# Cyclical and Commodity-Cycle Normalization

`D18-F10-A06` · Fundamental Analysis and Valuation → Sector-Specific Equity Scoring · archetype `record-transform` · difficulty 5/5 · verification **verified**

Full page: https://docs.thefintechbuilder.com/fundamental-analysis-and-valuation/sector-specific-equity-scoring/cyclical-and-commodity-cycle-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 { cyclicalAndCommodityCycleNormalization } from "fintech-algorithms/fundamental-analysis-and-valuation/sector-specific-equity-scoring/cyclical-and-commodity-cycle-normalization";
```

## Signature

```ts
cyclicalAndCommodityCycleNormalization(data)
```

Normalizes a commodity producer to mid-cycle by taking the median of its price, unit-cost and volume histories, rebuilding revenue and EBITDA from those medians less fixed costs, and scoring the resulting margin, leverage, free-cash-flow consistency and where the current price sits in its own history.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `data` | `RecordValue` | yes | A plain object. `framework` must be the string `cyclical-midcycle-teaching-v1`. `weights` must be an object holding exactly `normalized_margin`, `normalized_leverage`, `cash_consistency` and `cycle_balance`. `realized_price_history`, `unit_cost_history`, `volume_history` and `free_cash_flow_history` must each be an array of at least seven finite numbers, and all four must have the same length; the price, cost and volume series must additionally be strictly positive throughout. `fixed_costs` and `net_debt` must be nonnegative and `current_realized_price` strictly positive. · min_observations: 7 |

## Returns

`{ state: string; method: string; normalized_price: number; normalized_unit_cost: number; normalized_volume: number; normalized_revenue: number; normalized_ebitda: number; normalized_margin: number; normalized_net_leverage: number | null; positive_fcf_ratio: number; cycle_percentile: number; component_scores: Record<string, number>; weights: Record<string, number>; normalized_score: number; coverage_ratio: number; reason: string }`

`normalized_price`, `normalized_unit_cost` and `normalized_volume` are the medians of their series. `normalized_revenue` is median price times median volume; `normalized_ebitda` is median price less median unit cost, times median volume, less `fixed_costs`; `normalized_margin` is EBITDA over revenue. `normalized_net_leverage` is `net_debt` over that EBITDA, or null when EBITDA is not positive, in which case the leverage component scores 0. `positive_fcf_ratio` is the share of the free-cash-flow history above zero and `cycle_percentile` the share of historical prices at or below `current_realized_price`. `component_scores` holds four band scores on a 0-100 scale and `normalized_score` is their weighted sum. `state` is `nonpositive-midcycle-ebitda-review` when EBITDA is zero or negative, otherwise the band of the score: `strong-review-band` at 75 or more, `mixed-review-band` at 50 or more, `weak-review-band` below that. `method` is `cyclical-midcycle-normalization-v1` and `coverage_ratio` is 1.

## Errors

- When data is not a plain object — throws TypeError
- When framework is not a nonempty string, or a history entry is not a finite number — throws TypeError
- When framework is not cyclical-midcycle-teaching-v1 — throws RangeError
- When any of the four histories is not an array of at least 7 observations — throws RangeError
- When the four histories do not all have the same length — throws RangeError
- When any price, unit-cost or volume observation is zero or negative — throws RangeError
- When weights does not hold exactly the four component names, or its values do not sum to 1 within 1e-9 — throws RangeError
- When fixed_costs or net_debt is negative, or current_realized_price is not positive — throws RangeError

## Complexity

Time `O(n log n)`, space `O(n)`.

## 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
{
  "framework": "cyclical-midcycle-teaching-v1",
  "realized_price_history": [72, 64, 58, 83, 91, 77],
  "unit_cost_history": [43, 44, 45, 47, 49, 46],
  "volume_history": [9.2, 9.4, 9, 9.7, 9.8, 9.6],
  "free_cash_flow_history": [52, 18, -8, 74, 96, 61],
  "fixed_costs": 105,
  "net_debt": 690,
  "current_realized_price": 74,
  "weights": {
    "normalized_margin": 0.32,
    "normalized_leverage": 0.28,
    "cash_consistency": 0.22,
    "cycle_balance": 0.18
  }
}
```

### Call

```ts
cyclicalAndCommodityCycleNormalization(data)
```

### Returns

object with 16 fields: state, method, normalized_price, normalized_unit_cost, normalized_volume, normalized_revenue, normalized_ebitda, normalized_margin, …

```json
{
  "state": "mixed-review-band",
  "method": "cyclical-midcycle-normalization-v1",
  "normalized_price": 72,
  "normalized_unit_cost": 45,
  "normalized_volume": 9.4,
  "normalized_revenue": 676.8000000000001,
  "normalized_ebitda": 148.8,
  "normalized_margin": 0.21985815602836878,
  "normalized_net_leverage": 4.637096774193548,
  "positive_fcf_ratio": 0.7777777777777778,
  "cycle_percentile": 0.6666666666666666,
  "component_scores": {
    "normalized_margin": 73.28605200945627,
    "normalized_leverage": 0,
    "cash_consistency": 79.62962962962962,
    "cycle_balance": 83.33333333333333
  },
  "weights": {
    "normalized_margin": 0.32,
    "normalized_leverage": 0.28,
    "cash_consistency": 0.22,
    "cycle_balance": 0.18
  },
  "normalized_score": 55.97005516154452
}
```

Showing 14 of 16 fields.

## Other exports

`calculate`, `bankFundamentalScore`, `insuranceFundamentalScore`, `reitFundamentalScore`, `utilityFundamentalScore`, `earlyStageLiquidityAndRunwayScore`, `holdingCompanyLookThroughScore`, `sectorSpecificWeightCalibration`, `unsupportedScopeAndCoverageDecision`. 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/sector-specific-equity-scoring/cyclical-and-commodity-cycle-normalization/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/fundamental-analysis-and-valuation/sector-specific-equity-scoring/cyclical-and-commodity-cycle-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