One session, one formula, different amounts of warm-up

Name: One session, one formula, different amounts of warm-up
Creator: Strasmore
Published: 2026-08-17
License: https://www.strasmore.com/terms/

Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-08-17, from Why Your RSI Differs Between Platforms.

as of 2026-08-17 ranking 10×3read in context →

One session, one formula, different amounts of warm-up — 10 rows by 3 columns, computed 2026-08-17 from US exchange, SIP and OPRA data.
warmup_bars	rsi_wilder	rsi_simple
0	49.07	49.07
1	42.35	49.07
2	39.73	49.07
5	38.03	49.07
10	43.08	49.07
20	46.95	49.07
40	47.14	49.07
80	46.92	49.07
160	46.91	49.07
250	46.91	49.07

Rows × columns: 10 × 3
Computed: 2026-08-17
Completeness: No missing values
Source: US exchange, SIP and OPRA market data
Licence: Strasmore terms · free, no signup
Formats: JSON · CSV · the SQL below

What each column holds

Column definitions for One session, one formula, different amounts of warm-up, derived from the stored result.
Column	Type	Range
`warmup_bars`	text	10 distinct values (0, 1, 10…)
`rsi_wilder`	number	38.03 to 49.07
`rsi_simple`	number	every row is 49.07

Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on 2026-08-17, and the query below is what returned them.

the exact SQL behind every number

WITH
    daily AS
    (
        SELECT arraySort(groupArray((day, px))) AS pts
        FROM
        (
            SELECT
                date                  AS day,
                toFloat64(max(close)) AS px
            FROM global_markets.stocks_daily_aggs
            WHERE ticker = 'AAPL'
              AND date >= '2025-01-02'
              AND date <= '2026-06-30'
            GROUP BY day
        )
    ),
    steps AS
    (
        SELECT arrayPopFront(arrayDifference(arrayMap(p -> p.2, pts))) AS chg
        FROM daily
    ),
    grid AS
    (
        SELECT
            arrayMap(x -> greatest(x, 0.0),  chg) AS up,
            arrayMap(x -> greatest(-x, 0.0), chg) AS dn,
            toInt64(length(chg))                  AS e,
            toInt64(arrayJoin([0, 1, 2, 5, 10, 20, 40, 80, 160, 250])) AS warmup
        FROM steps
    ),
    seeded AS
    (
        SELECT
            warmup,
            (arraySum(arraySlice(up, e - warmup - 13, 14)) / 14) * pow(13.0 / 14.0, warmup)
                + arraySum(arrayMap((g, j) -> g * pow(13.0 / 14.0, toInt64(j) - 1),
                      arrayReverse(arraySlice(up, e - warmup + 1, warmup)),
                      arrayEnumerate(arraySlice(up, e - warmup + 1, warmup)))) / 14 AS up_wilder,
            (arraySum(arraySlice(dn, e - warmup - 13, 14)) / 14) * pow(13.0 / 14.0, warmup)
                + arraySum(arrayMap((g, j) -> g * pow(13.0 / 14.0, toInt64(j) - 1),
                      arrayReverse(arraySlice(dn, e - warmup + 1, warmup)),
                      arrayEnumerate(arraySlice(dn, e - warmup + 1, warmup)))) / 14 AS dn_wilder,
            arraySum(arraySlice(up, e - 13, 14)) / 14 AS up_simple,
            arraySum(arraySlice(dn, e - 13, 14)) / 14 AS dn_simple
        FROM grid
    )
SELECT
    toString(warmup)                                                      AS warmup_bars,
    round(100 - 100 / (1 + up_wilder / greatest(dn_wilder, 0.000001)), 2) AS rsi_wilder,
    round(100 - 100 / (1 + up_simple / greatest(dn_simple, 0.000001)), 2) AS rsi_simple
FROM seeded
ORDER BY warmup

Run your own version of this

The same 22 years of US equities and 12 years of options data are queryable in SQL or plain English. A free account runs 100 queries a day and takes no card.

More from this analysisWhy Your RSI Differs Between Platforms

Where the minute bars actually are, by ET hour ranking 16×3 → RSI(14) on identical closes: Wilder's smoothing against a simple average series 60×4 → Two definitions of one daily close, side by side series 21×4 → How many of the 390 session minutes each US symbol traded in ranking 10×2 → Trade condition codes that restrict high, low, and last ranking 7×3 → SPY volume by minute into the close, June 10 2026 series 25×3 → See all 2,170 queries →