wehub-resource-sync
725 lines
21 KiB
Python
725 lines
21 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Generate Stock Index from CSV File
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Input:
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- Tushare format: data/stock_list_{a,hk,us}.csv
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- Seed format: scripts/stock_index_seeds/stock_list_{jp,kr}.csv
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- AkShare format: logs/stock_basic_*.csv
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Output: apps/dsa-web/public/stocks.index.json
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Usage:
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python3 scripts/generate_index_from_csv.py # 默认使用 Tushare
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python3 scripts/generate_index_from_csv.py --source akshare
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python3 scripts/generate_index_from_csv.py --test # 测试模式
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"""
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import argparse
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import csv
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import json
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import re
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import sys
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import unicodedata
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from pathlib import Path
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from typing import List, Dict, Any, Optional
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# Add the project root to sys.path.
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sys.path.insert(0, str(Path(__file__).parent.parent))
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try:
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from pypinyin import lazy_pinyin, Style
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PYPINYIN_AVAILABLE = True
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except ImportError:
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lazy_pinyin = None
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Style = None
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PYPINYIN_AVAILABLE = False
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def require_pypinyin() -> bool:
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"""Ensure pypinyin is available before generating autocomplete assets."""
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if PYPINYIN_AVAILABLE:
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return True
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print("[Error] pypinyin not available; cannot generate stock autocomplete index.")
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print("[Info] Install dependencies with: pip install -r requirements.txt")
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return False
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def load_csv_data(csv_path: Path) -> List[Dict[str, Any]]:
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"""
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Load stock data from AkShare format CSV file
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Args:
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csv_path: CSV file path
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Returns:
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List of stock data
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"""
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stocks = []
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with open(csv_path, 'r', encoding='utf-8-sig') as f:
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reader = csv.DictReader(f)
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for row in reader:
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ts_code = row['ts_code'].strip()
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symbol = row['symbol'].strip()
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name = row['name'].strip()
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# Skip invalid rows.
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if not ts_code or not symbol or not name:
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continue
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stocks.append({
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'ts_code': ts_code,
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'symbol': symbol,
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'name': name,
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'area': row.get('area', ''),
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'industry': row.get('industry', ''),
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'list_date': row.get('list_date', ''),
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})
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return stocks
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def load_tushare_data(data_dir: Path) -> List[Dict[str, Any]]:
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"""
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从 Tushare CSV 文件加载多市场股票数据
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Args:
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data_dir: 数据目录路径
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Returns:
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合并后的股票列表
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"""
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all_stocks = []
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seed_dir = Path(__file__).parent / 'stock_index_seeds'
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default_data_dir = Path(__file__).parent.parent / 'data'
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use_seed_fallback = data_dir.resolve() == default_data_dir.resolve()
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def _csv_path(file_name: str) -> Path:
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data_path = data_dir / file_name
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if data_path.exists() or not use_seed_fallback:
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return data_path
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return seed_dir / file_name
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market_files = {
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'CN': data_dir / 'stock_list_a.csv',
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'HK': data_dir / 'stock_list_hk.csv',
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'US': data_dir / 'stock_list_us.csv',
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'JP': _csv_path('stock_list_jp.csv'),
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'KR': _csv_path('stock_list_kr.csv'),
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}
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for market_name, csv_file in market_files.items():
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if not csv_file.exists():
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print(f"[Warning] 未找到文件：{csv_file}")
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continue
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print(f" 正在读取 {market_name} 市场数据：{csv_file.name}")
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try:
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file_stocks = []
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selected_us_stocks: Dict[str, tuple[Dict[str, Any], int]] = {}
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with open(csv_file, 'r', encoding='utf-8-sig') as f:
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reader = csv.DictReader(f)
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for row in reader:
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# 传入市场参数以优化判断（对于特殊格式如 DUMMY）
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parsed = parse_stock_row(row, market_name)
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if not parsed:
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continue
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if market_name == 'US':
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# Tushare us_basic may include historical rows for a reused ticker.
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# Keep one deterministic row per ts_code before generating the index.
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delist_priority = get_us_delist_priority(row)
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existing = selected_us_stocks.get(parsed['ts_code'])
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if existing is None or delist_priority > existing[1]:
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selected_us_stocks[parsed['ts_code']] = (parsed, delist_priority)
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continue
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if parsed:
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all_stocks.append(parsed)
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file_stocks.append(parsed)
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if market_name == 'US':
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file_stocks = [item for item, _priority in selected_us_stocks.values()]
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all_stocks.extend(file_stocks)
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print(f" ✓ {market_name} 市场读取完成：{len(file_stocks)} 只股票")
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except Exception as e:
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print(f" [Error] 读取 {csv_file.name} 失败：{e}")
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return all_stocks
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def get_us_delist_priority(row: Dict[str, str]) -> int:
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"""
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为复用 ticker 的美股记录生成去重优先级。
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Tushare us_basic 导出的 delist_date 对当前记录并不总是稳定：
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- 空字符串通常表示当前仍在使用的 ticker
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- ``NaT`` 多见于历史记录或日期占位值
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- 实际日期表示明确退市
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因此前置去重时优先选择：
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1. delist_date 为空
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2. delist_date 为 NaT
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3. delist_date 为实际日期
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同优先级时保留 CSV 中最先出现的记录，避免在信息不足时随意切换名称。
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"""
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delist_date = (row.get('delist_date') or '').strip()
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if not delist_date:
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return 2
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if delist_date.upper() == 'NAT':
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return 1
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return 0
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def load_akshare_data(logs_dir: Path) -> List[Dict[str, Any]]:
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"""
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从 AkShare CSV 文件加载股票数据
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Args:
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logs_dir: 日志目录路径
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Returns:
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股票列表
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说明：
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AkShare 这条输入路径保留其原始 name 字段，不额外套用
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Tushare A 股那套 XD / XR / DR 状态前缀修正逻辑。这里的目标是
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复用 AkShare 已输出的展示名，而不是对其做二次归一化。
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"""
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csv_files = list(logs_dir.glob("stock_basic_*.csv"))
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if not csv_files:
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print("[Error] 未找到 CSV 文件：logs/stock_basic_*.csv")
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return []
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# 使用最新的 CSV 文件
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csv_file = sorted(csv_files)[-1]
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print(f" 正在读取 AkShare 数据：{csv_file.name}")
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stocks = []
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with open(csv_file, 'r', encoding='utf-8-sig') as f:
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reader = csv.DictReader(f)
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for row in reader:
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ts_code = row['ts_code'].strip()
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symbol = row['symbol'].strip()
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name = row['name'].strip()
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# Skip invalid rows.
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if not ts_code or not symbol or not name:
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continue
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stocks.append({
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'ts_code': ts_code,
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'symbol': symbol,
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'name': name,
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'area': row.get('area', ''),
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'industry': row.get('industry', ''),
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'list_date': row.get('list_date', ''),
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})
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print(f" ✓ 共读取 {len(stocks)} 只股票")
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return stocks
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def generate_pinyin(name: str) -> tuple:
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"""
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Generate pinyin for stock name
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Args:
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name: Stock name
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Returns:
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Tuple of (pinyin_full, pinyin_abbr)
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"""
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if not PYPINYIN_AVAILABLE:
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raise RuntimeError("pypinyin is required to generate stock autocomplete index")
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try:
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normalized_name = normalize_name_for_pinyin(name)
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# Full pinyin spelling.
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py_full = lazy_pinyin(normalized_name, style=Style.NORMAL)
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pinyin_full = ''.join(py_full)
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# Pinyin abbreviation.
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py_abbr = lazy_pinyin(normalized_name, style=Style.FIRST_LETTER)
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pinyin_abbr = ''.join(py_abbr)
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return (pinyin_full, pinyin_abbr)
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except Exception as e:
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print(f"[Warning] Failed to generate pinyin for {name}: {e}")
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return (None, None)
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def normalize_name_for_pinyin(name: str) -> str:
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"""
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Normalize stock name to avoid special prefixes and full-width characters polluting pinyin index
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Args:
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name: Original stock name
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Returns:
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Normalized name for pinyin generation
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"""
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normalized = unicodedata.normalize('NFKC', name).strip()
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# Strip common A-share prefixes while preserving the core name.
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normalized = re.sub(r'^(?:\*?ST|N)+', '', normalized, flags=re.IGNORECASE)
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return normalized.strip() or unicodedata.normalize('NFKC', name).strip()
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def normalize_stock_name_for_index(name: str, market: str) -> str:
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"""
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Normalize stock names before writing the long-lived autocomplete index.
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For A-shares (including BSE), ``XD``/``XR``/``DR`` are
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ex-dividend/ex-rights trading-day prefixes. They should not be stored in
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the official static index because they can become stale almost immediately.
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New-stock prefixes such as ``N``/``C`` and risk-warning prefixes such as
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``ST``/``*ST`` are preserved; they should be refreshed by the next
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stock-list update.
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"""
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normalized = unicodedata.normalize('NFKC', str(name or '')).strip()
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if market in {'CN', 'BSE'}:
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normalized = re.sub(r'^(?:XD|XR|DR)\s*', '', normalized, flags=re.IGNORECASE)
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return normalized.strip()
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def extract_symbol_from_ts_code(ts_code: str, market: str) -> Optional[str]:
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"""
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从 ts_code 提取 displayCode
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- A股：000001.SZ → 000001
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- 港股：00700.HK → 00700
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- 美股：AAPL → AAPL
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- 日股/韩股：7203.T / 005930.KS → 保留后缀，避免与其他市场裸代码冲突
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Args:
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ts_code: TS代码
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market: 市场代码
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Returns:
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displayCode 或 None
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"""
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if not ts_code:
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return None
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if market in {'US', 'JP', 'KR'}:
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# 美股常见 class/share 后缀、日韩 Yahoo 后缀都是代码身份的一部分。
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return ts_code
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if '.' in ts_code:
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# A股和港股：去除后缀
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return ts_code.split('.')[0]
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return ts_code
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def get_stock_name(row: Dict[str, str], market: str) -> Optional[str]:
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"""
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获取股票名称
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- A股/港股/日股/韩股：使用 name 字段
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- 美股：使用 enname 字段（英文名称）
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Args:
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row: CSV 行数据
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market: 市场代码
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Returns:
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股票名称或 None
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"""
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if market == 'US':
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# 美股使用英文名称
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name = row.get('enname', '').strip()
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return name if name else None
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else:
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# A股和港股使用中文名称
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name = row.get('name', '').strip()
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name = normalize_stock_name_for_index(name, market)
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return name if name else None
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def parse_aliases(row: Dict[str, str]) -> List[str]:
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"""Parse optional seed aliases from a CSV row."""
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raw_aliases = (row.get('aliases') or row.get('alias') or '').strip()
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if not raw_aliases:
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return []
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aliases: List[str] = []
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for alias in re.split(r'[|;,，、]+', raw_aliases):
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normalized = unicodedata.normalize('NFKC', alias).strip()
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if normalized and normalized not in aliases:
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aliases.append(normalized)
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return aliases
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def parse_stock_row(row: Dict[str, str], preferred_market: Optional[str] = None) -> Optional[Dict[str, Any]]:
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"""
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解析单行股票数据
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- 美股 DUMMY 过滤（严格过滤）
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- 空值校验
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- 自动判断市场类型（当无法判断时使用 preferred_market）
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- 返回统一格式的字典
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Args:
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row: CSV 行数据
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preferred_market: 当 ts_code 无法判断市场时使用（如美股 DUMMY 记录）
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Returns:
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解析后的股票字典，无效数据返回 None
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"""
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ts_code = row.get('ts_code', '').strip()
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if not ts_code:
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return None
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# 自动判断市场类型
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market = determine_market(ts_code)
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# 如果 ts_code 没有后缀（无法准确判断），且提供了 preferred_market，则使用它
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# 这主要用于处理美股的特殊格式（如 DUMMY 记录）
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if '.' not in ts_code and preferred_market:
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market = preferred_market
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# 美股特殊处理：严格过滤 DUMMY 记录
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if market == 'US':
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enname = row.get('enname', '').strip()
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if not enname or 'DUMMY' in enname.upper():
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return None
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# 获取股票名称
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name = get_stock_name(row, market)
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if not name:
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return None
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# 提取 displayCode
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display_code = extract_symbol_from_ts_code(ts_code, market)
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if not display_code:
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return None
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return {
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'ts_code': ts_code,
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'symbol': display_code,
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'name': name,
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'market': market,
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'aliases': parse_aliases(row),
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}
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def determine_market(ts_code: str) -> str:
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"""
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Determine market based on code
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Args:
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ts_code: Trading code (e.g., 000001.SZ, AAPL, BRK.B, 7203.T, 005930.KS)
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Returns:
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Market code (CN, HK, US, BSE, JP, KR)
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"""
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if '.' in ts_code:
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# 有后缀的情况
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suffix = ts_code.split('.')[1]
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# 检查是否为中国市场后缀
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if suffix in ['SH', 'SZ']:
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return 'CN'
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elif suffix == 'HK':
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return 'HK'
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elif suffix == 'BJ':
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return 'BSE'
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elif suffix == 'T':
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return 'JP'
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elif suffix in ['KS', 'KQ']:
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return 'KR'
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# 有后缀但不是中国市场后缀，检查是否为美股
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# 美股可能有点号后缀（如 BRK.B, GOOG.A, AAPL.U）
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prefix = ts_code.split('.')[0]
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if prefix.isalpha():
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return 'US'
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else:
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# 无后缀的情况
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# 纯字母代码为美股
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if ts_code.isalpha():
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return 'US'
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# 默认为 A股
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return 'CN'
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def generate_aliases(name: str, market: str) -> List[str]:
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"""
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Generate stock aliases
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Args:
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name: Stock name
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market: Market code
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Returns:
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List of aliases
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"""
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aliases = []
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# A股常见别名
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cn_alias_map = {
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'贵州茅台': ['茅台'],
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'中国平安': ['平安'],
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'平安银行': ['平银'],
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'招商银行': ['招行'],
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'五粮液': ['五粮'],
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'宁德时代': ['宁德'],
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'比亚迪': ['比亚'],
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'工商银行': ['工行'],
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'建设银行': ['建行'],
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'农业银行': ['农行'],
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'中国银行': ['中行'],
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'交通银行': ['交行'],
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'兴业银行': ['兴业'],
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'浦发银行': ['浦发'],
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'民生银行': ['民生'],
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'中信证券': ['中信'],
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'东方财富': ['东财'],
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'海康威视': ['海康'],
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'隆基绿能': ['隆基'],
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'中国神华': ['神华'],
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'长江电力': ['长电'],
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'中国石化': ['石化'],
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'中国石油': ['石油'],
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}
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# 港股常见别名
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hk_alias_map = {
|
||
'腾讯控股': ['腾讯', 'Tencent'],
|
||
'阿里巴巴-SW': ['阿里', '阿里巴巴', 'Alibaba'],
|
||
'美团-W': ['美团', 'Meituan'],
|
||
'小米集团-W': ['小米', 'Xiaomi'],
|
||
'京东集团-SW': ['京东', 'JD'],
|
||
'网易-S': ['网易', 'NetEase'],
|
||
'百度集团-SW': ['百度', 'Baidu'],
|
||
'中芯国际': ['中芯', 'SMIC'],
|
||
'中国移动': ['中移动', 'China Mobile'],
|
||
'中国海洋石油': ['中海油', 'CNOOC'],
|
||
}
|
||
|
||
# 美股常见别名
|
||
us_alias_map = {
|
||
'Apple Inc.': ['Apple', 'AAPL'],
|
||
'Microsoft Corporation': ['Microsoft', 'MSFT'],
|
||
'Amazon.com, Inc.': ['Amazon', 'AMZN'],
|
||
'Tesla Inc.': ['Tesla', 'TSLA'],
|
||
'Meta Platforms, Inc.': ['Meta', 'Facebook', 'META'],
|
||
'Alphabet Inc.': ['Google', 'Alphabet', 'GOOGL'],
|
||
'NVIDIA Corporation': ['NVIDIA', 'NVDA'],
|
||
'Netflix Inc.': ['Netflix', 'NFLX'],
|
||
'Intel Corporation': ['Intel', 'INTC'],
|
||
'Advanced Micro Devices': ['AMD', 'AMD'],
|
||
}
|
||
|
||
# 根据市场选择映射表
|
||
if market == 'CN':
|
||
alias_map = cn_alias_map
|
||
elif market == 'HK':
|
||
alias_map = hk_alias_map
|
||
elif market == 'US':
|
||
alias_map = us_alias_map
|
||
else:
|
||
alias_map = {}
|
||
|
||
if name in alias_map:
|
||
aliases.extend(alias_map[name])
|
||
|
||
return aliases
|
||
|
||
|
||
def build_stock_index(stocks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||
"""
|
||
Build the stock index.
|
||
|
||
Args:
|
||
stocks: Raw stock rows（已包含 market 字段）
|
||
|
||
Returns:
|
||
Stock index entries
|
||
"""
|
||
index = []
|
||
|
||
for stock in stocks:
|
||
ts_code = stock['ts_code']
|
||
symbol = stock['symbol']
|
||
name = stock['name']
|
||
market = stock.get('market', 'CN') # 优先使用已解析的市场，否则从 ts_code 判断
|
||
|
||
# 如果没有 market 字段，从 ts_code 判断
|
||
if market == 'CN' and '.' not in ts_code:
|
||
market = determine_market(ts_code)
|
||
|
||
# Generate pinyin fields.
|
||
pinyin_full, pinyin_abbr = generate_pinyin(name)
|
||
|
||
# Generate aliases.
|
||
aliases = generate_aliases(name, market)
|
||
for alias in stock.get('aliases', []):
|
||
if alias != name and alias not in aliases:
|
||
aliases.append(alias)
|
||
|
||
index.append({
|
||
"canonicalCode": ts_code, # Example: 000001.SZ, AAPL
|
||
"displayCode": symbol, # Example: 000001, AAPL
|
||
"nameZh": name,
|
||
"pinyinFull": pinyin_full,
|
||
"pinyinAbbr": pinyin_abbr,
|
||
"aliases": aliases,
|
||
"market": market,
|
||
"assetType": "stock",
|
||
"active": True,
|
||
"popularity": 100,
|
||
})
|
||
|
||
return index
|
||
|
||
|
||
def compress_index(index: List[Dict[str, Any]]) -> List[List]:
|
||
"""
|
||
压缩索引为数组格式以减少文件大小
|
||
|
||
Args:
|
||
index: 原始索引
|
||
|
||
Returns:
|
||
压缩后的索引
|
||
"""
|
||
compressed = []
|
||
for item in index:
|
||
compressed.append([
|
||
item["canonicalCode"],
|
||
item["displayCode"],
|
||
item["nameZh"],
|
||
item.get("pinyinFull"),
|
||
item.get("pinyinAbbr"),
|
||
item.get("aliases", []),
|
||
item["market"],
|
||
item["assetType"],
|
||
item["active"],
|
||
item.get("popularity", 0),
|
||
])
|
||
return compressed
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
parser = argparse.ArgumentParser(description='从 CSV 生成股票自动补全索引')
|
||
parser.add_argument(
|
||
'--source',
|
||
choices=['tushare', 'akshare'],
|
||
default='tushare',
|
||
help='数据源选择（默认: tushare）'
|
||
)
|
||
parser.add_argument(
|
||
'--test', '-t',
|
||
action='store_true',
|
||
help='测试模式：只验证不写入文件'
|
||
)
|
||
args = parser.parse_args()
|
||
|
||
print("=" * 60)
|
||
print("股票索引生成工具（从 CSV）")
|
||
print("=" * 60)
|
||
print(f"数据源：{args.source}")
|
||
|
||
if not require_pypinyin():
|
||
return 1
|
||
|
||
# 加载数据
|
||
print("\n[1/5] 读取 CSV 数据...")
|
||
if args.source == 'tushare':
|
||
data_dir = Path(__file__).parent.parent / 'data'
|
||
stocks = load_tushare_data(data_dir)
|
||
elif args.source == 'akshare':
|
||
logs_dir = Path(__file__).parent.parent / 'logs'
|
||
stocks = load_akshare_data(logs_dir)
|
||
else:
|
||
print(f"[Error] 不支持的数据源：{args.source}")
|
||
return 1
|
||
|
||
if not stocks:
|
||
print("[Error] 未加载到任何股票数据")
|
||
return 1
|
||
|
||
print(f" 共读取 {len(stocks)} 只股票")
|
||
|
||
print("\n[2/5] 生成索引数据...")
|
||
index = build_stock_index(stocks)
|
||
|
||
# 输出路径
|
||
output_path = (
|
||
Path(__file__).parent.parent / "apps" / "dsa-web" / "public" / "stocks.index.json"
|
||
)
|
||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||
|
||
print("\n[3/5] 压缩索引数据...")
|
||
compressed = compress_index(index)
|
||
|
||
if args.test:
|
||
print("\n[4/5] 测试模式：跳过写入文件")
|
||
print(f" 输出路径：{output_path}")
|
||
|
||
# 验证数据
|
||
print("\n[5/5] 验证数据...")
|
||
print(f" 压缩前：{len(index)} 条记录")
|
||
print(f" 压缩后：{len(compressed)} 条记录")
|
||
|
||
# 显示前5条示例
|
||
if compressed:
|
||
print("\n 前5条示例：")
|
||
for i, item in enumerate(compressed[:5]):
|
||
print(f" {i + 1}. {item}")
|
||
else:
|
||
print(f"\n[4/5] 写入文件：{output_path}")
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
f.write('[\n')
|
||
for i, item in enumerate(compressed):
|
||
json.dump(item, f, ensure_ascii=False, separators=(',', ':'))
|
||
if i < len(compressed) - 1:
|
||
f.write(',\n')
|
||
else:
|
||
f.write('\n')
|
||
f.write(']\n')
|
||
|
||
file_size = output_path.stat().st_size
|
||
print(f" 文件大小：{file_size / 1024:.2f} KB")
|
||
|
||
# 验证文件
|
||
print("\n[5/5] 验证文件...")
|
||
with open(output_path, 'r', encoding='utf-8') as f:
|
||
test_data = json.load(f)
|
||
print(f" 验证通过：{len(test_data)} 条记录")
|
||
|
||
# 统计信息
|
||
market_stats = {}
|
||
for item in index:
|
||
market = item['market']
|
||
market_stats[market] = market_stats.get(market, 0) + 1
|
||
|
||
print(f"\n{'=' * 60}")
|
||
print("生成完成！市场分布：")
|
||
for market, count in sorted(market_stats.items()):
|
||
print(f" - {market}: {count} 只")
|
||
print(f"{'=' * 60}")
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|