where-to-find-options-trade-data
venue_sharetable ·
2026-09-18 · 18×5
session_clockseries ·
2026-09-18 · 14×5
largest_printstable ·
2026-09-18 · 10×7
iv_skewtable ·
2026-09-18 · 29×4
what-is-the-cme-cvol-index
spy_term_structureranking ·
2026-09-18 · 7×2
spy_strike_weightsranking ·
2026-09-18 · 11×4
what-are-tokenized-stocks
volume_by_segmentranking ·
2026-09-18 · 5×3
off_hours_shareranking ·
2026-09-18 · 5×3
aapl_dividendsseries ·
2026-09-18 · 11×7
upcoming-ipo-lockup-expirations
prospectus_receiptstable ·
2026-09-18 · 50×4
price_vs_offertable ·
2026-09-18 · 51×5
lockup_pipelineseries ·
2026-09-18 · 13×3
lockup_calendartable ·
2026-09-18 · 57×8
early_release_watchtable ·
2026-09-18 · 25×6
upcoming-dividend-payment-dates
sector_gaptable ·
2026-09-18 · 7×5
pay_daysseries ·
2026-09-18 · 12×4
pay_calendartable ·
2026-09-18 · 30×8
gap_bucketsranking ·
2026-09-18 · 6×4
the-september-effect
worst_septembersranking ·
2026-09-18 · 3×2
septembers_by_yearranking ·
2026-09-18 · 22×2
monthlytable ·
2026-09-18 · 12×6
decadesranking ·
2026-09-18 · 3×4
stock-split-candidates
stubborntable ·
2026-09-18 · 12×5
split_historytable ·
2026-09-18 · 16×6
candidatestable ·
2026-09-18 · 15×8
aapl_traceseries ·
2026-09-18 · 84×4
aapl_splitstable ·
2026-09-18 · 3×5
nasdaq-opening-cross-explained
open_minute_shareranking ·
2026-09-18 · 5×4
open_conditionsranking ·
2026-09-18 · 14×3
aapl_open_minute_traceseries ·
2026-09-18 · 21×4
market-wide-circuit-breakers-explained
trigger_levelstable ·
2026-09-18 · 3×7
march_2020_sessionsseries ·
2026-09-18 · 22×6
halt_daysseries ·
2026-09-18 · 4×8
daily_recalcseries ·
2026-09-18 · 15×5
how-much-money-do-you-need-to-trade-options
spread_ladderranking ·
2026-09-18 · 8×3
put_traceseries ·
2026-09-18 · 31×5
put_laddertable ·
2026-09-18 · 8×5
call_laddertable ·
2026-09-18 · 8×5
how-long-can-a-stock-trade-under-1-dollar
sub_dollar_trendseries ·
2026-09-18 · 105×3
streaksranking ·
2026-09-18 · 4×3
reverse_splits_monthlyseries ·
2026-09-18 · 12×3
price_bucketsranking ·
2026-09-18 · 5×4
free-stock-data-api-in-python
avg_volume_by_tickerseries ·
2026-09-18 · 4×4
aapl_volume_traceseries ·
2026-09-18 · 34×3
are-0dte-options-high-risk
strike_gammaranking ·
2026-09-18 · 9×3
premium_laddertable ·
2026-09-18 · 5×5
greeks_ladderseries ·
2026-09-18 · 5×5
expiry_outcomesranking ·
2026-09-18 · 5×2
expiry_dayseries ·
2026-09-18 · 21×6
when-do-vix-options-expire
wednesday_expiriesranking ·
2026-09-17 · 12×3
what-is-the-3m10y-spread
recentseries ·
2026-09-17 · 60×6
monthlyseries ·
2026-09-17 · 241×4
inversionsseries ·
2026-09-17 · 9×6
front_endseries ·
2026-09-17 · 42×5
episodes_comparedtable ·
2026-09-17 · 3×9
what-does-cross-mean-in-trading
quote_statesranking ·
2026-09-17 · 3×4
cross_minutesseries ·
2026-09-17 · 7×4
cross_codestable ·
2026-09-17 · 13×5
Weekly Market Recap: The Week in Numbers
Where the volume went: most dollars traded over the past weekranking ·
2026-09-17 · 8×2
The eleven S&P 500 sectors over the past weekranking ·
2026-09-17 · 11×2
Biggest stock losers over the past weekranking ·
2026-09-17 · 7×3
The major index ETFs over the past weekranking ·
2026-09-17 · 4×4
Biggest stock gainers over the past weekranking ·
2026-09-17 · 7×3
Daily market breadth: advancers vs decliners each session (names trading $500M+)series ·
2026-09-17 · 5×3
Upcoming Stock Splits Calendar
Upcoming US stock splits: announced, with a future effective datetable ·
2026-09-17 · 30×5
Announced upcoming splits by directionranking ·
2026-09-17 · 2×2
Announced stock splits by effective month: forward vs reverseseries ·
2026-09-17 · 4×4
Unusual Volume Stocks This Week, Measured
How the whole qualifying universe traded this week, bucketed by relative volumeranking ·
2026-09-17 · 7×4
Persistence check: the eight leaders' daily relative volume across the five sessionstable ·
2026-09-17 · 8×5
Highest relative volume this week: trailing 5 sessions vs. the prior 40, for names trading $500M+ in the weekseries ·
2026-09-17 · 8×6
The board leader, day by day: daily relative volume and open-to-close change (last 15 sessions)series ·
2026-09-17 · 15×5
Wild multiples the dollar floor removes: highest relative volume among names trading under $500M this weekseries ·
2026-09-17 · 6×5
Unusual Options Activity: Last Session
Market-wide options volume by session, with monthly expirations labelledseries ·
2026-09-17 · 25×5
Calls or puts: the board's call and put contract volume on the same sessiontable ·
2026-09-17 · 10×5
What follows a heavy options session: next-session absolute move vs. the same names on an ordinary daytable ·
2026-09-17 · 5×6
What the session's contracts were made of: options volume by days to expiryranking ·
2026-09-17 · 6×4
Unusual options activity: last completed session vs. each underlying's own 20-session averagetable ·
2026-09-17 · 10×8
twap-vs-vwap-vs-pov-orders
volume_curveseries ·
2026-09-17 · 13×4
open_vs_lunchtable ·
2026-09-17 · 5×6
lunch_povseries ·
2026-09-17 · 13×4
curve_dispersionseries ·
2026-09-17 · 13×4
the-7-5-3-1-rule-mutual-funds
worst_windowstable ·
2026-09-17 · 8×6
step_uptable ·
2026-09-17 · 10×5
rolling_7yseries ·
2026-09-17 · 193×5
return_bucketsranking ·
2026-09-17 · 5×3
the-390-rule-in-options-trading
spy_trade_sizesranking ·
2026-09-17 · 5×3
spy_option_paceseries ·
2026-09-17 · 14×3
session_shapeseries ·
2026-09-17 · 16×3
one_lot_shareranking ·
2026-09-17 · 6×4
Short Squeeze Candidates This Week
Squeeze-shaped mechanics: crowded shorts among liquid names, with a rising priceranking ·
2026-09-17 · 12×4
The screened names ranked by short interest against shares outstanding (not float)table ·
2026-09-17 · 10×5
Every past screened name, by what it did over the next 30 daysranking ·
2026-09-17 · 6×3
From the whole settlement file down to the screened list, one rule at a timeranking ·
2026-09-17 · 4×2
Every input behind this screen, and how many days old it isseries ·
2026-09-17 · 3×3
Liquid names at 5+ and 10+ days to cover, settlement by settlementseries ·
2026-09-17 · 12×4
santa-claus-rally
worst_windowstable ·
2026-09-17 · 5×5
window_vs_decembertable ·
2026-09-17 · 3×8
window_volumeranking ·
2026-09-17 · 7×4
santa_windowstable ·
2026-09-17 · 23×5
folklore_testtable ·
2026-09-17 · 2×6
Next 100 →
page 1 of 24
aapl_trace
aapl_trace
| month | period_label | highest_close | lowest_close |
|---|---|---|---|
| 2014-01-01 | Jan 2014 | 557.36 | 499.78 |
| 2014-02-01 | Feb 2014 | 545.99 | 501.53 |
| 2014-03-01 | Mar 2014 | 544.99 | 524.69 |
| 2014-04-01 | Apr 2014 | 594.09 | 517.96 |
| 2014-05-01 | May 2014 | 635.38 | 585.54 |
| 2014-06-01 | Jun 2014 | 647.35 | 90.28 |
| 2014-07-01 | Jul 2014 | 99.02 | 93.09 |
| 2014-08-01 | Aug 2014 | 102.5 | 94.48 |
| 2014-09-01 | Sep 2014 | 103.3 | 97.87 |
| 2014-10-01 | Oct 2014 | 108 | 96.26 |
| 2014-11-01 | Nov 2014 | 119 | 108.6 |
| 2014-12-01 | Dec 2014 | 115.93 | 106.75 |
| 2015-01-01 | Jan 2015 | 118.9 | 105.99 |
| 2015-02-01 | Feb 2015 | 133 | 118.63 |
| 2015-03-01 | Mar 2015 | 129.36 | 122.24 |
| 2015-04-01 | Apr 2015 | 132.65 | 124.25 |
| 2015-05-01 | May 2015 | 132.54 | 125.01 |
| 2015-06-01 | Jun 2015 | 130.54 | 124.53 |
| 2015-07-01 | Jul 2015 | 132.07 | 120.07 |
| 2015-08-01 | Aug 2015 | 119.72 | 103.12 |
| 2015-09-01 | Sep 2015 | 116.41 | 107.72 |
| 2015-10-01 | Oct 2015 | 120.53 | 109.5 |
| 2015-11-01 | Nov 2015 | 122.57 | 112.34 |
| 2015-12-01 | Dec 2015 | 119.03 | 105.26 |
| 2016-01-01 | Jan 2016 | 105.35 | 93.42 |
| 2016-02-01 | Feb 2016 | 98.12 | 93.7 |
| 2016-03-01 | Mar 2016 | 109.56 | 100.53 |
| 2016-04-01 | Apr 2016 | 112.1 | 93.74 |
| 2016-05-01 | May 2016 | 100.41 | 90.34 |
| 2016-06-01 | Jun 2016 | 99.65 | 92.04 |
| 2016-07-01 | Jul 2016 | 104.34 | 94.99 |
| 2016-08-01 | Aug 2016 | 109.48 | 104.48 |
| 2016-09-01 | Sep 2016 | 115.57 | 103.13 |
| 2016-10-01 | Oct 2016 | 118.25 | 112.52 |
| 2016-11-01 | Nov 2016 | 111.8 | 105.71 |
| 2016-12-01 | Dec 2016 | 117.26 | 109.11 |
| 2017-01-01 | Jan 2017 | 121.95 | 116.02 |
| 2017-02-01 | Feb 2017 | 137.11 | 128.53 |
| 2017-03-01 | Mar 2017 | 144.12 | 138.68 |
| 2017-04-01 | Apr 2017 | 144.77 | 140.68 |
| 2017-05-01 | May 2017 | 156.1 | 146.53 |
| 2017-06-01 | Jun 2017 | 155.45 | 142.27 |
| 2017-07-01 | Jul 2017 | 153.46 | 142.73 |
| 2017-08-01 | Aug 2017 | 164 | 150.05 |
| 2017-09-01 | Sep 2017 | 164.05 | 150.55 |
| 2017-10-01 | Oct 2017 | 169.04 | 153.48 |
| 2017-11-01 | Nov 2017 | 176.24 | 166.89 |
| 2017-12-01 | Dec 2017 | 176.42 | 169.01 |
| 2018-01-01 | Jan 2018 | 179.26 | 166.97 |
| 2018-02-01 | Feb 2018 | 178.97 | 155.15 |
| 2018-03-01 | Mar 2018 | 181.72 | 164.94 |
| 2018-04-01 | Apr 2018 | 178.24 | 162.32 |
| 2018-05-01 | May 2018 | 190.04 | 169.1 |
| 2018-06-01 | Jun 2018 | 193.98 | 182.17 |
| 2018-07-01 | Jul 2018 | 194.82 | 183.92 |
| 2018-08-01 | Aug 2018 | 227.63 | 201.5 |
| 2018-09-01 | Sep 2018 | 228.36 | 217.66 |
| 2018-10-01 | Oct 2018 | 232.07 | 212.24 |
| 2018-11-01 | Nov 2018 | 222.22 | 172.29 |
| 2018-12-01 | Dec 2018 | 184.82 | 146.83 |
| 2019-01-01 | Jan 2019 | 166.44 | 142.19 |
| 2019-02-01 | Feb 2019 | 174.87 | 166.52 |
| 2019-03-01 | Mar 2019 | 195.09 | 172.5 |
| 2019-04-01 | Apr 2019 | 207.48 | 191.24 |
| 2019-05-01 | May 2019 | 211.75 | 175.07 |
| 2019-06-01 | Jun 2019 | 199.8 | 173.3 |
| 2019-07-01 | Jul 2019 | 213.04 | 200.02 |
| 2019-08-01 | Aug 2019 | 212.64 | 193.34 |
| 2019-09-01 | Sep 2019 | 223.97 | 205.7 |
| 2019-10-01 | Oct 2019 | 249.05 | 218.96 |
| 2019-11-01 | Nov 2019 | 267.84 | 255.82 |
| 2019-12-01 | Dec 2019 | 293.65 | 259.45 |
| 2020-01-01 | Jan 2020 | 324.34 | 297.43 |
| 2020-02-01 | Feb 2020 | 327.2 | 273.36 |
| 2020-03-01 | Mar 2020 | 302.74 | 224.37 |
| 2020-04-01 | Apr 2020 | 293.8 | 240.91 |
| 2020-05-01 | May 2020 | 319.23 | 289.07 |
| 2020-06-01 | Jun 2020 | 366.53 | 321.85 |
| 2020-07-01 | Jul 2020 | 425.04 | 364.11 |
| 2020-08-01 | Aug 2020 | 506.09 | 129.04 |
| 2020-09-01 | Sep 2020 | 134.18 | 106.84 |
| 2020-10-01 | Oct 2020 | 124.4 | 108.86 |
| 2020-11-01 | Nov 2020 | 120.3 | 108.77 |
| 2020-12-01 | Dec 2020 | 136.69 | 121.78 |
the exact SQL behind every number
WITH
on_file AS
(
SELECT
execution_date,
any(split_from) AS from_shares,
any(split_to) AS to_shares
FROM global_markets.stocks_splits
WHERE ticker = 'AAPL'
AND execution_date <= today()
GROUP BY execution_date
),
restated AS
(
SELECT
d.date AS date,
any(toFloat64(d.close)) * arrayProduct(groupArray(if(s.execution_date > d.date, toFloat64(s.to_shares) / toFloat64(s.from_shares), 1.0))) AS raw_close
FROM
(
SELECT date, close
FROM global_markets.stocks_daily_aggs
WHERE ticker = 'AAPL'
AND date >= '2014-01-01'
AND date < '2021-01-01'
) AS d
CROSS JOIN on_file AS s
GROUP BY d.date
)
SELECT
toString(toStartOfMonth(date)) AS month,
formatDateTime(toStartOfMonth(date), '%b %Y') AS period_label,
round(max(raw_close), 2) AS highest_close,
round(min(raw_close), 2) AS lowest_close
FROM restated
GROUP BY toStartOfMonth(date)
ORDER BY toStartOfMonth(date)
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