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Compressing Market Data: zstd vs gzip
Distinct prices per name, and how often each one repeatsranking · 2026-09-28 · 5×3Preview: 5 ranked values, smallest first. One session of trade prints, five household namesranking · 2026-09-28 · 5×3Preview: 5 ranked values, largest first. Uncompressed footprint of five names as retention growsranking · 2026-09-28 · 6×2Preview: 6 ranked values, smallest first. AAPL trade counts and distinct prices per quarter hourseries · 2026-09-28 · 64×3Preview: a 16-point series, roughly flat.
How to Store Market Data PCAPs: Split, Compress
Retention arithmetic for a 2 TB per day feed at three compression ratios (illustrative inputs)series · 2026-09-17 · 3×4Preview: a 3-point series, ending lower.
Distinct prices per name, and how often each one repeats

Distinct prices per name, and how often each one repeats

most recentas of ranking 5×3read in context →
Distinct prices per name, and how often each one repeats — 5 rows by 3 columns, computed from US exchange, SIP and OPRA data.
tickerdistinct_pricestrades_per_distinct_price
NVDA2270988
KO596340
AAPL1927236
SPY1953025
MSFT3310112
the exact SQL behind every number 
SELECT
    ticker,
    countDistinct(price)                              AS distinct_prices,
    toUInt32(round(count() / countDistinct(price)))   AS trades_per_distinct_price
FROM global_markets.stocks_trades
WHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'SPY', 'KO')
  AND sip_timestamp >= toDateTime64('2026-09-15 04:00:00', 9, 'UTC')
  AND sip_timestamp <  toDateTime64('2026-09-16 04:00:00', 9, 'UTC')
GROUP BY ticker
ORDER BY trades_per_distinct_price DESC
More from this analysisCompressing Market Data: zstd vs gzip
Uncompressed footprint of five names as retention grows ranking 6×2 → One session of trade prints, five household names ranking 5×3 → AAPL trade counts and distinct prices per quarter hour series 64×3 → Retention arithmetic for a 2 TB per day feed at three compression ratios (illustrative inputs) series 3×4 → See all 3,293 queries →
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