Hurst Exponent
Install and import#
npm install fintech-algorithmsimport { hurstExponent } from "fintech-algorithms/geometric-chart-patterns/market-structure-breakouts-and-regimes/hurst-exponent";Signature#
hurstExponent(input)A rolling rescaled-range estimate: over each window of period closes it takes the range of the mean-adjusted cumulative sum, divides by the window's standard deviation, and reports log(R/S) / log(period).
Parameters#
| Name | Type | Notes |
|---|---|---|
input | TopicInput | bars is the required OHLCV history, strictly ordered by timestamp and on a single adjustment basis. parameters.period (default 20, integer of at least 2) sets the window and the log base. |
Returns#
TopicResult
series and latest carry a single value key. The warm-up is period - 1 bars, so with the default ready_at is 19; value is additionally null on any window whose cumulative-deviation range or standard deviation is zero, which is what a perfectly flat stretch gives.
Warm-up#
The first period - 1 bars (19 by default) positions are null. The window spans indices i-period+1 through i inclusive, so index period - 1 is the first with a full window.
Errors#
- When
periodis supplied as a non-integer or as a value below 2 — throws Error - When a bar's
closeis not a finite number — 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#
{
"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#
hurstExponent(input)Returns#
object with 9 fields: topic_id, title, state, ready, ready_at, series, latest, parameters, …
{
"topic_id": "D08-F07-A09",
"title": "Hurst Exponent",
"state": "calculated",
"ready": true,
"ready_at": 19,
"series": {
"value": [null, null, null, null, null, null]
},
"latest": {
"value": 0.6970566842387678
},
"parameters": {},
"diagnostics": {
"causal": true,
"input_count": 96
}
}Diagrams#
Calculation flow#
Hurst Exponent calculation flow
flowchart LR
A["ordered OHLC observations, explicit lookbacks and threshol"] --> B["Validate order, basis, and finite values"]
B --> C["Apply the selected Hurst Exponent 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"]
Hurst Exponent 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.
References#
- CFA Institute technical-analysis overview — see linked primary or authoritative record
- TA-Lib maintained source — see linked primary or authoritative record
- Long-Term Storage Capacity of Reservoirs — see linked primary or authoritative record
- Evidence decision
- Level 1 evidence map