feat: 修复报错

python-app/app/market/service.py

@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+from typing import Any
+from time import time
+
+import pandas as pd
+
+from market.provider_base import MarketDataProvider
+
+
+DISPLAY_FIELDS = [
+    "代码",
+    "名称",
+    "最新价",
+    "涨跌幅",
+    "涨跌额",
+    "成交量",
+    "成交额",
+    "振幅",
+    "最高",
+    "最低",
+    "今开",
+    "昨收",
+    "市盈率-动态",
+    "市净率",
+    "总市值",
+    "流通市值",
+]
+
+
+def _safe(v: Any) -> Any:
+    if v is None:
+        return None
+    if isinstance(v, float) and pd.isna(v):
+        return None
+    return v
+
+
+def build_signals(hist: pd.DataFrame, sma_fast: int, sma_slow: int) -> list[dict[str, Any]]:
+    frame = hist.copy()
+    frame["sma_fast"] = frame["close"].rolling(sma_fast).mean()
+    frame["sma_slow"] = frame["close"].rolling(sma_slow).mean()
+    signals: list[dict[str, Any]] = []
+
+    for i in range(1, len(frame)):
+        prev = frame.iloc[i - 1]
+        curr = frame.iloc[i]
+        if pd.isna(prev["sma_fast"]) or pd.isna(prev["sma_slow"]) or pd.isna(curr["sma_fast"]) or pd.isna(curr["sma_slow"]):
+            continue
+        cross_up = prev["sma_fast"] <= prev["sma_slow"] and curr["sma_fast"] > curr["sma_slow"]
+        cross_down = prev["sma_fast"] >= prev["sma_slow"] and curr["sma_fast"] < curr["sma_slow"]
+        if cross_up:
+            signals.append({"date": curr["date"].strftime("%Y-%m-%d"), "type": "buy", "price": float(curr["close"])})
+        elif cross_down:
+            signals.append({"date": curr["date"].strftime("%Y-%m-%d"), "type": "sell", "price": float(curr["close"])})
+    return signals
+
+
+def resolve_candidates(provider: MarketDataProvider, query: str, limit: int) -> pd.DataFrame:
+    return provider.search_spot(query=query, limit=limit)
+
+
+def _pick_first_candidate(provider: MarketDataProvider, query: str) -> pd.Series:
+    candidates = resolve_candidates(provider, query=query, limit=1)
+    if candidates.empty:
+        raise RuntimeError(f"未找到匹配股票: {query}")
+    row = candidates.iloc[0]
+    code = str(row.get("代码", "")).strip()
+    if not code:
+        raise RuntimeError("行情返回缺少代码字段")
+    return row
+
+
+def _build_info(provider: MarketDataProvider, row: pd.Series) -> dict[str, Any]:
+    code = str(row.get("代码", "")).strip()
+    return {
+        "code": code,
+        "name": str(row.get("名称", "")),
+        "price": _safe(row.get("最新价")),
+        "change_pct": _safe(row.get("涨跌幅")),
+        "change": _safe(row.get("涨跌额")),
+        "volume": _safe(row.get("成交量")),
+        "turnover": _safe(row.get("成交额")),
+        "amplitude": _safe(row.get("振幅")),
+        "high": _safe(row.get("最高")),
+        "low": _safe(row.get("最低")),
+        "open": _safe(row.get("今开")),
+        "prev_close": _safe(row.get("昨收")),
+        "pe": _safe(row.get("市盈率-动态")),
+        "pb": _safe(row.get("市净率")),
+        "market_cap": _safe(row.get("总市值")),
+        "float_market_cap": _safe(row.get("流通市值")),
+        "provider": provider.provider_name,
+        "channel": provider.channel,
+    }
+
+
+def build_realtime_info(provider: MarketDataProvider, query: str) -> dict[str, Any]:
+    row = _pick_first_candidate(provider, query=query)
+    info = _build_info(provider=provider, row=row)
+    now = datetime.now()
+    price = info.get("price")
+    volume = info.get("volume")
+    realtime_point = {
+        "date": now.strftime("%Y-%m-%d"),
+        "close": float(price) if price is not None else None,
+        "volume": float(volume) if volume is not None else 0.0,
+    }
+    return {
+        "info": info,
+        "realtime_point": realtime_point,
+        "source": "realtime",
+        "updated_at": now.strftime("%Y-%m-%d %H:%M:%S"),
+    }
+
+
+_DASHBOARD_CACHE: dict[str, tuple[float, dict[str, Any]]] = {}
+_DASHBOARD_CACHE_TTL_SECONDS = 15.0
+
+
+def build_dashboard(
+    provider: MarketDataProvider,
+    query: str,
+    days: int,
+    sma_fast: int,
+    sma_slow: int,
+) -> dict[str, Any]:
+    if sma_fast >= sma_slow:
+        raise RuntimeError("sma_fast must be less than sma_slow")
+
+    row = _pick_first_candidate(provider, query=query)
+    code = str(row.get("代码", "")).strip()
+    cache_key = "|".join([provider.provider_name, provider.channel, code, str(days), str(sma_fast), str(sma_slow)])
+    now = time()
+    cached = _DASHBOARD_CACHE.get(cache_key)
+    if cached is not None and now - cached[0] <= _DASHBOARD_CACHE_TTL_SECONDS:
+        return cached[1]
+
+    end = datetime.now()
+    lookback_days = max(days + (sma_slow * 3), days + 30)
+    start = end - timedelta(days=lookback_days)
+    hist = provider.fetch_daily_kline(code=code, start=start, end=end).tail(days).reset_index(drop=True)
+    if hist.empty:
+        raise RuntimeError(f"未获取到 K 线数据: {code}")
+
+    signals = build_signals(hist, sma_fast=sma_fast, sma_slow=sma_slow)
+    hist["sma_fast"] = hist["close"].rolling(sma_fast).mean()
+    hist["sma_slow"] = hist["close"].rolling(sma_slow).mean()
+
+    points = [
+        {
+            "date": d.strftime("%Y-%m-%d"),
+            "close": float(c),
+            "sma_fast": _safe(f),
+            "sma_slow": _safe(s),
+            "volume": float(v),
+        }
+        for d, c, f, s, v in zip(hist["date"], hist["close"], hist["sma_fast"], hist["sma_slow"], hist["volume"])
+    ]
+
+    result = {"info": _build_info(provider=provider, row=row), "points": points, "signals": signals}
+    _DASHBOARD_CACHE[cache_key] = (now, result)
+    return result