Skill UI

This comprehensive framework allows users to validate and test both crypto and traditional trading strategies against extensive historical market data. It calculates essential performance metrics (Sharpe Ratio, Sortino Ratio, Max Drawdown), simulates trade execution, generates equity curves, and includes advanced parameter grid search optimization tools for robust strategy validation.

This skill functions as an expert-level web researcher, utilizing advanced search strategies (query optimization, domain filtering) to gather, synthesize, and validate information from multiple web sources. It is ideal for complex research tasks requiring fact verification, identifying trends, and producing structured, actionable insights, ensuring high credibility for the final output.

SciPy provides open-source algorithms and utilities for scientific computing, including integration, optimization, statistics, and signal processing, enabling researchers and engineers to build reliable numerical applications.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

A comprehensive guide helping authors position their research for the Conference on Learning Theory (COLT). It covers core topics like formal learning theory, statistical learning, optimization, and lower bounds. Use this tool to evaluate venue fit, structure the manuscript as 'theorem-first,' and adhere to specific academic conference submission standards.

This guide is designed for authors targeting top-tier journals like Foundations of Computational Mathematics (FoCM). It outlines the journal's high academic standards, requiring deep mathematical rigor, established theorems, and proofs (e.g., convergence, complexity bounds) at the math-computation interface. It emphasizes theoretical foundations over empirical results, making it ideal for advanced research in numerical analysis, optimization theory, and computational geometry.

This skill helps authors evaluate whether their research—particularly in algorithms, optimization, or machine learning for Operations Research (OR)—meets the high computational standards required by the INFORMS Journal on Computing (IJOC). It acts as a fit/venue-selection tool, guiding authors on methodological rigor, computational evidence, and structuring their submission to maximize acceptance chances.

This guide assists researchers in selecting and rigorously defending the appropriate methodology for Management Science (INFORMS) manuscripts. It covers both analytical modeling approaches (e.g., optimization, stochastic processes, game theory) and empirical designs (e.g., quasi-experiments, field trials). It ensures the chosen design not only matches the research question but also meets strict departmental standards and generates decision-relevant managerial insights.

This skill guides authors submitting work related to Operations Research (OR). It helps assess whether a manuscript provides a genuine theoretical or methodological advance in fields like optimization, stochastic processes, queueing theory, and mathematical modeling. It emphasizes the necessity of rigorous proofs, mathematical depth, and positioning the contribution against existing OR literature, rather than merely presenting a case study application.

A comprehensive guide for formulating rigorous Operations Research (OR) manuscripts. It covers defining mathematical models (optimization, stochastic, simulation), structuring claims (theorems, propositions), and rigorously justifying assumptions. Essential for transforming practical problems into mathematically provable, publication-ready academic work.

This guide helps researchers determine if their problem constitutes a rigorous Operations Research (OR) or Management Science (MS) methodological contribution. It provides a comprehensive fit test covering method novelty, mathematical rigor (proofs, guarantees), and significance to the OR community. Furthermore, it guides users on selecting the precise editorial area (e.g., Optimization, Stochastic Models, Simulation) for submission.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.