Skill UI

This skill executes comprehensive historical backtesting using the high-performance neural-trader engine. It supports features like walk-forward validation and captures key financial metrics (Sharpe, Drawdown). Critically, it signs the resulting artifact using Ed25519 cryptography, providing non-repudiable tamper evidence required for promoting research models to live trading systems.

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.

A comprehensive tool for analyzing the current market regime (bull, bear, ranging, volatile) using advanced neural models. It detects the prevailing market structure, provides a confidence score, and recommends suitable trading strategies. Ideal for market analysis, backtesting preparation, or general market condition inquiries.

Utilize Monte Carlo simulation to model complex marketing scenarios and predict revenue outcomes with quantified probability distributions. This tool is essential for assessing downside risk, understanding potential upside, and validating the probability of hitting specific revenue targets when testing budget shifts, channel mix changes, or new campaign launches.

A rigorous guide for academic researchers on establishing and testing the role of information mechanisms in accounting literature. It outlines how to shift focus from general pricing or financing channels to specific information channels—such as reducing information asymmetry, improving disclosure quality, or constraining earnings management. Provides advanced methodologies, including direct measurement of information variables and cross-sectional cutoff analysis.

This comprehensive guide details the methodological requirements for building a robust robustness section in accounting and empirical research manuscripts. It covers four critical pillars: substituting accounting measures, controlling for institutional/governance variables, executing placebo tests, and advanced endogeneity follow-ups. It helps authors ensure their primary findings withstand stringent academic scrutiny.

A comprehensive framework designed to rigorously test the stability of key macroeconomic findings. This program addresses potential biases arising from specification choices, sample periods (e.g., Great Moderation, ZLB, COVID breaks), and methodological variations (SVAR vs. LP). It ensures that the reported headline quantity remains robust even when challenged by a skeptical academic referee, providing a targeted defense rather than simply listing extra tables. Ideal for high-stakes empirical macro papers.

A comprehensive guide for formatting research exhibits, including Impulse Response Functions (IRFs), fan charts, and econometric tables, specifically for submissions to the American Economic Journal: Macroeconomics (AEJ: Macro). This ensures all figures are self-contained, meet rigorous standards for clarity, and adhere to the latest macro conventions, allowing reviewers to quickly assess the quantitative evidence.

This guide provides a rigorous framework for strengthening economic manuscripts submitted to top journals, such as AEJ: Micro. It details how to systematically test the robustness of main results through theoretical extensions (e.g., relaxing assumptions, perturbations) and applied/experimental methods (e.g., sensitivity analyses, placebo tests). Learn how to identify extensions that truly 'earn their place' and significantly enhance the overall academic contribution of your work.

This guide outlines how to structure a rigorous robustness program for economic policy manuscripts. It emphasizes defending the overall policy takeaway, rather than merely reporting coefficient significance. Users learn to address common econometric threats—such as specification changes, pre-trends, multiple testing, and selection on unobservables—using advanced techniques (e.g., Wild-cluster bootstrap, Oster bounds) to ensure the policy conclusion is stable and credible.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

A comprehensive guide for executing and reporting quantitative, computational, or experimental analyses for top-tier academic journals (e.g., BJPS). It outlines stringent norms for ensuring methodological rigor, transparency, and reproducibility, covering topics like uncertainty reporting, robustness testing, and measurement equivalence. This skill is essential when structuring the results section of a manuscript, particularly when facing expert peer review or preparing for data deposition.