How do I install the Pairs Trading Project skill?

Run npx skillmds@latest add knuckles-team/pairs-trading-project in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Pairs Trading Project free to use?

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Who published Pairs Trading Project?

Knuckles-Team (@knuckles-team) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pairs Trading Project by knuckles-team

Name: Pairs Trading Project
Author: Knuckles-Team

Pairs Trading Project

Build the pairs-trading portfolio project end-to-end: find two correlated stocks, test for cointegration, trade the z-score of their spread, backtest it leak-free, report full stats, and publish a GitHub repo. The best single learning project in quant finance — teaches hypothesis testing, z-scores, position sizing, and risk management at once.

Knuckles-Team 5592e2e 2 files · 3.6 KB Updated Sep 17, 2026

File contents

Pairs Trading Project Workflow

Project 1 of the quant portfolio. Mean reversion on a correlated pair (e.g. Coca-Cola / Pepsi): when the spread diverges, bet on convergence. See quant-career-docs/reference/backtesting.md and strategies.md.

Steps

Step 1: fetch-pair-data

Agent: quant_developer Tools: data-science-mcp

Pull historical price data for two candidate correlated stocks via yfinance. Align on a common point-in-time index. Avoid any forward-filled future data. Expected: aligned-price-series

Step 2: cointegration-test [depends_on: Step 1]

Agent: quant_researcher

Run a cointegration hypothesis test (e.g. Engle-Granger) on the pair. Confirm a statistically stable long-run spread before proceeding. Expected: cointegration-pvalue

Step 3: zscore-signal [depends_on: Step 2]

Agent: quant_researcher

Compute the rolling z-score of the spread. Define entry/exit thresholds (e.g. enter at |z|>2, exit near 0). Add position sizing and risk limits. Expected: entry-exit-signals

Step 4: backtest [depends_on: Step 3]

Agent: quant_developer

Backtest in Backtrader/Zipline with realistic fees and slippage. Audit explicitly for lookahead bias. Expected: backtest-equity-curve

Step 5: metrics [depends_on: Step 4]

Agent: risk_analyst

Compute Sharpe ratio, max drawdown, and CAGR vs benchmark. Identify the regimes where the strategy fails. Expected: performance-metrics

Step 6: github-publish [depends_on: Step 5]

Agent: quant_developer

Publish a GitHub repo: README (what it is + why it should theoretically work), metrics, clean commented code, and an honest "what didn't work" section. Expected: github-repo-url

Step 7: kg-persist [depends_on: Step 6]

Agent: quant_developer Tools: graph_write

Persist the project, its metrics, and the repo URL as typed nodes linked to the quant portfolio in the Knowledge Graph.

Output

A backtested, cointegration-based pairs-trading strategy
Full performance stats with honest failure analysis
A published GitHub portfolio repo

Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet depends_on run in parallel; dependents run after their prerequisites complete.

Run first (in parallel): Step 1 — fetch-pair-data
After level 0: Step 2 — cointegration-test
After level 1: Step 3 — zscore-signal
After level 2: Step 4 — backtest
After level 3: Step 5 — metrics
After level 4: Step 6 — github-publish
After level 5: Step 7 — kg-persist

Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.

---name: pairs-trading-projectdescription: Build the pairs-trading portfolio project end-to-end: find two correlated stocks, test for cointegration, trade the z-score of their spread, backtest it leak-free, report full stats, and publish a GitHub repo. The best single learning project in quant finance — teaches hypothesis testing, z-scores, position sizing, and risk management at once.---# Pairs Trading Project WorkflowProject 1 of the quant portfolio. Mean reversion on a correlated pair (e.g.Coca-Cola / Pepsi): when the spread diverges, bet on convergence. See`quant-career-docs/reference/backtesting.md` and `strategies.md`.## Steps### Step 1: fetch-pair-data**Agent**: `quant_developer`**Tools**: `data-science-mcp`Pull historical price data for two candidate correlated stocks via yfinance. Alignon a common point-in-time index. Avoid any forward-filled future data.Expected: `aligned-price-series`### Step 2: cointegration-test [depends_on: Step 1]**Agent**: `quant_researcher`Run a cointegration hypothesis test (e.g. Engle-Granger) on the pair. Confirm astatistically stable long-run spread before proceeding.Expected: `cointegration-pvalue`### Step 3: zscore-signal [depends_on: Step 2]**Agent**: `quant_researcher`Compute the rolling z-score of the spread. Define entry/exit thresholds (e.g.enter at |z|>2, exit near 0). Add position sizing and risk limits.Expected: `entry-exit-signals`### Step 4: backtest [depends_on: Step 3]**Agent**: `quant_developer`Backtest in Backtrader/Zipline with realistic fees and slippage. Audit explicitlyfor lookahead bias.Expected: `backtest-equity-curve`### Step 5: metrics [depends_on: Step 4]**Agent**: `risk_analyst`Compute Sharpe ratio, max drawdown, and CAGR vs benchmark. Identify the regimeswhere the strategy fails.Expected: `performance-metrics`### Step 6: github-publish [depends_on: Step 5]**Agent**: `quant_developer`Publish a GitHub repo: README (what it is + why it should theoretically work),metrics, clean commented code, and an honest "what didn't work" section.Expected: `github-repo-url`### Step 7: kg-persist [depends_on: Step 6]**Agent**: `quant_developer`**Tools**: `graph_write`Persist the project, its metrics, and the repo URL as typed nodes linked to thequant portfolio in the Knowledge Graph.## Output- A backtested, cointegration-based pairs-trading strategy- Full performance stats with honest failure analysis- A published GitHub portfolio repo## ExecutionRun this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.- **Run first (in parallel):** Step 1 — fetch-pair-data- **After level 0:** Step 2 — cointegration-test- **After level 1:** Step 3 — zscore-signal- **After level 2:** Step 4 — backtest- **After level 3:** Step 5 — metrics- **After level 4:** Step 6 — github-publish- **After level 5:** Step 7 — kg-persist**Execution:** If graph-os is reachable, offload the whole DAG via `graph_orchestrate action=execute_workflow` (or the `kg-delegate` skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet `depends_on` in parallel, then their dependents.

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