Show HN: Algo-Trading-Skills - 501 agent skills for trading infrastructure

Algo-Trading-Skills

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501 algorithmic trading skills · 16 engineering domains · 5 regulatory & exchange frameworks · 501 working reference implementations backed by 20,291 unit tests · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI and any tool that reads SKILL.md

Engineering Guidance, Not Financial, Legal, or Compliance Advice

Engineering Guidance, Not Financial, Legal, or Compliance Advice - Authorized & lawful use only. These skills encode production engineering practices for trading infrastructure. They do not constitute financial, legal, tax, or regulatory compliance advice, do not guarantee strategy profitability, and do not eliminate the risk of capital loss in live trading. Only deploy against paper accounts or live environments where risk limits are strictly enforced. Consult qualified legal, tax, and compliance professionals in your jurisdiction before deploying live trading systems. See SECURITY.md and CODE_OF_CONDUCT.md.

Why You Need These Skills

An AI coding agent can write a WebSocket client, a backtest loop, or an order-placement function that looks completely correct - right library calls, clean structure, plausible logic - and still fail catastrophically in production for reasons that have nothing to do with code syntax: a broker invalidates a token overnight in a way its docs don't mention, a backtest silently uses a bar's own close to predict its own direction, a risk limit lives inside the same function it's supposed to constrain, or a WebSocket callback blocks the read loop during exactly the volatility spike a strategy exists to catch. Your AI agent doesn't know these failure modes - unless you give it these skills.

What This Repository Contains

This repo contains 501 structured skills spanning 16 engineering domains, each following the agentskills.io open standard. The library maps across key financial regulatory & exchange frameworks - SEC Rule 15c3-5, Reg NMS / Reg SHO, FINRA, EU MiFID II / RTS 6 / MAR, UK FCA, ASIC, SEBI, and ISDA OTC derivative standards.

Frameworks & Standards

Example

Example - Each skill maps directly to regulatory mandates, broker APIs, and institutional standards:

Getting Started

Option 1: Claude Code

• install one domain, not the whole library
• /plugin marketplace add HimanshuJ16/Algo-Trading-Skills
• /plugin install algo-trading-risk-management
• /plugin install algo-trading-broker-integration

Option 2: skills CLI

• pick the skills you want interactively
• npx skills add HimanshuJ16/Algo-Trading-Skills

Option 3: git clone, then run the gates yourself

• git clone https://github.com/HimanshuJ16/Algo-Trading-Skills.git
• cd Algo-Trading-Skills
• pip install -r requirements-dev.txt
• python tools/validate_skills.py
• python tools/run_all_tests.py

How It Works

Install one domain, not all of them. Claude Code loads the name and description of every skill in an installed plugin into the model's context at the start of every session. The marketplace therefore ships one plugin per engineering domain (algo-trading-risk-management , algo-trading-execution-algorithms , …), each a few thousand tokens. An algo-trading-skills-all plugin exists for completeness, but it costs tens of thousands of tokens per session - reach for it only if you know you want that. One caveat on disk rather than context: every plugin entry points at the repository root, so installing a single domain checks out the whole tree (about 100 MB); only that domain's skills are loaded into the session.

Run one skill's suite on its own - the same command every skill quotes in its own Verification section:

python -m unittest discover -s skills/order-placement-idempotency/scripts

Supported Platforms

Works immediately with Claude Code, GitHub Copilot, OpenAI Codex CLI, Cursor, Gemini CLI, and any agentskills.io-compatible platform.

Quantitative Trading and Financial Software Engineering

The quantitative trading and financial software engineering domain requires deep practitioner knowledge across market microstructure, exchange protocols, and risk engineering. AI agents can help build and scale trading infrastructure - but only if they have structured practitioner playbooks to work from. Today's generic LLMs can write Python code and API wrappers, but they lack the operational context that separates code which works in a notebook from code that survives a live market.

Existing Trading Libraries

Existing trading libraries give you broker SDKs, indicator formulas, or naive strategy backtests. None of them give an AI agent the structured decision-making workflow a senior quant infrastructure engineer follows: when to use each technique, when not to, what prerequisites to check, how to execute step-by-step, and how to verify results in production. That is the gap this project fills.

Algo-Trading-Skills

Algo-Trading-Skills is not a collection of toy scripts. It is an AI-native knowledge base built from the ground up for the agentskills.io standard - YAML frontmatter for sub-second discovery, structured Markdown for step-by-step execution, and reference files for deep technical context. Every skill encodes real practitioner workflows, not generic LLM summaries.

Skill Libraries

Skill libraries are easy to generate and hard to trust. Everything in this table is re-checked by CI on every push and pull request, on Python 3.10, 3.12 and 3.13, so the claims stay true or the build goes red.

Statistics

Machine-Enforced Contract

tools/validate_skills.py checks the frontmatter contract, the seven required body sections, scripts/ layout, every skill cross-reference in both skills and repo docs, that every documented test command runs from the repository root, and that the plugin manifests cover every skill exactly once.

Specification, Not Our Reading of It

CI also runs skills-ref , the official agentskills.io reference validator, against every skill. It needs Python 3.11+, so that job runs on the newer interpreters while the library itself stays 3.10-compatible.

Descriptions that Say When to Trigger

Every description starts with "Use when …" and fits in 280 characters - enforced, because it is the only thing an agent reads before choosing a skill, and it costs context on every session.

Generated Files Can't Drift

index.json and the plugin marketplace are generated and carry no timestamp; --check modes fail CI if either is stale.

Verification You Can Paste

Every skill quotes a runnable command in its ## Verification section, alongside the concrete assertions to check by hand.

Sourced, or Explicitly Unsourced

458 of 501 references/standards.md cite at least one primary source - the rule text, the exchange notice, the vendor spec. Where no external standard exists, the file says so and labels its numbers as configurable defaults rather than inventing an authority for them.

Stated Scope Boundaries

When NOT to Use on every skill, naming the excluded cases and handing each to the skill that owns it.

Examples that Use the Real Code

The three walkthroughs in examples/ import the actual skill helpers rather than re-implementing them, and CI runs all three.

CI Workflow

The CI workflow is .github/workflows/validate-skills.yml.

Library Coverage

The library covers 16 core engineering domains spanning domestic and global markets - crypto exchanges, forex brokers, multi-currency and multi-timezone data handling, regulatory compliance, multi-asset derivatives, execution algorithms, custody/security, cross-strategy portfolio management, market microstructure, alternative-data research, and tax/accounting.

Domain Breakdown