Initial commit: stock market platform
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admin
332
backend/app/services/stock_service.py
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332
backend/app/services/stock_service.py
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"""
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Stock data service — wraps AKShare with Redis caching.
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All AKShare calls are blocking I/O; run them in a thread pool via asyncio.
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"""
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import asyncio
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import json
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from functools import lru_cache
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from typing import Optional
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from loguru import logger
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try:
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import akshare as ak
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AK_AVAILABLE = True
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except Exception:
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AK_AVAILABLE = False
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logger.warning("AKShare not available, using mock data")
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# ── helpers ──────────────────────────────────────────────────────────────────
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def _run_sync(fn, *args, **kwargs):
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"""Run a sync blocking function in the default thread pool."""
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loop = asyncio.get_running_loop()
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return loop.run_in_executor(None, lambda: fn(*args, **kwargs))
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# ── market overview ───────────────────────────────────────────────────────────
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MAJOR_INDICES = [
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("sh000001", "上证指数"),
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("sz399001", "深证成指"),
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("sz399006", "创业板指"),
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("sh000688", "科创50"),
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("sh000300", "沪深300"),
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]
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async def get_market_overview() -> list[dict]:
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if not AK_AVAILABLE:
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return _mock_market_overview()
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try:
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df = await _run_sync(ak.stock_zh_index_spot_em)
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result = []
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code_map = {code: name for code, name in MAJOR_INDICES}
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for _, row in df.iterrows():
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code = str(row.get("代码", ""))
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if code in code_map:
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result.append({
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"index_code": code,
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"index_name": code_map[code],
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"current": float(row.get("最新价", 0)),
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"change": float(row.get("涨跌额", 0)),
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"change_pct": float(row.get("涨跌幅", 0)),
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})
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return result
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except Exception as e:
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logger.error(f"get_market_overview error: {e}")
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return _mock_market_overview()
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# ── real-time quotes (A-share spot) ──────────────────────────────────────────
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async def get_all_stocks_spot() -> list[dict]:
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"""All A-share real-time quotes — used for heatmap."""
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if not AK_AVAILABLE:
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return _mock_heatmap_data()
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try:
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df = await _run_sync(ak.stock_zh_a_spot_em)
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result = []
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for _, row in df.iterrows():
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pct = float(row.get("涨跌幅", 0) or 0)
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result.append({
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"symbol": str(row.get("代码", "")),
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"name": str(row.get("名称", "")),
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"price": float(row.get("最新价", 0) or 0),
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"change": float(row.get("涨跌额", 0) or 0),
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"change_pct": pct,
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"open": float(row.get("今开", 0) or 0),
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"high": float(row.get("最高", 0) or 0),
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"low": float(row.get("最低", 0) or 0),
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"prev_close": float(row.get("昨收", 0) or 0),
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"volume": float(row.get("成交量", 0) or 0),
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"amount": float(row.get("成交额", 0) or 0),
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})
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return result
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except Exception as e:
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logger.error(f"get_all_stocks_spot error: {e}")
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return _mock_heatmap_data()
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async def get_stock_quote(symbol: str) -> Optional[dict]:
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"""Single stock real-time quote."""
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all_stocks = await get_all_stocks_spot()
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for s in all_stocks:
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if s["symbol"] == symbol:
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return s
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return None
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# ── K-line data ───────────────────────────────────────────────────────────────
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PERIOD_MAP = {
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"daily": "daily",
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"weekly": "weekly",
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"monthly": "monthly",
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}
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async def get_kline(symbol: str, period: str = "daily", adjust: str = "qfq", limit: int = 250) -> list[dict]:
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"""
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period: daily | weekly | monthly
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adjust: qfq (前复权) | hfq (后复权) | "" (不复权)
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"""
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if not AK_AVAILABLE:
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return _mock_kline(limit)
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ak_period = PERIOD_MAP.get(period, "daily")
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try:
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df = await _run_sync(
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ak.stock_zh_a_hist,
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symbol=symbol,
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period=ak_period,
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adjust=adjust,
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)
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df = df.tail(limit)
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result = []
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for _, row in df.iterrows():
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result.append({
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"date": str(row.get("日期", "")),
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"open": float(row.get("开盘", 0)),
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"high": float(row.get("最高", 0)),
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"low": float(row.get("最低", 0)),
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"close": float(row.get("收盘", 0)),
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"volume": float(row.get("成交量", 0)),
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"amount": float(row.get("成交额", 0)),
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"change_pct": float(row.get("涨跌幅", 0)),
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})
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return result
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except Exception as e:
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logger.error(f"get_kline {symbol} {period} error: {e}")
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return _mock_kline(limit)
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async def get_intraday(symbol: str) -> list[dict]:
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"""Today's minute-level data."""
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if not AK_AVAILABLE:
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return _mock_intraday()
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try:
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df = await _run_sync(ak.stock_zh_a_hist_min_em, symbol=symbol, period="1", adjust="")
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result = []
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for _, row in df.iterrows():
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result.append({
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"time": str(row.get("时间", "")),
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"price": float(row.get("收盘", 0)),
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"volume": float(row.get("成交量", 0)),
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"amount": float(row.get("成交额", 0)),
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"avg_price": float(row.get("均价", 0) or 0),
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})
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return result
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except Exception as e:
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logger.error(f"get_intraday {symbol} error: {e}")
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return _mock_intraday()
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async def get_five_day(symbol: str) -> list[dict]:
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"""5-day minute-level data."""
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if not AK_AVAILABLE:
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return _mock_intraday(days=5)
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try:
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df = await _run_sync(ak.stock_zh_a_hist_min_em, symbol=symbol, period="1", adjust="")
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result = []
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for _, row in df.iterrows():
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result.append({
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"time": str(row.get("时间", "")),
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"price": float(row.get("收盘", 0)),
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"volume": float(row.get("成交量", 0)),
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"amount": float(row.get("成交额", 0) or 0),
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"avg_price": float(row.get("均价", 0) or 0),
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})
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return result[-5 * 240:]
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except Exception as e:
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logger.error(f"get_five_day {symbol} error: {e}")
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return _mock_intraday(days=5)
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# ── search ────────────────────────────────────────────────────────────────────
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@lru_cache(maxsize=1)
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def _get_stock_list_cached() -> list[dict]:
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"""Cache the full stock list in memory (refreshed on process restart)."""
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if not AK_AVAILABLE:
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return []
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try:
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import akshare as ak
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df = ak.stock_info_a_code_name()
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return [{"symbol": str(r["code"]), "name": str(r["name"]), "market": "A股"} for _, r in df.iterrows()]
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except Exception as e:
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logger.error(f"_get_stock_list_cached error: {e}")
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return []
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async def search_stocks(query: str, limit: int = 20) -> list[dict]:
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stock_list = await _run_sync(_get_stock_list_cached)
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query = query.strip().lower()
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results = [
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s for s in stock_list
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if query in s["symbol"].lower() or query in s["name"].lower()
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]
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return results[:limit]
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# ── sector data for heatmap ───────────────────────────────────────────────────
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async def get_sector_spot() -> list[dict]:
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"""Board/sector change pct for heatmap grouping."""
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if not AK_AVAILABLE:
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return _mock_sectors()
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try:
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df = await _run_sync(ak.stock_board_industry_name_em)
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result = []
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for _, row in df.iterrows():
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result.append({
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"sector": str(row.get("板块名称", "")),
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"change_pct": float(row.get("涨跌幅", 0) or 0),
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"volume": float(row.get("成交量", 0) or 0),
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"amount": float(row.get("成交额", 0) or 0),
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})
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return result
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except Exception as e:
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logger.error(f"get_sector_spot error: {e}")
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return _mock_sectors()
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# ── mock data (fallback when market is closed or AKShare unavailable) ─────────
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import random
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import math
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from datetime import date, timedelta
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def _mock_market_overview() -> list[dict]:
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return [
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{"index_code": "sh000001", "index_name": "上证指数", "current": 3312.46, "change": -28.84, "change_pct": -0.86},
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{"index_code": "sz399001", "index_name": "深证成指", "current": 10573.99, "change": -93.16, "change_pct": -0.87},
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{"index_code": "sz399006", "index_name": "创业板指", "current": 2105.37, "change": -18.42, "change_pct": -0.87},
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{"index_code": "sh000688", "index_name": "科创50", "current": 968.12, "change": -9.56, "change_pct": -0.98},
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{"index_code": "sh000300", "index_name": "沪深300", "current": 3843.20, "change": -30.11, "change_pct": -0.78},
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]
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def _mock_heatmap_data() -> list[dict]:
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sectors = ["银行", "电力设备", "食品饮料", "医药生物", "电子", "汽车", "非银金融", "计算机", "有色金属", "化工"]
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stocks = []
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for i in range(80):
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pct = round(random.uniform(-5, 5), 2)
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sector = sectors[i % len(sectors)]
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stocks.append({
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"symbol": f"{600000 + i:06d}",
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"name": f"测试股票{i+1:02d}",
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"price": round(random.uniform(5, 100), 2),
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"change": round(pct * 0.1, 2),
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"change_pct": pct,
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"open": round(random.uniform(5, 100), 2),
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"high": round(random.uniform(5, 100), 2),
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"low": round(random.uniform(5, 100), 2),
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"prev_close": round(random.uniform(5, 100), 2),
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"volume": random.randint(100000, 10000000),
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"amount": random.randint(1000000, 100000000),
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"sector": sector,
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})
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return stocks
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def _mock_kline(limit: int = 250) -> list[dict]:
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bars = []
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price = 20.0
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today = date.today()
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for i in range(limit):
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d = today - timedelta(days=limit - i)
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pct = random.uniform(-0.05, 0.05)
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close = round(price * (1 + pct), 2)
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high = round(max(price, close) * random.uniform(1.0, 1.03), 2)
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low = round(min(price, close) * random.uniform(0.97, 1.0), 2)
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bars.append({
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"date": d.isoformat(),
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"open": round(price, 2),
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"high": high,
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"low": low,
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"close": close,
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"volume": random.randint(500000, 5000000),
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"amount": random.randint(5000000, 50000000),
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"change_pct": round(pct * 100, 2),
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})
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price = close
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return bars
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def _mock_intraday(days: int = 1) -> list[dict]:
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bars = []
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price = 20.0
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for d in range(days):
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for minute in range(240):
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h = 9 + minute // 60
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m = (minute % 60) + (30 if d == 0 and minute < 60 else 0)
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if h == 11 and m >= 30:
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continue
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pct = random.uniform(-0.01, 0.01)
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price = round(price * (1 + pct), 2)
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bars.append({
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"time": f"2026-06-0{d+1} {h:02d}:{m % 60:02d}",
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"price": price,
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"volume": random.randint(10000, 500000),
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"amount": random.randint(100000, 5000000),
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"avg_price": price,
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})
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return bars
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def _mock_sectors() -> list[dict]:
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sectors = [
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"银行", "电力设备", "食品饮料", "医药生物", "电子",
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"汽车", "非银金融", "计算机", "有色金属", "化工",
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"机械设备", "建筑材料", "传媒", "房地产", "交通运输",
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]
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return [{"sector": s, "change_pct": round(random.uniform(-3, 3), 2), "volume": 0, "amount": 0}
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for s in sectors]
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