מיקרו-מבנה לעומק: 29 ביוני 2026

WITH
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date = '2026-06-29') AS holiday_rows,
    (
        SELECT countIf(window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00')
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY' AND window_start >= '2026-06-29 00:00:00' AND window_start < '2026-06-30 00:00:00'
    ) AS spy_bars
SELECT
    holiday_rows AS holiday_rows_jun29,
    spy_bars AS spy_regular_session_bars,
    round(count() / 1e6, 1) AS trades_m,
    round(100.0 * countIf(size < 100) / count(), 2) AS odd_lot_pct_of_trades,
    round(100.0 * toFloat64(sumIf(size, size < 100)) / toFloat64(sum(size)), 2) AS odd_lot_pct_of_shares,
    multiIf(
        countIf(size <= 19) >= 0.5 * count(), 19,
        countIf(size <= 20) >= 0.5 * count(), 20,
        countIf(size <= 21) >= 0.5 * count(), 21,
        countIf(size <= 22) >= 0.5 * count(), 22,
        countIf(size <= 23) >= 0.5 * count(), 23,
        0) AS median_print_shares,
    round(countIf(size = 1) / 1e6, 1) AS one_share_trades_m,
    round(100.0 * countIf(size != round(size)) / count(), 2) AS fractional_pct_of_trades
FROM global_markets.stocks_trades
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'

SELECT
    count() AS excluded_codes,
    sum(id) AS excluded_id_checksum,
    arrayStringConcat(arraySort(groupArray(concat(toString(id), ' ', name))), ' · ') AS codes
FROM global_markets.stocks_condition_codes
WHERE asset_class = 'stocks'
  AND type = 'sale_condition'
  AND JSONExtractBool(update_rules, 'consolidated', 'updates_volume') = 0

SELECT
    round(toFloat64(sum(size)) / 1e9, 2) AS raw_shares_bn,
    round(toFloat64(sumIf(size, NOT hasAny(conditions, [15, 16, 38]))) / 1e9, 2) AS matched_shares_bn,
    round(toFloat64(sumIf(size, hasAny(conditions, [15, 16, 38]))) / 1e9, 2) AS excluded_shares_bn,
    countIf(hasAny(conditions, [15, 16, 38])) AS excluded_prints
FROM global_markets.stocks_trades
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'

SELECT
    ticker,
    formatDateTime(toTimeZone(sip_timestamp, 'America/New_York'), '%H:%i:%S') AS et_time,
    round(toFloat64(size) / 1e6, 2) AS shares_m,
    toFloat64(price) AS price,
    round(toFloat64(size) * toFloat64(price) / 1e6, 1) AS notional_usd_m,
    round(100 * (toFloat64(size) * toFloat64(price)) / max(toFloat64(size) * toFloat64(price)) OVER (), 1) AS pct_of_biggest_dollar_print,
    arrayStringConcat(arrayMap(c -> toString(c), conditions), ', ') AS condition_id
FROM global_markets.stocks_trades
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
  AND size >= 10000000
  AND NOT hasAny(conditions, [15, 16, 38])
ORDER BY size DESC
LIMIT 10

SELECT
    round(toFloat64(maxIf(size, has(conditions, 8))) / 1e6, 2) AS auction_print_shares_m,
    round(toFloat64(maxIf(size, has(conditions, 15))) / 1e6, 2) AS official_close_reprint_shares_m,
    round(toFloat64(maxIf(size, has(conditions, 8))) - toFloat64(maxIf(size, has(conditions, 15))), 2) AS share_difference,
    formatDateTime(toTimeZone(argMaxIf(sip_timestamp, size, has(conditions, 15)), 'America/New_York'), '%H:%i:%S') AS reprint_et,
    argMaxIf(exchange, size, has(conditions, 15)) AS reprint_exchange_id,
    countIf(has(conditions, 15)) AS cag_code15_prints
FROM global_markets.stocks_trades
WHERE ticker = 'CAG'
  AND sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'

SELECT
    formatDateTime(toTimeZone(toStartOfMinute(sip_timestamp), 'America/New_York'), '%H:%i') AS et_minute,
    round(count() / 1e6, 2) AS trades_m
FROM global_markets.stocks_trades
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
GROUP BY et_minute
ORDER BY trades_m DESC
LIMIT 10

SELECT
    countIf(NOT (et_minute BETWEEN '09:30' AND '09:39' OR et_minute BETWEEN '15:50' AND '16:00')) AS entries_outside_open_close,
    countIf(et_minute BETWEEN '09:30' AND '09:39' OR et_minute BETWEEN '15:50' AND '16:00') AS entries_in_open_close,
    maxIf(et_minute, NOT (et_minute BETWEEN '09:30' AND '09:39' OR et_minute BETWEEN '15:50' AND '16:00')) AS outside_entry_minute,
    maxIf(trades_m, NOT (et_minute BETWEEN '09:30' AND '09:39' OR et_minute BETWEEN '15:50' AND '16:00')) AS outside_entry_trades_m
FROM (
    SELECT
        formatDateTime(toTimeZone(toStartOfMinute(sip_timestamp), 'America/New_York'), '%H:%i') AS et_minute,
        round(count() / 1e6, 2) AS trades_m
    FROM global_markets.stocks_trades
    WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
    GROUP BY et_minute
    ORDER BY trades_m DESC
    LIMIT 10
)

WITH
    (
        SELECT (round(countIf(ticker = 'QQQ') / 1e6, 2), round(countIf(ticker = 'NVDA') / 1e6, 2),
                round(countIf(ticker = 'AAPL') / 1e6, 2), round(countIf(ticker = 'TSLA') / 1e6, 2))
        FROM global_markets.cache_stocks_quotes
        WHERE ticker IN ('QQQ', 'NVDA', 'AAPL', 'TSLA')
          AND sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
    ) AS pinned
SELECT
    round(count() / 1e6, 2) AS updates_m,
    pinned.1 AS qqq_updates_m,
    pinned.2 AS nvda_updates_m,
    round(pinned.1 - pinned.2, 2) AS qqq_minus_nvda_updates_m,
    round(pinned.2 - greatest(pinned.3, pinned.4), 2) AS nvda_minus_next_single_m
FROM global_markets.cache_stocks_quotes
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'

SELECT ticker, round(count() / 1e6, 2) AS updates_m
FROM global_markets.cache_stocks_quotes
WHERE ticker IN ('QQQ', 'SPY', 'TQQQ', 'IWM', 'SQQQ', 'NVDA', 'AAPL', 'TSLA')
  AND sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
GROUP BY ticker
ORDER BY updates_m DESC

WITH
    (
        SELECT (formatDateTime(toTimeZone(toStartOfSecond(sip_timestamp), 'America/New_York'), '%H:%i:%S'), count())
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'SPY' AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 20:00:00'
        GROUP BY toStartOfSecond(sip_timestamp)
        ORDER BY count() DESC, toStartOfSecond(sip_timestamp) ASC
        LIMIT 1
    ) AS busiest_sec,
    ordered AS (
        SELECT
            toFloat64(ask_price - bid_price) AS spread,
            bid_price,
            ask_price,
            toFloat64(sip_timestamp - lagInFrame(sip_timestamp) OVER (ORDER BY sip_timestamp, sequence_number)) AS gap_s,
            greatest(toFloat64(least(leadInFrame(sip_timestamp, 1, toDateTime64('2026-06-29 20:00:00', 9)) OVER (ORDER BY sip_timestamp, sequence_number ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING), toDateTime64('2026-06-29 20:00:00', 9)) - sip_timestamp), 0) AS dwell_s,
            rowNumberInAllBlocks() AS rn
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'SPY' AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 20:00:00'
    )
SELECT
    round(count() / 1e6, 2) AS spy_updates_m,
    busiest_sec.1 AS busiest_second_et,
    toUInt8(startsWith(busiest_sec.1, '10:15')) AS busiest_second_in_1015,
    busiest_sec.2 AS busiest_second_updates,
    round(minIf(gap_s, rn > 0 AND gap_s > 0) * 1e9) AS min_gap_between_updates_ns,
    round(quantileExactIf(0.5)(gap_s, rn > 0) * 1e6, 1) AS median_gap_us,
    countIf(rn > 0 AND gap_s = 0) AS identical_timestamp_pairs,
    round(sumIf(spread * dwell_s, bid_price > 0 AND ask_price >= bid_price) / sumIf(dwell_s, bid_price > 0 AND ask_price >= bid_price) * 100, 2) AS time_weighted_avg_spread_cents,
    round(avgIf(spread, bid_price > 0 AND ask_price >= bid_price) * 100, 2) AS per_update_avg_spread_cents,
    round((avgIf(spread, bid_price > 0 AND ask_price >= bid_price) - sumIf(spread * dwell_s, bid_price > 0 AND ask_price >= bid_price) / sumIf(dwell_s, bid_price > 0 AND ask_price >= bid_price)) * 100, 2) AS per_update_premium_cents,
    countIf(bid_price > 0 AND ask_price > 0 AND ask_price < bid_price) AS crossed_updates,
    countIf(bid_price <= 0 OR ask_price <= 0) AS zero_bid_or_ask_updates
FROM ordered

SELECT
    formatDateTime(toTimeZone(toStartOfInterval(sip_timestamp, INTERVAL 30 MINUTE), 'America/New_York'), '%H:%i') AS et_time,
    round(quantileExactIf(0.5)(toFloat64(ask_price - bid_price), bid_price > 0 AND ask_price >= bid_price) * 100, 1) AS median_spread_cents,
    count() AS quote_updates
FROM global_markets.cache_stocks_quotes
WHERE ticker = 'SPY' AND sip_timestamp >= '2026-06-29 08:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
GROUP BY et_time
ORDER BY et_time

SELECT
    date,
    uniqExact(ticker) AS tickers,
    min(ticker) AS first_ticker,
    max(ticker) AS last_ticker,
    countIf(ticker = 'TSLA') AS tsla_rows,
    round(100 * sum(short_volume) / sum(total_volume), 2) AS short_pct_of_reported
FROM global_markets.stocks_short_volume
WHERE date BETWEEN '2026-06-26' AND '2026-06-30'
GROUP BY date
ORDER BY date