francy51
24aa8e33d4
- Add core.computed.json with 32 ratio definitions (filing + market derived) - Add Rust types for ComputedDefinition and ComputationSpec - Create generate-taxonomy.ts to generate TypeScript from Rust JSON - Generate lib/generated/ (gitignored) with surfaces, computed, kpis - Update financial-metrics.ts to use generated definitions - Add build-time generation via 'bun run generate' - Add taxonomy architecture documentation Two-phase ratio computation: - Filing-derived: margins, returns, per-share, growth (Rust computes) - Market-derived: valuation ratios (TypeScript computes with price data) All 32 ratios defined in core.computed.json: - Margins: gross, operating, ebitda, net, fcf - Returns: roa, roe, roic, roce - Financial health: debt_to_equity, net_debt_to_ebitda, cash_to_debt, current_ratio - Per-share: revenue, fcf, book_value - Growth: yoy metrics + 3y/5y cagr - Valuation: market_cap, ev, p/e, p/fcf, p/b, ev/sales, ev/ebitda, ev/fcf
83 lines
2.5 KiB
TypeScript
83 lines
2.5 KiB
TypeScript
import type {
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FinancialSurfaceKind,
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RatioRow,
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StandardizedFinancialRow,
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StructuredKpiRow,
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TrendSeries
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} from '@/lib/types';
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import { RATIO_CATEGORIES } from '@/lib/generated';
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import { KPI_CATEGORY_ORDER } from '@/lib/server/financials/kpi-registry';
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function toTrendSeriesRow(row: {
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key: string;
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label: string;
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category: string;
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unit: TrendSeries['unit'];
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values: Record<string, number | null>;
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}) {
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return {
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key: row.key,
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label: row.label,
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category: row.category,
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unit: row.unit,
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values: row.values
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} satisfies TrendSeries;
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}
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export function buildFinancialCategories(rows: Array<{ category: string }>, surfaceKind: FinancialSurfaceKind) {
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const counts = new Map<string, number>();
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for (const row of rows) {
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counts.set(row.category, (counts.get(row.category) ?? 0) + 1);
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}
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const order = surfaceKind === 'ratios'
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? [...RATIO_CATEGORIES]
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: surfaceKind === 'segments_kpis'
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? [...KPI_CATEGORY_ORDER]
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: [...counts.keys()];
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return order
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.filter((key) => (counts.get(key) ?? 0) > 0)
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.map((key) => ({
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key,
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label: key.replace(/_/g, ' ').replace(/\b\w/g, (char) => char.toUpperCase()),
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count: counts.get(key) ?? 0
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}));
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}
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export function buildTrendSeries(input: {
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surfaceKind: FinancialSurfaceKind;
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statementRows?: StandardizedFinancialRow[];
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ratioRows?: RatioRow[];
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kpiRows?: StructuredKpiRow[];
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}) {
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switch (input.surfaceKind) {
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case 'income_statement':
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return (input.statementRows ?? [])
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.filter((row) => row.key === 'revenue' || row.key === 'net_income')
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.map(toTrendSeriesRow);
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case 'balance_sheet':
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return (input.statementRows ?? [])
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.filter((row) => row.key === 'total_assets' || row.key === 'cash_and_equivalents' || row.key === 'total_debt')
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.map(toTrendSeriesRow);
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case 'cash_flow_statement':
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return (input.statementRows ?? [])
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.filter((row) => row.key === 'operating_cash_flow' || row.key === 'free_cash_flow' || row.key === 'capital_expenditures')
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.map(toTrendSeriesRow);
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case 'ratios':
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return (input.ratioRows ?? [])
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.filter((row) => row.category === 'margins')
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.map(toTrendSeriesRow);
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case 'segments_kpis': {
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const rows = input.kpiRows ?? [];
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const firstCategory = buildFinancialCategories(rows, 'segments_kpis')[0]?.key ?? null;
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return rows
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.filter((row) => row.category === firstCategory)
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.slice(0, 4)
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.map(toTrendSeriesRow);
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}
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default:
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return [];
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}
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}
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