Skill UI

This tool analyzes cryptocurrency options flow from major derivatives exchanges (e.g., Deribit). It tracks open interest, calculates put/call ratios, and analyzes implied volatility to identify institutional positioning, gauge market sentiment, and detect significant trading activity (block trades) in BTC and ETH options. It is essential for quantitative traders and market analysts seeking to predict market movements.

This tool analyzes cryptocurrency options flow from major derivatives exchanges (e.g., Deribit). It tracks open interest, calculates put/call ratios, and analyzes implied volatility to identify institutional positioning, gauge market sentiment, and detect significant trading activity (block trades) in BTC and ETH options. It is essential for quantitative traders and market analysts seeking to predict market movements.

This tool detects profitable arbitrage opportunities across centralized exchanges (CEX), decentralized exchanges (DEX), and cross-chain markets in real-time. It aggregates price data, calculates net profit after fees, and identifies various arbitrage paths, including direct, triangular, and cross-chain opportunities. It is designed for quantitative traders needing to monitor market efficiency and exploit price discrepancies.

This skill simulates the role of a quantitative analyst, providing comprehensive tools for building complex financial models, backtesting trading strategies, and analyzing market data. It covers advanced topics like portfolio optimization (Markowitz, Black-Litterman), calculating risk metrics (VaR, Sharpe ratio), and performing statistical arbitrage. Ideal for quantitative finance professionals needing rigorous data analysis, robust backtesting, and risk-adjusted performance evaluation.

This skill acts as an expert risk manager, providing comprehensive guidance on monitoring portfolio risk, calculating R-multiples, and optimizing position limits. It covers advanced quantitative finance topics such as Value at Risk (VaR), correlation analysis, and stress testing using Monte Carlo simulations. Use it for systematic risk assessment, developing hedging strategies (options/futures), and implementing rigorous risk controls in trading workflows.

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

QuTiP is a Python toolbox for simulating open and closed quantum systems, offering solvers for master equations, trajectory methods, visualization, and analysis tailored to quantum research, education, and exploratory physics studies.

This skill provides zero-shot time series forecasting using Google's TimesFM foundation model. It forecasts any univariate time series (like sales, sensor data, or weather) without requiring custom model training. The model delivers point forecasts along with calibrated prediction intervals (quantiles), offering a robust and probabilistic view of future trends. It is ideal for rapid, reliable forecasting across diverse time-based datasets.

Analyzes comprehensive fitness logs to track quantitative trends in volume, intensity, and frequency. It tracks progress across multiple disciplines (running, strength) and correlates exercise metrics with vital health indicators like blood pressure and weight, providing personalized, science-backed training and recovery advice.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

Provides a concise quantitative analysis experience for financial or research scenarios by structuring numerical data insights and interpretations.

Placeholder skill entry for risk modeling workflows that outlines basic analytics and scenario planning used to quantify potential losses and guide mitigation decisions.