Initial commit: stock market platform

backend/app/services/__init__.py

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+

backend/app/services/stock_service.py

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