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Quantitative Statistics Methods

Run stationarity, cointegration, GARCH volatility and regression diagnostics correctly with a shared library

Source Download skill
Repository
HKUDS/Vibe-Trading
Stars
37,433
Licence
MIT
Files
1 file

What it does

This skill teaches an AI agent to run standard quantitative finance statistics on price or return data: unit-root (ADF) tests, cointegration and pair-trading hedge ratios, Granger causality, GARCH volatility modeling, regression diagnostics, and bootstrap methods. It instructs the agent to call tested functions from the project's own quant library instead of rewriting formulas, and to interpret results with explicit rules (p-value thresholds, half-life sanity checks, annualising volatility). It also tells the agent to report missing-package errors to the user rather than quietly switching methods.

Great for

  • Quant researchers building pair-trading or factor research who want correct statistical checks
  • Students learning time-series tests like ADF, cointegration and GARCH with worked examples
  • Developers using the Vibe-Trading codebase who need the agent to call its existing statistics helpers

Skip it if

  • Anyone trading real money on the output without understanding the statistics: p-values and half-lives can look good on noisy or non-stationary data, and the skill does not replace professional judgement or risk controls
  • Users without the Vibe-Trading repository: the functions live in src.quantlib.timeseries and the skill assumes that code is present

Try asking

  • “Test whether my two ETF price series are cointegrated and compute a hedge ratio and half-life for a pair trade”
  • “Check if my daily return series is stationary before fitting a regression”
  • “Fit a GARCH(1,1) model to these returns and forecast volatility for the next 5 days, annualised”
  • “Run Granger causality tests of volume on returns with up to 5 lags and explain the results”
Step-by-step guide: how to use Quantitative Statistics MethodsInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 1 original files, the MIT licence and an attribution note.

    quant-statistics.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip quant-statistics.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip quant-statistics.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Ask the agent to use the library functions directly rather than rewriting formulas, and to show the raw output dictionaries
  • Check that both price legs are non-stationary and share the same date index before running cointegration tests
  • Compare the pair's half-life with your intended holding period, and treat very high persistence in GARCH as a warning sign

What's inside

  • SKILL.md

Redistributed unmodified from HKUDS/Vibe-Trading under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.