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code-review-for-quant

Rank silent corruption risks in Python, Go, and SQL quant code.

Updated Apr 29, 2026
Search Tags:#code review#data integrity#time-series#backtesting#lookahead bias#quant research#financial pipelines
One-click install
npx skills add https://github.com/jefrnc/quant-llm-skills --skill code-review-for-quant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review-for-quant
Source: https://github.com/jefrnc/quant-llm-skills/tree/main/skills/code-review-for-quant
Command: npx skills add https://github.com/jefrnc/quant-llm-skills --skill code-review-for-quant

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents time-series and research pipelines from producing plausible-looking results that are silently corrupted by quant-specific failure modes like lookahead bias, snapshot misuse, and incorrect event-time handling.

Core Features & Use Cases

  • Quant time-semantics checklist: Enforces known-date correctness using query_date and filing/acceptance timestamps, rejecting period_end and “current snapshot” fallacies.
  • Data-shape and aggregation hygiene: Reduces silent errors from missing tags, XBRL 404 fallbacks, multi-class share conversions, and 13D/13F/144 dedup rules.
  • Numerical and friction realism: Guards against NaN/None gaps, division-by-zero, float drift, and unrealistic assumptions like zero slippage or missing microcap spread and halt handling.
  • Reproducibility and performance traps: Ensures deterministic runs via explicit seeds and flags performance patterns that can hide research drift or quadratic slowdowns.

Quick Start

Ask the AI to run a code review checklist on your snippet and return the bugs ranked by silent-corruption risk, citing any leaking datapoints and proposing fixes aligned to quant known-date rules.

Frequently Asked Questions about code-review-for-quant

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect lookahead bias in my backtesting code?▼

Detect lookahead bias in backtesting code by enforcing publication-time semantics, checking query-time filters, and rejecting known-date misuse like period_end to ensure only data available at query_date is used.

What causes silent data corruption in quant research pipelines?▼

Silent data corruption in quant research pipelines stems from incorrect event-time handling, snapshot misuse, XBRL fallback failures, and multi-class share conversion errors that produce plausible but invalid historical market data results.

How do you validate insider ownership aggregation rules for 13D and 13F filings?▼

Validate insider ownership aggregation by checking dedup rules for 13D, 13F, and 144 filings, verifying XBRL and text fallback parsing, and ensuring multi-class share conversions preserve data integrity.

How do I review Python and SQL functions for quant time-series snapshot misuse?▼

Review Python and SQL functions for time-series snapshot misuse by rejecting current snapshot fallacies, validating filing and acceptance timestamps, and enforcing known-date correctness using query_date.

What are common numerical and reproducibility traps in trading system code?▼

Common numerical and reproducibility traps in trading system code include NaN gaps, division-by-zero, float drift, zero slippage assumptions, missing microcap spread handling, and non-deterministic runs lacking explicit seeds.

Does this code review checklist work with Go and Python financial pipelines?▼

This code review checklist works with Python, Go, and SQL financial pipelines, applying publication-time semantics to query historical market data, parse filings, compute signals, and run backtests.

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