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$500 a month into SPY vs one lump sum, 2016 to 2026 (portfolio value, $ thousands)

most recentas of 2026-07-16 series 126×4read in context →

$500 a month into SPY vs one lump sum, 2016 to 2026 (portfolio value, $ thousands) — 126 rows by 4 columns, computed 2026-07-16 from US exchange, SIP and OPRA data.
month	dca_value	lumpsum_value	invested
2016-01-01	0.5	63	0.5
2016-02-01	1	60.7	1
2016-03-01	1.5	62.1	1.5
2016-04-01	2.1	64.8	2
2016-05-01	2.6	65.2	2.5
2016-06-01	3.1	65.9	3
2016-07-01	3.6	65.8	3.5
2016-08-01	4.2	68	4
2016-09-01	4.7	68.1	4.5
2016-10-01	5.2	67.6	5
2016-11-01	5.6	66.1	5.5
2016-12-01	6.3	68.8	6
2017-01-01	7	70.6	6.5
2017-02-01	7.5	71.4	7
2017-03-01	8.5	75.1	7.5
2017-04-01	8.8	73.8	8
2017-05-01	9.4	74.8	8.5
2017-06-01	10.1	76.3	9
2017-07-01	10.6	76	9.5
2017-08-01	11.3	77.5	10
2017-09-01	11.8	77.7	10.5
2017-10-01	12.5	79.1	11
2017-11-01	13.3	80.7	11.5
2017-12-01	14.1	82.9	12
2018-01-01	14.9	84.3	12.5
2018-02-01	16.1	88.3	13
2018-03-01	15.8	83.9	13.5
2018-04-01	15.7	80.7	14
2018-05-01	16.6	83.1	14.5
2018-06-01	17.7	85.8	15
2018-07-01	18.1	85.2	15.5
2018-08-01	19.2	88.1	16
2018-09-01	20.3	90.9	16.5
2018-10-01	20.9	91.4	17
2018-11-01	20.1	85.7	17.5
2018-12-01	21	87.5	18
2019-01-01	19.3	78.4	18.5
2019-02-01	21.4	84.7	19
2019-03-01	22.7	87.9	19.5
2019-04-01	23.6	89.6	20
2019-05-01	24.6	91.5	20.5
2019-06-01	23.7	86.1	21
2019-07-01	26	92.7	21.5
2019-08-01	26.4	92.4	22
2019-09-01	26.5	91.1	22.5
2019-10-01	27.3	91.9	23
2019-11-01	29	96	23.5
2019-12-01	30	97.7	24
2020-01-01	31.8	101.8	24.5
2020-02-01	32.2	101.6	25
2020-03-01	31.2	96.9	25.5
2020-04-01	25.3	77.1	26
2020-05-01	29.6	88.6	26.5
2020-06-01	32.5	95.8	27
2020-07-01	33.5	97.3	27.5
2020-08-01	36	103	28
2020-09-01	39.1	110.5	28.5
2020-10-01	37.9	105.6	29
2020-11-01	37.6	103.5	29.5
2020-12-01	42.2	114.7	30
2021-01-01	43	115.6	30.5
2021-02-01	44.4	117.9	31
2021-03-01	46.5	122.1	31.5
2021-04-01	48.3	125.5	32
2021-05-01	50.9	131.1	32.5
2021-06-01	51.6	131.5	33
2021-07-01	53.4	134.9	33.5
2021-08-01	54.8	137.2	34
2021-09-01	57.1	141.6	34.5
2021-10-01	55.3	136.1	35
2021-11-01	59.1	144.2	35.5
2021-12-01	58.4	141.2	36
2022-01-01	62.4	149.8	36.5
2022-02-01	59.7	142	37
2022-03-01	57.2	134.8	37.5
2022-04-01	60.7	142	38
2022-05-01	56.1	129.9	38.5
2022-06-01	55.9	128.4	39
2022-07-01	52.5	119.5	39.5
2022-08-01	57.1	128.7	40
2022-09-01	55.6	124.3	40.5
2022-10-01	51.9	114.9	41
2022-11-01	55	120.5	41.5
2022-12-01	58.7	127.7	42
2023-01-01	55.4	119.4	42.5
2023-02-01	60.3	128.8	43
2023-03-01	58.4	123.7	43.5
2023-04-01	61.3	128.8	44
2023-05-01	62.5	130.2	44.5
2023-06-01	63.9	132.2	45
2023-07-01	67.8	139.1	45.5
2023-08-01	70.2	143.1	46
2023-09-01	69.9	141.4	46.5
2023-10-01	66.7	134	47
2023-11-01	66.5	132.5	47.5
2023-12-01	72.7	143.9	48
2024-01-01	75.3	148.1	48.5
2024-02-01	78.5	153.3	49
2024-03-01	82.8	160.7	49.5
2024-04-01	84.8	163.7	50
2024-05-01	81.7	156.8	50.5
2024-06-01	86.7	165.4	51
2024-07-01	90.1	170.9	51.5
2024-08-01	90.2	170.2	52
2024-09-01	92.2	173.1	52.5
2024-10-01	95.5	178.3	53
2024-11-01	96.4	179	53.5
2024-12-01	102.4	189.2	54
2025-01-01	99.7	183.2	54.5
2025-02-01	102.4	187.3	55
2025-03-01	100.5	183	55.5
2025-04-01	97.1	175.9	56
2025-05-01	97.2	175	56.5
2025-06-01	103.6	185.8	57
2025-07-01	108.5	193.6	57.5
2025-08-01	109.7	194.9	58
2025-09-01	113.5	200.7	58.5
2025-10-01	119	209.5	59
2025-11-01	122.1	214.2	59.5
2025-12-01	122.1	213.2	60
2026-01-01	123.1	214.1	60.5
2026-02-01	125.8	218	61
2026-03-01	124.6	215.1	61.5
2026-04-01	119.5	205.4	62
2026-05-01	131.9	225.9	62.5
2026-06-01	139.3	237.7	63

the exact SQL behind every number

WITH
monthly AS (
    SELECT toStartOfMonth(dt) AS mo, argMin(c, dt) AS px
    FROM (
        SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS dt,
               argMax(toFloat64(close), window_start) AS c
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY'
          AND window_start >= '2016-01-01 00:00:00' AND window_start < '2026-07-01 00:00:00'
          AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
               + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
        GROUP BY dt
    )
    GROUP BY mo
),
agg AS (
    SELECT argMin(px, mo) AS first_px, sum(500.0) AS total_invested FROM monthly
)
SELECT mo AS month,
       round(sum(500.0 / px) OVER (ORDER BY mo) * px / 1000, 1) AS dca_value,
       round((total_invested / first_px) * px / 1000, 1) AS lumpsum_value,
       round(sum(500.0) OVER (ORDER BY mo) / 1000, 1) AS invested
FROM monthly CROSS JOIN agg
ORDER BY mo

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