Total 30,538 skills, Data Processing has 1462 skills
Showing 12 of 1462 skills
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Guide for creating GreptimeDB triggers, by which we can trigger external webhook like Alertmanager. This feature can be used as alternative to Prometheus alerting rule.
Map "Profit Growth × Financial Conditions (Financial Environment)" to the "Investment Clock" to determine the current quadrant, whether it has been rotating clockwise or counterclockwise recently, and the position difference compared to the previous cycle.
Quantitative trading expertise for DeFi and crypto derivatives. Use when building trading strategies, signals, risk management. Triggers on signal, backtest, alpha, sharpe, volatility, correlation, position size, risk.
Guide for creating GreptimeDB Pipeline, by which user can add a process layer to GreptimeDB between ingestion and storage, to transform data.
SQLiteData queries, @Table models, Point-Free SQLite, RETURNING clause, FTS5 full-text search, CloudKit sync, CTEs, JSON aggregation, @DatabaseFunction
Statistics, probability, linear algebra, and mathematical foundations for data science
Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations. Use PROACTIVELY for business intelligence or strategic analysis.
Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.
Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.
Build trading systems in the style of Renaissance Technologies, the most successful quantitative hedge fund in history. Emphasizes statistical arbitrage, signal processing, and rigorous scientific methodology. Use when developing alpha research, signal extraction, or systematic trading strategies.
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.