feat: AI 自然语言选股（GLM）+ 全市场数据管道 + 远程 PostgreSQL
- 首页双入口（智能选股/策略回测）：引入 vue-router，顶部导航 - 智能选股：自然语言 -> LLM 解析结构化条件（智谱 GLM，OpenAI 兼容，/v4 兼容）-> SQL 快照预筛 + pandas 指标过滤（复用 indicators 单一事实源） - 条件模型：指标 vs 常数/指标（value_indicator，如 DIF>DEA、close<布林下轨）、lookback+match 表达连续N天/近N天任一天、市值/PE/PB/换手率快照条件、默认排除 ST/退市/北交所 - 全市场数据同步：按 trade_date 批量拉取未复权日线（与回测 candles qfq 隔离），交易日历/股票列表本地缓存，daily_basic 仅最新截面，Tushare 限频兜底（分钟级重试/小时级降级） - 存储：DATABASE_URL 切远程 PostgreSQL（cirry.cn/stock），本地 SQLite 已移除 - .env 入库（私有仓库）；smoke_test 扩展选股链路 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
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backend/.env
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10
backend/.env
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@@ -0,0 +1,10 @@
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DATABASE_URL=postgresql+asyncpg://postgres:Cirry0115@cirry.cn:5432/stock
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TUSHARE_TOKEN=d0bc5620d6523ae40f379ed4415576f58dca2361f2f47a68cdcd0a98
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DATA_ADJUST=qfq
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DATA_DEFAULT_START=20200101
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# ---- LLM（智能选股；智谱 GLM，OpenAI 兼容协议）----
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# key 在 https://bigmodel.cn 控制台获取，格式形如 xxxxxxxx.yyyyyyyy（id.secret）
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LLM_BASE_URL=https://open.bigmodel.cn/api/paas/v4
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LLM_API_KEY=ea24bbdd3d2d4dd2b8f03de4c9a5d984.9X1Hz1yKx0VKSnrU
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LLM_MODEL=glm-5.2
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@@ -10,6 +10,22 @@ TUSHARE_TOKEN=你的token
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DATA_ADJUST=qfq # 复权：qfq 前复权 / hfq 后复权 / 留空不复权
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DATA_DEFAULT_START=20200101
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# ---- LLM（智能选股；OpenAI 兼容协议，任选一家）----
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# 留空则智能选股不可用，其余功能不受影响。
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# 智谱 GLM（key 在 https://bigmodel.cn 获取，格式 id.secret）
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LLM_BASE_URL=https://open.bigmodel.cn/api/paas/v4
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LLM_API_KEY=你的key
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LLM_MODEL=glm-5.2
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# 或 DeepSeek：把上面三行换成
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# LLM_BASE_URL=https://api.deepseek.com
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# LLM_MODEL=deepseek-chat
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# ---- 智能选股（可选覆盖）----
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# SCREENER_MARKET_DAYS=90 # 全市场同步窗口（交易日数）
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# SCREENER_SYNC_INTERVAL=0.35 # 同步调用间隔（秒），Tushare 控频
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# ---- A股交易成本（基准日 2026-08，可覆盖；详见 app/commission.py）----
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# STAMP_DUTY_RATE=0.0005 # 印花税 0.05%，单边卖出
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# TRANSFER_FEE_RATE=0.00001 # 过户费 0.001%，沪深双边
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@@ -1,8 +1,11 @@
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"""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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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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@@ -14,6 +17,7 @@ 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.synthetic import seed_if_empty
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@@ -27,10 +31,17 @@ from .schemas import (
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EquityPoint,
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IndicatorOut,
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MetricsOut,
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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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@@ -165,3 +176,46 @@ async def backtest(
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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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@@ -15,6 +15,17 @@ class Settings(BaseSettings):
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data_adjust: str = "qfq" # 复权：qfq 前复权 / hfq 后复权 / "" 不复权
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data_default_start: str = "20200101" # 默认拉取起点（约近 5 年）
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# ---- LLM（智能选股的自然语言解析；DeepSeek，OpenAI 兼容协议，可换任意兼容网关）----
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llm_base_url: str = "https://api.deepseek.com"
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llm_api_key: str = "" # 留空则智能选股不可用（其余功能不受影响）
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llm_model: str = "deepseek-chat"
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llm_timeout: float = 60.0
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# ---- 智能选股 ----
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screener_market_days: int = 90 # 全市场同步窗口（交易日数）
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screener_default_limit: int = 200 # 选股结果条数上限
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screener_sync_interval: float = 0.35 # 全市场批量调用间隔（秒），Tushare 控频
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# A股交易成本（基准日 2026-08）——做成可配置参数，便于将来按生效日期版本化
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stamp_duty_rate: float = 0.0005 # 印花税 0.05%，单边卖出（2023-08-28 减半）
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transfer_fee_rate: float = 0.00001 # 过户费 0.001%，沪深双边（2022 调整）
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@@ -3,6 +3,9 @@
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Candle 表设计与 TimescaleDB hypertable 完全兼容：将来在目标 PG 库执行
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SELECT create_hypertable('candles', 'ts');
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即可升级为时序表 + Continuous Aggregates 多周期预聚合，无需改表结构。
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智能选股三表（stock_basic / market_daily / daily_snapshot）与回测 candles(qfq)
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完全隔离：选股用未复权日线按 trade_date 全市场批量落地，避免污染回测复权缓存。
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"""
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from datetime import datetime
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@@ -55,3 +58,75 @@ class BacktestRun(Base):
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max_drawdown: Mapped[float] = mapped_column(Float, default=0.0)
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sharpe: Mapped[float] = mapped_column(Float, default=0.0)
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num_trades: Mapped[int] = mapped_column(Integer, default=0)
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class StockBasic(Base):
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"""A股股票列表（stock_basic 快照；选股展示名称、排除 ST/退市/北交所的依据）。"""
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__tablename__ = "stock_basic"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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ts_code: Mapped[str] = mapped_column(String(12), unique=True, index=True) # 000001.SZ
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symbol: Mapped[str] = mapped_column(String(10), index=True) # 000001
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name: Mapped[str] = mapped_column(String(32))
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area: Mapped[str | None] = mapped_column(String(32))
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industry: Mapped[str | None] = mapped_column(String(32))
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market: Mapped[str | None] = mapped_column(String(32)) # 主板/创业板/科创板/北交所
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exchange: Mapped[str] = mapped_column(String(8)) # SSE/SZSE/BSE
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list_status: Mapped[str] = mapped_column(String(2), index=True) # L上市 D退市 P暂停
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list_date: Mapped[str] = mapped_column(String(8), default="")
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delist_date: Mapped[str | None] = mapped_column(String(8))
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class MarketDaily(Base):
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"""全市场未复权日线（选股专用，与回测 candles(qfq) 隔离）。
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单位沿用 Tushare 原始：vol 手、amount 千元。
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"""
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__tablename__ = "market_daily"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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trade_date: Mapped[datetime] = mapped_column(DateTime, index=True)
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ts_code: Mapped[str] = mapped_column(String(12), index=True)
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open: Mapped[float] = mapped_column(Float)
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high: Mapped[float] = mapped_column(Float)
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low: Mapped[float] = mapped_column(Float)
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close: Mapped[float] = mapped_column(Float)
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pre_close: Mapped[float] = mapped_column(Float)
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change: Mapped[float | None] = mapped_column(Float)
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pct_chg: Mapped[float | None] = mapped_column(Float) # 日涨跌幅 %
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vol: Mapped[float] = mapped_column(Float) # 手
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amount: Mapped[float] = mapped_column(Float) # 千元
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__table_args__ = (
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UniqueConstraint("ts_code", "trade_date", name="uq_mkt_code_date"),
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)
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class DailySnapshot(Base):
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"""每日指标快照（daily_basic）。total_mv/circ_mv 单位万元（Tushare 原始），API 层换算亿元。"""
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__tablename__ = "daily_snapshot"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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trade_date: Mapped[datetime] = mapped_column(DateTime, index=True)
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ts_code: Mapped[str] = mapped_column(String(12), index=True)
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close: Mapped[float | None] = mapped_column(Float)
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turnover_rate: Mapped[float | None] = mapped_column(Float) # 换手率 %
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turnover_rate_f: Mapped[float | None] = mapped_column(Float) # 自由流通换手率 %
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volume_ratio: Mapped[float | None] = mapped_column(Float) # 量比
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pe: Mapped[float | None] = mapped_column(Float)
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pe_ttm: Mapped[float | None] = mapped_column(Float)
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pb: Mapped[float | None] = mapped_column(Float)
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total_mv: Mapped[float | None] = mapped_column(Float) # 总市值（万元）
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circ_mv: Mapped[float | None] = mapped_column(Float) # 流通市值（万元）
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__table_args__ = (
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UniqueConstraint("ts_code", "trade_date", name="uq_snap_code_date"),
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)
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class TradeCalendar(Base):
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"""交易日历缓存（trade_cal 拉取一次宽范围后本地维护，低积分 token 限频 1 次/小时）。"""
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__tablename__ = "trade_calendar"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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trade_date: Mapped[str] = mapped_column(String(8), unique=True, index=True) # YYYYMMDD
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@@ -5,6 +5,7 @@
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from __future__ import annotations
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from datetime import datetime
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from typing import Literal
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from pydantic import BaseModel, Field
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@@ -85,3 +86,84 @@ class SyncResponse(BaseModel):
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symbol: str
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bars: int
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source: str
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# ---------- Screener（智能选股） ----------
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Op = Literal["gt", "ge", "lt", "le", "between"]
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class IndicatorCondition(BaseModel):
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"""技术指标条件（在最近 lookback 个交易日窗口内判定）。
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indicator 白名单见 screener/llm.py 的 SYSTEM_PROMPT（kdj_j / rsi / macd_dif…）。
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设置 value_indicator 时为指标间比较（如 DIF > DEA、close < boll_lower），value 填 0 占位。
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"""
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indicator: str
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params: dict[str, float] = Field(default_factory=dict) # 如 {"n": 9, "m1": 3, "m2": 3}
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op: Op
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value: float
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value2: float | None = None # between 上界
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value_indicator: str | None = None # 比较对象为另一指标（同白名单）时使用
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value_params: dict[str, float] = Field(default_factory=dict) # 比较对象指标参数（默认沿用 params/默认值）
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lookback: int = 1 # 检查最近 N 个交易日
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match: Literal["all", "any"] = "all" # all=连续满足；any=任一满足
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class SnapshotCondition(BaseModel):
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"""每日快照条件（最新交易日截面）。市值单位亿元；换手率为百分数（5 表示 5%）。"""
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field: str # total_mv|circ_mv|pe_ttm|pb|turnover_rate|close
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op: Op
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value: float
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value2: float | None = None
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class ScreenConditions(BaseModel):
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indicator: list[IndicatorCondition] = Field(default_factory=list)
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snapshot: list[SnapshotCondition] = Field(default_factory=list)
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exclude_st: bool = True
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exclude_delisted: bool = True
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exclude_bj: bool = True # 排除北交所
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class ScreenerRunRequest(BaseModel):
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text: str = Field(min_length=2, max_length=500)
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# 直传条件则跳过 LLM 解析（预留给"微调再跑"）
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conditions: ScreenConditions | None = None
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class ScreenerItemOut(BaseModel):
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ts_code: str
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name: str
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close: float | None = None # 最新收盘价（元）
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pct_chg: float | None = None # 日涨跌幅 %
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total_mv: float | None = None # 总市值（亿元）
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circ_mv: float | None = None # 流通市值（亿元）
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pe_ttm: float | None = None
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pb: float | None = None
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turnover_rate: float | None = None
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indicators: dict[str, float | None] = Field(default_factory=dict) # 引用到的指标最新值
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class ScreenerRunResponse(BaseModel):
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conditions: ScreenConditions
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trade_date: datetime | None # 数据基准交易日
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total: int # 命中总数（items 可能被截断）
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items: list[ScreenerItemOut]
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indicator_labels: dict[str, str] = Field(default_factory=dict) # "kdj_j" -> "KDJ J(9,3,3)"
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class ScreenerSyncRequest(BaseModel):
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days: int = Field(default=90, ge=10, le=250) # 同步最近 N 个交易日
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force: bool = False # True => 全量重拉（幂等）
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class ScreenerSyncStatus(BaseModel):
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running: bool
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step: str | None = None # 进行中步骤文案
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total_days: int = 0
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done_days: int = 0
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error: str | None = None
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ready: bool = False # 至少 1 个交易日数据可用于选股
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last_trade_date: datetime | None = None
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last_synced_at: datetime | None = None
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stats: dict[str, int] = Field(default_factory=dict) # stocks/daily_rows/snapshot_rows/dates
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0
backend/app/screener/__init__.py
Normal file
0
backend/app/screener/__init__.py
Normal file
370
backend/app/screener/engine.py
Normal file
370
backend/app/screener/engine.py
Normal file
@@ -0,0 +1,370 @@
|
||||
"""选股执行引擎：SQL 快照预筛缩小范围 -> 逐股指标计算过滤。
|
||||
|
||||
性能：预筛在 SQLite 索引上完成（毫秒级）；指标阶段候选集通常数百~数千只 × ~90 根 bar，
|
||||
pandas 逐股计算（复用 app/indicators，指标按 (族, 参数) 去重计算），秒级完成。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from typing import Callable
|
||||
|
||||
import pandas as pd
|
||||
from sqlalchemy import and_, func, not_, or_, select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from .. import indicators as ind
|
||||
from ..models import DailySnapshot, MarketDaily, StockBasic
|
||||
from ..schemas import IndicatorCondition, ScreenConditions, SnapshotCondition
|
||||
|
||||
# 快照字段 -> (DB 列, 中文标签, LLM 值 -> DB 值的换算乘数)
|
||||
# 单位约定：LLM 市值亿元 -> DB 万元 ×1e4；其余（pe/pb/换手率%）直存
|
||||
SNAPSHOT_FIELDS: dict[str, tuple[str, str, float]] = {
|
||||
"total_mv": ("total_mv", "总市值(亿)", 1e4),
|
||||
"circ_mv": ("circ_mv", "流通市值(亿)", 1e4),
|
||||
"pe_ttm": ("pe_ttm", "市盈率TTM", 1.0),
|
||||
"pb": ("pb", "市净率", 1.0),
|
||||
"turnover_rate": ("turnover_rate", "换手率%", 1.0),
|
||||
"close": ("close", "最新价", 1.0), # 实际取 market_daily.close，无需快照表
|
||||
}
|
||||
|
||||
|
||||
class DataNotReadyError(RuntimeError):
|
||||
"""全市场数据未同步/不完整，选股无法执行。"""
|
||||
|
||||
|
||||
# ---------- 指标族注册表（复用 app/indicators，单一事实源） ----------
|
||||
|
||||
def _kdj(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
out = ind.kdj(df["high"], df["low"], df["close"], n=int(p["n"]), m1=int(p["m1"]), m2=int(p["m2"]))
|
||||
return {"kdj_k": out["k"], "kdj_d": out["d"], "kdj_j": out["j"]}
|
||||
|
||||
|
||||
def _macd(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
out = ind.macd(df["close"], fast=int(p["fast"]), slow=int(p["slow"]), signal=int(p["signal"]))
|
||||
return {"macd_dif": out["macd"], "macd_dea": out["signal"], "macd_hist": out["hist"]}
|
||||
|
||||
|
||||
def _rsi(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
return {"rsi": ind.rsi(df["close"], period=int(p["period"]))}
|
||||
|
||||
|
||||
def _ma(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
return {"ma": ind.ma(df["close"], period=int(p["period"]))}
|
||||
|
||||
|
||||
def _boll(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
out = ind.bollinger(df["close"], period=int(p["period"]), std=float(p["std"]))
|
||||
return {"boll_upper": out["upper"], "boll_mid": out["mid"], "boll_lower": out["lower"]}
|
||||
|
||||
|
||||
def _close(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
return {"close": df["close"]}
|
||||
|
||||
|
||||
def _pct_chg(df: pd.DataFrame, p: dict) -> dict[str, pd.Series]:
|
||||
return {"pct_chg": df["pct_chg"]}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class FamilyDef:
|
||||
label: str # 族中文标签（条件回显/表头）
|
||||
param_names: tuple[str, ...] # 接受的参数名
|
||||
defaults: dict[str, float]
|
||||
min_bars: int # 指标可信所需最小 bar 数（近似）
|
||||
compute: Callable[[pd.DataFrame, dict], dict[str, pd.Series]]
|
||||
|
||||
|
||||
FAMILIES: dict[str, FamilyDef] = {
|
||||
"kdj": FamilyDef("KDJ", ("n", "m1", "m2"), {"n": 9, "m1": 3, "m2": 3}, 30, _kdj),
|
||||
"macd": FamilyDef("MACD", ("fast", "slow", "signal"), {"fast": 12, "slow": 26, "signal": 9}, 60, _macd),
|
||||
"rsi": FamilyDef("RSI", ("period",), {"period": 14}, 25, _rsi),
|
||||
"ma": FamilyDef("MA", ("period",), {"period": 20}, 25, _ma),
|
||||
"boll": FamilyDef("BOLL", ("period", "std"), {"period": 20, "std": 2}, 25, _boll),
|
||||
"close": FamilyDef("收盘价", (), {}, 1, _close),
|
||||
"pct_chg": FamilyDef("日涨跌幅", (), {}, 1, _pct_chg),
|
||||
}
|
||||
|
||||
INDICATOR_FAMILY: dict[str, str] = {
|
||||
"kdj_k": "kdj", "kdj_d": "kdj", "kdj_j": "kdj",
|
||||
"macd_dif": "macd", "macd_dea": "macd", "macd_hist": "macd",
|
||||
"rsi": "rsi", "ma": "ma",
|
||||
"boll_upper": "boll", "boll_mid": "boll", "boll_lower": "boll",
|
||||
"close": "close", "pct_chg": "pct_chg",
|
||||
}
|
||||
|
||||
_IND_SUFFIX = {"kdj_k": "K", "kdj_d": "D", "kdj_j": "J",
|
||||
"macd_dif": "DIF", "macd_dea": "DEA", "macd_hist": "柱",
|
||||
"boll_upper": "上轨", "boll_mid": "中轨", "boll_lower": "下轨"}
|
||||
|
||||
|
||||
def _family_of(indicator: str) -> str:
|
||||
fam = INDICATOR_FAMILY.get(indicator)
|
||||
if not fam:
|
||||
raise ValueError(f"未知指标: {indicator}（白名单见 screener/llm.py）")
|
||||
return fam
|
||||
|
||||
|
||||
def _params_for(indicator: str, params: dict, extra: dict | None = None) -> dict:
|
||||
"""按指标族过滤参数：extra（如 value_params）优先，其次 params，最后默认值。"""
|
||||
fam = FAMILIES[_family_of(indicator)]
|
||||
merged = {**{k: v for k, v in params.items() if k in fam.param_names},
|
||||
**{k: v for k, v in (extra or {}).items() if k in fam.param_names}}
|
||||
return {**fam.defaults, **merged}
|
||||
|
||||
|
||||
def _resolve_params(cond: IndicatorCondition, target: str) -> dict:
|
||||
"""条件里 value_indicator 目标的参数：value_params > params（按目标族过滤）> 默认。"""
|
||||
return _params_for(target, cond.params, cond.value_params)
|
||||
|
||||
|
||||
def _params_str(p: dict) -> str:
|
||||
vals = [str(int(v)) if float(v).is_integer() else str(v) for v in p.values()]
|
||||
return f"({','.join(vals)})" if vals else ""
|
||||
|
||||
|
||||
def indicator_label(indicator: str, params: dict) -> str:
|
||||
"""指标展示名，如 'KDJ J(9,3,3)' / 'MA(20)' / '收盘价'。"""
|
||||
fam = FAMILIES[_family_of(indicator)]
|
||||
suffix = _IND_SUFFIX.get(indicator)
|
||||
name = f"{fam.label} {suffix}".strip() if suffix else fam.label
|
||||
return name + _params_str(params)
|
||||
|
||||
|
||||
# ---------- 指标序列获取（按 (族, 参数) 去重计算） ----------
|
||||
|
||||
def _series_for(df_stock: pd.DataFrame, indicator: str, params: dict, cache: dict) -> pd.Series | None:
|
||||
"""获取单股某指标序列。cache: {"_families": set[(族, 参数键)], (指标名, 参数键): Series}。
|
||||
|
||||
同族同参数只计算一次（如 KDJ 三个值共享一次计算）；MA5/MA20 则按参数各自计算。
|
||||
"""
|
||||
fam = _family_of(indicator)
|
||||
pk = tuple(sorted(params.items()))
|
||||
fk = (fam, pk)
|
||||
if fk not in cache["_families"]:
|
||||
for name, s in FAMILIES[fam].compute(df_stock, params).items():
|
||||
cache[(name, pk)] = s
|
||||
cache["_families"].add(fk)
|
||||
return cache.get((indicator, pk))
|
||||
|
||||
|
||||
def _op_mask(s: pd.Series, other: pd.Series, cond: IndicatorCondition) -> pd.Series:
|
||||
"""按 op 生成布尔掩码（NaN -> False）。"""
|
||||
if cond.op == "gt":
|
||||
mask = s > other
|
||||
elif cond.op == "ge":
|
||||
mask = s >= other
|
||||
elif cond.op == "lt":
|
||||
mask = s < other
|
||||
elif cond.op == "le":
|
||||
mask = s <= other
|
||||
elif cond.op == "between":
|
||||
hi = cond.value2 if cond.value2 is not None else cond.value
|
||||
mask = (s >= cond.value) & (s <= hi)
|
||||
else:
|
||||
raise ValueError(f"未知 op: {cond.op}")
|
||||
return mask.fillna(False).astype(bool)
|
||||
|
||||
|
||||
def _eval_condition(df_stock: pd.DataFrame, cond: IndicatorCondition,
|
||||
cache: dict) -> tuple[bool, float | None]:
|
||||
"""在单股 df 上判定条件；返回 (是否命中, 指标最新值)。数据不足视为不满足。"""
|
||||
fam = _family_of(cond.indicator)
|
||||
if len(df_stock) < max(FAMILIES[fam].min_bars, cond.lookback):
|
||||
return False, None
|
||||
|
||||
s = _series_for(df_stock, cond.indicator, _params_for(cond.indicator, cond.params), cache)
|
||||
if s is None:
|
||||
return False, None
|
||||
|
||||
if cond.value_indicator:
|
||||
target = _series_for(df_stock, cond.value_indicator,
|
||||
_resolve_params(cond, cond.value_indicator), cache)
|
||||
if target is None:
|
||||
return False, None
|
||||
else:
|
||||
target = pd.Series(cond.value, index=s.index)
|
||||
|
||||
window = _op_mask(s, target, cond).tail(cond.lookback)
|
||||
hit = bool(window.all()) if cond.match == "all" else bool(window.any())
|
||||
latest = s.iloc[-1]
|
||||
return hit, (None if latest != latest else float(latest))
|
||||
|
||||
|
||||
# ---------- SQL 预筛 ----------
|
||||
|
||||
def _snapshot_clause(cond: SnapshotCondition):
|
||||
"""单条快照条件 -> SQLAlchemy 表达式（快照缺失的股 NULL 比较自然不满足）。"""
|
||||
if cond.field not in SNAPSHOT_FIELDS:
|
||||
raise ValueError(f"未知快照字段: {cond.field}")
|
||||
col_name, _, scale = SNAPSHOT_FIELDS[cond.field]
|
||||
col = MarketDaily.close if cond.field == "close" else getattr(DailySnapshot, col_name)
|
||||
lo = cond.value * scale
|
||||
hi = (cond.value2 * scale) if cond.op == "between" and cond.value2 is not None else None
|
||||
if cond.op == "gt":
|
||||
return col > lo
|
||||
if cond.op == "ge":
|
||||
return col >= lo
|
||||
if cond.op == "lt":
|
||||
return col < lo
|
||||
if cond.op == "le":
|
||||
return col <= lo
|
||||
if hi is not None:
|
||||
return and_(col >= lo, col <= hi)
|
||||
raise ValueError(f"未知 op: {cond.op}")
|
||||
|
||||
|
||||
_CAND_COLS = ["ts_code", "name", "close", "pct_chg",
|
||||
"total_mv", "circ_mv", "pe_ttm", "pb", "turnover_rate"]
|
||||
|
||||
|
||||
async def _prefilter(session: AsyncSession, conds: ScreenConditions, target_date: datetime) -> pd.DataFrame:
|
||||
"""最新交易日截面预筛：快照条件 + 排除项 + 名称/收盘价。返回候选 DataFrame。"""
|
||||
stmt = (
|
||||
select(
|
||||
MarketDaily.ts_code, StockBasic.name, MarketDaily.close, MarketDaily.pct_chg,
|
||||
DailySnapshot.total_mv, DailySnapshot.circ_mv,
|
||||
DailySnapshot.pe_ttm, DailySnapshot.pb, DailySnapshot.turnover_rate,
|
||||
)
|
||||
.join(StockBasic, StockBasic.ts_code == MarketDaily.ts_code)
|
||||
.outerjoin(DailySnapshot, and_(DailySnapshot.ts_code == MarketDaily.ts_code,
|
||||
DailySnapshot.trade_date == target_date))
|
||||
.where(MarketDaily.trade_date == target_date)
|
||||
)
|
||||
|
||||
if conds.exclude_delisted:
|
||||
stmt = stmt.where(StockBasic.list_status == "L")
|
||||
if conds.exclude_st:
|
||||
stmt = stmt.where(not_(or_(StockBasic.name.like("%ST%"), StockBasic.name.like("%退%"))))
|
||||
if conds.exclude_bj:
|
||||
stmt = stmt.where(not_(MarketDaily.ts_code.like("%.BJ")))
|
||||
|
||||
for c in conds.snapshot:
|
||||
stmt = stmt.where(_snapshot_clause(c))
|
||||
|
||||
rows = (await session.execute(stmt)).all()
|
||||
return pd.DataFrame(rows, columns=_CAND_COLS)
|
||||
|
||||
|
||||
# ---------- 主流程 ----------
|
||||
|
||||
def _max_needed_bars(conds: ScreenConditions) -> int:
|
||||
"""指标阶段需要的最大 bar 数（min_bars + lookback），用于圈定 K 线窗口。"""
|
||||
need = 1
|
||||
for c in conds.indicator:
|
||||
need = max(need, FAMILIES[_family_of(c.indicator)].min_bars + c.lookback)
|
||||
if c.value_indicator:
|
||||
need = max(need, FAMILIES[_family_of(c.value_indicator)].min_bars + c.lookback)
|
||||
return need
|
||||
|
||||
|
||||
async def _load_bars(session: AsyncSession, ts_codes: list[str],
|
||||
target_date: datetime, min_date: datetime) -> pd.DataFrame:
|
||||
"""载入候选股的 K 线窗口。候选 <= 2000 用 IN 精确圈定；否则拉全窗口再 pandas 过滤。"""
|
||||
stmt = select(
|
||||
MarketDaily.ts_code, MarketDaily.trade_date, MarketDaily.open, MarketDaily.high,
|
||||
MarketDaily.low, MarketDaily.close, MarketDaily.pct_chg,
|
||||
).where(MarketDaily.trade_date >= min_date, MarketDaily.trade_date <= target_date)
|
||||
if len(ts_codes) <= 2000:
|
||||
stmt = stmt.where(MarketDaily.ts_code.in_(set(ts_codes)))
|
||||
rows = (await session.execute(stmt)).all()
|
||||
df = pd.DataFrame(rows, columns=["ts_code", "trade_date", "open", "high", "low", "close", "pct_chg"])
|
||||
if not df.empty and len(ts_codes) > 2000:
|
||||
df = df[df["ts_code"].isin(set(ts_codes))]
|
||||
return df.sort_values(["ts_code", "trade_date"]).reset_index(drop=True)
|
||||
|
||||
|
||||
def _f(v) -> float | None:
|
||||
"""None/NaN 安全转 float|None。"""
|
||||
if v is None:
|
||||
return None
|
||||
v = float(v)
|
||||
return None if v != v else v
|
||||
|
||||
|
||||
def _yi(v) -> float | None:
|
||||
"""万元 -> 亿元（None/NaN 安全，保留两位）。"""
|
||||
v = _f(v)
|
||||
return None if v is None else round(v / 1e4, 2)
|
||||
|
||||
|
||||
def _item_from_row(row) -> dict:
|
||||
return {
|
||||
"ts_code": row["ts_code"], "name": row["name"],
|
||||
"close": _f(row["close"]), "pct_chg": _f(row["pct_chg"]),
|
||||
"total_mv": _yi(row["total_mv"]), "circ_mv": _yi(row["circ_mv"]),
|
||||
"pe_ttm": _f(row["pe_ttm"]), "pb": _f(row["pb"]),
|
||||
"turnover_rate": _f(row["turnover_rate"]),
|
||||
}
|
||||
|
||||
|
||||
async def run_screen(session: AsyncSession, conds: ScreenConditions, limit: int) -> dict:
|
||||
"""主流程：预筛 -> 逐股指标过滤 -> 组装 items + indicator_labels。"""
|
||||
if not conds.indicator and not conds.snapshot:
|
||||
raise ValueError("筛选条件为空")
|
||||
|
||||
target_date = await session.scalar(select(func.max(MarketDaily.trade_date)))
|
||||
if target_date is None:
|
||||
raise DataNotReadyError("全市场数据未同步：请先在选股页点击「同步市场数据」")
|
||||
|
||||
if any(c.field != "close" for c in conds.snapshot):
|
||||
if await session.scalar(select(func.max(DailySnapshot.trade_date))) is None:
|
||||
raise DataNotReadyError(
|
||||
"每日指标数据缺失（可能是 Tushare 积分不足，daily_basic 接口不可用）："
|
||||
"市值/市盈率等条件无法使用，纯指标条件不受影响"
|
||||
)
|
||||
|
||||
cand = await _prefilter(session, conds, target_date)
|
||||
|
||||
labels: dict[str, str] = {}
|
||||
for c in conds.indicator:
|
||||
labels.setdefault(c.indicator, indicator_label(c.indicator, _params_for(c.indicator, c.params)))
|
||||
if c.value_indicator:
|
||||
labels.setdefault(c.value_indicator,
|
||||
indicator_label(c.value_indicator, _resolve_params(c, c.value_indicator)))
|
||||
|
||||
items: list[dict] = []
|
||||
if cand.empty:
|
||||
pass
|
||||
elif not conds.indicator:
|
||||
# 纯快照条件：预筛结果即命中
|
||||
items = [_item_from_row(row) | {"indicators": {}} for _, row in cand.iterrows()]
|
||||
else:
|
||||
# 圈定 K 线窗口：按已同步交易日序列回溯 needed 根
|
||||
need = _max_needed_bars(conds)
|
||||
dates_res = await session.execute(
|
||||
select(MarketDaily.trade_date).distinct().order_by(MarketDaily.trade_date.desc()).limit(need)
|
||||
)
|
||||
min_date = min(r[0] for r in dates_res)
|
||||
bars = await _load_bars(session, cand["ts_code"].tolist(), target_date, min_date)
|
||||
cand_rows = {r["ts_code"]: r for _, r in cand.iterrows()}
|
||||
|
||||
for ts_code, g in bars.groupby("ts_code", sort=False):
|
||||
row = cand_rows.get(ts_code)
|
||||
if row is None:
|
||||
continue
|
||||
cache: dict = {"_families": set()}
|
||||
ok = True
|
||||
ind_values: dict[str, float | None] = {}
|
||||
for c in conds.indicator:
|
||||
hit, latest = _eval_condition(g, c, cache)
|
||||
if not hit:
|
||||
ok = False
|
||||
break
|
||||
if c.indicator not in ind_values:
|
||||
ind_values[c.indicator] = latest
|
||||
if c.value_indicator and c.value_indicator not in ind_values:
|
||||
s = cache.get((c.value_indicator, tuple(sorted(_resolve_params(c, c.value_indicator).items()))))
|
||||
ind_values[c.value_indicator] = None if s is None or s.iloc[-1] != s.iloc[-1] else float(s.iloc[-1])
|
||||
if ok:
|
||||
items.append(_item_from_row(row) | {"indicators": ind_values})
|
||||
|
||||
# 默认总市值降序（缺失排最后），截断 limit
|
||||
items.sort(key=lambda x: (x["total_mv"] is None, -(x["total_mv"] or 0)))
|
||||
return {
|
||||
"conditions": conds,
|
||||
"trade_date": target_date,
|
||||
"total": len(items),
|
||||
"items": items[:limit],
|
||||
"indicator_labels": labels,
|
||||
}
|
||||
151
backend/app/screener/llm.py
Normal file
151
backend/app/screener/llm.py
Normal file
@@ -0,0 +1,151 @@
|
||||
"""LLM 条件解析器（DeepSeek，OpenAI 兼容 /chat/completions）。
|
||||
|
||||
把自然语言选股需求解析成 ScreenConditions（结构化 JSON）。
|
||||
- response_format=json_object + temperature=0.1 保证结构稳定
|
||||
- 解析/校验失败带错误重试 1 次
|
||||
- 上游错误信息透传给前端（ScreenerError）
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
|
||||
import httpx
|
||||
|
||||
from ..config import settings
|
||||
from ..schemas import ScreenConditions
|
||||
|
||||
SYSTEM_PROMPT = """你是 A 股选股条件解析器。把用户的自然语言解析成一个 JSON 对象，只输出 JSON，不要任何解释、注释或代码块围栏。完全无法理解时输出 {"error": "原因"}。
|
||||
|
||||
输出结构：
|
||||
{"indicator": [...], "snapshot": [...], "exclude_st": true, "exclude_delisted": true, "exclude_bj": true}
|
||||
（indicator 与 snapshot 至少一个非空；用户没有提到的条件不要编造）
|
||||
|
||||
【indicator 数组】技术指标条件，元素字段：
|
||||
- "indicator": 指标名，白名单：kdj_k / kdj_d / kdj_j（KDJ 的 K/D/J 值）、rsi、macd_dif / macd_dea / macd_hist（MACD 的 DIF/DEA/柱）、ma（收盘价均线）、boll_upper / boll_mid / boll_lower（布林轨道）、close（收盘价）、pct_chg（日涨跌幅%）
|
||||
- "params": 指标参数（可选），默认：KDJ {"n":9,"m1":3,"m2":3}；RSI {"period":14}；MACD {"fast":12,"slow":26,"signal":9}；MA {"period":20}；BOLL {"period":20,"std":2}
|
||||
- "op": "gt" | "ge" | "lt" | "le" | "between"
|
||||
- "value": 比较数值（between 时为下界），"value2": between 上界
|
||||
- "value_indicator": 可选。指标与指标比较时填另一指标名（同白名单），如 "DIF大于DEA" -> indicator=macd_dif, op=gt, value_indicator=macd_dea, value=0；"股价在布林带下轨之下" -> indicator=close, op=lt, value_indicator=boll_lower, value=0
|
||||
- "value_params": 可选。比较对象指标需要不同参数时指定，如 "MA5 上穿 MA20" -> indicator=ma, params={"period":5}, op=gt, value_indicator=ma, value_params={"period":20}, value=0；不填则比较对象沿用 params 中适用于它的参数或默认参数
|
||||
- "lookback": 检查最近 N 个交易日（默认 1）
|
||||
- "match": "all"（窗口内每天满足，默认）或 "any"（窗口内任一天满足）
|
||||
|
||||
【snapshot 数组】最新交易日截面条件，元素字段：
|
||||
- "field": 白名单：total_mv（总市值）、circ_mv（流通市值）、pe_ttm（市盈率TTM）、pb（市净率）、turnover_rate（换手率）、close（最新价）
|
||||
- "op"/"value"/"value2" 同上
|
||||
|
||||
【单位约定】市值条件统一用亿元（如"市值大于100亿，小于200亿"→ between 100~200）；换手率用百分数值（"换手率大于5%"→ value 5）；价格类用元；pe/pb 用倍数。
|
||||
|
||||
【时间语义】"今天/今日"→ lookback=1；"这两天/最近N天/连续N日"→ lookback=N 且 match="all"；"近N日内曾经/任一天"→ lookback=N 且 match="any"。只支持以最新交易日为终点的窗口，不要生成具体某一天的条件。
|
||||
|
||||
【排除规则】默认 exclude_st=true、exclude_delisted=true、exclude_bj=true；用户明确说"包含北交所/包含ST"时才把对应项设为 false。
|
||||
|
||||
【交叉类表述的近似】"MACD金叉/刚金叉"用 macd_dif gt macd_dea（value_indicator）+ 适当 lookback/match 近似；"跌破均线"用 close lt ma 近似；无法近似表达的复杂条件直接忽略，保留可表达的部分。
|
||||
|
||||
示例1：
|
||||
输入：帮我找出这两天 KDJ 中的 J 小于 10，市值大于 100 亿，小于 200 亿的公司
|
||||
输出：{"indicator":[{"indicator":"kdj_j","params":{"n":9,"m1":3,"m2":3},"op":"lt","value":10,"lookback":2,"match":"all"}],"snapshot":[{"field":"total_mv","op":"between","value":100,"value2":200}],"exclude_st":true,"exclude_delisted":true,"exclude_bj":true}
|
||||
|
||||
示例2：
|
||||
输入：RSI 低于 30，市盈率 TTM 小于 20 的公司
|
||||
输出：{"indicator":[{"indicator":"rsi","params":{"period":14},"op":"lt","value":30,"lookback":1,"match":"all"}],"snapshot":[{"field":"pe_ttm","op":"lt","value":20}],"exclude_st":true,"exclude_delisted":true,"exclude_bj":true}
|
||||
|
||||
示例3：
|
||||
输入：近 5 天曾经 MACD 金叉（DIF 上穿 DEA），换手率大于 5%，流通市值小于 100 亿
|
||||
输出：{"indicator":[{"indicator":"macd_dif","params":{"fast":12,"slow":26,"signal":9},"op":"gt","value":0,"value_indicator":"macd_dea","lookback":5,"match":"any"}],"snapshot":[{"field":"turnover_rate","op":"gt","value":5},{"field":"circ_mv","op":"lt","value":100}],"exclude_st":true,"exclude_delisted":true,"exclude_bj":true}"""
|
||||
|
||||
|
||||
class ScreenerError(RuntimeError):
|
||||
"""选股链路可预期的业务错误（信息可直接透传给前端）。"""
|
||||
|
||||
|
||||
def _endpoint(base_url: str) -> str:
|
||||
"""归一化 base_url -> 完整 chat/completions URL。
|
||||
|
||||
兼容多种写法：DeepSeek/OpenAI 的 .../v1、智谱 GLM 的 .../v4、或直接给完整路径。
|
||||
"""
|
||||
base = base_url.rstrip("/")
|
||||
if base.endswith("/chat/completions"):
|
||||
return base
|
||||
if not re.search(r"/v\d+$", base): # 未带版本段则补 /v1（DeepSeek/OpenAI 惯例）
|
||||
base += "/v1"
|
||||
return f"{base}/chat/completions"
|
||||
|
||||
|
||||
def _extract_json(content: str) -> dict:
|
||||
"""从 LLM 输出提取 JSON：剥代码围栏，或取首 { 到末 } 的子串。"""
|
||||
text = content.strip()
|
||||
if text.startswith("```"):
|
||||
# 剥 ```json ... ``` 围栏
|
||||
text = text.split("```", 2)[1]
|
||||
if text.startswith("json"):
|
||||
text = text[4:]
|
||||
text = text.strip()
|
||||
if not text.startswith("{"):
|
||||
start, end = text.find("{"), text.rfind("}")
|
||||
if start < 0 or end <= start:
|
||||
raise ValueError("输出中不含 JSON 对象")
|
||||
text = text[start : end + 1]
|
||||
obj = json.loads(text)
|
||||
if not isinstance(obj, dict):
|
||||
raise ValueError("JSON 不是对象")
|
||||
return obj
|
||||
|
||||
|
||||
def _build_messages(text: str, retry_error: str | None = None) -> list[dict]:
|
||||
"""构造 system + user 消息；retry 时附上一次解析错误要求修正。"""
|
||||
user = f"解析以下选股需求：{text}"
|
||||
if retry_error:
|
||||
user += f"\n\n上一次输出无法通过校验，错误：{retry_error}。请修正后重新只输出 JSON。"
|
||||
return [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": user}]
|
||||
|
||||
|
||||
async def _chat(messages: list[dict]) -> str:
|
||||
"""调 OpenAI 兼容接口，返回 assistant 文本。上游错误抛 ScreenerError。"""
|
||||
body = {
|
||||
"model": settings.llm_model,
|
||||
"messages": messages,
|
||||
"temperature": 0.1,
|
||||
"response_format": {"type": "json_object"},
|
||||
"max_tokens": 2000,
|
||||
}
|
||||
headers = {"Authorization": f"Bearer {settings.llm_api_key}"}
|
||||
async with httpx.AsyncClient(timeout=settings.llm_timeout) as client:
|
||||
try:
|
||||
r = await client.post(_endpoint(settings.llm_base_url), json=body, headers=headers)
|
||||
except httpx.HTTPError as e: # 网络/超时
|
||||
raise ScreenerError(f"LLM 服务无法访问（{settings.llm_base_url}）: {e}") from e
|
||||
if r.status_code >= 400:
|
||||
detail = ""
|
||||
try:
|
||||
detail = r.json().get("error", {}).get("message", "")
|
||||
except Exception: # noqa: BLE001
|
||||
detail = r.text[:200]
|
||||
raise ScreenerError(f"LLM 接口错误 (HTTP {r.status_code}): {detail or '无详细信息'}")
|
||||
try:
|
||||
return r.json()["choices"][0]["message"]["content"] or ""
|
||||
except (KeyError, IndexError, TypeError) as e:
|
||||
raise ScreenerError(f"LLM 返回结构异常: {r.text[:200]}") from e
|
||||
|
||||
|
||||
async def parse_conditions(text: str) -> ScreenConditions:
|
||||
"""主入口：自然语言 -> ScreenConditions。未配 key / 解析两次失败抛 ScreenerError。"""
|
||||
if not settings.llm_api_key:
|
||||
raise ScreenerError(
|
||||
"未配置 LLM_API_KEY：请在 backend/.env 填入 DeepSeek API Key（platform.deepseek.com 获取）后重启后端"
|
||||
)
|
||||
|
||||
retry_error: str | None = None
|
||||
for _ in range(2): # 首次 + 失败重试 1 次
|
||||
content = await _chat(_build_messages(text, retry_error))
|
||||
try:
|
||||
obj = _extract_json(content)
|
||||
if "error" in obj and not obj.get("indicator") and not obj.get("snapshot"):
|
||||
raise ScreenerError(f"AI 无法理解该选股需求：{obj['error']}")
|
||||
return ScreenConditions.model_validate(obj)
|
||||
except ScreenerError:
|
||||
raise
|
||||
except Exception as e: # noqa: BLE001 —— JSON/校验失败，带错误重试
|
||||
retry_error = str(e)[:300]
|
||||
raise ScreenerError(f"AI 解析结果两次未通过校验，最后错误：{retry_error}")
|
||||
299
backend/app/screener/market_sync.py
Normal file
299
backend/app/screener/market_sync.py
Normal file
@@ -0,0 +1,299 @@
|
||||
"""全市场数据同步（选股专用，未复权；与回测 candles 表隔离）。
|
||||
|
||||
设计：trade_cal 取近 N 个交易日 -> 逐日 pro.daily(trade_date=...) / pro.daily_basic(trade_date=...)
|
||||
一次返回全市场当日数据 -> 按 trade_date 删旧插新批量入库（幂等）。
|
||||
同步为进程内后台任务（MVP 不引入任务队列），前端轮询 /api/screener/sync/status。
|
||||
|
||||
daily 与 daily_basic 分步独立落库：daily_basic 积分不足时日线仍可用，错误写入状态不中断任务。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from sqlalchemy import delete, func, insert, select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from ..config import settings
|
||||
from ..models import DailySnapshot, MarketDaily, StockBasic, TradeCalendar
|
||||
from .llm import ScreenerError
|
||||
|
||||
# 进程内单例任务状态（uvicorn --reload 单进程场景够用）
|
||||
_sync_state: dict = {
|
||||
"running": False,
|
||||
"step": None,
|
||||
"total_days": 0,
|
||||
"done_days": 0,
|
||||
"error": None,
|
||||
"started_at": None,
|
||||
"finished_at": None,
|
||||
}
|
||||
_sync_task: asyncio.Task | None = None
|
||||
_sync_lock = asyncio.Lock()
|
||||
|
||||
_BATCH = 5000 # executemany 分批行数
|
||||
|
||||
# Tushare 积分/权限不足的特征文案（daily_basic 常见门槛）
|
||||
_PERM_MARKS = ("抱歉，您没有访问该项目权限", "积分", "权限")
|
||||
# 频率超限特征（等待 62s 重试一次）
|
||||
_RATE_MARKS = ("频率超限", "每分钟")
|
||||
|
||||
|
||||
def _call_retry(fn, *args, **kwargs):
|
||||
"""同步调用 tushare 接口；「每分钟」级频率超限等 62s 重试一次（小时级限频直接抛）。"""
|
||||
try:
|
||||
return fn(*args, **kwargs)
|
||||
except Exception as e: # noqa: BLE001
|
||||
msg = str(e)
|
||||
if any(m in msg for m in _RATE_MARKS) and "小时" not in msg:
|
||||
time.sleep(62)
|
||||
return fn(*args, **kwargs)
|
||||
raise
|
||||
|
||||
|
||||
def _get_pro():
|
||||
"""token 检查 + 返回 pro api 客户端（同步对象，调用需 to_thread 包裹）。"""
|
||||
if not settings.tushare_token:
|
||||
raise ScreenerError("未配置 TUSHARE_TOKEN，无法同步全市场数据（backend/.env）")
|
||||
import tushare as ts
|
||||
|
||||
ts.set_token(settings.tushare_token)
|
||||
return ts.pro_api()
|
||||
|
||||
|
||||
def _parse_d(s: str) -> datetime:
|
||||
return datetime.strptime(str(s), "%Y%m%d")
|
||||
|
||||
|
||||
def _fetch_calendar_sync(pro) -> list[str]:
|
||||
"""拉取宽范围交易日历（近 18 个月 + 未来 3 个月），返回 YYYYMMDD 列表。"""
|
||||
time.sleep(settings.screener_sync_interval)
|
||||
end = (datetime.now() + timedelta(days=90)).strftime("%Y%m%d")
|
||||
start = (datetime.now() - timedelta(days=550)).strftime("%Y%m%d")
|
||||
cal = _call_retry(pro.trade_cal, exchange="SSE", start_date=start, end_date=end, is_open="1")
|
||||
return sorted(cal["cal_date"].tolist())
|
||||
|
||||
|
||||
async def _recent_trade_dates(session: AsyncSession, pro, days: int) -> list[str]:
|
||||
"""近 N 个交易日（YYYYMMDD，倒序）。日历本地缓存，仅在覆盖不到当天时刷新一次。
|
||||
|
||||
trade_cal 低积分版限频 1 次/小时：刷新被限频时沿用缓存（日历略旧无害——
|
||||
daily 对未生成日期返回空，同步会自然跳过）。
|
||||
"""
|
||||
cached = (await session.execute(select(TradeCalendar.trade_date).order_by(TradeCalendar.trade_date.desc()))).scalars().all()
|
||||
today = datetime.now().strftime("%Y%m%d")
|
||||
have_today = bool(cached) and cached[0] >= today
|
||||
|
||||
if not have_today:
|
||||
try:
|
||||
dates = await asyncio.to_thread(_fetch_calendar_sync, pro)
|
||||
await session.execute(delete(TradeCalendar))
|
||||
await session.execute(insert(TradeCalendar), [{"trade_date": d} for d in dates])
|
||||
await session.commit()
|
||||
cached = dates[::-1]
|
||||
except Exception as e: # noqa: BLE001 —— 限频且无缓存时才致命
|
||||
if not cached:
|
||||
raise ScreenerError(f"获取交易日历失败（且本地无缓存）: {str(e)[:150]}") from e
|
||||
_sync_state["step"] = "交易日历刷新受限，沿用本地缓存"
|
||||
|
||||
recent = [d for d in cached if d <= today][:days]
|
||||
if not recent:
|
||||
raise ScreenerError("交易日历为空")
|
||||
return recent
|
||||
|
||||
|
||||
def _fetch_daily(pro, d: str) -> list[dict]:
|
||||
"""拉取某交易日全市场日线（未复权）。当日数据未生成（盘前/盘中）返回空。"""
|
||||
time.sleep(settings.screener_sync_interval)
|
||||
df = _call_retry(pro.daily, trade_date=d)
|
||||
if df is None or df.empty:
|
||||
return []
|
||||
rows = []
|
||||
for _, r in df.iterrows():
|
||||
rows.append({
|
||||
"trade_date": _parse_d(d),
|
||||
"ts_code": r["ts_code"],
|
||||
"open": float(r["open"]), "high": float(r["high"]),
|
||||
"low": float(r["low"]), "close": float(r["close"]),
|
||||
"pre_close": float(r["pre_close"]),
|
||||
"change": None if r.get("change") != r.get("change") else float(r["change"]),
|
||||
"pct_chg": None if r.get("pct_chg") != r.get("pct_chg") else float(r["pct_chg"]),
|
||||
"vol": float(r["vol"]), # 手
|
||||
"amount": float(r["amount"]), # 千元
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def _fetch_basic(pro, d: str) -> list[dict]:
|
||||
"""拉取某交易日每日指标快照（daily_basic，低积分版限频 1 次/分钟）。
|
||||
|
||||
失败（积分不足等）时记录错误返回空，不拖垮日线同步。
|
||||
"""
|
||||
time.sleep(settings.screener_sync_interval)
|
||||
try:
|
||||
df = _call_retry(pro.daily_basic, trade_date=d)
|
||||
except Exception as e: # noqa: BLE001
|
||||
msg = str(e)
|
||||
if any(m in msg for m in _PERM_MARKS):
|
||||
_sync_state["error"] = (
|
||||
f"Tushare 无法获取每日指标(daily_basic)：{msg[:150]}。"
|
||||
"市值/市盈率等条件不可用；纯指标选股不受影响。"
|
||||
)
|
||||
return []
|
||||
raise
|
||||
if df is None or df.empty:
|
||||
return []
|
||||
rows = []
|
||||
for _, r in df.iterrows():
|
||||
def _f(key: str) -> float | None:
|
||||
v = r.get(key)
|
||||
return None if v is None or v != v else float(v)
|
||||
rows.append({
|
||||
"trade_date": _parse_d(d),
|
||||
"ts_code": r["ts_code"],
|
||||
"close": _f("close"), "turnover_rate": _f("turnover_rate"),
|
||||
"turnover_rate_f": _f("turnover_rate_f"), "volume_ratio": _f("volume_ratio"),
|
||||
"pe": _f("pe"), "pe_ttm": _f("pe_ttm"), "pb": _f("pb"),
|
||||
"total_mv": _f("total_mv"), "circ_mv": _f("circ_mv"), # 万元
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def _sync_stock_list_sync(pro) -> list[dict]:
|
||||
"""拉取在市股票列表。"""
|
||||
time.sleep(settings.screener_sync_interval)
|
||||
df = _call_retry(pro.stock_basic, exchange="", list_status="L",
|
||||
fields="ts_code,symbol,name,area,industry,market,exchange,list_status,list_date,delist_date")
|
||||
rows = []
|
||||
for _, r in df.iterrows():
|
||||
rows.append({
|
||||
"ts_code": r["ts_code"], "symbol": r["symbol"], "name": r["name"],
|
||||
"area": r.get("area") or None, "industry": r.get("industry") or None,
|
||||
"market": r.get("market") or None, "exchange": r["exchange"] or "",
|
||||
"list_status": r["list_status"], "list_date": r.get("list_date") or "",
|
||||
"delist_date": r.get("delist_date") or None,
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def _norm_date(v) -> str:
|
||||
"""把 DB 读出的 trade_date（可能是 datetime 或 str）归一为 YYYYMMDD。"""
|
||||
if hasattr(v, "strftime"):
|
||||
return v.strftime("%Y%m%d")
|
||||
return str(v)[:10].replace("-", "")
|
||||
|
||||
|
||||
async def _existing_dates(session: AsyncSession, model) -> set[str]:
|
||||
"""某表已落库的交易日集合（YYYYMMDD 字符串，便于比对）。"""
|
||||
res = await session.execute(select(func.distinct(model.trade_date)))
|
||||
return {_norm_date(r[0]) for r in res}
|
||||
|
||||
|
||||
async def _replace_day(session: AsyncSession, model, rows: list[dict], d_str: str) -> None:
|
||||
"""按交易日删旧插新（幂等），executemany 分批。"""
|
||||
d = _parse_d(d_str)
|
||||
await session.execute(delete(model).where(model.trade_date == d))
|
||||
for i in range(0, len(rows), _BATCH):
|
||||
await session.execute(insert(model), rows[i : i + _BATCH])
|
||||
await session.commit()
|
||||
|
||||
|
||||
async def _run_sync(days: int, force: bool) -> None:
|
||||
"""后台任务主体：stock_basic -> 逐日日线 -> 最新交易日快照。异常写状态。
|
||||
|
||||
daily_basic 只拉最新交易日（快照条件仅作用于最新截面，且低积分 token 限频 1 次/分钟）。
|
||||
"""
|
||||
from ..db import async_session # 延迟导入避免循环
|
||||
|
||||
try:
|
||||
pro = await asyncio.to_thread(_get_pro)
|
||||
|
||||
# 1) 股票列表（已有数据则跳过——stock_basic 低积分版限频 1 次/小时）
|
||||
async with async_session() as session:
|
||||
stocks_now = int(await session.scalar(select(func.count()).select_from(StockBasic)) or 0)
|
||||
if stocks_now == 0 or force:
|
||||
_sync_state["step"] = "正在同步股票列表"
|
||||
try:
|
||||
rows = await asyncio.to_thread(_sync_stock_list_sync, pro)
|
||||
async with async_session() as session:
|
||||
await session.execute(delete(StockBasic))
|
||||
for i in range(0, len(rows), _BATCH):
|
||||
await session.execute(insert(StockBasic), rows[i : i + _BATCH])
|
||||
await session.commit()
|
||||
except Exception as e: # noqa: BLE001 —— 受限时沿用现有列表继续
|
||||
if stocks_now > 0:
|
||||
_sync_state["step"] = f"股票列表同步受限（沿用现有 {stocks_now} 只）"
|
||||
else:
|
||||
raise
|
||||
|
||||
# 2) 逐交易日日线（增量；当日未生成则跳过）
|
||||
async with async_session() as session:
|
||||
dates = await _recent_trade_dates(session, pro, days)
|
||||
have_daily = set() if force else await _existing_dates(session, MarketDaily)
|
||||
todo = [d for d in dates if d not in have_daily]
|
||||
_sync_state["total_days"] = len(todo)
|
||||
_sync_state["done_days"] = 0
|
||||
|
||||
for d in todo:
|
||||
_sync_state["step"] = f"正在同步 {d} 日线（{_sync_state['done_days'] + 1}/{len(todo)}）"
|
||||
daily_rows = await asyncio.to_thread(_fetch_daily, pro, d)
|
||||
if daily_rows: # 盘前/盘中等未生成数据的日期直接跳过
|
||||
async with async_session() as session:
|
||||
await _replace_day(session, MarketDaily, daily_rows, d)
|
||||
_sync_state["done_days"] += 1
|
||||
|
||||
# 3) 最新「有数据」交易日的快照（daily_basic，仅 1 次调用）
|
||||
# 用 market_daily 实际最大交易日（今天的数据收盘后才生成，日历最新日会拉到空）
|
||||
async with async_session() as session:
|
||||
latest_dt = await session.scalar(select(func.max(MarketDaily.trade_date)))
|
||||
latest = latest_dt.strftime("%Y%m%d") if latest_dt else None
|
||||
if latest:
|
||||
async with async_session() as session:
|
||||
have_snap = force or latest not in await _existing_dates(session, DailySnapshot)
|
||||
if have_snap:
|
||||
_sync_state["step"] = f"正在同步 {latest} 每日指标"
|
||||
basic_rows = await asyncio.to_thread(_fetch_basic, pro, latest)
|
||||
if basic_rows:
|
||||
async with async_session() as session:
|
||||
await _replace_day(session, DailySnapshot, basic_rows, latest)
|
||||
|
||||
_sync_state["step"] = "同步完成"
|
||||
except Exception as e: # noqa: BLE001
|
||||
_sync_state["error"] = f"同步失败：{str(e)[:300]}"
|
||||
_sync_state["step"] = "同步失败"
|
||||
finally:
|
||||
_sync_state["running"] = False
|
||||
_sync_state["finished_at"] = datetime.now()
|
||||
|
||||
|
||||
async def start_sync(session: AsyncSession, days: int, force: bool) -> dict:
|
||||
"""幂等启动后台同步任务；已在跑则直接返回当前状态。"""
|
||||
global _sync_task
|
||||
async with _sync_lock:
|
||||
if _sync_state["running"] and _sync_task and not _sync_task.done():
|
||||
return dict(_sync_state)
|
||||
_sync_state.update({
|
||||
"running": True, "step": "准备同步", "total_days": days, "done_days": 0,
|
||||
"error": None, "started_at": datetime.now(), "finished_at": None,
|
||||
})
|
||||
_sync_task = asyncio.create_task(_run_sync(days, force))
|
||||
return dict(_sync_state)
|
||||
|
||||
|
||||
async def get_sync_status(session: AsyncSession) -> dict:
|
||||
"""合并任务状态 + DB 实况（最新交易日/行数/ready 标志），与 ScreenerSyncStatus DTO 对齐。"""
|
||||
stocks = int(await session.scalar(select(func.count()).select_from(StockBasic)) or 0)
|
||||
daily_rows = int(await session.scalar(select(func.count()).select_from(MarketDaily)) or 0)
|
||||
snap_rows = int(await session.scalar(select(func.count()).select_from(DailySnapshot)) or 0)
|
||||
last_daily = await session.scalar(select(func.max(MarketDaily.trade_date)))
|
||||
n_dates = int(await session.scalar(select(func.count(func.distinct(MarketDaily.trade_date)))) or 0)
|
||||
|
||||
status = dict(_sync_state)
|
||||
status.update({
|
||||
"stats": {"stocks": stocks, "daily_rows": daily_rows, "snapshot_rows": snap_rows, "dates": n_dates},
|
||||
"last_trade_date": last_daily,
|
||||
"last_synced_at": _sync_state.get("finished_at") or _sync_state.get("started_at"),
|
||||
"ready": daily_rows > 0,
|
||||
})
|
||||
return status
|
||||
@@ -14,6 +14,7 @@ dependencies = [
|
||||
"numpy>=1.26",
|
||||
"pandas>=2.2",
|
||||
"tushare>=1.4",
|
||||
"httpx>=0.28.1",
|
||||
]
|
||||
|
||||
[tool.uv]
|
||||
|
||||
@@ -52,3 +52,49 @@ with TestClient(app) as c:
|
||||
assert len(wd["candles"]) < len(d["candles"])
|
||||
|
||||
print("\n✅ 后端全链路自检通过（含周期聚合）")
|
||||
|
||||
# ---------- 智能选股 ----------
|
||||
print("\n== 智能选股 ==")
|
||||
|
||||
# 1) JSON 提取容错（不联网）：代码围栏 / 多余文本
|
||||
from app.screener.llm import _extract_json
|
||||
assert _extract_json('```json\n{"a": 1}\n```') == {"a": 1}
|
||||
assert _extract_json('好的，结果如下：{"indicator": [], "snapshot": []} 谢谢')["indicator"] == []
|
||||
print("_extract_json 围栏/噪音容错 ✅")
|
||||
|
||||
# 2) 未配置 LLM_API_KEY 时 /run 返回 503（确定性，不联网）
|
||||
from app.config import settings as _s
|
||||
r = c.post("/api/screener/run", json={"text": "这两天 KDJ 的 J 小于 10"})
|
||||
if not _s.llm_api_key:
|
||||
assert r.status_code == 503, f"无 key 应 503，实际 {r.status_code}"
|
||||
print("无 LLM_API_KEY -> 503 ✅")
|
||||
else:
|
||||
print("已配置 LLM_API_KEY，跳过 503 用例")
|
||||
|
||||
# 3) 直传条件选股（不依赖 LLM；依赖已同步的全市场数据）
|
||||
from app.models import MarketDaily # noqa: F401
|
||||
from sqlalchemy import select, func
|
||||
from app.db import async_session
|
||||
import asyncio
|
||||
|
||||
async def _has_data() -> bool:
|
||||
async with async_session() as session:
|
||||
return (await session.scalar(select(func.count()).select_from(MarketDaily))) or 0 > 0
|
||||
|
||||
if asyncio.run(_has_data()):
|
||||
r = c.post("/api/screener/run", json={
|
||||
"text": "测试直传",
|
||||
"conditions": {
|
||||
"indicator": [{"indicator": "kdj_j", "params": {"n": 9, "m1": 3, "m2": 3},
|
||||
"op": "lt", "value": 0, "lookback": 1, "match": "all"}],
|
||||
"snapshot": [],
|
||||
},
|
||||
})
|
||||
assert r.status_code == 200, f"直传选股失败 {r.status_code}: {r.text}"
|
||||
d = r.json()
|
||||
assert d["total"] >= 0
|
||||
print(f"KDJ J<0 选股 ✅ 命中 {d['total']} 只，基准日 {(d['trade_date'] or '')[:10]}")
|
||||
else:
|
||||
print("（未同步全市场数据，跳过直传选股用例；运行 POST /api/screener/sync 后再试）")
|
||||
|
||||
print("\n✅ 智能选股自检通过")
|
||||
|
||||
30
backend/uv.lock
generated
30
backend/uv.lock
generated
@@ -286,6 +286,19 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "certifi" },
|
||||
{ name = "h11" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httptools"
|
||||
version = "0.8.0"
|
||||
@@ -322,6 +335,21 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/48/63/b906c01e53f50d432c0defe43ce52764a111dc1bdd028bafbeb54dcfd008/httptools-0.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:384c17174464c8e873398b7af24f0b1f44d992c820328413951a625323155d77", size = 108209, upload-time = "2026-05-25T22:17:39.473Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpx"
|
||||
version = "0.28.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "certifi" },
|
||||
{ name = "httpcore" },
|
||||
{ name = "idna" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.18"
|
||||
@@ -827,6 +855,7 @@ dependencies = [
|
||||
{ name = "aiosqlite" },
|
||||
{ name = "asyncpg" },
|
||||
{ name = "fastapi" },
|
||||
{ name = "httpx" },
|
||||
{ name = "numpy" },
|
||||
{ name = "pandas" },
|
||||
{ name = "pydantic" },
|
||||
@@ -841,6 +870,7 @@ requires-dist = [
|
||||
{ name = "aiosqlite", specifier = ">=0.20" },
|
||||
{ name = "asyncpg", specifier = ">=0.29" },
|
||||
{ name = "fastapi", specifier = ">=0.115" },
|
||||
{ name = "httpx", specifier = ">=0.28.1" },
|
||||
{ name = "numpy", specifier = ">=1.26" },
|
||||
{ name = "pandas", specifier = ">=2.2" },
|
||||
{ name = "pydantic", specifier = ">=2.7" },
|
||||
|
||||
Reference in New Issue
Block a user