Stock Screener (Demo)
Filter and sort a demo dataset of 30 well-known stocks.
このツールについて
Explore a fictional dataset of ~30 well-known stocks and ETFs to learn how a stock screener works. Filter by sector, P/E ratio range, market cap range, and price range. Sort results by any column — ticker, price, P/E, market cap, or 52-week change. All data is hardcoded for educational purposes only; no real financial data is presented.
使い方
- 1 Use the Sector dropdown to narrow results to a specific industry.
- 2 Drag the P/E, market cap, and price range sliders to set your criteria.
- 3 Click any column header to sort by that field.
- 4 Review the filtered and sorted results in the table below.
What a stock screener actually does
A stock screener is a filter for a list of companies. Instead of reading thousands of individual quotes, you set numeric thresholds — "show me only technology stocks under a P/E of 30 with a market cap above $200 billion" — and the screener returns the handful that match every rule at once. This demo runs that exact logic against a fixed set of 30 well-known names and ETFs so you can learn how the mechanics work without any live data or account. Every figure here is fictional and frozen; nothing updates, nothing is fetched from a market feed, and you should never base a real trade on it.
The four filters in this tool combine with AND logic: a row survives only if it passes the sector test and the P/E range and the market-cap range and the price range simultaneously. Narrowing one filter can drop rows that another filter would have kept, which is the single most important behaviour to internalise before you use any screener in the real world.
The metrics, and what they mean
| Column | Meaning | How to read it |
|---|---|---|
| Price | Quoted share price in dollars | Says nothing about value on its own |
| P/E | Price divided by earnings per share | Lower can mean cheaper; higher implies growth expectations |
| Mkt Cap | Price × shares outstanding | Company size; here shown in billions, rolled to $X.XT above $1,000B |
| 52W % | One-year price change | Green for gains, red for losses — momentum, not value |
Note that the ETFs in the dataset (SPY, QQQ, VTI) carry no P/E or market cap — those fields are blank because an index fund is a basket, not a single company. The tool renders those as a dash and, importantly, skips them when you apply a P/E or cap filter rather than excluding them, so an ETF only disappears if its price falls outside your price range.
A worked example
Suppose you want large, reasonably priced technology companies. Set the Sector to Technology, the P/E range to 0–30, and the market-cap range to 1000–3000 (i.e. $1T and up). Walk through the dataset: Apple (P/E 28.4, cap $2,800B) passes. Microsoft (P/E 32.1) fails the P/E ceiling. Alphabet (P/E 24.6, cap $1,800B) passes. NVIDIA (P/E 55.8) fails. The result is a short list of the cheapest mega-cap tech names — exactly the kind of shortlist a value-minded investor builds before doing deeper research. Loosen the P/E ceiling to 60 and Microsoft and NVIDIA reappear instantly.
Sorting to find the extremes
Click any sortable column header to order the visible rows. The first click sorts descending (highest first); click the same header again to flip to ascending. This is how you surface the outliers in a filtered set — sort by 52W % descending to see the year's biggest movers, or by P/E ascending to push the lowest-multiple companies to the top. Sorting operates only on the rows that passed your filters, so combining a tight filter with a sort is the fastest way to answer a specific question.
Common mistakes screeners expose
- Treating a low P/E as automatically "cheap". A bank at a P/E of 9 and a software firm at 55 are priced for entirely different growth and risk. Compare like with like — use the sector filter first.
- Ignoring why a field is blank. A dash here means "not applicable" (an ETF), not "zero". Filtering blindly on P/E silently keeps those rows; understand your screener's null-handling.
- Over-filtering into an empty set. Stack enough narrow ranges and you get "No stocks match". That is information too — it tells you your criteria are unrealistic, not that the market is broken.
- Confusing momentum with value. The 52-week column and the P/E column answer different questions. A stock can be both up 195% and expensive at the same time.
From demo to the real thing
A production screener pulls live fundamentals and offers dozens more filters — dividend yield, debt-to-equity, revenue growth, analyst ratings. The interaction model, though, is identical to what you practise here: define numeric rules, combine them with AND logic, then sort the survivors. Because this version ships its entire dataset inside the page, it runs instantly and offline, and there is no risk of mistaking practice numbers for a live quote — they are clearly labelled as fictional and never change.