提交

backend/app/api.py

@@ -3,8 +3,8 @@
   GET  /api/health               健康检查
   GET  /api/candles/{sym}        取 K 线（支持 1d/1w/1M/1y 周期，日线为基底聚合）
   GET  /api/stocks               全市场股票列表（基本信息 + 最新行情 + 缓存条数）
-  GET  /api/stock/{code}/chips   个股筹码峰（cyq_chips/cyq_perf，按复权口径换算）
   GET  /api/market/overview      主页大盘总览（A 股/港美指数 + 两市市值成交统计）
+  GET  /api/market/index-candles 上证指数全量 K 线（1d/1w/1M/1y 聚合）
   POST /api/backtest             跑回测，返回 K线+指标+买卖点+净值+绩效
   POST /api/screener/run         智能选股：自然语言 -> 条件 -> 全市场筛选
   POST /api/screener/sync        启动全市场数据同步（后台任务）
@@ -18,6 +18,7 @@ import json
 from datetime import datetime
 
 import pandas as pd
+from pydantic import TypeAdapter
 from fastapi import APIRouter, Depends, File, HTTPException, Response, UploadFile
 from fastapi.responses import StreamingResponse
 from sqlalchemy import delete, func, select, text
@@ -30,8 +31,9 @@ from .auth import require_user
 from .backtest.events import EventEngineError, run_event_backtest
 from .backtest.strategies import build_strategy
 from .config import settings
-from .data import fetcher, repository, tushare_provider
+from .data import fetcher, repository
 from .data.aggregation import bars_per_year, resample_bars
+from .data.index_series import SH_INDEX, get_index_daily
 from .data.market_overview import MarketOverviewError, fetch_overview
 from .data.symbols import plain_code
 from .db import async_session, get_session
@@ -53,8 +55,6 @@ from .schemas import (
     BacktestRequest,
     BacktestResponse,
     CandleOut,
-    ChipRowOut,
-    ChipsResponse,
     EquityPoint,
     EventBacktestRequest,
     EventBacktestResponse,
@@ -133,6 +133,8 @@ def _rows_to_bars(rows) -> list[Bar]:
 _ADJUST_MODES = ("bfq", "qfq", "hfq")
 # MA 全量集合（前端已改为本地计算 MA，后端始终返回此集合以保证缓存一致）
 _FULL_MA_SET = (5, 10, 20, 30, 60, 120, 250)
+# 指数 K 线支持的周期（日线基底聚合）
+_INDEX_TIMEFRAMES = ("1d", "1w", "1M", "1y")
 
 # 信息卡一条 SQL 拿全：stock_basic 基本信息 + 「优先与行情同日、缺则最新日」的 daily_snapshot
 # （LATERAL 单条替换原两条查询，语义不变：target 为 NULL 时全按最新日兜底）
@@ -409,6 +411,32 @@ async def get_market_overview(session: AsyncSession = Depends(get_session)) -> M
     return MarketOverviewResponse(**data)
 
 
+@router.get("/market/index-candles", response_model=list[CandleOut])
+async def get_index_candles(timeframe: str = "1d") -> Response:
+    """上证指数全量 K 线：日线为基底（tushare index_daily，进程内+Redis 缓存），
+    聚合到 1d/1w/1M/1y。收盘口径（数据随 EOD 更新，与总览 spark 一致）。"""
+    if timeframe not in _INDEX_TIMEFRAMES:
+        raise HTTPException(status_code=400, detail=f"timeframe 仅支持 {'/'.join(_INDEX_TIMEFRAMES)}")
+    key = f"idxkj:{cache.digest('idxc', SH_INDEX, timeframe)}"
+    cached = await _cached_json_response(key)
+    if cached is not None:
+        return cached
+    try:
+        bars = resample_bars(await get_index_daily(), timeframe)
+    except Exception as e:  # noqa: BLE001
+        raise HTTPException(status_code=502, detail=f"指数数据获取失败: {e}")
+    outs = [
+        CandleOut(ts=b.ts, open=b.open, high=b.high, low=b.low, close=b.close,
+                  volume=b.volume, amount=b.amount, turnover=None)
+        for b in bars
+    ]
+    # pydantic-core 序列化（与 _raw_json 同款），历史不可变、TTL 兜到当日更新
+    raw = TypeAdapter(list[CandleOut]).dump_json(outs).decode()
+    cache.local_set(key, raw, ttl=300)
+    await cache.cache_set(key, raw, ttl=7200)
+    return Response(content=raw, media_type="application/json")
+
+
 @router.post("/backtest", response_model=BacktestResponse)
 async def backtest(
     req: BacktestRequest,
@@ -1112,84 +1140,3 @@ async def screener_preview(
     cache.local_set(f"pvj:{cache_key}", raw, ttl=120)
     asyncio.create_task(cache.cache_set(f"pvj:{cache_key}", raw, ttl=600))
     return Response(content=raw, media_type="application/json")
-
-
-@router.get("/stock/{ts_code}/chips", response_model=ChipsResponse)
-async def stock_chips(
-    ts_code: str, date: str | None = None, adjust: str = "qfq",
-    session: AsyncSession = Depends(get_session),
-) -> Response:
-    """个股筹码峰（Tushare cyq_chips + cyq_perf，数据自 2018 年起）。
-
-    date=YYYY-MM-DD 为参考日（日 K 传当日；周/月 K 由前端传周期末）：返回
-    <=date 的最近有筹码数据的交易日截面；缺省取最新。
-    价格/成本均按 adjust（bfq/qfq/hfq）用 adj_factor 本地换算，与 K 线同口径。
-    """
-    if adjust not in _ADJUST_MODES:
-        raise HTTPException(status_code=400, detail=f"adjust 仅支持 {'/'.join(_ADJUST_MODES)}")
-    ref: str | None = None
-    if date:
-        try:
-            ref = datetime.strptime(date.strip()[:10], "%Y-%m-%d").strftime("%Y%m%d")
-        except ValueError:
-            raise HTTPException(status_code=400, detail="date 格式应为 YYYY-MM-DD")
-
-    # 历史截面不可变；键带 candles 版本号（adj_factor 随同步更新后旧缓存失效）。
-    # 同 preview：存序列化 JSON 直返（j: 前缀），跳过 pydantic 校验/序列化。
-    cache_key = cache.digest("chips", ts_code, ref or "latest", adjust, await cache.get_version("candles"))
-    cached = await _cached_json_response(f"chipsj:{cache_key}")
-    if cached is not None:
-        return cached
-
-    try:
-        perf, rows = await asyncio.to_thread(tushare_provider.fetch_chips, ts_code, ref)
-    except Exception as e:  # noqa: BLE001
-        raise HTTPException(status_code=502, detail=f"筹码数据获取失败: {e}")
-    if not perf:
-        resp = ChipsResponse(
-            ts_code=ts_code, trade_date=None, adjust=adjust,
-            error="无筹码数据（cyq 数据自 2018 年起，或参考日早于数据起点）",
-        )
-        raw = _raw_json(resp)
-        cache.local_set(f"chipsj:{cache_key}", raw, ttl=120)
-        await cache.cache_set(f"chipsj:{cache_key}", raw, ttl=3600)
-        return Response(content=raw, media_type="application/json")
-
-    d = datetime.strptime(str(perf["trade_date"]), "%Y%m%d")
-
-    # 复权换算（与 _adjust_bars 同口径）：qfq=f(d)/f_latest，hfq=f(d)，bfq=1
-    mult = 1.0
-    if adjust != "bfq":
-        factors = (await session.execute(
-            select(AdjFactor.trade_date, AdjFactor.adj_factor)
-            .where(AdjFactor.ts_code == ts_code, AdjFactor.trade_date <= d)
-            .order_by(AdjFactor.trade_date)
-        )).all()
-        if factors:
-            latest_f = (await session.execute(
-                select(AdjFactor.trade_date, AdjFactor.adj_factor).where(AdjFactor.ts_code == ts_code)
-                .order_by(AdjFactor.trade_date.desc()).limit(1)
-            )).first()
-            f_at = float(factors[-1][1])  # <=d 的最近因子（因子是阶梯函数）
-            f_latest = float(latest_f[1]) if latest_f else f_at
-            mult = f_at / f_latest if adjust == "qfq" else f_at
-
-    def _px(v) -> float | None:
-        return None if v is None or v != v else round(float(v) * mult, 3)
-
-    resp = ChipsResponse(
-        ts_code=ts_code,
-        trade_date=str(perf["trade_date"]),
-        adjust=adjust,
-        rows=[ChipRowOut(price=round(p * mult, 3), percent=v) for p, v in rows],
-        his_low=_px(perf.get("his_low")), his_high=_px(perf.get("his_high")),
-        cost_5pct=_px(perf.get("cost_5pct")), cost_15pct=_px(perf.get("cost_15pct")),
-        cost_50pct=_px(perf.get("cost_50pct")), cost_85pct=_px(perf.get("cost_85pct")),
-        cost_95pct=_px(perf.get("cost_95pct")),
-        weight_avg=_px(perf.get("weight_avg")),
-        winner_rate=_px(perf.get("winner_rate")),
-    )
-    raw = _raw_json(resp)
-    cache.local_set(f"chipsj:{cache_key}", raw, ttl=120)
-    await cache.cache_set(f"chipsj:{cache_key}", raw, ttl=21600)
-    return Response(content=raw, media_type="application/json")