[{"data":1,"prerenderedAt":12426},["ShallowReactive",2],{"wrun-open-editor-url":3,"indicators:functions/series-functions":4},"https://openmarket.xyz/chart/#wrun-open=v1",{"slug":5,"filePath":6,"frontmatter":7,"rawMarkdown":12,"tree":13},"functions/series-functions","functions/series-functions.md",{"title":8,"description":9,"order":10,"section":11},"Series functions","Series functions answer the questions that come up constantly when writing an indicator: did two lines just cross, is a series rising, what is the highest high…",46,"functions","---\ntitle: \"Series functions\"\ndescription: \"Series functions answer the questions that come up constantly when writing an indicator: did two lines just cross, is a series rising, what is the highest high…\"\norder: 46\nsection: \"functions\"\n---\n\n\u003C!-- source: docs/indicators/functions/series-functions.md; generated by packages/cli/scripts/gen-indicator-docs.ts, do not edit -->\n\n# Series functions\n\nSeries functions answer the questions that come up constantly when\nwriting an indicator: did two lines just cross, is a series rising, what\nis the highest high in the last 10 bars, what is the window total or\nz-score, how many bars since a condition was true, and is this value real\nor missing. A wrun indicator sees one\nbar at a time ([Execution model](../core-concepts/execution-model.md)),\nso each is a small class in `./sdk/ta` that keeps the history it needs\nand folds one bar per `update()` call, or a two-line function over\n`NaN`. Every class on this page ships in the kit; the full catalog is on\nthe [TA library](ta-library.md) page.\n\nThe one series question this page does not answer is `source[n]`, the\nvalue a few bars back. That is `History` from `./sdk/stats`: construct\nit in `onStart()`, `push()` once per bar, read `ago(n)`, `NaN` until the\nwindow holds the bar. The same module does the list math (`mean`,\n`stdev`, `slope`, `correlation`, `median`, `percentile`) over a\n`StaticArray\u003Cf64>` without allocating, and `./sdk/ta-plus` adds the\nrunning extremes, `PercentRank`, `Range` and `Mode` over a window; all\non [Extra indicators](extra-indicators.md). The pivots below are also\nthe raw material of the [Market structure kit](market-structure-kit.md),\nwhose `Swings` turns them into confirmed swing highs and lows.\n\n## Price-source helpers\n\n`hl2`, `hlc3`, `ohlc4`, and `hlcc4` are arithmetic on the bar's fields.\nRead the fields you need and write the formula:\n\n| Helper | Formula |\n| --- | --- |\n| `hl2` | `(high + low) / 2.0` |\n| `hlc3` | `(high + low + close) / 3.0` |\n| `ohlc4` | `(open + high + low + close) / 4.0` |\n| `hlcc4` | `(high + low + close + close) / 4.0` |\n\nThere is no implicit source: a wrun indicator names every field it\nreads, `bar.high()` for the high, `bar.close()` for the close.\n\n```typescript sample=fn-price-helpers\noutput(\"hl2\", line, overlay, { color: \"#2563eb\", width: 1, description: \"Bar midpoint\" });\noutput(\"hlc3\", line, overlay, { color: \"#16a34a\", width: 1, description: \"Typical price\" });\noutput(\"ohlc4\", line, overlay, { color: \"#f97316\", width: 1, description: \"Four-price average\" });\noutput(\"hlcc4\", line, overlay, { color: \"#7c3aed\", width: 1, description: \"Close-weighted average\" });\n\nfunction onBar(): void {\n  const open = bar.open();\n  const high = bar.high();\n  const low = bar.low();\n  const close = bar.close();\n  out_hl2((high + low) / 2.0);\n  out_hlc3((high + low + close) / 3.0);\n  out_ohlc4((open + high + low + close) / 4.0);\n  out_hlcc4((high + low + close + close) / 4.0);\n}\n```\n\n## Crossovers and signals\n\n`Cross` ships in `./sdk/ta`: `new Cross()`, `.update(a, b): i32`\nreturns `+1` on the bar `a` crosses above `b`, `-1` on the bar it crosses\nbelow, and `0` otherwise, including the first bar and any bar where either\nside is `NaN`. The edge rule is the engine's: a crossover is `a` below `b`\non the previous bar and `a` at or above `b` on this bar (touching counts\non the current bar, not on the previous one), and a crossunder is the\nmirror image. One object answers all three questions:\n\n| You want | wrun form |\n| --- | --- |\n| a cross above | `cross.update(a, b) == 1` |\n| a cross below | `cross.update(a, b) == -1` |\n| a cross in either direction | `cross.update(a, b) != 0` |\n\nCall `update()` exactly once per bar per pair: it remembers the previous\npair, so a second call on the same bar would compare the bar to itself.\nKeep the result in a local and test it as many times as you like. A\ncrossover against a constant (`rsi` over `70`) is `cross.update(value,\n70.0)`.\n\nThe classic signal-line pattern is a `Cross` over the MACD line and its\nsignal; the result is a `0`/`1` step you can draw, or a gate for a\n`shape` output that marks the price bar ([Plotting](../presentation/plotting.md)):\n\n```typescript\nconst crossed = cross.update(macd.macd, macd.signal);\nbullish = crossed == 1 ? 1.0 : 0.0;\n```\n\n## Trend, extremes, and counting\n\n| You want | wrun form | Returns |\n| --- | --- | --- |\n| is the series rising | `new Rising(period)`, `.update(x)` | `1` when `x` is strictly above every one of the previous `period` values, else `0`; `NaN` for the first `period` bars |\n| is the series falling | `new Falling(period)`, `.update(x)` | `1` when `x` is strictly below every one of the previous `period` values, else `0`; `NaN` for the first `period` bars |\n| the change over `n` bars | `new Change(n)`, `.update(x)` | `x` now minus `x` `n` bars ago (`n` defaults to 1); `NaN` for the first `n` bars |\n| the window high | `new Highest(period)`, `.update(x)`, field `bars` | the highest value in the window; `bars` is the same offset `HighestBars` reports for the window |\n| the window low | `new Lowest(period)`, `.update(x)`, field `bars` | the lowest value in the window; `bars` is the same offset `LowestBars` reports |\n| how long ago the window high was | `new HighestBars(period)`, `.update(x)`, field `value` | how many bars ago the window's high sits, as `0` or a negative number (`0` = this bar, `-3` = three bars ago); `value` is the high itself |\n| how long ago the window low was | `new LowestBars(period)`, `.update(x)`, field `value` | the same offset for the window's low |\n| bars since a condition | `new BarsSince()`, `.update(cond)` | bars elapsed since `cond` was last true (`0` on that bar), `NaN` until it has been true once |\n| the value when a condition last held | `new ValueWhen(occurrence)`, `.update(cond, x)` | `x` on the `occurrence`-th most recent bar where `cond` was true (`0` = the latest, the current bar counts) |\n| a percentile over a window | `new Percentile(period, pct)`, `.update(x)` | the nearest-rank `pct`-th percentile over the window |\n| a median over a window | `new Median(period)`, `.update(x)` | the middle value (the mean of the two middle values on an even window) |\n| a correlation over a window | `new Correlation(period)`, `.update(a, b)` | the rolling Pearson correlation, -1..1 |\n| a window total | `new Sum(period)`, `.update(x)` | the sum of the last `period` values; no warm-up, bar 0 already returns the partial window |\n| a z-score over a window | `new Zscore(period)`, `.update(x)` | `(x - mean) / stdev` over the window; `0` when the deviation is exactly `0` |\n\nA condition is an `f64`: true means finite and not `0`, so a `0`/`1`\nseries or a comparison cast with `? 1.0 : 0.0` both work. You feed the\nclass the series it should scan, so `new Highest(20)` over `bar.high()`\nis the 20-bar high and a `Lowest(20)`\nover `bar.low()` is the 20-bar low. Among equal values the newest bar wins\nthe offset. The `bars` fields are handy for \"is the high in the window\nrecent?\" logic, and `BarsSince` counts up from the last time a condition\nfired, the natural cooldown and recency check.\n\nConstruct the windowed ones in `onStart()` from a param and update each\nonce per bar:\n\n```typescript\nlet highs = new Highest(20);\nlet lows = new Lowest(20);\nlet p80 = new Percentile(20, 80.0);\nlet median = new Median(20);\nconst rising = new Rising(3);\n\n// In onBar(): the return value is the extreme, the field is its offset.\n// const hi = highs.update(high);   // highs.bars: 0 = this bar, -3 = three bars ago\n// const lo = lows.update(low);\n// const up = rising.update(close); // 1, 0, or NaN for the first 3 bars\n```\n\nEvery windowed class here follows the strict window rule:\n`NaN` until `period` bars exist, and `NaN` whenever any value inside the\nwindow is not finite. `Percentile` sorts the window and takes rank\n`ceil(pct / 100 * period)` (1-based, `pct` clamped to 0..100); `Median`\nsorts and takes the middle, averaging the two middle values on an even\nperiod; `Correlation` returns `NaN` when either side has no variance. None\nof them allocates after construction. `Sum` and `Zscore` are the two that\nskip a `NaN` bar instead of refusing the window (`Zscore` still divides\nits variance by `period`), and `Sum` has no warm-up at all: a 20-bar `Sum`\ndraws from the first bar where a 20-bar `Stdev` draws from bar 19.\n\n## Pivot confirmation\n\n`PivotHigh` and `PivotLow` are causal confirmation signals. A pivot only\nemits after `rightbars` later bars have closed, so the emitted value\nappears on the confirmation bar and lags the true pivot bar by\n`rightbars`. Nothing reads a future bar and no earlier bar repaints; an\nindicator could not do otherwise, because `onBar()` sees one bar.\n`new PivotHigh(leftbars, rightbars)` and `new PivotLow(leftbars,\nrightbars)` keep `left + right + 1` bars and, once the window is full,\ntest the candidate `rightbars` back: strictly higher than every other\nvalue in the window is a pivot high, strictly lower a pivot low (a tie\nnever counts), and `update(x)` returns the candidate's value on the\nconfirmation bar only and `NaN` everywhere else, including any window\nwith a non-finite value. Feed `PivotHigh` the high and `PivotLow` the\nlow.\n\n```typescript\nconst pivotHigh = new PivotHigh(2, 2);\nconst pivotLow = new PivotLow(2, 2);\nconst lastHigh = new Fixnan();\n\n// In onBar(): a value on the confirmation bar, NaN between.\n// const confirmed = pivotHigh.update(bar.high());\n// const level = lastHigh.update(confirmed); // the last confirmed pivot, carried forward\n```\n\nA confirmed pivot is a sparse series: one value on the confirmation bar,\n`NaN` between. To carry it forward into a continuous line, run it\nthrough `Fixnan` below.\n\n## Handling missing values\n\nWarm-up is `NaN`, and any arithmetic touching `NaN` stays `NaN`. `isNaN`\nand `isFinite` are AssemblyScript builtins on `f64`; there is no separate\nmissing-value type, so a missing value is `NaN` everywhere and these two\nare the whole vocabulary. The helpers are two lines each:\n\n| You want | wrun form |\n| --- | --- |\n| is the value missing | `isNaN(x)` |\n| is the value a real number | `isFinite(x)` (finite and not `NaN`) |\n| a replacement for a missing value | `nz(x, replacement)` below |\n| the last real value carried forward | `new Fixnan()`, `.update(x)`: carries the last finite value forward, `NaN` until the first one |\n\n```typescript\nfunction nz(x: f64, replacement: f64): f64 {\n  return isNaN(x) ? replacement : x;\n}\n\nconst fixnan = new Fixnan();\n// In onBar(): fixnan.update(sparse) repeats the last finite value across every NaN bar.\n```\n\n## Every series function in one module\n\nA `sparse` series (forced `NaN` for the first 10 bars) drives the missing\nvalue helpers so you can see them flip; the rest run over real bars. The\nclasses are the shipped ones from `./sdk/ta`.\n\n```typescript sample=fn-series-tour\nparam(\"fast\", 5, { min: 1, max: 200 });\nparam(\"slow\", 13, { min: 2, max: 400 });\nparam(\"window\", 10, { min: 2, max: 200, description: \"Window for the extremes, percentile, median, and correlation\" });\noutput(\"crossover\", line, lower, { color: \"#2563eb\", description: \"1 on the bar the fast average crosses above the slow\" });\noutput(\"crossunder\", line, lower, { color: \"#dc2626\", description: \"1 on the bar it crosses below\" });\noutput(\"cross\", line, lower, { color: \"#7c3aed\", description: \"1 on either cross\" });\noutput(\"rising\", line, lower, { color: \"#16a34a\", description: \"1 while the close is above its previous 3 values\" });\noutput(\"falling\", line, lower, { color: \"#ea580c\", description: \"1 while the close is below its previous 3 values\" });\noutput(\"barssince\", line, lower, { color: \"#0891b2\", description: \"Bars since the close was above the fast average\" });\noutput(\"change\", line, lower, { color: \"#4b5563\", description: \"3-bar change\" });\noutput(\"highest\", line, lower, { color: \"#0f766e\", description: \"Window highest high\" });\noutput(\"lowest\", line, lower, { color: \"#be123c\", description: \"Window lowest low\" });\noutput(\"highestbars\", line, lower, { color: \"#9333ea\", description: \"Offset of the window high (0 = this bar, negative = bars ago)\" });\noutput(\"lowestbars\", line, lower, { color: \"#1d4ed8\", description: \"Offset of the window low\" });\noutput(\"valuewhen\", line, lower, { color: \"#0e7490\", description: \"The close on the most recent bullish cross\" });\noutput(\"percentile\", line, lower, { color: \"#b45309\", description: \"80th percentile of the close over the window\" });\noutput(\"median\", line, lower, { color: \"#a16207\", description: \"Median close over the window\" });\noutput(\"correlation\", line, lower, { color: \"#15803d\", description: \"Correlation between open and close over the window\" });\noutput(\"nz\", line, lower, { color: \"#6d28d9\", description: \"nz over the sparse series, the open as the replacement\" });\noutput(\"isna\", line, lower, { color: \"#0e7490\", description: \"1 while the sparse series is NaN\" });\noutput(\"isnum\", line, lower, { color: \"#374151\", description: \"1 while the sparse series is a finite number\" });\noutput(\"fixnan\", line, lower, { color: \"#3b82f6\", description: \"The sparse series with gaps carried forward\" });\noutput(\"pivot_high\", line, lower, { color: \"#2563eb\", description: \"Confirmed 2/2 pivot highs, carried forward\" });\noutput(\"pivot_low\", line, lower, { color: \"#dc2626\", description: \"Confirmed 2/2 pivot lows, carried forward\" });\n\nfunction nz(x: f64, replacement: f64): f64 {\n  return isNaN(x) ? replacement : x;\n}\n\nlet fast = new Sma(5);\nlet slow = new Sma(13);\nconst cross = new Cross();\nconst rising = new Rising(3);\nconst falling = new Falling(3);\nconst barsSince = new BarsSince();\nconst change = new Change(3);\nlet highs = new Highest(10);\nlet lows = new Lowest(10);\nconst valueWhen = new ValueWhen(0);\nlet percentile = new Percentile(10, 80.0);\nlet median = new Median(10);\nlet correlation = new Correlation(10);\nconst fixnan = new Fixnan();\nconst pivotHigh = new PivotHigh(2, 2);\nconst pivotLow = new PivotLow(2, 2);\nconst stableHigh = new Fixnan();\nconst stableLow = new Fixnan();\nlet barIndex: i32 = 0;\n\nfunction onStart(): void {\n  fast = new Sma(i32(p_fast()));\n  slow = new Sma(i32(p_slow()));\n  const window = i32(p_window());\n  highs = new Highest(window);\n  lows = new Lowest(window);\n  percentile = new Percentile(window, 80.0);\n  median = new Median(window);\n  correlation = new Correlation(window);\n}\n\nfunction onBar(): void {\n  const close = bar.close();\n  const openValue = bar.open();\n  const f = fast.update(close);\n  const s = slow.update(close);\n  const crossed = cross.update(f, s);\n  const risingValue = rising.update(close);\n  const fallingValue = falling.update(close);\n  const since = barsSince.update(!isNaN(f) && close > f ? 1.0 : 0.0);\n  const changeValue = change.update(close);\n  const highValue = highs.update(bar.high());\n  const lowValue = lows.update(bar.low());\n  const whenValue = valueWhen.update(crossed == 1 ? 1.0 : 0.0, close);\n  const pctValue = percentile.update(close);\n  const medianValue = median.update(close);\n  const corrValue = correlation.update(openValue, close);\n  const sparse = barIndex \u003C 10 ? NaN : close;\n  const fixed = fixnan.update(sparse);\n  const pivotHighValue = stableHigh.update(pivotHigh.update(bar.high()));\n  const pivotLowValue = stableLow.update(pivotLow.update(bar.low()));\n  barIndex += 1;\n  out_crossover(crossed == 1 ? 1.0 : 0.0);\n  out_crossunder(crossed == -1 ? 1.0 : 0.0);\n  out_cross(crossed != 0 ? 1.0 : 0.0);\n  out_rising(risingValue);\n  out_falling(fallingValue);\n  out_barssince(since);\n  out_change(changeValue);\n  out_highest(highValue);\n  out_lowest(lowValue);\n  out_highestbars(highs.bars);\n  out_lowestbars(lows.bars);\n  out_valuewhen(whenValue);\n  out_percentile(pctValue);\n  out_median(medianValue);\n  out_correlation(corrValue);\n  out_nz(nz(sparse, openValue));\n  out_isna(isNaN(sparse) ? 1.0 : 0.0);\n  out_isnum(isFinite(sparse) ? 1.0 : 0.0);\n  out_fixnan(fixed);\n  out_pivot_high(pivotHighValue);\n  out_pivot_low(pivotLowValue);\n}\n```\n\n`barIndex` is state the module counts itself: there is no `barIndex`\nglobal, so it lives at module level with the rest of the state that\nspans bars.\n\n## A Donchian breakout\n\nA 20-bar high and low channel as a `Highest` over the high\nand a `Lowest` over the low, with a gated mark on the bar that closes\nabove the channel. The gate is a data-only output, and the `shape` output\ndraws only where it is nonzero.\n\n```typescript sample=fn-donchian-breakout\nparam(\"period\", 20, { min: 2, max: 400, description: \"Channel lookback\" });\noutput(\"hi\", line, overlay, { color: \"#16a34a\", width: 1, description: \"Upper Donchian band\" });\noutput(\"lo\", line, overlay, { color: \"#dc2626\", width: 1, description: \"Lower Donchian band\" });\noutput(\"breakout_mark\", shape, overlay, { color: \"#16a34a\", shape_where: \"breakout\", description: \"The close on a breakout bar\" });\noutput(\"breakout\", none, overlay, { description: \"1 when the close is above the previous bar's upper band\" });\n\nlet highs = new Highest(20);\nlet lows = new Lowest(20);\nlet prevHi: f64 = NaN;\n\nfunction onStart(): void {\n  highs = new Highest(i32(p_period()));\n  lows = new Lowest(i32(p_period()));\n}\n\nfunction onBar(): void {\n  const close = bar.close();\n  // Test against the channel as it stood BEFORE this bar, so the bar cannot break its own high.\n  const breakout = !isNaN(prevHi) && close > prevHi ? 1.0 : 0.0;\n  const hi = highs.update(bar.high());\n  const lo = lows.update(bar.low());\n  prevHi = hi;\n  if (isNaN(hi)) return;\n  out_hi(hi);\n  out_lo(lo);\n  out_breakout_mark(close);\n  out_breakout(breakout);\n}\n```\n\n## Window statistics, a channel, and a cross\n\nThe window statistics (`Highest`, `Lowest`, `Sum`, `Stdev`), a `Donchian`\nchannel, a `Cross` over two averages, and an `isFinite` gate in one\nmodule. `Stdev` and `Donchian` are explained with their families, on\n[Trend and volatility](trend-indicators.md#volatility-primitives) and\n[Moving averages](moving-averages.md#donchian).\n\n```typescript sample=fn-utility-tour\nparam(\"period\", 20, { min: 2, max: 400 });\noutput(\"low20\", line, overlay, { color: \"#dc2626\", width: 1, description: \"Lowest low over the period\" });\noutput(\"high20\", line, overlay, { color: \"#16a34a\", width: 1, description: \"Highest high over the period\" });\noutput(\"donchian_mid\", line, overlay, { color: \"#94a3b8\", width: 1, description: \"Donchian midpoint\" });\noutput(\"volume_sum\", line, lower, { color: \"#0891b2\", description: \"Volume summed over the period\" });\noutput(\"volatility\", line, lower, { color: \"#7c3aed\", description: \"Standard deviation of the close\" });\noutput(\"bullish\", none, lower, { description: \"1 on the bar the fast average crosses above the slow\" });\noutput(\"bearish\", none, lower, { description: \"1 on the bar it crosses below\" });\noutput(\"any_cross\", none, lower, { description: \"1 on either\" });\noutput(\"valid\", none, lower, { description: \"1 when every input on the bar is a finite number\" });\n\nlet highs = new Highest(20);\nlet lows = new Lowest(20);\nlet volumeSum = new Sum(20);\nlet stdev = new Stdev(20);\nlet donchian = new Donchian(20);\nlet fast = new Sma(5);\nlet slow = new Sma(20);\nconst cross = new Cross();\n\nfunction onStart(): void {\n  const period = i32(p_period());\n  highs = new Highest(period);\n  lows = new Lowest(period);\n  volumeSum = new Sum(period);\n  stdev = new Stdev(period);\n  donchian = new Donchian(period);\n  fast = new Sma(5);\n  slow = new Sma(period);\n}\n\nfunction onBar(): void {\n  const close = bar.close();\n  const high = bar.high();\n  const low = bar.low();\n  const volume = bar.volume();\n  const valid = isFinite(close) && isFinite(high) && isFinite(low) && isFinite(volume) ? 1.0 : 0.0;\n  const highValue = highs.update(high);\n  const lowValue = lows.update(low);\n  const sumValue = volumeSum.update(volume);\n  const volatility = stdev.update(close);\n  donchian.update(high, low);\n  const crossed = cross.update(fast.update(close), slow.update(close));\n  out_low20(lowValue);\n  out_high20(highValue);\n  out_donchian_mid(donchian.basis);\n  out_volume_sum(sumValue);\n  out_volatility(volatility);\n  out_bullish(crossed == 1 ? 1.0 : 0.0);\n  out_bearish(crossed == -1 ? 1.0 : 0.0);\n  out_any_cross(crossed != 0 ? 1.0 : 0.0);\n  out_valid(valid);\n}\n```\n\n## Warm-up and the missing-value window\n\nWindowed functions cannot produce a value until they have seen enough\nbars: a 20-bar `Percentile` or `Correlation` returns `NaN` until 20 bars\nexist, just as a 20-bar `Sma` does. When the underlying series is itself\n`NaN` for a stretch, the warm-up shifts forward by the same amount,\nbecause the windowed classes refuse a window with a `NaN` inside it.\n`Fixnan` shows the recovery: it produces its first real value the moment\nthe source has one, then holds it across every gap. Feed the `sparse`\nseries from the tour into a 5-bar `Percentile` and the `percentile`\noutput stays `NaN` through bar 14, not bar 4.\n\n## Practices that carry over\n\n- **Lookback periods.** Choose them for the interval you trade: short\n  windows for scalping and lower timeframes, longer ones for swing\n  context. Declare the range on the param (`min`, `max`) and size buffers\n  from `max` so the module never allocates per bar.\n- **One object per statistic.** Compute a statistic once per bar and reuse\n  the return value or field rather than constructing a second object over\n  the same series.\n- **Cross detection.** Combine a cross with a trend read (`Adx`, a slope,\n  a higher-timeframe fold) before treating it as a signal; in a chop the\n  cross flips every few bars.\n- **Donchian channels.** Breakout logic in trends, support and resistance\n  in ranges: the same two outputs, different rules on top.\n- **Data validation.** Check `isFinite` before a division, and write `NaN`\n  rather than a made-up number when a value has no answer; a `NaN` draws\n  nothing and never trips an alert.\n",{"type":14,"children":15,"data":12419,"position":12421},"root",[16,26,29,30,159,160,579,580,596,597,676,678,906,907,956,957,1529,1530,1544,1545,1800,1802,1973,1974,2061,2062,2168,2169,2264,2265,2280,2282,3940,3941,4150,4151,4181,4182,4434,4435,4746,4747,4762,4763,5001,5002,5164,5165,5216,5217,5232,5233,5370,5371,5637,5638,5798,5799,5813,5814,5879,5880,9175,9176,9220,9221,9236,9237,9307,9308,10096,10097,10112,10113,10323,10324,11887,11888,11903,11904,12114,12115,12130,12131],{"type":17,"value":18,"position":19},"comment"," source: docs/indicators/functions/series-functions.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},133,132,{"type":27,"value":28},"text","\n",{"type":27,"value":28},{"type":31,"tagName":32,"properties":33,"children":34,"position":156},"element","p",{},[35,45,64,72,86,94,107,114,127,134,149],{"type":27,"value":36,"position":37},"Series functions answer the questions that come up constantly when\nwriting an indicator: did two lines just cross, is a series rising, what\nis the highest high in the last 10 bars, what is the window total or\nz-score, how many bars since a condition was true, and is this value real\nor missing. 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