Simon
f3586adb25
- domain/entities/selection.py：SelectionQuery（universe+method+factors+top_n/top_pct/ min_score+as_of+预热）与 SelectionResult/Candidate/Statistics（v2 §14.2/§21.1 DTO）； ConditionSpec 字段就位供 M6.2 条件选股 - quant/selection.py：Selection Engine method=score —— 复合分（zscore×权重×方向） → TopN/Top% 截断；observation_date=<=as_of 最近交易日（防未来函数，v2 §9）； 候选带 factor_values 与 selection_reason（可解释） - application/services/selection_service.py：选股用例（universe 过滤 → 装配 → 引擎） - quant/service.py：抽取公共 load_daily_df 供研究/选股共用（行为不变） - tests/test_selection.py：11 例 —— TopN/排序/理由、as_of 防未来函数、ST/上市天数/ 退市过滤、top_pct/min_score、空数据与查询校验；全量 pytest 通过
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
"""选股用例入口（ARCHITECTURE_v2 §14 Selection Engine · 业务层）。
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- 输入：SelectionQuery（universe + method + factors/conditions + top_n/pct + as_of）
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- 装配：股票池（universe 过滤）→ 行情长表（含预热窗口）→ Selection Engine
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- 输出：SelectionResult（可解释：factor_values / selection_reason）
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- 未来函数红线：全部数据只取到 <= as_of（v2 §9）；财务条件（后续）只取已公告值
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MVP 为同步执行（单日全市场因子计算量轻）；如需异步可复用 Job 链路（M6.3 决策）。
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"""
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from __future__ import annotations
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from datetime import date, timedelta
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import pandas as pd
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from app.domain.entities.selection import SelectionQuery, SelectionResult
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from app.domain.repositories.market import DailyBarRepository, StockRepository
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from app.quant.selection import factor_columns, run_score_selection
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from app.quant.service import filter_stocks, load_daily_df
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class SelectionService:
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"""选股用例入口：select(query) → SelectionResult（当前或历史 as_of）。"""
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def __init__(
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self,
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stock_repo: StockRepository,
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daily_repo: DailyBarRepository,
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) -> None:
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self._stock_repo = stock_repo
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self._daily_repo = daily_repo
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def select(self, query: SelectionQuery) -> SelectionResult:
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as_of = query.as_of or date.today()
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stocks = filter_stocks(self._stock_repo.list(), query.universe, as_of=as_of)
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if not stocks:
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return run_score_selection(pd.DataFrame(), query, as_of)
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symbols = [s.symbol for s in stocks]
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columns = sorted(factor_columns(query)) if query.method == "score" else ["close"]
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daily = load_daily_df(
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self._daily_repo,
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symbols,
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as_of - timedelta(days=query.warmup_days),
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as_of,
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columns,
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)
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if daily.empty:
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return run_score_selection(daily, query, as_of)
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return run_score_selection(daily, query, as_of)
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