Earnings-Quality Composite

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/** Canonical TypeScript parity implementation for D18-F09. */

type RecordLike = Record<string, any>;
const finite = (value: any, name: string): number => {
  if (typeof value !== "number" || !Number.isFinite(value)) throw new TypeError(`${name} must be a finite number`);
  return value;
};
const positive = (data: RecordLike, name: string): number => {
  const value = finite(data[name], name);
  if (value <= 0) throw new RangeError(`${name} must be positive`);
  return value;
};
const nonnegative = (data: RecordLike, name: string): number => {
  const value = finite(data[name], name);
  if (value < 0) throw new RangeError(`${name} must be nonnegative`);
  return value;
};
const isoDate = (value: any, name: string): string => {
  if (typeof value !== "string" || !/^\d{4}-\d{2}-\d{2}$/.test(value)) throw new TypeError(`${name} must be YYYY-MM-DD`);
  return value;
};
const clamp = (value: number, low = 0, high = 100): number => Math.max(low, Math.min(high, value));
const score = (data: RecordLike, name: string): number => {
  const value = finite(data[name], name);
  if (value < 0 || value > 100) throw new RangeError(`${name} must be between 0 and 100`);
  return value;
};
const band = (value: number): string => value >= 75 ? "strong" : value >= 50 ? "watch" : "weak";

function assemble(data: RecordLike): RecordLike {
  const cutoff = isoDate(data.knowledge_cutoff, "knowledge_cutoff");
  const periodEnd = isoDate(data.period_end, "period_end");
  if (typeof data.currency !== "string" || data.currency.length === 0) throw new TypeError("currency must be nonempty text");
  if (typeof data.scale !== "string" || data.scale.length === 0) throw new TypeError("scale must be nonempty text");
  if (!Array.isArray(data.required_fields) || data.required_fields.length === 0 || data.required_fields.some((x: any) => typeof x !== "string" || x.length === 0)) throw new TypeError("required_fields must be a nonempty string list");
  if (!Array.isArray(data.facts)) throw new TypeError("facts must be a list");
  const accepted: RecordLike = {};
  const rejected: RecordLike[] = [];
  for (const field of data.required_fields as string[]) {
    const candidates = (data.facts as any[]).filter((fact: any) => fact && fact.field === field && typeof fact.value === "number" && Number.isFinite(fact.value) && typeof fact.available_at === "string" && typeof fact.period_end === "string" && fact.available_at <= cutoff && fact.period_end === periodEnd && fact.currency === data.currency && fact.scale === data.scale && fact.revision === "original");
    if (candidates.length) {
      candidates.sort((a: any, b: any) => a.available_at.localeCompare(b.available_at));
      accepted[field] = candidates[candidates.length - 1].value;
    } else rejected.push({ field, reason: "no original, aligned fact available by the knowledge cutoff" });
  }
  const completeness = Object.keys(accepted).length / data.required_fields.length;
  return { state: completeness === 1 ? "ready" : "incomplete", method: "point-in-time-filing-fact-assembly", as_of: data.knowledge_cutoff, accepted_fields: Object.keys(accepted).sort(), selected_values: accepted, rejected_fields: rejected, completeness, ready: completeness === 1, clock_policy: "availability_at <= knowledge_cutoff; original revision; exact period/currency/scale" };
}

function cohort(data: RecordLike): RecordLike {
  const asOf = isoDate(data.as_of, "as_of");
  if (typeof data.target_id !== "string" || typeof data.target_sector !== "string" || typeof data.target_country !== "string") throw new TypeError("target identifiers and cohort dimensions must be text");
  const minCap = nonnegative(data, "min_market_cap");
  const minPeers = Math.trunc(positive(data, "min_peers"));
  if (!Array.isArray(data.universe) || data.universe.length === 0) throw new TypeError("universe must be a nonempty list");
  const target = (data.universe as any[]).find((row: any) => row && row.id === data.target_id);
  if (!target) throw new RangeError("target_id must occur in universe");
  const targetCap = positive(target, "market_cap");
  const eligible: any[] = [];
  const exclusions: RecordLike[] = [];
  for (const row of data.universe as any[]) {
    if (!row || row.id === data.target_id) continue;
    let reason: string | null = null;
    let cap = 0;
    try { cap = positive(row, "market_cap"); isoDate(row.available_at, "universe.available_at"); } catch { reason = "invalid market-cap or availability fact"; }
    if (!reason && row.available_at > asOf) reason = "not available at as-of date";
    else if (!reason && row.listed !== true) reason = "not listed under the selected scope";
    else if (!reason && row.sector !== data.target_sector) reason = "sector mismatch";
    else if (!reason && row.country !== data.target_country) reason = "country mismatch";
    else if (!reason && cap < minCap) reason = "below market-cap floor";
    if (reason) exclusions.push({ id: String(row?.id ?? "?"), reason }); else eligible.push(row);
  }
  eligible.sort((a, b) => Math.abs(a.market_cap - targetCap) - Math.abs(b.market_cap - targetCap) || String(a.id).localeCompare(String(b.id)));
  const ids = eligible.map(row => String(row.id));
  return { state: ids.length >= minPeers ? "resolved" : "abstain", method: "point-in-time-sector-country-market-cap-cohort", as_of: data.as_of, eligible_ids: ids, cohort_count: ids.length, minimum_required: minPeers, coverage: ids.length / minPeers, exclusions, tie_break: "absolute market-cap distance, then stable entity id" };
}

function normalize(data: RecordLike): RecordLike {
  if (!Array.isArray(data.metrics) || data.metrics.length === 0) throw new TypeError("metrics must be a nonempty list");
  const minPeers = Math.trunc(positive(data, "min_peers"));
  const results: RecordLike[] = [];
  for (const metric of data.metrics as any[]) {
    if (!metric || typeof metric.name !== "string" || !Array.isArray(metric.peers)) throw new TypeError("each metric needs name and peers");
    const target = finite(metric.target, "target");
    const peers = metric.peers.filter((value: any) => typeof value === "number" && Number.isFinite(value)) as number[];
    if (peers.length < minPeers) throw new RangeError(`${metric.name} has fewer than min_peers observations`);
    const equal = peers.filter(value => Math.abs(value - target) <= 1e-12).length;
    const less = peers.filter(value => value < target - 1e-12).length;
    const percentile = 100 * (less + 0.5 * equal) / peers.length;
    if (typeof metric.higher_is_better !== "boolean") throw new TypeError(`${metric.name}.higher_is_better must be boolean`);
    const normalized = metric.higher_is_better ? percentile : 100 - percentile;
    results.push({ name: metric.name, target, peer_count: peers.length, percentile, higher_is_better: metric.higher_is_better, normalized_score: normalized });
  }
  const aggregate = results.reduce((sum, row) => sum + row.normalized_score, 0) / results.length;
  return { state: "calculated", method: "midrank-empirical-percentile-with-direction", metrics: results, aggregate_score: aggregate, metric_count: results.length, invariant: "higher-is-better reverses the percentile only; peer values are not z-scored" };
}

const modelRules: Record<string, { population: string; eligible: (target: RecordLike) => boolean; variant: string }> = {
  altman_z_original: { population: "public industrial manufacturer", eligible: t => t.is_public === true && t.sector === "industrial" && Number(t.market_cap) > 0, variant: "Use D18-F04-A01 original public-manufacturer coefficients" },
  piotroski_f: { population: "non-financial issuer with two annual periods", eligible: t => !["bank", "insurance", "reit", "utility"].includes(t.sector) && Number(t.annual_periods) >= 2, variant: "Use D18-F04-A02 nine-signal contract" },
  beneish_m: { population: "non-financial issuer with two annual periods", eligible: t => !["bank", "insurance", "reit", "utility"].includes(t.sector) && Number(t.annual_periods) >= 2, variant: "Use D18-F04-A03 eight-index contract" },
  ohlson_o: { population: "industrial public research screen", eligible: t => t.is_public === true && Number(t.annual_periods) >= 2, variant: "Use D18-F04-A05 Model 1 convention" },
  dividend_safety: { population: "issuer with declared dividend and cash-flow facts", eligible: t => t.dividends_known === true && Number(t.annual_periods) >= 1, variant: "Use D18-F09-A07 payout contract" },
  balance_sheet_resilience: { population: "issuer with aligned balance-sheet and coverage facts", eligible: t => Number(t.total_assets) > 0 && ["US-GAAP", "IFRS"].includes(t.framework), variant: "Use D18-F09-A08 resilience contract" },
};

function route(data: RecordLike): RecordLike {
  if (!data.target || typeof data.target !== "object" || !Array.isArray(data.requested_models) || data.requested_models.length === 0) throw new TypeError("target and requested_models are required");
  const routes: RecordLike[] = [];
  for (const model of data.requested_models as any[]) {
    if (typeof model !== "string") throw new TypeError("requested model names must be text");
    const rule = modelRules[model];
    if (!rule) { routes.push({ model, status: "unsupported", variant: null, reason: "no frozen rule for this model label" }); continue; }
    const eligible = rule.eligible(data.target);
    routes.push({ model, status: eligible ? "eligible" : "reroute", variant: eligible ? rule.variant : null, population: rule.population, reason: eligible ? "all required scope facts pass" : `target does not meet ${rule.population} contract` });
  }
  const eligibleCount = routes.filter(row => row.status === "eligible").length;
  return { state: eligibleCount ? "routed" : "abstain", method: "explicit-model-applicability-router", routes, eligible_count: eligibleCount, requested_count: routes.length, coverage: eligibleCount / routes.length };
}

function weighted(data: RecordLike, names: string[], weights: number[], output: string, method: string): RecordLike {
  const values = names.map(name => score(data, name));
  const components: RecordLike = {}, weightMap: RecordLike = {}, contributions: RecordLike = {};
  names.forEach((name, i) => { components[name] = values[i]; weightMap[name] = weights[i]; contributions[name] = values[i] * weights[i]; });
  const total = Object.values(contributions).reduce((sum: number, value: any) => sum + value, 0);
  return { state: "calculated", method, components, weights: weightMap, contributions, [output]: total, band: band(total), coverage: 1 };
}
const health = (data: RecordLike): RecordLike => weighted(data, ["profitability_score", "cash_flow_score", "liquidity_score", "leverage_score"], [0.3, 0.3, 0.2, 0.2], "financial_health_score", "accounting-financial-health-weighted-composite");
const earnings = (data: RecordLike): RecordLike => weighted(data, ["accrual_quality_score", "cash_conversion_score", "revenue_quality_score", "manipulation_safety_score"], [0.3, 0.25, 0.25, 0.2], "earnings_quality_score", "earnings-quality-weighted-composite");

function dividend(data: RecordLike): RecordLike {
  const dividends = positive(data, "dividends_paid");
  const fcf = finite(data.free_cash_flow, "free_cash_flow");
  const netIncome = finite(data.net_income, "net_income");
  const interestCoverage = nonnegative(data, "interest_coverage");
  const cash = nonnegative(data, "cash_and_equivalents");
  const components = { free_cash_flow_coverage: clamp(fcf / dividends / 2 * 100), earnings_coverage: clamp(netIncome / dividends / 2 * 100), interest_coverage: clamp(interestCoverage / 10 * 100), cash_buffer: clamp(cash / dividends / 4 * 100) };
  const weights = { free_cash_flow_coverage: 0.35, earnings_coverage: 0.25, interest_coverage: 0.2, cash_buffer: 0.2 };
  const contributions: RecordLike = {}; Object.keys(components).forEach(key => { contributions[key] = (components as any)[key] * (weights as any)[key]; });
  const value = Object.values(contributions).reduce((sum: number, item: any) => sum + item, 0);
  return { state: "calculated", method: "coverage-and-liquidity-dividend-safety", components, weights, contributions, dividend_safety_score: value, band: band(value), coverage_policy: "coverage is capped at two times and cash buffer at four times" };
}

function resilience(data: RecordLike): RecordLike {
  const currentAssets = positive(data, "current_assets"), currentLiabilities = positive(data, "current_liabilities"), totalDebt = positive(data, "total_debt"), ebitda = positive(data, "ebitda"), interest = positive(data, "interest_expense"), cash = nonnegative(data, "cash_and_equivalents"), due = nonnegative(data, "debt_due_12m");
  if (due > totalDebt) throw new RangeError("debt_due_12m cannot exceed total_debt");
  const components = { liquidity: clamp(currentAssets / currentLiabilities / 2 * 100), net_leverage: clamp((1 - (totalDebt - cash) / (4 * ebitda)) * 100), interest_coverage: clamp(ebitda / interest / 10 * 100), maturity_headroom: clamp((1 - due / totalDebt) * 100) };
  const weights = { liquidity: 0.3, net_leverage: 0.3, interest_coverage: 0.25, maturity_headroom: 0.15 };
  const contributions: RecordLike = {}; Object.keys(components).forEach(key => { contributions[key] = (components as any)[key] * (weights as any)[key]; });
  const value = Object.values(contributions).reduce((sum: number, item: any) => sum + item, 0);
  return { state: "calculated", method: "liquidity-leverage-coverage-maturity-resilience", components, weights, contributions, balance_sheet_resilience_score: value, band: band(value) };
}

function ensemble(data: RecordLike): RecordLike {
  if (!Array.isArray(data.models) || data.models.length === 0) throw new TypeError("models must be a nonempty list");
  const eligible: [string, number, number][] = [];
  for (const model of data.models as any[]) {
    if (!model || typeof model !== "object") throw new TypeError("each model must be an object");
    if (model.eligible !== true) continue;
    const probability = finite(model.distress_probability, "distress_probability"), weight = positive(model, "weight");
    if (probability < 0 || probability > 1) throw new RangeError("distress_probability must be between zero and one");
    eligible.push([String(model.name ?? "model"), probability, weight]);
  }
  if (!eligible.length) throw new RangeError("at least one eligible model is required");
  const weightSum = eligible.reduce((sum, row) => sum + row[2], 0);
  const probability = eligible.reduce((sum, row) => sum + row[1] * row[2], 0) / weightSum;
  const variance = eligible.reduce((sum, row) => sum + row[2] * (row[1] - probability) ** 2, 0) / weightSum;
  const values = eligible.map(row => row[1]), dispersion = Math.max(...values) - Math.min(...values);
  return { state: "calculated", method: "weighted-distress-probability-ensemble", eligible_models: eligible.map(row => ({ name: row[0], probability: row[1], weight: row[2] })), model_count: eligible.length, distress_probability: probability, weighted_stddev: Math.sqrt(variance), disagreement_range: dispersion, agreement: 1 - dispersion, band: probability >= 0.66 ? "high-review" : probability >= 0.33 ? "watch" : "lower-review", calibration_boundary: "weighted aggregation preserves supplied probabilities; it does not recalibrate them" };
}

function conflict(data: RecordLike): RecordLike {
  if (!Array.isArray(data.components) || data.components.length === 0) throw new TypeError("components must be a nonempty list");
  const groupCap = finite(data.group_cap, "group_cap"), threshold = finite(data.conflict_threshold, "conflict_threshold");
  if (groupCap <= 0 || groupCap > 1 || threshold <= 0 || threshold > 100) throw new RangeError("group_cap must be in (0,1] and conflict_threshold in (0,100]");
  const groups = new Map<string, RecordLike[]>(); let rawWeight = 0, rawNumerator = 0;
  for (const item of data.components as any[]) {
    if (!item || typeof item.evidence_group !== "string") throw new TypeError("each component needs an evidence_group");
    const value = score(item, "score"), weight = positive(item, "weight"), group = item.evidence_group;
    if (!groups.has(group)) groups.set(group, []);
    groups.get(group)!.push({ name: String(item.name ?? "component"), score: value, weight }); rawWeight += weight; rawNumerator += value * weight;
  }
  const adjusted: RecordLike[] = [], conflicts: string[] = []; let denominator = 0, numerator = 0;
  [...groups.entries()].sort((a, b) => a[0].localeCompare(b[0])).forEach(([group, items]) => {
    const weight = items.reduce((sum, item) => sum + item.weight, 0), mean = items.reduce((sum, item) => sum + item.score * item.weight, 0) / weight, values = items.map(item => item.score), range = Math.max(...values) - Math.min(...values), isConflict = range >= threshold, capped = Math.min(weight, groupCap);
    if (isConflict) conflicts.push(group);
    adjusted.push({ group, raw_weight: weight, capped_weight: capped, mean_score: mean, range, conflict: isConflict }); denominator += capped; numerator += capped * mean;
  });
  const resolved = numerator / denominator, raw = rawNumerator / rawWeight;
  return { state: conflicts.length ? "conflict-detected" : "resolved", method: "evidence-group-cap-and-conflict-resolver", raw_score: raw, resolved_score: resolved, double_counting_adjustment: resolved - raw, groups: adjusted, conflict_groups: conflicts, conflict_count: conflicts.length, group_cap: groupCap, conflict_threshold: threshold };
}

const overall = (data: RecordLike): RecordLike => weighted(data, ["financial_health_score", "earnings_quality_score", "dividend_safety_score", "balance_sheet_resilience_score", "distress_safety_score", "valuation_score"], [0.24, 0.18, 0.14, 0.18, 0.16, 0.10], "overall_stock_score", "explainable-six-pillar-stock-score");

function confidence(data: RecordLike): RecordLike {
  const baseScore = score(data, "base_score"), required = Math.trunc(positive(data, "required_components")), available = Math.trunc(nonnegative(data, "available_components")), maxConflicts = Math.trunc(positive(data, "max_conflicts")), conflicts = Math.trunc(nonnegative(data, "conflict_count")), minimum = Math.trunc(positive(data, "minimum_components")), staleDays = nonnegative(data, "stale_days");
  if (available > required || conflicts > maxConflicts) throw new RangeError("available components or conflicts exceed their declared maxima");
  const coverage = available / required, missingPenalty = 1 - coverage, stalenessPenalty = Math.min(staleDays / 365, 1) * 0.2, conflictPenalty = Math.min(conflicts / maxConflicts, 1) * 0.2, confidenceValue = coverage * (1 - stalenessPenalty) * (1 - conflictPenalty), adjusted = clamp(baseScore - 15 * missingPenalty - 10 * conflictPenalty), abstain = available < minimum || confidenceValue < 0.6;
  return { state: abstain ? "abstain" : "usable-with-confidence", method: "coverage-staleness-conflict-confidence-gate", base_score: baseScore, coverage, missing_penalty: missingPenalty, staleness_penalty: stalenessPenalty, conflict_penalty: conflictPenalty, confidence: confidenceValue, adjusted_score: adjusted, abstain, reason: abstain ? "minimum component or confidence gate failed" : "coverage and confidence gates passed" };
}

function screen(data: RecordLike): RecordLike {
  if (!Array.isArray(data.universe) || data.universe.length === 0) throw new TypeError("universe must be a nonempty list");
  const floor = score(data, "screen_floor"), minConfidence = finite(data.min_confidence, "min_confidence"), minLiquidity = nonnegative(data, "min_liquidity");
  if (minConfidence < 0 || minConfidence > 1) throw new RangeError("min_confidence must be between zero and one");
  const selected: RecordLike[] = [], excluded: RecordLike[] = [];
  for (const row of data.universe as any[]) {
    if (!row || typeof row.id !== "string") throw new TypeError("each universe row needs an id");
    const rowScore = score(row, "score"), confidenceValue = finite(row.confidence, "confidence"), liquidity = nonnegative(row, "liquidity");
    if (confidenceValue < 0 || confidenceValue > 1) throw new RangeError("universe confidence must be between zero and one");
    const reasons: string[] = []; if (row.eligible !== true) reasons.push("not eligible"); if (rowScore < floor) reasons.push("below score floor"); if (confidenceValue < minConfidence) reasons.push("below confidence floor"); if (liquidity < minLiquidity) reasons.push("below liquidity floor");
    const candidate: RecordLike = { id: row.id, score: rowScore, confidence: confidenceValue, liquidity, sector: row.sector ?? "unspecified" };
    if (reasons.length) excluded.push({ ...candidate, reasons }); else selected.push(candidate);
  }
  selected.sort((a, b) => b.score - a.score || b.confidence - a.confidence || a.id.localeCompare(b.id));
  const ranked = selected.map((row, index) => ({ rank: index + 1, ...row }));
  return { state: selected.length ? "ranked" : "abstain", method: "eligibility-confidence-score-floor-ranking", ranked, screened_count: ranked.length, universe_count: data.universe.length, coverage: ranked.length / data.universe.length, score_floor: floor, confidence_floor: minConfidence, liquidity_floor: minLiquidity, excluded, tie_break: "score descending, confidence descending, stable id ascending" };
}

function history(data: RecordLike): RecordLike {
  if (!Array.isArray(data.history) || data.history.length < 2) throw new RangeError("history needs at least two points");
  const rows = (data.history as any[]).map(row => { if (!row || typeof row.components !== "object") throw new TypeError("each history row needs components"); return { date: isoDate(row.as_of, "history.as_of"), row }; });
  for (let index = 0; index + 1 < rows.length; index += 1) if (rows[index].date >= rows[index + 1].date) throw new RangeError("history must be strictly chronological");
  const previous = rows[rows.length - 2].row, latest = rows[rows.length - 1].row, previousScore = score(previous, "overall_score"), latestScore = score(latest, "overall_score");
  const keys = [...new Set([...Object.keys(previous.components), ...Object.keys(latest.components)])].sort();
  const deltas: RecordLike = {}; for (const key of keys) deltas[key] = score(latest.components, key) - score(previous.components, key);
  const topDriver = keys.reduce((best, key) => Math.abs(deltas[key]) > Math.abs(deltas[best]) || (Math.abs(deltas[key]) === Math.abs(deltas[best]) && key > best) ? key : best, keys[0]);
  const delta = latestScore - previousScore, priorBand = String(previous.band), latestBand = String(latest.band), migration = priorBand === latestBand ? "unchanged" : `${priorBand} -> ${latestBand}`, order: Record<string, number> = { abstain: 0, watch: 1, eligible: 2, strong: 3 };
  return { state: delta > 1e-12 ? "improved" : delta < -1e-12 ? "deteriorated" : "unchanged", method: "point-in-time-score-history-change-attribution", previous_as_of: previous.as_of, latest_as_of: latest.as_of, previous_score: previousScore, latest_score: latestScore, score_change: delta, prior_band: priorBand, latest_band: latestBand, migration, migration_direction: (order[latestBand] ?? 0) - (order[priorBand] ?? 0), component_deltas: deltas, top_driver: topDriver, clock_policy: "only knowledge-available snapshots are comparable" };
}

export function calculate(topicId: string, data: RecordLike): RecordLike {
  const functions: Record<string, (input: RecordLike) => RecordLike> = { "D18-F09-A01": assemble, "D18-F09-A02": cohort, "D18-F09-A03": normalize, "D18-F09-A04": route, "D18-F09-A05": health, "D18-F09-A06": earnings, "D18-F09-A07": dividend, "D18-F09-A08": resilience, "D18-F09-A09": ensemble, "D18-F09-A10": conflict, "D18-F09-A11": overall, "D18-F09-A12": confidence, "D18-F09-A13": screen, "D18-F09-A14": history };
  if (!functions[topicId]) throw new RangeError(`unsupported topic id: ${topicId}`);
  return functions[topicId](data);
}