quant-research-skill: Claude Code Quantitative Finance Research Plugin and Multi-Agent Review Mechanism

quant-research-skill is a quantitative finance research plugin for Claude Code, designed to be an intelligent assistant for researchers. It adopts a hypothesis-driven workflow, supports time series validation and robustness testing, and introduces a two-layer multi-agent review mechanism (correctness × claim-evidence) to enhance the reliability of AI-generated content in the financial field, demonstrating the application potential of AI agents in serious financial research.

The traditional quantitative research process is time-consuming and requires significant manual input. With the development of large language models and AI agent technologies, this field is undergoing transformation. quant-research-skill is positioned as an AI-assisted tool, not to replace researchers, but to automate repetitive parts, allowing researchers to focus on creative thinking and key decisions.

Hypothesis-Driven Workflow: Starting from a clear hypothesis, it formalizes the null/alternative hypotheses, test statistics, and significance levels; automatically plans data requirements (data source identification, quality check, alignment, feature engineering); assists in model selection and estimation; and performs hypothesis testing (statistical and economic significance evaluation).

Time Series Validation: Tailored to the characteristics of financial data, it provides forward validation (simulating real deployment to avoid look-ahead bias), out-of-sample testing (multiple partitioning schemes), and structural change detection (Chow test, CUSUM test, etc.).

To avoid overfitting, the plugin provides comprehensive tests:

• Parameter sensitivity analysis: grid search, random perturbation, parameter stability evaluation;
• Data perturbation testing: Bootstrap resampling, Monte Carlo simulation, data pruning;
• Market condition changes: performance under bull/bear markets, volatility states, and liquidity conditions;
• Transaction costs and frictions: slippage models, commission deductions, market impact simulation.

The plugin's innovation lies in its two-layer review: First layer: Correctness review: code review (syntax/logic vulnerabilities), statistical review (computational correctness, method applicability), data review (citation/preprocessing norms); Second layer: Claim-evidence review: identify claims, verify supporting evidence, review constraints, counterexample testing; Collaborative process: draft generation → parallel review → issue aggregation → iterative correction → re-review → human intervention (when in dispute).

Applicable to multiple scenarios:

• Academic research: accelerate hypothesis testing and robustness testing, focus on theoretical construction;
• Buy-side research: quickly screen factors, evaluate strategies, and improve output;
• Strategy backtesting: identify overfitting risks, increase live trading success rate;
• Teaching and training: demonstrate standardized processes to facilitate learning.

Limitations: data dependence, model risk, black-box problem, unclear responsibility attribution; Outlook: multi-modal capabilities (processing unstructured information), real-time learning (optimization from live trading), collaborative research (multi-agent shared discoveries).