352 lines
14 KiB
Python
352 lines
14 KiB
Python
"""HTTP 路由（OpenAPI 契约的载体）。
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GET /api/health 健康检查
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GET /api/candles/{sym} 取 K 线（支持 1d/1w/1M/1y 周期，日线为基底聚合）
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POST /api/backtest 跑回测，返回 K线+指标+买卖点+净值+绩效
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POST /api/screener/run 智能选股：自然语言 -> 条件 -> 全市场筛选
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POST /api/screener/sync 启动全市场数据同步（后台任务）
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GET /api/screener/sync/status 同步任务状态与数据实况
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"""
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from __future__ import annotations
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import json
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import pandas as pd
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from .backtest.engine import BacktestConfig, run_backtest
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from .backtest.strategies import build_strategy
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from .config import settings
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from .data import fetcher, repository
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from .data.aggregation import bars_per_year, resample_bars
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from .data.symbols import plain_code
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from .data.synthetic import seed_if_empty
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from .db import get_session
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from .domain import Bar
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from . import indicators as ind
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from .models import BacktestRun, DailySnapshot, MarketDaily, StockBasic
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from .schemas import (
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BacktestRequest,
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BacktestResponse,
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CandleOut,
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EquityPoint,
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IndicatorOut,
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MetricsOut,
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PreviewInfoOut,
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PreviewResponse,
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ScreenerRunRequest,
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ScreenerRunResponse,
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ScreenerSyncRequest,
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ScreenerSyncStatus,
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SignalOut,
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SyncRequest,
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SyncResponse,
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)
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from .screener import engine, market_sync
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from .screener.engine import DataNotReadyError
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from .screener.llm import ScreenerError, parse_conditions
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router = APIRouter(prefix="/api")
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def _series_to_jsonable(s: pd.Series) -> list[float | None]:
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"""NaN -> None（lightweight-charts 的 whitespace data，跳过指标预热期）。"""
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out: list[float | None] = []
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for v in s.tolist():
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if v is None or (isinstance(v, float) and v != v):
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out.append(None)
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else:
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out.append(float(v))
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return out
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def _rows_to_bars(rows) -> list[Bar]:
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return [Bar(ts=r.ts, open=r.open, high=r.high, low=r.low, close=r.close, volume=r.volume) for r in rows]
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@router.get("/health")
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async def health() -> dict:
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return {"status": "ok"}
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@router.get("/candles/{symbol}", response_model=list[CandleOut])
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async def get_candles(
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symbol: str,
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timeframe: str = "1d",
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limit: int = 5000,
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session: AsyncSession = Depends(get_session),
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) -> list[CandleOut]:
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await seed_if_empty(session, symbol="DEMO")
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# 始终以日线为基底，再聚合到目标周期
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rows = await repository.get_candles(session, symbol, "1d", limit=limit)
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bars = resample_bars(_rows_to_bars(rows), timeframe)
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return [CandleOut(ts=b.ts, open=b.open, high=b.high, low=b.low, close=b.close, volume=b.volume) for b in bars]
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@router.post("/data/sync", response_model=SyncResponse)
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async def sync_data(req: SyncRequest, session: AsyncSession = Depends(get_session)) -> SyncResponse:
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"""主动拉取并缓存某标的的日线（Tushare 主 -> AKShare 兜底）。"""
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try:
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res = await fetcher.sync_symbol(
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session, req.symbol, start=req.start, end=req.end, source=req.source, force=req.force
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)
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return SyncResponse(**res)
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except Exception as e: # noqa: BLE001
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raise HTTPException(status_code=502, detail=str(e))
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@router.post("/backtest", response_model=BacktestResponse)
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async def backtest(
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req: BacktestRequest,
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session: AsyncSession = Depends(get_session),
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) -> BacktestResponse:
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await seed_if_empty(session, symbol="DEMO")
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# 非演示标的：首次自动拉取真实数据并缓存
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if req.symbol != "DEMO" and not await fetcher.is_cached(session, req.symbol):
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try:
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await fetcher.sync_symbol(session, req.symbol, source="auto")
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except Exception as e: # noqa: BLE001
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raise HTTPException(status_code=502, detail=f"数据拉取失败: {e}")
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# 日线为基底，聚合到请求周期
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rows = await repository.get_candles(
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session, req.symbol, "1d", start=req.start, end=req.end, limit=100000
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)
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if not rows:
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raise HTTPException(status_code=404, detail=f"无数据: symbol={req.symbol}")
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bars = resample_bars(_rows_to_bars(rows), req.timeframe)
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if len(bars) < 2:
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raise HTTPException(status_code=400, detail=f"周期 {req.timeframe} 下数据不足，无法回测")
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try:
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strategy = build_strategy(req.strategy, req.params)
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except Exception as e: # noqa: BLE001
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raise HTTPException(status_code=400, detail=f"策略构建失败: {e}")
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cfg = BacktestConfig(
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initial_cash=req.initial_cash,
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fast_mode=req.fast_mode,
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bars_per_year=bars_per_year(req.timeframe),
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)
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result = run_backtest(bars, strategy, cfg)
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df: pd.DataFrame = result["df"]
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m = result["metrics"]
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# 记录到回测运行注册表（可复现/可审计的基础）
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session.add(
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BacktestRun(
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symbol=req.symbol,
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strategy=req.strategy,
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timeframe=req.timeframe,
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params_json=json.dumps(req.params, ensure_ascii=False),
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initial_cash=req.initial_cash,
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total_return=m["total_return"],
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max_drawdown=m["max_drawdown"],
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sharpe=m["sharpe"],
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num_trades=m["num_trades"],
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)
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)
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await session.commit()
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candles = [
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CandleOut(ts=r["ts"], open=r["open"], high=r["high"], low=r["low"],
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close=r["close"], volume=r["volume"])
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for _, r in df.iterrows()
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]
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signals = [
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SignalOut(ts=f.ts, side=f.side.value, price=f.price, qty=f.qty)
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for f in result["fills"]
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]
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indicators = IndicatorOut(
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strategy=req.strategy,
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data={col: _series_to_jsonable(df[col]) for col in result["indicator_cols"]},
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)
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equity = [EquityPoint(ts=t.to_pydatetime(), value=float(v))
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for t, v in result["equity"].items()]
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return BacktestResponse(
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symbol=req.symbol,
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timeframe=req.timeframe,
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strategy=req.strategy,
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candles=candles,
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indicators=indicators,
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signals=signals,
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equity=equity,
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metrics=MetricsOut(**m),
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final_cash=result["final_cash"],
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final_position=result["final_position"],
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initial_cash=req.initial_cash,
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)
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# ---------- 智能选股 ----------
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@router.post("/screener/run", response_model=ScreenerRunResponse)
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async def screener_run(
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req: ScreenerRunRequest, session: AsyncSession = Depends(get_session)
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) -> ScreenerRunResponse:
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"""自然语言 -> LLM 解析条件 -> 全市场筛选。也可直传 conditions 跳过 LLM（微调再跑）。"""
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try:
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conds = req.conditions or await parse_conditions(req.text)
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if not conds.indicator and not conds.snapshot:
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raise HTTPException(status_code=400, detail="AI 未从描述中解析出任何筛选条件，请换种说法")
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result = await engine.run_screen(session, conds, settings.screener_default_limit)
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return ScreenerRunResponse(**result)
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except HTTPException:
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raise
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except DataNotReadyError as e:
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raise HTTPException(status_code=409, detail=str(e))
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except ValueError as e: # 未知指标/字段、条件为空
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raise HTTPException(status_code=400, detail=str(e))
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except ScreenerError as e:
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code = 503 if "未配置 LLM_API_KEY" in str(e) else 502
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raise HTTPException(status_code=code, detail=str(e))
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@router.post("/screener/sync", response_model=ScreenerSyncStatus)
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async def screener_sync_start(
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req: ScreenerSyncRequest, session: AsyncSession = Depends(get_session)
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) -> ScreenerSyncStatus:
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"""启动全市场数据同步（后台任务，立即返回状态）。"""
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try:
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await market_sync.start_sync(session, req.days, req.force)
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except ScreenerError as e:
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raise HTTPException(status_code=503, detail=str(e))
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status = await market_sync.get_sync_status(session)
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return ScreenerSyncStatus(**{k: status.get(k) for k in ScreenerSyncStatus.model_fields})
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@router.get("/screener/sync/status", response_model=ScreenerSyncStatus)
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async def screener_sync_status(session: AsyncSession = Depends(get_session)) -> ScreenerSyncStatus:
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"""同步任务状态 + 数据实况（最新交易日/行数/ready）。"""
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status = await market_sync.get_sync_status(session)
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return ScreenerSyncStatus(**{k: status.get(k) for k in ScreenerSyncStatus.model_fields})
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@router.get("/screener/preview/{ts_code}", response_model=PreviewResponse)
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async def screener_preview(
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ts_code: str, limit: int = 260, session: AsyncSession = Depends(get_session)
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) -> PreviewResponse:
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"""个股详情预览：日线（qfq 全量缓存，未缓存/过期自动拉取，失败退 market_daily 近段）
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+ 全套指标（indicators.py 单一事实源）+ 最新截面信息卡。"""
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symbol = plain_code(ts_code)
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# 先取 market_daily 最新行：既做缓存过期判断，也做信息卡数据源
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md = (
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await session.execute(
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select(MarketDaily).where(MarketDaily.ts_code == ts_code).order_by(MarketDaily.trade_date.desc()).limit(1)
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)
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).scalars().first()
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# --- 日线：candles(qfq 全量) 优先；未缓存拉取，缓存落后于全市场最新交易日则强制刷新（每日至多一次） ---
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rows = await repository.get_candles(session, symbol, "1d", limit=100000)
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source = "qfq"
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try:
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if not rows:
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await fetcher.sync_symbol(session, symbol, source="auto")
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rows = await repository.get_candles(session, symbol, "1d", limit=100000)
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elif md is not None and rows and rows[-1].ts.date() < md.trade_date.date():
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await fetcher.sync_symbol(session, symbol, source="auto", force=True)
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rows = await repository.get_candles(session, symbol, "1d", limit=100000)
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except Exception: # noqa: BLE001 —— tushare/写库失败时回滚会话（否则毒化后兜底查询 500）
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await session.rollback()
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if not rows:
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rows = []
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bars = _rows_to_bars(rows)
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if not bars:
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source = "market"
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res = await session.execute(
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select(MarketDaily).where(MarketDaily.ts_code == ts_code).order_by(MarketDaily.trade_date)
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)
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bars = [
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Bar(ts=r.trade_date, open=r.open, high=r.high, low=r.low, close=r.close, volume=r.vol * 100.0)
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for r in res.scalars()
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]
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if not bars:
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raise HTTPException(status_code=404, detail=f"无数据: {ts_code}（可先点「同步市场数据」）")
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# --- 指标（在全量历史上计算后截尾，保证预热正确） ---
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df = pd.DataFrame({"close": [b.close for b in bars], "high": [b.high for b in bars], "low": [b.low for b in bars]})
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closes, highs, lows = df["close"], df["high"], df["low"]
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macd = ind.macd(closes)
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kdj = ind.kdj(highs, lows, closes)
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boll = ind.bollinger(closes)
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indicators: dict[str, dict[str, list[float | None]]] = {
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"ma": {f"ma{p}": _series_to_jsonable(ind.ma(closes, p)) for p in (5, 10, 20, 60)},
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"macd": {
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"dif": _series_to_jsonable(macd["macd"]),
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"dea": _series_to_jsonable(macd["signal"]),
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"hist": _series_to_jsonable(macd["hist"]),
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},
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"kdj": {k: _series_to_jsonable(kdj[k]) for k in ("k", "d", "j")},
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"rsi": {
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"rsi6": _series_to_jsonable(ind.rsi(closes, 6)),
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"rsi12": _series_to_jsonable(ind.rsi(closes, 12)),
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"rsi24": _series_to_jsonable(ind.rsi(closes, 24)),
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},
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"boll": {k: _series_to_jsonable(boll[k]) for k in ("upper", "mid", "lower")},
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}
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limit = max(30, min(limit, len(bars)))
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for group in indicators.values():
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for key in group:
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group[key] = group[key][-limit:]
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# --- 信息卡：stock_basic + 最新 market_daily + 与其对齐的快照（避免混用不同交易日） ---
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sb = (await session.execute(select(StockBasic).where(StockBasic.ts_code == ts_code))).scalars().first()
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ds = None
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if md is not None:
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# 优先取与行情同日的快照；缺当日快照时退最新（字段可能与行情差日期，罕见）
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ds = (
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await session.execute(
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select(DailySnapshot).where(
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DailySnapshot.ts_code == ts_code, DailySnapshot.trade_date == md.trade_date
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)
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)
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).scalars().first()
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if ds is None:
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ds = (
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await session.execute(
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select(DailySnapshot).where(DailySnapshot.ts_code == ts_code).order_by(DailySnapshot.trade_date.desc()).limit(1)
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)
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).scalars().first()
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def _yi(v) -> float | None:
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if v is None:
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return None
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v = float(v)
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return None if v != v else round(v / 1e4, 2) # 万元 -> 亿元
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info = PreviewInfoOut(
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ts_code=ts_code,
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symbol=symbol,
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name=sb.name if sb else ts_code,
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industry=sb.industry if sb else None,
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area=sb.area if sb else None,
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market=sb.market if sb else None,
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list_date=sb.list_date if sb else None,
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trade_date=md.trade_date if md else None,
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open=md.open if md else None,
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high=md.high if md else None,
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low=md.low if md else None,
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close=md.close if md else None,
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pre_close=md.pre_close if md else None,
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pct_chg=md.pct_chg if md else None,
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volume_hand=round(md.vol, 0) if md else None,
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amount_yi=round(md.amount / 100000, 2) if md else None, # 千元 -> 亿元
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turnover_rate=ds.turnover_rate if ds else None,
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pe_ttm=ds.pe_ttm if ds else None,
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pb=ds.pb if ds else None,
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total_mv=_yi(ds.total_mv) if ds else None,
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circ_mv=_yi(ds.circ_mv) if ds else None,
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)
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candles = [
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CandleOut(ts=b.ts, open=b.open, high=b.high, low=b.low, close=b.close, volume=b.volume)
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for b in bars[-limit:]
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]
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return PreviewResponse(ts_code=ts_code, symbol=symbol, source=source, info=info, candles=candles, indicators=indicators)
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