61 Algorithmic Trading의 Machine Learning for Algorithmic Trading, 3rd Edition에서 거래 워크플로우를 위한 AI 코딩 조수를 가르치는 독립 기술.

이 저장소는 책의 쿼터 연구 분야를 코딩 에이전트에 대한 런타임 지도로 전환합니다. 각 기술은 일반적인 ML4T 실패를 피하기 위해 필요한 패턴을 제공합니다: 누설, lookahead Bias, overfit backtests, stale data joins, 깨진 cross-validation, unalistic transaction cost, and unsafe autonomous jobs.

각 기술은 YAML frontmatter와 concise procedural 지도를 가진 일반 SKILL.md 파일입니다. 그(것)들을 설치하면 에이전트가 기술에 대해 볼 수 있는 각 개별 기술 디렉토리를 넣는 것을 의미한다. scripts/install.sh는 clone 또는 unpacked release archive에서 한다.

다음 무료 세션: 트레이딩 전략 연구를위한 AI 에이전트를 사용하는 방법, 30 분 라이브 세션 ** 월, 28, 2026, 11:00 AM ET / 15:00 UTC**. 모든 코스, 워크샵 및 무료 수업.

왜 이러한 기술을 사용

코딩 에이전트는 작성 코드에 강하지만, 금융 ML은 늦은 발견하기 쉽고 값 비싼 실패 모드가 있습니다.

ML4T Agent Skills

61 standalone skills that teach AI coding assistants the Machine Learning for Trading workflow from Machine Learning for Algorithmic Trading, 3rd Edition.

This repository turns the book’s quant research discipline into runtime guidance for coding agents. Each skill gives an agent the pattern it needs to avoid common ML4T failures: leakage, lookahead bias, overfit backtests, stale data joins, broken cross-validation, unrealistic transaction costs, and unsafe autonomous workflows.

Each skill is a plain SKILL.md file with YAML frontmatter and concise procedural guidance. Installing them means putting each individual skill directory where your agent looks for skills, which scripts/install.sh does from a clone or from an unpacked release archive.

Next free session: How to use AI Agents for Trading Strategy Research, a 30-minute live session on Wednesday, October 28, 2026, 11:00 AM ET / 15:00 UTC. All courses, workshops, and free lessons.

Why Use These Skills

Coding agents are strong at writing code, but financial ML has failure modes that are easy to miss and expensive to discover late. These skills package the reviewer-grade checks that should happen before a strategy result is trusted:

• Method discipline: every skill teaches a concept-first WRONG/CORRECT pattern.
• Book alignment: skills map to the chapters and case-study workflow in Machine Learning for Algorithmic Trading.
• Production handoff: stable patterns point to the corresponding ml4t-data, ml4t-engineer, ml4t-backtest, ml4t-diagnostic, or ml4t-live API.
• Agent portability: the files are standard Markdown and can be used by Claude Code, Codex/OpenAI agents, or any tool that can discover and read local skill files.

Installation

The canonical distribution is this Git repository. There is no npm package, Python package, or build step, because the runtime artifact is the Markdown file itself.

Agents discover skills one directory level deep, at //SKILL.md. This repository groups skills into category directories so it can be read, so cloning it into a skills directory as a single folder will not work: the SKILL.md files end up three levels down and nothing finds them. scripts/install.sh flattens the categories for you.

git clone https://github.com/ml4t/skills.git ~/.ml4t-skills
cd ~/.ml4t-skills

./scripts/install.sh                    # ~/.claude/skills, the default
./scripts/install.sh .agents/skills     # Codex, project-local
./scripts/install.sh ~/.claude/skills --copy   # copies instead of symlinking