Skill from video 1 of the Quant Series: I Re-Created A Nobel Prize Quant Trading Model With Claude Code.
The model family is Engle's ARCH / Bollerslev's GARCH — the volatility machinery that won the 2003 Sveriges Riksbank Prize (the economics Nobel) and that real risk desks run daily. I'm the guy who turned it into a Claude Code skill.
One sentence: every other trading tool tries to answer which way. This one answers how much — and how much is the question that decides whether you survive.
In Claude Code:
/plugin marketplace add [YOUR-USERNAME]/garchmethod
/plugin install garch-method@garchmethod
Then just ask in plain English: "what's the vol forecast on BTC-USD", "how big should my SPY position be", "test my strategy with and without vol targeting" — Claude fires the skill automatically.
No API keys. No accounts. No sudo. Dependencies resolve on first run via uv (PEP 723 inline metadata) — nothing to pip-install yourself.
It answers one question for any asset: how violent is this market, and how big should my position be?
- Fits GARCH(1,1) walk-forward — parameters re-estimated on an expanding window, every forecast made using only data that existed before that day. Zero lookahead.
- Produces a 1-day-ahead volatility forecast (daily + annualized)
- Classifies the vol regime: calm / normal / storm (percentile vs trailing year)
- Converts the forecast into a position size:
target_vol / forecast_vol, capped [0.25x, 2.0x] - Runs the honest test (
compare.py): the same strategy signals sized two ways — fixed vs vol-targeted — with both equity curves and Sharpe / max drawdown / worst month side by side - Ships with an EMA 9/21 crossover demo strategy; bring your own via a signals CSV (
date, signal in {-1,0,1})
It takes either a ticker (--ticker BTC-USD, fetched via yfinance) or your own CSV (--csv my_prices.csv, just a date + close column) — so it drops into whatever data pipeline you already run, on whatever asset you trade.
It's built to compose: use it as a sizing layer on a strategy you already have, a standalone risk throttle, or a comparison harness — without rewriting your strategy. See skills/garch/SKILL.md for the JSON contract and three worked composition patterns. It also composes cleanly with regime-direction skills (Markov-style bull/bear classifiers): theirs answers which way, this answers how much. Multiply the two.
What it will never do: predict direction. GARCH forecasts the magnitude of moves — the skill says this in its own output, every time.
garch-method.md is the original one-shot onboarding prompt — the version built live on camera. Paste it into Claude Code (agent mode) and it builds the whole skill from scratch in front of you: detects your OS, installs uv, writes every file, runs the sanity check (a full fixed-vs-vol-targeted comparison on BTC as proof-of-life).
It's kept here as the zero-trust path: if you don't want to install a plugin from a marketplace, this builds the identical logic locally so you can read every line as it's written. Most people should use the two-command plugin install above — this is the transparent fallback and the on-camera artifact.
pine-script/storm-gauge.pine — TradingView v5 indicator that puts the framework on a chart: vol bands on price (so you can see the clustering), storm tint, and a corner gauge showing market violence vs your limit, its 1-year percentile, the weather, and — if you enter — your size, in dollars.
Honest label, on the panel itself: the gauge uses realized vol (Pine-native math). The walk-forward GARCH forecast lives in the skill — the chart tells you how violent the market is; the skill forecasts tomorrow.
Open TradingView → Pine Editor → paste the .pine → Save → Add to Chart. Use the daily chart. Inputs: 365 periods/year for crypto, 252 for stocks.
- Model family: Robert Engle (ARCH, Nobel 2003) and Tim Bollerslev (GARCH, 1986). Read the originals — the math is theirs.
- Skill + installer + Pine: Miles Deutscher.
MIT.