342 lines
9.6 KiB
TypeScript
342 lines
9.6 KiB
TypeScript
/**
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* 概念/行业分析 — 数据适配层
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*
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* 处理两种扩展数据结构：
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* - 结构 A（个股维度）：每行一只股票，维度字段（如 concept）存该股票所属的概念/行业
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* - 结构 B（板块维度）：每行一个概念/行业，带成分股列表（如 constituents: [...]）
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*
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* 两种结构统一输出为 DimensionGroup[]，供页面组件消费。
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*/
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import type { ExtDataConfig, ExtDataField, ExtDataRowsResult } from '@/lib/api'
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// ===== 公共类型 =====
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export interface StockRow {
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symbol: string
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code?: string
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name?: string
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[key: string]: unknown
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}
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export interface DimensionGroup {
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/** 维度名称（概念名/行业名） */
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key: string
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/** 成分股数量 */
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count: number
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/** 成分股原始行 */
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stocks: StockRow[]
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/** 聚合指标（如涨跌幅均值等） */
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metrics: Record<string, number | null>
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}
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export interface ResolvedDimension {
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/** 是否成功解析 */
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ok: boolean
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/** 数据结构类型 */
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structure: 'per_stock' | 'per_dimension' | 'unknown'
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/** 维度字段名 */
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dimensionField: string
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/** 所有解析出的分组 */
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groups: DimensionGroup[]
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/** 原始全部行（结构 A 下为原始行，结构 B 下展平后的全部成分股） */
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allStocks: StockRow[]
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/** 解析提示 */
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hint?: string
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}
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// ===== 结构探测 =====
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const SEPARATORS = /[、,，;；|/\s]+/
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const CONSTITUENT_KEYS = [
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'constituents', '成分股', 'stocks', 'members', 'codes', 'list',
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'symbol_list', 'stock_list', 'member_list',
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]
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const DIMENSION_NAME_KEYS = [
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'name', '概念名称', '概念', '行业名称', '行业', '板块名称', '板块',
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'concept', 'industry', 'sector', 'theme', 'title', 'label',
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]
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/** 检测行是否是"板块维度"结构（含成分股列表字段） */
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function detectConstituentField(fields: ExtDataField[]): string | null {
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return fields.find(f =>
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CONSTITUENT_KEYS.some(k => f.name.toLowerCase() === k.toLowerCase() || f.label?.includes(k))
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)?.name ?? null
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}
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/** 检测维度名称字段 */
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function detectDimensionNameField(fields: ExtDataField[]): string | null {
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return fields.find(f =>
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DIMENSION_NAME_KEYS.some(k => f.name.toLowerCase() === k.toLowerCase() || f.label?.includes(k))
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)?.name ?? null
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}
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/** 从候选名中选取最佳维度字段（结构 A） */
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export function pickDimensionField(
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fields: ExtDataField[],
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candidates: string[],
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): string {
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const nonMeta = fields.filter(f =>
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!['symbol', 'code', 'name', '股票简称', '股票代码', 'date'].includes(f.name)
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)
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for (const c of candidates) {
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const m = nonMeta.find(f =>
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f.name.toLowerCase().includes(c.toLowerCase()) ||
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f.label?.toLowerCase().includes(c.toLowerCase())
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)
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if (m) return m.name
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}
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// 回退：第一个非数值字段
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return nonMeta.find(f => f.dtype !== 'int' && f.dtype !== 'float')?.name ?? nonMeta[0]?.name ?? ''
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}
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/** 判断字段是否为数值类型 */
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function isNumericField(f: ExtDataField): boolean {
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return f.dtype === 'int' || f.dtype === 'float'
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}
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// ===== 结构 A 解析：个股维度 =====
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function parsePerStock(
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rows: Record<string, any>[],
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dimensionField: string,
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numericFields: string[],
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): DimensionGroup[] {
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const map = new Map<string, StockRow[]>()
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for (const row of rows) {
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const raw = row[dimensionField]
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if (raw == null) continue
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const text = String(raw).trim()
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if (!text) continue
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// 支持多值分隔（如 "人工智能,芯片,5G"）
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const values = text.split(SEPARATORS).map(s => s.trim()).filter(Boolean)
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const stock: StockRow = { ...row, symbol: row.symbol ?? row.code ?? '' }
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for (const v of values) {
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const list = map.get(v) ?? []
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list.push(stock)
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map.set(v, list)
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}
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}
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return [...map.entries()]
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.map(([key, stocks]) => ({
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key,
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count: stocks.length,
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stocks,
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metrics: computeMetrics(stocks, numericFields),
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}))
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.sort((a, b) => b.count - a.count)
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}
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// ===== 结构 B 解析：板块维度 =====
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function parsePerDimension(
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rows: Record<string, any>[],
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constituentField: string,
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nameField: string,
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numericFields: string[],
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): DimensionGroup[] {
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const allStocks: StockRow[] = []
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const groups = rows.map(row => {
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const key = String(row[nameField] ?? row[constituentField] ?? '').trim()
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if (!key) return null
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// 成分股可能是字符串数组、对象数组、逗号分隔字符串
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const rawList = row[constituentField]
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const stocks = parseConstituents(rawList)
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stocks.forEach(s => { if (s.symbol) allStocks.push(s) })
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// 维度自身的数值指标也保留
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const metrics = computeMetrics(stocks, numericFields)
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// 补上行级别的数值
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for (const f of numericFields) {
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if (typeof row[f] === 'number') {
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metrics[`__dim_${f}`] = row[f]
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}
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}
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return { key, count: stocks.length, stocks, metrics } as DimensionGroup
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}).filter((g): g is DimensionGroup => g !== null && g.key !== '')
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return groups.sort((a, b) => b.count - a.count)
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}
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/** 解析成分股字段（支持多种格式） */
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function parseConstituents(raw: unknown): StockRow[] {
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if (raw == null) return []
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if (typeof raw === 'string') {
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// 逗号/分隔符分隔的股票代码字符串
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return raw.split(SEPARATORS).map(s => s.trim()).filter(Boolean).map(s => ({
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symbol: normalizeSymbol(s),
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code: s,
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}))
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}
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if (Array.isArray(raw)) {
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return raw.map(item => {
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if (typeof item === 'string') {
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return { symbol: normalizeSymbol(item), code: item }
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}
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if (typeof item === 'object' && item !== null) {
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const obj = item as Record<string, any>
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return {
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symbol: obj.symbol ?? obj.code ?? obj.股票代码 ?? '',
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code: obj.code ?? obj.symbol ?? '',
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name: obj.name ?? obj.股票简称 ?? obj.名称 ?? '',
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...obj,
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}
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}
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return { symbol: String(item) }
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})
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}
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return []
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}
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function normalizeSymbol(s: string): string {
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// 尝试补全为 6 位代码
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if (/^\d{6}$/.test(s)) return s
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return s
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}
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// ===== 聚合指标计算 =====
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function computeMetrics(
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stocks: StockRow[],
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numericFields: string[],
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): Record<string, number | null> {
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const result: Record<string, number | null> = {}
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for (const f of numericFields) {
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const vals = stocks
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.map(s => s[f])
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.filter((v): v is number => typeof v === 'number' && Number.isFinite(v))
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if (vals.length === 0) {
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result[f] = null
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} else {
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result[f] = vals.reduce((a, b) => a + b, 0) / vals.length
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}
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}
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return result
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}
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// ===== 主入口：自动探测 + 解析 =====
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export function resolveDimension(
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data: ExtDataRowsResult | null | undefined,
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config: ExtDataConfig | null | undefined,
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candidateFields: string[],
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): ResolvedDimension {
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if (!data || !config || !data.rows.length) {
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return { ok: false, structure: 'unknown', dimensionField: '', groups: [], allStocks: [] }
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}
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const fields = data.fields ?? config.fields
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const rows = data.rows
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const numericFields = fields.filter(f => isNumericField(f)).map(f => f.name)
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// 先检测是否为结构 B（板块维度）
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const constituentField = detectConstituentField(fields)
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if (constituentField) {
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const nameField = detectDimensionNameField(fields) ?? 'name'
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const groups = parsePerDimension(rows, constituentField, nameField, numericFields)
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const allStocks = groups.flatMap(g => g.stocks)
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return {
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ok: true,
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structure: 'per_dimension',
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dimensionField: nameField,
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groups,
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allStocks,
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hint: `检测到板块维度结构（成分股字段: ${constituentField}）`,
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}
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}
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// 结构 A（个股维度）
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const dimensionField = pickDimensionField(fields, candidateFields)
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if (!dimensionField) {
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return {
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ok: false,
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structure: 'unknown',
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dimensionField: '',
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groups: [],
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allStocks: rows as StockRow[],
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hint: '未找到合适的维度字段',
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}
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}
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const groups = parsePerStock(rows, dimensionField, numericFields)
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const allStocks = rows as StockRow[]
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return {
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ok: true,
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structure: 'per_stock',
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dimensionField,
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groups,
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allStocks,
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hint: `按 ${dimensionField} 分组，共 ${groups.length} 个维度`,
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}
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}
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// ===== 行情数据关联 =====
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export interface QuoteMap {
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symbol: string
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price?: number
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pct?: number
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change_pct?: number
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name?: string
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[key: string]: unknown
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}
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/** 构建 symbol → quote 的快速查找 */
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export function buildQuoteMap(quotes: QuoteMap[]): Map<string, QuoteMap> {
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const map = new Map<string, QuoteMap>()
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for (const q of quotes) {
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if (q.symbol) map.set(q.symbol, q)
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// 也用纯数字代码做索引
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const code = q.symbol?.replace(/\.\w+$/, '')
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if (code) map.set(code, q)
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}
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return map
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}
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/** 为分组计算行情聚合指标 */
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export function computeQuoteMetrics(
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stocks: StockRow[],
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quoteMap: Map<string, QuoteMap>,
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): {
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avgPct: number | null
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upCount: number
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downCount: number
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flatCount: number
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totalVolume: number
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} {
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let up = 0, down = 0, flat = 0, totalVol = 0
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let sumPct = 0, countPct = 0
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for (const s of stocks) {
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const sym = String(s.symbol ?? '')
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const q = quoteMap.get(sym) ?? quoteMap.get(sym.replace(/\.\w+$/, ''))
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if (!q) continue
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const pct = q.pct ?? q.change_pct
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if (pct != null && typeof pct === 'number' && Number.isFinite(pct)) {
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sumPct += pct
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countPct++
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if (pct > 0) up++
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else if (pct < 0) down++
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else flat++
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}
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totalVol += (typeof q.price === 'number' ? 1 : 0) // 简化计数
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}
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return {
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avgPct: countPct > 0 ? sumPct / countPct : null,
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upCount: up,
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downCount: down,
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flatCount: flat,
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totalVolume: totalVol,
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}
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}
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