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Gaussian Moving Average

Install and import#

bash
npm install fintech-algorithms
ts
import { gaussianMovingAverage } from "fintech-algorithms/technical-indicators/trend-smoothing/gaussian-moving-average";

Signature#

gaussianMovingAverage(input)

Averages close under a Gaussian weight curve centred at offset * (period - 1) with width period / sigma. The default offset of 0.5 puts the peak in the middle of the window, which is what separates this from ALMA's recency-shifted variant of the same kernel.

Parameters#

NameTypeNotes
inputTopicInputbars is a non-empty array of OHLCV records with strictly increasing timestamp, finite open, high, low, close and non-negative volume, all sharing one adjustment basis. From parameters this topic reads period, an integer of at least 2, default 14; offset, a finite number between 0 and 1, default 0.5; and sigma, a finite number of at least 0.000001, default 6.

Returns#

TopicResult

series and latest carry a single key, value, the weight-normalised average. The first period - 1 entries are null while the window fills, so with the default period ready_at is 13.

Warm-up#

The first period - 1 bars (13 at the default period of 14) positions are null. The kernel is applied only to complete period-bar windows, so ready_at is period - 1. Centring the peak makes the filter symmetric in weighting but the output is still computed only from bars up to and including the current one.

Errors#

  • When parameters.offset is not a finite number between 0 and 1 — throws Error
  • When parameters.sigma is not a finite number of at least 0.000001 — throws Error
  • When parameters.period is not an integer of at least 2 — throws Error

Complexity: time O(n * period), space O(n).

Worked example#

executed Captured by running this function on the input its own test provides. Real output of real code — but not asserted against a published figure.

Input#

input
{
  "bars": [
    {
      "timestamp": "2024-01-02",
      "basis": "synthetic-unadjusted",
      "open": 100,
      "high": 101.45,
      "low": 98.695,
      "close": 100,
      "volume": 750000,
      "benchmark": 200
    },
    {
      "timestamp": "2024-01-03",
      "basis": "synthetic-unadjusted",
      "open": 101.49111452,
      "high": 103.38381693,
      "low": 100.05480022,
      "close": 101.78791214,
      "volume": 795117,
      "benchmark": 200.56326135
    },
    {
      "timestamp": "2024-01-04",
      "basis": "synthetic-unadjusted",
      "open": 102.45519048,
      "high": 104.6701838,
      "low": 100.91147007,
      "close": 102.9549389,
      "volume": 840234,
      "benchmark": 201.11020913
    }
  ],
  "parameters": {}
}

Call#

gaussianMovingAverage(input)

Returns#

object with 9 fields: topic_id, title, state, ready, ready_at, series, latest, parameters, …

{
  "topic_id": "D07-F01-A22",
  "title": "Gaussian Moving Average",
  "state": "calculated",
  "ready": true,
  "ready_at": 13,
  "series": {
    "value": [null, null, null, null, null, null]
  },
  "latest": {
    "value": 105.48707931937686
  },
  "parameters": {},
  "diagnostics": {
    "causal": true,
    "input_count": 96
  }
}

Diagrams#

Gaussian Moving Average — article hero
Gaussian Moving Average — concept map
Gaussian Moving Average — decision comparison
Gaussian Moving Average — worked example

Calculation flow#

Gaussian Moving Average calculation flow 
flowchart LR
    A["an oldest-to-newest finite price series, declared price so"] --> B["Validate order, basis, and finite values"]
    B --> C["Apply the selected Gaussian Moving Average convention"]
    C --> D["Emit value, readiness, and diagnostics"]
    D --> E["Interpret descriptively; test outcomes separately"]
    B -->|invalid or insufficient| X["Withhold output with a reason"]
Gaussian Moving Average readiness and evidence states 
stateDiagram-v2
    [*] --> Waiting
    Waiting --> Ready: enough valid causal observations
    Waiting --> Rejected: malformed or unsupported input
    Ready --> Calculated: selected formula applied
    Calculated --> Interpreted: diagnostic and limitation retained
    Interpreted --> Ready: next observation arrives
    Rejected --> Waiting: corrected input and deterministic reset

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

  • TA-Lib function groups — see linked primary or authoritative record
  • TA-Lib C/C++ API — see linked primary or authoritative record
  • TA-Lib maintained source — see linked primary or authoritative record
  • Evidence decision
  • Level 1 evidence map

The rest of the Trend Smoothing family#