{"slug":"block-trade-discounts-and-bought-deals","qid":"size_tiers","label":"How one ordinary AAPL session splits across print sizes","post_title":"Block Trade Discounts and Bought Deals","post_url":"/blog/block-trade-discounts-and-bought-deals#q-size_tiers","columns":["print_size","print_count","share_of_volume_pct"],"rows":[{"print_size":"under 100 shares","print_count":643334,"share_of_volume_pct":22.84},{"print_size":"100 to 999 shares","print_count":78404,"share_of_volume_pct":24.89},{"print_size":"1,000 to 9,999 shares","print_count":1119,"share_of_volume_pct":4.35},{"print_size":"10,000 shares and up","print_count":77,"share_of_volume_pct":47.92}],"shape":"ranking","sql":"SELECT\n    b.print_size                                 AS print_size,\n    b.print_count                                AS print_count,\n    round(100 * b.tier_shares / t.day_shares, 2) AS share_of_volume_pct\nFROM\n(\n    SELECT\n        multiIf(size <   100, 'under 100 shares',\n                size <  1000, '100 to 999 shares',\n                size < 10000, '1,000 to 9,999 shares',\n                              '10,000 shares and up') AS print_size,\n        multiIf(size < 100, 1, size < 1000, 2, size < 10000, 3, 4) AS tier_rank,\n        count()                                       AS print_count,\n        toFloat64(sum(size))                          AS tier_shares\n    FROM global_markets.stocks_trades\n    WHERE ticker = 'AAPL'\n      AND sip_timestamp >= toDateTime('2026-06-17 08:00:00', 'UTC')\n      AND sip_timestamp <  toDateTime('2026-06-18 02:00:00', 'UTC')\n    GROUP BY print_size, tier_rank\n) AS b\nCROSS JOIN\n(\n    SELECT toFloat64(sum(size)) AS day_shares\n    FROM global_markets.stocks_trades\n    WHERE ticker = 'AAPL'\n      AND sip_timestamp >= toDateTime('2026-06-17 08:00:00', 'UTC')\n      AND sip_timestamp <  toDateTime('2026-06-18 02:00:00', 'UTC')\n) AS t\nORDER BY b.tier_rank","computed_at":"2026-10-05T15:49:02.356543+00:00","elapsed":0.003455608}