What a one cent tick costs at each price level
Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-10-07, from The Real Disadvantages of a Stock Split.
| price_bucket | tickers_count | median_close_usd | tick_spread_bps |
|---|---|---|---|
| Under $5 | 1241 | 1.75 | 57.143 |
| $5 to $20 | 1567 | 11.1 | 9.009 |
| $20 to $50 | 1603 | 32.12 | 3.113 |
| $50 to $100 | 920 | 69.37 | 1.442 |
| $100 to $250 | 602 | 142.5 | 0.702 |
| $250 to $500 | 203 | 320.51 | 0.312 |
| $500 and up | 66 | 673.3 | 0.149 |
- Rows × columns
- 7 × 4
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
price_bucket |
text | 7 distinct values ($100 to $250, $20 to $50, $250 to $500…) | |
tickers_count |
number | 66 to 1,603 | count |
median_close_usd |
number | 1.75 to 673.3 | US dollars |
tick_spread_bps |
number | 0.149 to 57.143 |
Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on , and the query below is what returned them.
Run it yourself
This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.
WITH monthly AS (
SELECT
ticker,
argMax(toFloat64(close), date) AS last_close,
avg(volume) AS avg_volume,
count() AS sessions
FROM global_markets.stocks_daily_aggs
WHERE date BETWEEN '2026-09-01' AND '2026-09-30'
AND ticker NOT IN ('SPCX')
GROUP BY ticker
HAVING sessions >= 15
AND avg_volume >= 100000
AND last_close > 0
)
SELECT
multiIf(last_close < 5, 'Under $5',
last_close < 20, '$5 to $20',
last_close < 50, '$20 to $50',
last_close < 100, '$50 to $100',
last_close < 250, '$100 to $250',
last_close < 500, '$250 to $500',
'$500 and up') AS price_bucket,
count() AS tickers_count,
round(quantileDeterministic(0.5)(last_close, cityHash64(ticker)), 2) AS median_close_usd,
round(100.0 / quantileDeterministic(0.5)(last_close, cityHash64(ticker)), 3) AS tick_spread_bps
FROM monthly
GROUP BY price_bucket
ORDER BY min(last_close)
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