# Fundamental Metric Direction and Peer Normalization

`D18-F09-A03` · Fundamental Analysis and Valuation → Integrated Equity Scoring · archetype `record-transform` · difficulty 3/5 · verification **verified**

Full page: https://docs.thefintechbuilder.com/fundamental-analysis-and-valuation/integrated-equity-scoring/fundamental-metric-direction-and-peer-normalization/
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 { fundamentalMetricDirectionAndPeerNormalization } from "fintech-algorithms/fundamental-analysis-and-valuation/integrated-equity-scoring/fundamental-metric-direction-and-peer-normalization";
```

## Signature

```ts
fundamentalMetricDirectionAndPeerNormalization(data)
```

Turns each metric's target value into a midrank empirical percentile against its peer values, flips that percentile where lower is better, and averages the resulting scores across metrics.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `data` | `{ min_peers: number; metrics: Array<{ name: string; target: number; peers: number[]; higher_is_better: boolean }> }` | yes | The normalisation request. `min_peers` is truncated to an integer and is the number of finite `peers` each metric must supply. Each metric needs a text `name`, a finite `target`, a `peers` list whose non-finite entries are dropped, and a boolean `higher_is_better` that decides whether the percentile is kept or subtracted from 100. |

## Returns

`{ state: string; method: string; metrics: Array<{ name: string; target: number; peer_count: number; percentile: number; higher_is_better: boolean; normalized_score: number }>; aggregate_score: number; metric_count: number; invariant: string }`

One row per input metric carrying `peer_count`, the `percentile` formed as peers strictly below the target plus half the ties, over the peer count and times 100, and the direction-adjusted `normalized_score`. `aggregate_score` is the unweighted mean of those scores and `metric_count` their number.

## Errors

- When metrics is not a nonempty list, or a metric lacks a text name or a peers list — throws TypeError
- When a metric target is not a finite number, or higher_is_better is not a boolean — throws TypeError
- When min_peers is not positive — throws RangeError
- When a metric supplies fewer finite peer values than min_peers — throws RangeError

## Complexity

Time `O(m * p)`, space `O(m + p)`.

## 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

`data`:

```json
{
  "min_peers": 3,
  "metrics": [
    {
      "name": "roic",
      "target": 0.12,
      "peers": [0.08, 0.1, 0.14, 0.16],
      "higher_is_better": true
    },
    {
      "name": "net_debt_to_ebitda",
      "target": 1.5,
      "peers": [0.8, 1.2, 2, 3],
      "higher_is_better": false
    },
    {
      "name": "fcf_margin",
      "target": 0.1,
      "peers": [0.04, 0.08, 0.12, 0.16],
      "higher_is_better": true
    }
  ]
}
```

### Call

```ts
fundamentalMetricDirectionAndPeerNormalization(data)
```

### Returns

object with 6 fields: state, method, metrics, aggregate_score, metric_count, invariant

```json
{
  "state": "calculated",
  "method": "midrank-empirical-percentile-with-direction",
  "metrics": [
    {
      "name": "roic",
      "target": 0.12,
      "peer_count": 4,
      "percentile": 50,
      "higher_is_better": true,
      "normalized_score": 50
    },
    {
      "name": "net_debt_to_ebitda",
      "target": 1.5,
      "peer_count": 4,
      "percentile": 50,
      "higher_is_better": false,
      "normalized_score": 50
    },
    {
      "name": "fcf_margin",
      "target": 0.1,
      "peer_count": 4,
      "percentile": 50,
      "higher_is_better": true,
      "normalized_score": 50
    }
  ],
  "aggregate_score": 50,
  "metric_count": 3,
  "invariant": "higher-is-better reverses the percentile only; peer values are not z-scored"
}
```

## Other exports

`calculate`, `pointInTimeStockScoringInputAssembly`, `stockScoringPeerCohortResolver`, `modelApplicabilityAndVariantRouter`, `accountingFinancialHealthComposite`, `earningsQualityComposite`, `dividendSafetyScore`, `balanceSheetResilienceScore`, `distressModelEnsemble`, `crossModelConflictAndDoubleCountingResolver`, `overallExplainableStockScore`, `scoreConfidenceMissingDataPenaltyAndAbstention`, `marketWideStockScreeningAndRanking`, `stockScoreHistoryMigrationAndChangeAttribution`. 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: **verified** (via D).

The worked example below is the figure published in this algorithm's article, replayed and asserted by the test suite on every build. The arithmetic cannot drift without the build failing.

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

Generated from the docs.json payload shipped inside fintech-algorithms@0.13.0.
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/fundamental-analysis-and-valuation/integrated-equity-scoring/fundamental-metric-direction-and-peer-normalization/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/fundamental-analysis-and-valuation/integrated-equity-scoring/fundamental-metric-direction-and-peer-normalization/impl.ts
- Package on npm: https://www.npmjs.com/package/fintech-algorithms
- Domain index for agents: https://docs.thefintechbuilder.com/fundamental-analysis-and-valuation/llms.txt