CommunityResearch & Data Analysisgithub.com

koreal6803/finlab

Comprehensive guide for the FinLab quantitative trading package, covering global stock markets (TW, US, KR, JP, HK) including equities and ETFs. Use for trading strategies, backtesting, stock data, factor analysis, and US-market specifics.

What is finlab?

This skill provides a comprehensive reference for the FinLab quantitative trading package, enabling developers to build and backtest trading strategies across global markets including Taiwan, the US, Korea, Japan, and Hong Kong. It covers both single-name equities and ETFs/funds, with special emphasis on US-market specifics such as data availability maps, filing-date-aligned quarterly fundamentals, US universe construction, and ETF backtesting. The skill supports the full workflow from data access and FinLabDataFrame manipulation to factor analysis, stock selection, and strategy development. It is designed for use with AI coding agents like Claude Code, Cursor, or Codex, and is particularly useful when users mention trading, quant trading, US equity indices (S&P 500/NASDAQ 100), SPY/QQQ, sector or leveraged ETFs, ETF rotation, or stock market analysis in Chinese or English contexts.

Works with~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/koreal6803/finlab-ai/tree/main/skills/finlab

Installed? Explore more Research & Data Analysis skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →

View original→Browse all skills

Ask in your favorite AI

Open a new chat with this agent skill pre-loaded.

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

Documentation

What does finlab do?

Comprehensive guide for the FinLab quantitative trading package across global stock markets (TW, US, KR, JP, HK; both single-name equities and ETFs/funds). Use when working with trading strategies, backtesting, stock data, FinLabDataFrame, factor analysis, stock selection, or when the user mentions FinLab, trading, quant trading, US equity, S&P 500 / NASDAQ 100, SPY / QQQ, sector or leveraged ETFs, ETF rotation, 美股, or stock market analysis. Includes data access, strategy development, backtesting workflows, best practices, and US-market specifics (data availability map, filing-date-aligned quarterly fundamentals, US universe construction, USMarket vs. USFundMarket defaults, and ETF backtesting).

Related Skills

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

community

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

community

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

community

affaan-m/everything-claude-code

The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

community

forrestchang/andrej-karpathy-skills

Agent skill repository discovered by 10x-chat research.

community

langgenius/backend-code-review

Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.

community
← More Research & Data Analysis skills