# Divergence Strength and Quality Scoring

`D08-F05-A05` · Geometric Chart Patterns → Indicator Divergence Detection · archetype `record-transform` · difficulty 4/5 · verification **contract**

Full page: https://docs.thefintechbuilder.com/geometric-chart-patterns/indicator-divergence-detection/divergence-strength-and-quality-scoring/
Agent skill: `npx skills add IslamBaraka90/Fintech-Algorithms-Library` — https://docs.thefintechbuilder.com/guides/agent-skill/

## Install and import

```bash
npm install fintech-algorithms
```

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

## Signature

```ts
divergenceStrengthAndQualityScoring(payload)
```

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

## Parameters

| Name | Type | Required | 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 }[] }` | yes | 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 `undefined`, space `undefined`.

## Worked example

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`:

```json
{
  "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

```ts
divergenceStrengthAndQualityScoring(payload)
```

### Returns

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

```json
{
  "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

`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.

## Verification and provenance

Tier: **contract**.

The module loads, the entry point is callable and its declared signature matches the compiled code. The example below is real captured output, but no independently published figure asserts the numbers.

Both tiers guarantee the signature. Full explanation: https://docs.thefintechbuilder.com/guides/verification/

Generated from the docs.json payload shipped inside fintech-algorithms@0.12.0.
The signature and parameter list are checked against the compiled implementation at build time,
so a description that contradicts the code fails the build rather than reaching this file.

## Links

- Article (how it works, step by step): https://thefintechbuilder.com/geometric-chart-patterns/indicator-divergence-detection/divergence-strength-and-quality-scoring/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/geometric-chart-patterns/indicator-divergence-detection/divergence-strength-and-quality-scoring/impl.ts
- Package on npm: https://www.npmjs.com/package/fintech-algorithms
- Domain index for agents: https://docs.thefintechbuilder.com/geometric-chart-patterns/llms.txt