Divergence Strength and Quality Scoring

Install and import#

bash

npm install fintech-algorithms

import { divergenceStrengthAndQualityScoring } from "fintech-algorithms/geometric-chart-patterns/indicator-divergence-detection/divergence-strength-and-quality-scoring";

Signature#

divergenceStrengthAndQualityScoring(payload)

Scores the first detected divergence from explicit geometry, prominence, alignment, and separation components.

Parameters#

Name	Type	Notes
`payload`	{ topic_id: string; data_class: string; as_of: string; bars: { timestamp: string; open: number; high: number; low: number; close: number; finalized: boolean }[]; price_pivots: { kind: string; event_index: number; confirmation_index: number; price: number; prominence: number }[]; indicators: { name: string; family: string; polarity: number; scale: number; pivots: { kind: string; event_index: number; confirmation_index: number; value: number; prominence: number }[] }[]; parameters: { max_lag: number; min_separation: number; max_separation: number; min_price_fraction: number; min_indicator_delta: number; confirmation_horizon: number; invalidation_buffer: number; minimum_indicators: number }; weights: { RSI: number; MACD-Histogram: number; Stochastic-K: number }; as_of_index: number; universe: { symbol: string; state: string; divergence_type: string; confluence_score: number; freshness: number; liquidity: number; data_quality: number; available_index: number; eligible: boolean }[] }	Topic payload containing the price pivots, indicators, and detection parameters needed to produce a divergence event.

Returns#

{ topic_id, state, event?, score, reason? }

One scoring record: not-detected with null score and a reason, or scored with the event and component score object.

Errors#

When the underlying pivot, indicator, detection, or score inputs are invalid — throws

Complexity: time , space .

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#

payload

{
  "topic_id": "D08-F05-A05",
  "data_class": "synthetic-teaching",
  "as_of": "2026-01-08T13:30:00Z",
  "bars": [
    {
      "timestamp": "2026-01-05T14:30:00Z",
      "open": 105.75,
      "high": 107,
      "low": 105,
      "close": 106,
      "finalized": true
    },
    {
      "timestamp": "2026-01-05T15:30:00Z",
      "open": 106.112,
      "high": 107.362,
      "low": 105.362,
      "close": 106.362,
      "finalized": true
    },
    {
      "timestamp": "2026-01-05T16:30:00Z",
      "open": 106.4614,
      "high": 107.7114,
      "low": 105.7114,
      "close": 106.7114,
      "finalized": true
    }
  ],
  "price_pivots": [
    {
      "kind": "low",
      "event_index": 18,
      "confirmation_index": 21,
      "price": 98,
      "prominence": 0.72
    },
    {
      "kind": "low",
      "event_index": 42,
      "confirmation_index": 45,
      "price": 94,
      "prominence": 0.84
    }
  ],
  "indicators": [
    {
      "name": "RSI",
      "family": "bounded-momentum",
      "polarity": 1,
      "scale": 100,
      "pivots": [
        {
          "kind": "low",
          "event_index": 19,
          "confirmation_index": 22,
          "value": 28,
          "prominence": 0.76
        },
        {
          "kind": "low",
          "event_index": 41,
          "confirmation_index": 44,
          "value": 36,
          "prominence": 0.86
        }
      ]
    },
    {
      "name": "MACD-Histogram",
      "family": "moving-average-momentum",
      "polarity": 1,
      "scale": 5,
      "pivots": [
        {
          "kind": "low",
          "event_index": 18,
          "confirmation_index": 21,
          "value": -2.2,
          "prominence": 0.7
        },
        {
          "kind": "low",
          "event_index": 42,
          "confirmation_index": 45,
          "value": -1,
          "prominence": 0.81
        }
      ]
    },
    {
      "name": "Stochastic-K",
      "family": "range-momentum",
      "polarity": 1,
      "scale": 100,
      "pivots": [
        {
          "kind": "low",
          "event_index": 17,
          "confirmation_index": 20,
          "value": 18,
          "prominence": 0.68
        },
        {
          "kind": "low",
          "event_index": 43,
          "confirmation_index": 46,
          "value": 29,
          "prominence": 0.78
        }
      ]
    }
  ],
  "parameters": {
    "max_lag": 3,
    "min_separation": 5,
    "max_separation": 40,
    "min_price_fraction": 0.005,
    "min_indicator_delta": 0.05,
    "confirmation_horizon": 12,
    "invalidation_buffer": 0.005,
    "minimum_indicators": 2
  },
  "weights": {
    "RSI": 0.4,
    "MACD-Histogram": 0.35,
    "Stochastic-K": 0.25
  },
  "as_of_index": 71,
  "universe": [
    {
      "symbol": "SYNTH-A",
      "state": "confirmed",
      "divergence_type": "regular-bullish",
      "confluence_score": 86,
      "freshness": 92,
      "liquidity": 80,
      "data_quality": 98,
      "available_index": 47,
      "eligible": true
    },
    {
      "symbol": "SYNTH-B",
      "state": "candidate",
      "divergence_type": "regular-bearish",
      "confluence_score": 91,
      "freshness": 98,
      "liquidity": 72,
      "data_quality": 94,
      "available_index": 54,
      "eligible": true
    },
    {
      "symbol": "SYNTH-C",
      "state": "confirmed",
      "divergence_type": "hidden-bullish",
      "confluence_score": 74,
      "freshness": 70,
      "liquidity": 96,
      "data_quality": 90,
      "available_index": 39,
      "eligible": true
    }
  ]
}

Call#

divergenceStrengthAndQualityScoring(payload)

Returns#

object with 4 fields: topic_id, state, event, score

{
  "topic_id": "D08-F05-A05",
  "state": "scored",
  "event": {
    "type": "regular-bullish",
    "direction": "bullish",
    "kind": "low",
    "indicator": "MACD-Histogram",
    "indicator_family": "moving-average-momentum",
    "price_pair": [18, 42],
    "indicator_pair": [18, 42],
    "price_values": [98, 94],
    "indicator_values": [-0.44000000000000006, -0.2],
    "price_delta_fraction": -0.04081632653061224,
    "indicator_delta": 0.24000000000000005,
    "separation": 24,
    "max_alignment_lag": 0,
    "prominence": 0.7675
  },
  "score": {
    "score": 95.815,
    "quality": "exceptional",
    "components": {
      "price_geometry": 1,
      "indicator_geometry": 1,
      "prominence": 0.7675,
      "alignment": 1,
      "separation": 1
    },
    "interpretation": "definition-strength score; not probability or expected return"
  }
}

Other exports#

This module also exports adaptIndicator, alignPivots, detectDivergences, scoreDivergence, advanceState, combineConfluence, rankUniverse, runTopic, priceIndicatorPivotAlignment, regularBullishBearishDivergenceDetection, hiddenBullishBearishDivergenceDetection, multiIndicatorDivergenceAdapters, divergenceConfirmationAndInvalidationStateMachine, multiIndicatorDivergenceConfluence, marketWideDivergenceScannerAndRanking. 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#

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#

TradingView — RSI divergence indicator — TradingView
TradingView — MACD indicator — TradingView
TradingView Pine Script — Repainting — TradingView Pine Script
TradingView Pine Script — Visuals FAQ — TradingView Pine Script
Fidelity — Relative Strength Index — Fidelity
Fidelity — MACD — Fidelity
TA-Lib — Function index — TA-Lib
TA-Lib — RSI — TA-Lib
SciPy — find_peaks — SciPy
Bailey et al. — Effects of Backtest Overfitting — Bailey et al.
Historical-example decision
Topic-specific applicability map

Divergence Strength and Quality Scoring

Install and import#

Signature#

Parameters#

Returns#

Errors#

Worked example#

Input#

Call#

Returns#

Other exports#

Diagrams#

How it works#

References#

The rest of the Indicator Divergence Detection family#