Spread Scanner

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The scanner finds names whose volatility has compressed — coiled springs that tend to release with an outsized move. The options layer then reads what the market charges for that move, and that is what decides which side of the trade you want to be on:

One trade, placed in the same week every year. Set the week you would buy, how long you would hold it and how far the name has to move — this counts the years that closed past that move against the years that did not, over the same weekly history behind the Charts tab. The verdict is the exit: eight weeks at +1% asks whether the name is 1% up at the close of week eight, not whether it got there and gave it back. Whether it was ever touched is reported beside it, never as it.
The target can be zero or negative. An in-the-money vertical is already past its breakeven at today's price, so its real question is did it hold up rather than did it travel — set −3% and this counts the years that closed no worse than 3% down.
A target is a distance, not a level: $250 meant something very different in 2016, so you set a percentage and each year's target price is worked out from that year's own entry. Every one of them is in the table.

Name

Direction

Buy week — 37

Hold, weeks Target move by exit, % Years to repeat

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Price it — real cash in, cash out, net

A rule, run over every name and every week of the same history the Repeat test walks. Pick what would have made you buy — a squeeze, a new high, a close under the average — say how long you would hold it and how far it has to move, and this finds every time that rule ever fired and counts what the next weeks did.
The rule is decided on the week's own close and the weeks before it, never on anything after, and the trade is entered at that close. The verdict is the exit, as on the Repeat test: where the trade closed, not the best price it saw on the way.
The rate on its own means nothing — these names rose over this decade, so almost any rule looks good. What this leads with is the edge: the same names, hold and target entered on every week, subtracted from the rule's own record. A rule that beats that gap picked the weeks; a rule that does not was just holding the market.

Rule Lookback, weeks And also

Direction

Hold, weeks Target move by exit, % History, years Name, for the trade list

Count every firing, even inside a running trade Off, one position at a time — what you could actually have held.

Loading the weekly history…

Every definition below ships inside data/scan.json alongside the numbers it describes, so an explanation here can never drift from the field it explains.

What the numbers mean

The strategies

How the pipeline works

Backend — data only

Each weekday after the US close a GitHub Action fetches the ETF holdings that define the universe, re-screens them on industry plus debt and cash ratios, downloads daily prices, recomputes the indicators without look-ahead, reads the option chains for the top-ranked names — the front month and a ≈13-month expiry — runs both strategy engines, and writes JSON:

data/scan.json — signals, IV read, recommendations, and this copy
data/charts.json — downsampled price history, and the same closes grouped by calendar month
data/weekly.json — the same history as one row per ISO week, which the Repeat test walks
data/backtest.json · data/calibration.json
data/regime.json — the Market regime tab, rewritten weekly by its own workflow
data/signals.csv — the flat table, for spreadsheets

Python renders no HTML at all. Deciding what to trade got complicated enough that it needed to be data, not markup.

Frontend — this page

Hand-written, checked into public/, no build step and no external assets. It fetches the JSON above and renders it. Nothing regenerates it, so you can restyle the dashboard without touching the scanner — and read the same data from anything else that speaks JSON.

Run it locally with python run.py then python -m http.server --directory public 8765. Opening the file over file:// will not work: the browser blocks fetch there.