fintech-algorithms
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Trend-Context Filter

Install and import#

bash
npm install fintech-algorithms
ts
import { trendContextFilter } from "fintech-algorithms/price-action-and-candlesticks/candle-foundations/trend-context-filter";

Signature#

trendContextFilter(inputs)

Classifies prior closes as an uptrend, downtrend, or sideways context from normalized net movement and path efficiency.

Parameters#

NameTypeNotes
inputs{ prior_closes: number[]; prior_ranges: number[]; lookback: number; minimum_efficiency: number; minimum_move_scale: number }Record containing aligned prior_closes, prior_ranges, lookback, minimum_efficiency, and minimum_move_scale fields.

Returns#

{ state, trend_context, history_count, net_move, path_move, efficiency, range_scale, normalized_move }

One readiness record. state is warmup until lookback observations exist, zero-scale when the range scale is zero, and ready otherwise; this is not a positional warmup series.

Warm-up#

The first lookback prior observations positions are state: warmup. The function returns one readiness record until the configured lookback is available; it does not emit a positional null prefix.

Errors#

  • When lookback is below 3, arrays are misaligned, or thresholds are outside their allowed ranges — throws

Complexity: time O(n log n), 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#

inputs
{
  "prior_closes": [100, 99, 98, 97, 96, 95],
  "prior_ranges": [2, 2, 2, 2, 2, 2],
  "lookback": 8,
  "minimum_efficiency": 0.6,
  "minimum_move_scale": 2
}

Call#

trendContextFilter(inputs)

Returns#

object with 8 fields: state, trend_context, history_count, net_move, path_move, efficiency, range_scale, normalized_move

{
  "state": "ready",
  "trend_context": "downtrend",
  "history_count": 8,
  "net_move": -7,
  "path_move": 7,
  "efficiency": 1,
  "range_scale": 2,
  "normalized_move": -3.5
}

Other exports#

This module also exports calculate, shadowToBodyRatio, gapClassification, doji, dragonflyDoji, gravestoneDoji, marubozu, spinningTop, hammer, hangingMan, invertedHammer, shootingStar, bullishEngulfing, bearishEngulfing, bullishHarami, bearishHarami, piercingLine, darkCloudCover, tweezerTop, tweezerBottom. Every module additionally exports run as an alias of its primary function, and a meta object carrying its catalog id, domain, family, shape and article URL.

Diagrams#

Trend-Context Filter — system map

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#

The rest of the Candle Foundations family#

  • Candle AnatomyDecomposes one candle into body, upper and lower shadow, and direction.
  • Scale-Aware Body ClassificationClassifies a candle body as long, short or doji **relative to recent bodies** rather than against a fixed threshold.
  • Shadow-to-Body RatioDecomposes one OHLC candle and reports raw and tick-stabilized shadow-to-body ratios without hiding the zero-body case.
  • Gap ClassificationClassifies full-range, body, or opening gaps in either direction using an explicit tick-scaled minimum distance.