traction data provided.

The author claims the tool addresses a core issue in quant research: that backtests can appear profitable yet be untrustworthy due to:

• Duplicate trades;
• Repeated events;
• Leakage across splits;
• Unstable setup progression.

It positions itself as an audit-ready governance tool, not a trading system or signal generator. The author emphasizes reproducibility and transparency of decision-making.

The claim evolution shows a shift from general problem identification (backtests can be flawed) to specific solution design (a two-gate framework with evidence export).

Evidence strength Self-reported. No external validation or market positioning data.

Not evidenced.

The description does not name target customers, nor does it describe any segmentation strategy or ideal customer profile (ICP). It only describes the tool's functionality and use case within a research context.

Not evidenced.

There is no mention of pricing models, monetization strategies, or business structure. The tool is described as a local application with no external integrations or commercial features.

The tool is built using:

• Python standard library;
• Vanilla HTML/CSS/JS for UI;
• Codex and GPT-5.6 for architecture and implementation support;
• No third-party runtime dependencies;
• Unit and integration tests (37 total);
• Fully synthetic demonstrations with no sensitive data.

It supports:

• Row-level evidence generation;
• Portable audit artifacts (HTML, JSON, CSV);
• Deterministic decision-making;
• Provenance tracking across exports.

Evidence strength Self-reported. No independent verification of technical claims or delivery quality.

Not evidenced.

There is no evidence of revenue, customers, user base, or product adoption beyond the author’s own prototype. The project is described as a hackathon submission with synthetic data and zero external dependencies.

Not evidenced.

No mention of competitors, market landscape, or how this tool compares to existing solutions in quant research governance or backtesting quality control.

• Prototype-only: The tool is described as a hackathon prototype with no real-world usage.
• No external dependencies: While this may be a strength for security, it also implies limited scalability or integration capability.
• Self-reported only: All claims are unverified; there is no third-party validation of functionality or effectiveness.
• No commercialization path: No indication of how the tool would transition from prototype to product or service.
• Single-person team: The entire project was built by one person, raising questions about long-term maintenance and development capacity.

1. What specific quant research workflows does this tool aim to integrate into?
2. Has it been tested in real-world scenarios beyond the synthetic examples?
3. Are there plans to support external data sources or APIs for integration?
4. How would you scale this from a single-person prototype to a product with multiple users?
5. What is your roadmap for monetization or commercial viability?