[{"data":1,"prerenderedAt":15610},["ShallowReactive",2],{"wrun-open-editor-url":3,"indicators:functions/ta-library":4},"https://openmarket.xyz/chart/#wrun-open=v1",{"slug":5,"filePath":6,"frontmatter":7,"rawMarkdown":12,"tree":13},"functions/ta-library","functions/ta-library.md",{"title":8,"description":9,"order":10,"section":11},"TA library","Every wrun indicator can use ./sdk/ta, which ships with the editor: 52 stateful classes covering averages, statistics, oscillators, ranges, trend systems, and…",41,"functions","---\ntitle: \"TA library\"\ndescription: \"Every wrun indicator can use ./sdk/ta, which ships with the editor: 52 stateful classes covering averages, statistics, oscillators, ranges, trend systems, and…\"\norder: 41\nsection: \"functions\"\n---\n\n\u003C!-- source: docs/indicators/functions/ta-library.md; generated by packages/cli/scripts/gen-indicator-docs.ts, do not edit -->\n\n# TA library\n\nEvery wrun indicator can use `./sdk/ta`, which ships with the editor:\n52 stateful classes covering averages, statistics, oscillators, ranges,\ntrend systems, and events. Each class folds one bar per `update()` call,\nkeeps its own window, and has fixed arithmetic, warm-up, and\nmissing-value rules, checked bit-exact against a reference run. This\npage is the catalog: every class with its constructor, its `update()`\narguments, its fields, and the one convention you need to know about\nit. The function pages ([Moving averages](moving-averages.md),\n[Oscillators](oscillators.md), [Trend and volatility](trend-indicators.md),\n[Volume and VWAP](volume-indicators.md),\n[Series functions](series-functions.md)) go deeper on each group with\ncompiled examples.\n\nTwo sibling modules pick up where the catalog stops,\nthere with classes of the same shape: `./sdk/ta-plus` adds the textbook\nindicators the catalog never had (the double and triple EMAs, TRIX,\nKaufman's adaptive average, the ultimate oscillator, Vortex, Aroon,\nchoppiness, the volume lines and more), and `./sdk/stats` adds\nallocation-free list math over a `StaticArray\u003Cf64>` plus `History`, the\n`x[n]` window every indicator needs sooner or later. Both are on\n[Extra indicators](extra-indicators.md); the kits for text, time,\ncolors, order flow, levels and market structure sit beside it in this\nsection.\n\n## How every class works\n\n- **Allocate in the constructor, never in `update()`**, so per-bar memory\n  stays flat.\n- **`update(...)` folds one bar and returns the current value**, `NaN`\n  until the class is warm (a few classes report a partial window from bar\n  0 instead; the tables say which).\n- **Multi-output classes fill fields.** `update()` returns the primary\n  line and the other lines sit in public fields you read after the call.\n- **`reset()` restores the just-constructed state.** Nothing on the\n  chart calls it for you: a revised forming bar replays from a snapshot\n  of the module taken after the last closed bar, so a live update never\n  folds the same bar twice.\n- **Periods are `i32`, params are `f64`.** Construct in `onStart()` from\n  a param (`new Sma(i32(p_period()))`), keep the object in a module-level\n  `let`, and update it once per bar in `onBar()`. A period below 1 is\n  clamped to 1 (except `Donchian`, which uses the period as given).\n\n## The shipped classes\n\n### Averages and smoothing\n\n| Class | Construct | Per bar | Convention |\n| --- | --- | --- | --- |\n| `Sma` | `new Sma(period)` | `.update(x)` | plain window mean; `NaN` until `period` bars exist and whenever the window holds a non-finite value |\n| `Ema` | `new Ema(period)` | `.update(x)` | the mean of the first `period` finite values seeds it, then `x * alpha + prev * (1 - alpha)` with `alpha = 2 / (period + 1)`; a non-finite input after the seed makes it `NaN` for good |\n| `Rma` | `new Rma(period)` | `.update(x)` | Wilder smoothing, the accumulator inside `Rsi` and `Atr`: same seed as `Ema`, then `(prev * (period - 1) + x) / period` |\n| `Wma` | `new Wma(period)` | `.update(x)` | linear weights, the newest value weighs `period`, the oldest 1; strict window |\n| `Hma` | `new Hma(period)` | `.update(x)` | `wma(2 * wma(x, round(period / 2)) - wma(x, period), round(sqrt(period)))`; first value at bar `period - 1 + round(sqrt(period)) - 1` |\n| `Vwma` | `new Vwma(period)` | `.update(x, volume)` | `sum(x * volume) / sum(volume)`; `NaN` when the volume sum is 0 |\n| `Alma` | `new Alma(length, offset = 0.85, sigma = 6)` | `.update(x)` | Gaussian weights centered at `offset * (length - 1)`, width `length / sigma`; strict window |\n| `Swma` | `new Swma()` | `.update(x)` | `(x[3] + 2 x[2] + 2 x[1] + x[0]) / 6` with `x[0]` the newest; first value at bar 3 |\n| `Linreg` | `new Linreg(period, offset = 0)` | `.update(x)` | least-squares line through the window evaluated at `period - 1 - offset` (offset truncated to an integer); strict window |\n\n### Statistics and series math\n\n| Class | Construct | Per bar | Convention |\n| --- | --- | --- | --- |\n| `Sum` | `new Sum(period)` | `.update(x)` | no warm-up: bar 0 already returns the partial window; `NaN` inputs are skipped |\n| `Median` | `new Median(period)` | `.update(x)` | middle of the sorted window, the mean of the two middle values on an even period; strict window |\n| `Percentile` | `new Percentile(period, pct)` | `.update(x)` | nearest rank: `rank = ceil(pct / 100 * period)`, result `sorted[max(0, rank - 1)]`; `pct` clamped to 0..100; strict window |\n| `Variance` | `new Variance(period)` | `.update(x)` | population variance (divide by `period`, not `period - 1`); strict window |\n| `Stdev` | `new Stdev(period)` | `.update(x)` | population standard deviation, the square root of `Variance`; strict window |\n| `Zscore` | `new Zscore(period)` | `.update(x)` | `(x - mean) / stdev` over the window; `NaN` bars inside the window are skipped for the sums, the variance still divides by `period`; `0` when the deviation is exactly 0 |\n| `Correlation` | `new Correlation(period)` | `.update(a, b)` | Pearson correlation of two series; `NaN` when either window holds a non-finite value or the denominator is 0 |\n| `Change` | `new Change(n = 1)` | `.update(x)` | `x - x[n]`; `NaN` for the first `n` bars |\n| `Mom` | `new Mom(n)` | `.update(x)` | the same math as `Change` under another name |\n| `Roc` | `new Roc(period)` | `.update(x)` | `(x - x[n]) / x[n] * 100`; `NaN` for the first `n` bars and when `x[n]` is 0 |\n| `Cum` | `new Cum()` | `.update(x)` | running sum from bar 0; a non-finite bar makes it `NaN` for good |\n| `Fixnan` | `new Fixnan()` | `.update(x)` | repeats the last finite value over a non-finite bar; `NaN` until the first finite value |\n\n### Oscillators and momentum\n\n| Class | Construct | Per bar | Convention |\n| --- | --- | --- | --- |\n| `Rsi` | `new Rsi(period)` | `.update(x)` | Wilder RSI; bar 0 feeds nothing, first value at bar `period`; a zero average loss returns 100, so a flat window is 100 |\n| `Cmo` | `new Cmo(length)` | `.update(x)` | `100 * (up - down) / (up + down)` over the last `length` changes; first value at bar `length`; a zero total returns 0 |\n| `Tsi` | `new Tsi(short = 13, long = 25)` | `.update(x)` | double EMA (long, then short) of momentum over the double EMA of its absolute value; the engine's parameter order is `(short, long)`; first value at bar `long + short - 1` |\n| `Cci` | `new Cci(period = 20, constant = 0.015)` | `.update(high, low, close)` | over typical price `(high + low + close) / 3`; first value at bar `period - 1`; 0 when the mean deviation is 0 |\n| `Mfi` | `new Mfi(period = 14)` | `.update(high, low, close, volume)` | money flow over typical price times volume; first value at bar `period`; a zero negative flow returns 100 |\n| `Wpr` | `new Wpr(length = 14)` | `.update(high, low, close)` | Williams %R; first value at bar `length - 1`; a flat window returns 0 |\n| `Stoch` | `new Stoch(periodK, smoothK, periodD)` | `.update(high, low, close)` returns `k` | fields `k`, `d`; raw %K is 0 on a flat window; first `k` at bar `periodK + smoothK - 2`, first `d` at bar `periodK + smoothK + periodD - 3` |\n| `Stochastic` | `new Stochastic(kPeriod = 14, kSmoothing = 3, dPeriod = 3)` | `.update(high, low, close)` returns `k` | the other spelling, with its own rules: `k = d = 0` before bar `kPeriod - 1` (never `NaN`), 50 on a flat window, a `NaN` `k` or `d` is reported as 50 |\n| `Macd` | `new Macd(fast = 12, slow = 26, signal = 9)` | `.update(x)` returns `macd` | fields `macd`, `signal`, `hist`; the line appears at bar `slow - 1`, the signal at bar `slow + signal - 2`; `hist = macd - signal` |\n| `Obv` | `new Obv()` | `.update(close, volume)` | on-balance volume; bar 0 returns 0; an unchanged close adds nothing |\n\n### Ranges and bands\n\n| Class | Construct | Per bar | Convention |\n| --- | --- | --- | --- |\n| `Tr` | `new Tr()` | `.update(high, low, close)` | `max(high - low, abs(high - prevClose), abs(low - prevClose))`; bar 0 is plain `high - low` |\n| `Atr` | `new Atr(period = 14)` | `.update(high, low, close)` | `Rma` of `Tr`; first value at bar `period - 1`; a non-finite range after the seed makes it `NaN` for good |\n| `Bb` | `new Bb(period, mult)` | `.update(x)` returns `basis` | fields `basis`, `upper`, `lower`; `Sma` basis and population `Stdev` width; all three `NaN` while the window holds a non-finite value |\n| `Keltner` | `new Keltner(period, mult, atrPeriod)` | `.update(x, high, low, close)` returns `basis` | fields `basis`, `upper`, `lower`; `Ema` basis plus `Atr` width; the basis is reported as soon as the EMA is seeded, the bands once the ATR is finite |\n| `Donchian` | `new Donchian(period = 12)` | `.update(high, low)` returns `basis` | fields `basis` (the midline `update()` returns), `upper`, `lower`; no warm-up, the window is partial at the start; older `NaN` highs and lows are skipped, the current bar's `NaN` propagates |\n| `Highest` | `new Highest(period = 12)` | `.update(x)` | the window maximum; field `bars` is the offset of that maximum (0 = this bar, negative = bars ago, the newest bar wins a tie); pass the high for a bar-high window |\n| `Lowest` | `new Lowest(period = 12)` | `.update(x)` | the window minimum; field `bars` is the offset of that minimum; pass the low for a bar-low window |\n| `HighestBars` | `new HighestBars(period)` | `.update(x)` | the offset as the primary value (0 or negative); field `value` is the matching high |\n| `LowestBars` | `new LowestBars(period)` | `.update(x)` | the offset as the primary value; field `value` is the matching low |\n\n### Trend systems\n\n| Class | Construct | Per bar | Convention |\n| --- | --- | --- | --- |\n| `Adx` | `new Adx(period = 14)` | `.update(high, low, close)` returns `adx` | fields `adx`, `plusDi`, `minusDi`; the seed is the plain sum of the first `period` true ranges and directional moves, then `s = s - s / period + x` (the engine's form, not an `Rma`); `+DI` and `-DI` appear at bar `period`, ADX at bar `2 * period - 1`; a non-finite bar after the seed leaves every output `NaN` for good |\n| `Ichimoku` | `new Ichimoku(conversionPeriod = 9, basePeriod = 26, laggingSpanPeriod = 52, displacement = 26)` | `.update(high, low, close)` returns `tenkan` | fields `tenkan`, `kijun`, `senkouA`, `senkouB`, `chikou`; windows are partial from bar 0 (no `NaN` warm-up), the first `displacement` bars use the current bar's values instead of shifted ones, a `NaN` output is reported as 0; `chikou` is the current close (see the accuracy section) |\n| `Psar` | `new Psar(start = 0.02, increment = 0.02, maxValue = 0.2)` | `.update(high, low, close)` | the stop-and-reverse level; bar 0 is `NaN`, bar 1 picks the first trend from `close[1] >= close[0]`; a non-finite bar makes every later bar `NaN`, as the engine's full recompute does |\n| `Supertrend` | `new Supertrend(factor, atrPeriod)` | `.update(high, low, close)` returns `line` | fields `line`, `direction` (`1` up, the line sits below price; `-1` down); both `NaN` while the ATR is `NaN`, and the band state is left untouched across such a gap |\n| `Vwap` | `new Vwap(anchor = \"\", price = \"hlc3\")` | `.update(open, high, low, close, volume, tsMs = NaN)` | `anchor` is `\"\"` (one accumulation from bar 0), `\"day\"`, `\"week\"`, `\"month\"`, `\"quarter\"`, `\"year\"` (UTC calendar boundaries) or a numeric string bucket width in milliseconds; `tsMs` is the bar's open time in milliseconds since the epoch and is read only when an anchor is set (the `time` source hands seconds, multiply by 1000); `price` is `hlc3`, `hl2`, `ohlc4`, `hlcc4`, or `close`; a non-finite bar makes the current bucket `NaN` until the next one starts |\n\n### Events and conditions\n\n| Class | Construct | Per bar | Convention |\n| --- | --- | --- | --- |\n| `Rising` | `new Rising(period)` | `.update(x)` | 1 when `x` is strictly above every one of the previous `period` values, else 0; `NaN` while the bar index is below `period` |\n| `Falling` | `new Falling(period)` | `.update(x)` | the mirror of `Rising` |\n| `PivotHigh` | `new PivotHigh(leftbars, rightbars)` | `.update(high)` | the value of the bar `rightbars` back when it beats every value `leftbars` before and `rightbars` after it, reported only on the confirming bar, `NaN` otherwise; ties never count |\n| `PivotLow` | `new PivotLow(leftbars, rightbars)` | `.update(low)` | the mirror of `PivotHigh` |\n| `ValueWhen` | `new ValueWhen(occurrence)` | `.update(condition, x)` | `x` on the most recent bar where `condition` was true (`occurrence` 0), or the one before (`1`); a condition is true when it is finite and not 0; the current bar counts |\n| `BarsSince` | `new BarsSince()` | `.update(condition)` | bars since the condition was last true, 0 on a true bar; `NaN` until the first true bar |\n| `Cross` | `new Cross()` | `.update(a, b): i32` | `+1` when `a` crosses above `b`, `-1` below, `0` otherwise; the engine's previous-bar rule: crossover is `prevA \u003C prevB && a >= b`, crossunder is `prevA > prevB && a \u003C= b`; any `NaN` among the four values gives 0, and so does the first bar |\n\n`Cross` folds three questions into one return value: test `> 0`, `\u003C 0`,\nor `!= 0`.\n\nA composite over the shipped classes: a `Macd` read through its three\nfields, a `Cross` gate over the line and its signal, and an `Rsi`. The\ndeclarations at the top name the four params and the four outputs; the\nmodule below them only folds the bars:\n\n```typescript sample=fn-ta-tour\nparam(\"fast\", 12, { min: 1, max: 200 });\nparam(\"slow\", 26, { min: 2, max: 400 });\nparam(\"signal\", 9, { min: 1, max: 200 });\nparam(\"rsi_len\", 14, { min: 2, max: 200 });\noutput(\"macd\", line, lower);\noutput(\"signal\", line, lower);\noutput(\"rsi\", line, lower);\n// Data-only: +1 on a bullish MACD cross, -1 on a bearish one, 0 otherwise.\noutput(\"crossed\", none);\n\nlet macd = new Macd(12, 26, 9);\nlet rsi = new Rsi(14);\nlet cross = new Cross();\n\nfunction onStart(): void {\n  macd = new Macd(i32(p_fast()), i32(p_slow()), i32(p_signal()));\n  rsi = new Rsi(i32(p_rsi_len()));\n  cross = new Cross();\n}\n\nfunction onBar(): void {\n  const close = bar.close();\n  macd.update(close);\n  const strength = rsi.update(close);\n  const crossed = f64(cross.update(macd.macd, macd.signal));\n  if (isNaN(macd.signal) || isNaN(strength)) return;\n  out_macd(macd.macd);\n  out_signal(macd.signal);\n  out_rsi(strength);\n  out_crossed(crossed);\n}\n```\n\n## The catalog by page\n\nEvery class grouped by what it does. Every class ships in `./sdk/ta`;\nthe page column is where the group is explained with compiled examples.\n\n**Moving averages and smoothing**\n\n| Class | Where |\n| --- | --- |\n| `Sma` | [Moving averages](moving-averages.md) |\n| `Ema` | [Moving averages](moving-averages.md) |\n| `Rma` | [Moving averages](moving-averages.md) |\n| `Wma` | [Moving averages](moving-averages.md) |\n| `Hma` | [Moving averages](moving-averages.md) |\n| `Vwma`, `update(x, volume)` | [Moving averages](moving-averages.md) |\n| `Alma` | [Moving averages](moving-averages.md) |\n| `Swma` | [Moving averages](moving-averages.md) |\n| `Linreg` | [Moving averages](moving-averages.md) |\n\n**Oscillators and momentum**\n\n| Class | Where |\n| --- | --- |\n| `Rsi` | [Oscillators](oscillators.md) |\n| `Wpr` | [Oscillators](oscillators.md) |\n| `Cmo` | [Oscillators](oscillators.md) |\n| `Tsi` | [Oscillators](oscillators.md) |\n| `Macd`, fields `macd`, `signal`, `hist` | [Oscillators](oscillators.md) |\n| `Stoch`, fields `k`, `d` | [Oscillators](oscillators.md) |\n| `Stochastic`, fields `k`, `d` | [Oscillators](oscillators.md) |\n| `Cci` | [Oscillators](oscillators.md) |\n| `Mfi` | [Oscillators](oscillators.md) |\n| `Change` | [Series functions](series-functions.md) |\n| `Mom` | [Oscillators](oscillators.md) |\n| `Roc` | [Oscillators](oscillators.md) |\n\n**Trend and volatility**\n\n| Class | Where |\n| --- | --- |\n| `Adx`, fields `adx`, `plusDi`, `minusDi` | [Trend and volatility](trend-indicators.md) |\n| `Ichimoku`, five fields | [Trend and volatility](trend-indicators.md) |\n| `Psar` | [Trend and volatility](trend-indicators.md) |\n| `Supertrend`, fields `line`, `direction` | [Trend and volatility](trend-indicators.md) |\n| `Tr` | [Trend and volatility](trend-indicators.md) |\n| `Atr` | [Trend and volatility](trend-indicators.md) |\n| `Bb`, fields `basis`, `upper`, `lower` | [Moving averages](moving-averages.md) |\n| `Keltner`, fields `basis`, `upper`, `lower` | [Moving averages](moving-averages.md) |\n| `Donchian`, fields `basis`, `upper`, `lower` | [Moving averages](moving-averages.md) |\n| `Stdev` | [Trend and volatility](trend-indicators.md) |\n| `Variance` | [Trend and volatility](trend-indicators.md) |\n| price helpers (`hl2`, `hlc3`, `ohlc4`, `hlcc4`): arithmetic on the declared inputs | [Series functions](series-functions.md) |\n\n**Volume**\n\n| Class | Where |\n| --- | --- |\n| `Obv`, `update(close, volume)` | [Volume and VWAP](volume-indicators.md) |\n| `Vwap`, anchored on the bar's open time | [Volume and VWAP](volume-indicators.md) |\n| `Cum` | [Volume and VWAP](volume-indicators.md) |\n\n**Statistics**\n\n| Class | Where |\n| --- | --- |\n| `Sum` | [Series functions](series-functions.md) |\n| `Median` | [Series functions](series-functions.md) |\n| `Percentile` | [Series functions](series-functions.md) |\n| `Correlation`, `update(a, b)` | [Series functions](series-functions.md) |\n| `Zscore` | [Series functions](series-functions.md) |\n\n**Bars and events**\n\n| Class | Where |\n| --- | --- |\n| `Highest`, `Lowest`, field `bars` | [Series functions](series-functions.md) |\n| `HighestBars`, `LowestBars`, field `value` | [Series functions](series-functions.md) |\n| `PivotHigh`, `PivotLow` | [Series functions](series-functions.md) |\n| `Rising`, `Falling` | [Series functions](series-functions.md) |\n| `ValueWhen` | [Series functions](series-functions.md) |\n| `BarsSince` | [Series functions](series-functions.md) |\n| `Cross`, `+1` / `-1` / `0` | [Series functions](series-functions.md) |\n| `Fixnan` | [Series functions](series-functions.md) |\n| `isNaN(x)` | [Series functions](series-functions.md) |\n| `nz(x, replacement)`, a two-line helper | [Series functions](series-functions.md) |\n\nThe order book and volume profile scans are not classes: they are loops\nover a celled input on the [Order flow](order-flow-kit.md) page.\n\n## Conventions (read this once)\n\nThese rules hold across the library and explain nearly every edge case.\n\n**Warm-up is `NaN`, with named exceptions.** A windowed class\nreturns `NaN` until it has a full window and never fabricates an early\nvalue. The classes that report a partial window from bar 0:\n`Sum`, `Donchian`, `Ichimoku`, `Cum` (bar 0 is `x`), `Obv`\n(bar 0 is 0), and `Stochastic` (0 before its window fills). Decide per\nbar whether to `return` from `onBar()` before writing or to write the\n`NaN`; either way the chart draws nothing on that bar\n([Execution model](../core-concepts/execution-model.md)).\n\n**Windows are strict; accumulators poison.** A class that recomputes over\nits window (`Sma`, `Wma`, `Alma`, `Linreg`, `Median`, `Percentile`,\n`Variance`, `Stdev`, `Correlation`, `Bb`, `Highest`, `Lowest`, and the\nrest of the window family) yields `NaN` while any value inside the window\nis not finite and heals as soon as it leaves. A class that carries a\nrunning accumulator (`Ema`, `Rma`, `Atr`, `Rsi`, `Macd`, `Adx`, `Cum`,\n`Psar`, `Supertrend`'s ATR) restarts its seed when a non-finite value\narrives before the seed completes, and turns `NaN` for good when one\narrives after, because the engine never reseeds. `Sum` and `Zscore` skip\n`NaN` bars instead. Run a sparse source through `Fixnan` or an `isNaN`\nguard before an accumulator.\n\n**Columns are yours to choose.** The classes take one value per bar:\npass `bar.high()` to `Highest`, `HighestBars`, and `PivotHigh` for a\nbar-high window, `bar.low()` to their counterparts, or the close for a\nclose-based one.\n\n**Classes compose.** `update()` takes any `f64`, including another\nclass's output (`rsi.update(sma.update(close))`) and any series derived\nfrom a celled input. Warm-up propagates through the composition because\n`NaN` propagates through arithmetic.\n\n**Values carry; events do not.** A missing scalar carries the latest\neligible value forward by default (the `missing` policy on the source\ndecides), so math keeps working. Events are stricter: `Cross.update()`\nreturns `0` on any bar where either side is `NaN`, `BarsSince` and\n`ValueWhen` read a condition you computed (finite and not 0), so stale\ndata cannot fabricate a signal.\n\n**`Rma` is Wilder.** `Rsi`, `Atr`, `Keltner`, and `Supertrend` share\nthe same accumulator, so an indicator built by hand over `Rma` agrees\nwith the shipped classes.\n\n## Multi-output indicators\n\nAn indicator with several streams is a class with several fields.\nThere are no tuple outputs and nothing indexes into an array:\n`update()` returns the primary line, and you read the other fields after\nthe call and write each to its own output\n([Named streams](../core-concepts/named-streams.md)).\n\n| Class | `update()` returns | Fields |\n| --- | --- | --- |\n| `Bb`, `Keltner`, `Donchian` | `basis` | `basis`, `upper`, `lower` |\n| `Macd` | `macd` | `macd`, `signal`, `hist` |\n| `Stoch`, `Stochastic` | `k` | `k`, `d` |\n| `Supertrend` | `line` | `line`, `direction` |\n| `Adx` | `adx` | `adx`, `plusDi`, `minusDi` |\n| `Ichimoku` | `tenkan` | `tenkan`, `kijun`, `senkouA`, `senkouB`, `chikou` |\n| `Highest`, `Lowest` | the extreme | `bars` (the offset of that extreme) |\n| `HighestBars`, `LowestBars` | the offset | `value` (the extreme at that offset) |\n\nFields feed a band declaration or a box the same way outputs do: write\n`bb.upper` and `bb.lower` to two drawn outputs and declare a `range()`\nbetween their names ([Styling](../presentation/styling.md)), or a `box`\nbetween their handles ([Drawing objects](../presentation/drawing-objects.md)).\n\n## Over microstructure\n\nEvery windowed class also runs over order-flow series, which is where the\nlibrary stops being a price-only kit. A celled `volume_profile` input\ndelivers each bar's `[low, high, buy, sell]` tuples; sum the buy and sell\ncolumns into a per-bar delta and the delta is just an `f64` any class can\nfold: an `Rsi` over it is delta-RSI, a running sum is cumulative volume\ndelta. Declaring the celled input switches the derived sheet to the\nsecond runtime contract, and the numeric classes compute exactly as\nbefore.\n\n```typescript sample=fn-delta-rsi\nparam(\"period\", 14, { min: 2, max: 200, description: \"RSI window over the per-bar delta\" });\ninput(\"close\", ohlcv.close);\ninput(\"profile\", volume_profile.cells, { max_cells: 8192 });\noutput(\"delta\", line, lower, { color: \"#94a3b8\", width: 1, description: \"Buy minus sell volume across the bar's profile\" });\noutput(\"delta_rsi\", line, lower, { color: \"#7c3aed\", width: 2, description: \"RSI of the per-bar delta\" });\noutput(\"cvd\", line, lower, { color: \"#22d3ee\", width: 2, description: \"Cumulative volume delta\" });\n\nlet rsi = new Rsi(14);\nlet cvd: f64 = 0.0;\n\nfunction onStart(): void {\n  rsi = new Rsi(i32(p_period()));\n}\n\nfunction onBar(): void {\n  const n = in_profile_cells();\n  if (n \u003C 0) return; // this bar carries no block\n  let delta = 0.0;\n  if (n > 0) {\n    const cells = in_profile_view();\n    for (let i = 0; i + 3 \u003C n; i += 4) delta += cells[i + 2] - cells[i + 3];\n  }\n  cvd += delta;\n  const deltaRsi = rsi.update(delta);\n  out_delta(delta);\n  out_delta_rsi(deltaRsi);\n  out_cvd(cvd);\n}\n```\n\nThe [Order flow](order-flow-kit.md) page has every footprint read as a\nscan over the same tuples, and does the same over book levels for a\nbook-imbalance average.\n\n## Accuracy as a contract\n\n**Every class is checked bit-exact against a reference implementation.**\nThe reference run covers a 690-bar 1h BTCUSDT window; each class is\ncompiled through the real build and executed through the real runtime\nover the same bars, and every output is compared per bar: a maximum\nabsolute deviation of 0 and identical `NaN` placement (a bar that is\n`NaN` in the reference is `NaN` here, and only that bar). The\nconventions on this page are written down so that comparison holds, and\na change that drifts a class from the reference numbers fails the build.\n\n**Two honest exceptions.**\n\n- **`Ichimoku.chikou` is the current close.** The engine computes the\n  lagging span by reading `close[i + displacement]`, a bar in the future\n  of bar `i`, and only falls back to the current close on the last\n  `displacement` bars of the series. A class that sees one bar at a time\n  cannot read ahead, so the field carries the current close on every bar;\n  it matches the engine on those last bars only. The other four fields\n  match bar for bar.\n- **`Vwap` anchors beyond `\"\"`, `\"day\"`, and a numeric millisecond width\n  are unproven on that window.** The `\"week\"`, `\"month\"`,\n  `\"quarter\"`, and `\"year\"` boundaries follow the engine's UTC calendar\n  arithmetic, but the 690-bar window does not exercise a deep-history\n  quarter or year, and the engine's session-calendar bucketing and RTH\n  session filter (venues with a trading-session calendar) are not\n  mirrored, since a per-bar class never sees a session calendar.\n\nEverything else in the catalog, warm-up bars included, matches the\nreference bar for bar.\n",{"type":14,"children":15,"data":15603,"position":15605},"root",[16,26,29,30,228,229,364,365,382,383,753,754,769,770,785,787,1850,1851,1866,1868,3135,3136,3151,3153,4413,4414,4428,4429,5570,5571,5586,5588,6781,6782,6796,6798,7728,7729,7805,7806,7870,7871,8762,8763,8777,8778,8808,8809,8830,8832,9295,9296,9317,9319,9993,9994,10014,10015,10836,10837,10859,10861,11066,11067,11088,11090,11379,11380,11402,11404,12026,12027,12062,12063,12078,12079,12093,12094,12348,12349,12838,12839,12955,12956,13051,13052,13187,13188,13310,13311,13326,13327,13379,13381,14133,14134,14262,14263,14278,14279,14368,14369,15184,15185,15219,15220,15235,15236,15317,15318,15339,15340,15587,15588],{"type":17,"value":18,"position":19},"comment"," source: docs/indicators/functions/ta-library.md; generated by packages/cli/scripts/gen-indicator-docs.ts, do not edit ",{"start":20,"end":23},{"line":21,"column":21,"offset":22},1,0,{"line":21,"column":24,"offset":25},127,126,{"type":27,"value":28},"text","\n",{"type":27,"value":28},{"type":31,"tagName":32,"properties":33,"children":34,"position":225},"element","p",{},[35,44,58,66,79,87,99,107,125,132,148,155,173,179,195,201,217],{"type":27,"value":36,"position":37},"Every wrun indicator can use ",{"start":38,"end":41},{"line":39,"column":21,"offset":40},5,142,{"line":39,"column":42,"offset":43},30,171,{"type":31,"tagName":45,"properties":46,"children":47,"position":55},"code",{},[48],{"type":27,"value":49,"position":50},"./sdk/ta",{"start":51,"end":52},{"line":39,"column":42,"offset":43},{"line":39,"column":53,"offset":54},40,181,{"start":56,"end":57},{"line":39,"column":42,"offset":43},{"line":39,"column":53,"offset":54},{"type":27,"value":59,"position":60},", which ships with the editor:\n52 stateful classes covering averages, statistics, oscillators, ranges,\ntrend systems, and events. 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