Simon
40bd603b44
按用户目标把原来「一个策略 = 全套参数」拆开（已确认的设计决策）：
- 公共配置 GlobalConfig（全局唯一）：佣金/印花税/滑点/最低佣金/复权口径/基准
- 选股策略 SelectionStrategy（原 StrategyDefinition 改名）：只剩股票池+因子+条件，
不再持有 selection/rebalance/costs/portfolio/区间/资金
- 回测组合 BacktestCombo：引用若干选股策略 + 回测时才定的参数
（起始资金、持仓数 N、持仓天数区间 [Tmin,Tmax]、调仓时机 日/周/月、区间）
引擎（app/quant/combo_engine.py，新增）：
- 多策略打分 = 并集 + Borda 秩和（各策略 1/名次 求和；不假设不同策略分值可比，
能容纳各策略股票池不同）；抽出纯函数 borda_combine 便于单测
- 持仓天数区间 [Tmin,Tmax]：Tmax **每个交易日**强制了结（安全阀，月频下也不超期）；
Tmin 仅在调仓日保护（掉出 TopN 但未满 Tmin 暂留，防频繁换手）；调仓日为增量调仓
（只卖超期/掉队且满 Tmin 的，从 TopN 补买至 N 只，不主动减持以尊重 Tmin）
- 调仓时机 daily/weekly/monthly（local_engine.rebalance_dates 新增日频分支）
- 产出与旧 runner 同构的 BacktestResult，前端可视化无需改动；config_snapshot 固化
ComboRunSpec（组合+当时各策略定义+当时成本/复权）保证可复现
数据层：
- 新表 global_config（默认行：万三/hfq/最低佣金5元）、backtest_combo
- 迁移 b4c5d6e7f8a9：建两表 + 把存量 strategy.config_json 的回测参数键剥掉、
spec_type 收敛为 selection（已在真实 MariaDB 验证：STG-16BFBF08 清洗后只剩
universe/factors/conditions）
- 仓储 SqlAlchemyGlobalConfigRepository / SqlAlchemyComboRepository + Protocol
API：
- /api/config GET/PUT；/api/combos CRUD + /{id}/run + /run（kind=combo 异步 Job）
- job_executor 新增 combo 分支：取齐策略+读公共配置→ComboService.run，归档 kind
记 backtest（结果结构相同）
- /api/strategies 切到 SelectionStrategy，移除已废弃的 /{id}/expand
- strategy_doc.describe_strategy 支持 SelectionStrategy（只讲「怎么选」，如实声明
资金/持仓/调仓/成本/区间在回测组合里定）
旧的 ResearchSpec + /api/backtests 保留（因子测试与既有契约自检仍用），
作为底层 escape hatch；用户产品路径改为回测组合。
测试：新增 test_combo_engine(6)/test_combo_service(3)/test_combo_api(5)，
改写 test_strategies/test_strategy_doc 适配新模型。全量 403 passed（原 388）。
255 lines
10 KiB
Python
255 lines
10 KiB
Python
"""回测组合服务：把「组合 + 选股策略 + 公共配置」解析并执行成 BacktestResult。
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职责（应用层用例，AGENT.md §16/§17）：
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- 装配行情数据（复用 ResearchService 的 load_daily_df / universe 过滤 / 名称回填）；
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- 为每个选股策略构造「as_of → 合格股票集」闭包（复用 selection 求值器，保证与
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`/api/selections` 同口径，v2 §25）；
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- 调 combo_engine.run_combo_backtest（多策略 Borda + 持仓区间 + 日/周/月）；
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- 把可复现的 ComboRunSpec 写进结果 config_snapshot（已在引擎内完成）。
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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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from typing import Any
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import pandas as pd
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from app.domain.entities.combo import (
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BacktestCombo,
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ComboRunSpec,
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GlobalConfig,
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SelectionStrategyRef,
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)
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from app.domain.entities.research import BacktestResult, UniverseSpec
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from app.domain.entities.strategy import SelectionStrategy
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from app.quant.combo_engine import run_combo_backtest
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from app.quant.selection import build_condition_fields, eligible_symbols
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from app.quant.service import _fill_names, load_daily_df, split_factor_columns
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from app.quant.universe import filter_stocks, names_as_of, resolve_members
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class ComboService:
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"""回测组合用例入口。依赖注入各 Repository + 引擎无关的数据装配函数。"""
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def __init__(
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self,
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stock_repo,
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daily_repo,
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*,
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index_repo=None,
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basic_repo=None,
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financial_repo=None,
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name_repo=None,
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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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self._index_repo = index_repo
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self._basic_repo = basic_repo
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self._financial_repo = financial_repo
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self._name_repo = name_repo
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self._last_stocks: list = []
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def run(
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self,
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combo: BacktestCombo,
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strategies: list[SelectionStrategy],
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config: GlobalConfig,
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on_stage=None,
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) -> BacktestResult:
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if not strategies:
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raise ValueError("回测组合至少需要引用一个选股策略")
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# 校验引用的策略 id 与传入一致（防御性：调用方应已按 combo.strategy_ids 取齐）
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given = {s.id for s in strategies}
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missing = [sid for sid in combo.strategy_ids if sid not in given]
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if missing:
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raise ValueError(f"组合引用的选股策略未提供：{missing}")
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_stage(on_stage, "data_loading")
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daily = self._load_daily(combo, strategies, config)
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_stage(on_stage, "backtesting")
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eligibility_fns = [self._build_eligibility(s, daily) for s in strategies]
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refs = [_to_ref(s) for s in strategies]
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result = run_combo_backtest(
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combo=combo,
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strategies=refs,
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costs=config.to_cost_spec(),
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price_adjustment=config.price_adjustment,
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daily=daily,
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eligibility_fns=eligibility_fns,
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)
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_stage(on_stage, "analysis")
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return _fill_names(result, self._last_stocks)
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# ---- 数据装配（与 ResearchService 同口径，复用底层函数） ----
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def _merged_universe(self, strategies: list[SelectionStrategy]) -> UniverseSpec:
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"""合并各策略的股票池口径用于「装哪些股票的行情」。
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取并集语义：symbols 白名单取并集；exclude_st / min_listing_days 取**最宽松**
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（任一策略不剔 ST 则不剔，min_listing_days 取最小）—— 因为最终选股由各策略
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自己的 eligibility 闭包再过滤，这里只为「行情装配覆盖足够多的股票」。
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index_code 不一致时无法合并 → 报错（同一组合里混用不同指数成分没有明确语义）。
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"""
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indices = {s.universe.index_code for s in strategies if s.universe.index_code}
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if len(indices) > 1:
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raise ValueError(
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f"组合内各选股策略的指数成分不一致（{sorted(indices)}），无法合并股票池；"
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"请统一指数或改用 symbols 白名单"
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)
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symbols: set[str] = set()
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for s in strategies:
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symbols.update(s.universe.symbols)
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return UniverseSpec(
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market=strategies[0].universe.market,
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exclude_st=all(s.universe.exclude_st for s in strategies),
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exclude_suspended=all(s.universe.exclude_suspended for s in strategies),
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min_listing_days=min(s.universe.min_listing_days for s in strategies),
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index_code=indices.pop() if indices else None,
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symbols=sorted(symbols),
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)
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def _load_daily(
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self, combo: BacktestCombo, strategies: list[SelectionStrategy], config: GlobalConfig
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) -> pd.DataFrame:
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start, end = combo.period
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data_start = start - timedelta(days=300) # 因子 warmup 余量
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all_stocks = self._stock_repo.list()
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merged = self._merged_universe(strategies)
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name_at, _applied = names_as_of(all_stocks, start, self._name_repo)
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stocks = filter_stocks(
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all_stocks, merged, as_of=start,
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members=resolve_members(self._index_repo, merged, start),
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name_at=name_at,
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)
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self._last_stocks = stocks
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# 所需列 = 所有策略因子 + 所有策略条件引用列 + close
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needed = {"close"}
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for s in strategies:
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from app.domain.entities.research import ResearchSpec
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from app.quant.engine import factor_required_columns
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# 借用既有列裁剪逻辑：构造一个临时 spec 只为算 required_columns
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tmp = ResearchSpec(
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type="backtest", universe=s.universe, factors=s.factors,
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conditions=s.conditions, period=combo.period,
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)
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needed |= factor_required_columns(tmp)
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bar_cols, basic_cols = split_factor_columns(needed)
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symbols = [st.symbol for st in stocks]
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daily = load_daily_df(
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self._daily_repo, symbols, data_start, end, sorted(bar_cols),
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adjust="none", price_adjust=config.price_adjustment,
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)
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if basic_cols:
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daily = self._attach_basic(daily, symbols, data_start, end, sorted(basic_cols))
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return daily
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def _attach_basic(self, daily, symbols, start, end, columns) -> pd.DataFrame:
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from app.quant.service import load_basic_df, merge_basic_into_daily
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if self._basic_repo is None:
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raise ValueError(
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f"选股策略条件/因子需要每日指标列 {columns}（daily_basic），但未注入 DailyBasicRepository"
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)
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basic = load_basic_df(self._basic_repo, symbols, start, end, columns)
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if basic.empty:
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raise ValueError(
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f"daily_basic 在 {start}~{end} 无数据，无法计算需要 {columns} 的因子/条件"
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)
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return merge_basic_into_daily(daily, basic)
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def _build_eligibility(self, strategy: SelectionStrategy, daily: pd.DataFrame):
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"""单策略的「as_of → 合格股票集」闭包（与 ResearchService._build_eligibility 同口径）。"""
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if not self._last_stocks:
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if not strategy.conditions and not strategy.universe.exclude_st:
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return None
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raise ValueError("universe 过滤结果为空，无法构造选股条件求值器")
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statics = {s.symbol: s.model_dump() for s in self._last_stocks}
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candidates = sorted(statics)
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st_fn = self._build_st_filter(strategy, candidates)
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if not strategy.conditions:
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if st_fn is None:
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return None
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allowed: dict[date, set[str]] = {}
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def _st_only(as_of: date) -> set[str]:
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if as_of not in allowed:
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allowed[as_of] = set(candidates) - st_fn(as_of)
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return allowed[as_of]
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return _st_only
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uses_fundamental = any(
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f.startswith("fundamental.")
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for c in strategy.conditions
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for f in (c.field, c.ref or "")
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)
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cache: dict[date, set[str]] = {}
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def _fn(as_of: date) -> set[str]:
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if as_of in cache:
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return cache[as_of]
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financial = self._load_financial(candidates, as_of) if uses_fundamental else {}
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fields = build_condition_fields(daily, strategy.conditions, pd.Timestamp(as_of))
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if not fields:
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cache[as_of] = set()
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return cache[as_of]
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passed = set(eligible_symbols(candidates, strategy.conditions, statics, fields, financial))
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if st_fn is not None:
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passed -= st_fn(as_of)
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cache[as_of] = passed
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return cache[as_of]
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return _fn
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def _build_st_filter(self, strategy: SelectionStrategy, candidates: list[str]):
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if not strategy.universe.exclude_st or self._name_repo is None:
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return None
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cache: dict[date, set[str]] = {}
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def _fn(as_of: date) -> set[str]:
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if as_of not in cache:
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name_at, applied = names_as_of(self._last_stocks, as_of, self._name_repo)
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if not applied[0]:
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cache[as_of] = set()
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else:
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st_syms: set[str] = set()
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for st in self._last_stocks:
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nm = (name_at or {}).get(st.symbol) or st.name
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if nm and "ST" in nm.upper():
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st_syms.add(st.symbol)
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cache[as_of] = st_syms
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return cache[as_of]
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return _fn
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def _load_financial(self, symbols: list[str], as_of: date) -> dict[str, Any]:
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if self._financial_repo is None:
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raise ValueError("条件引用了 fundamental.* 字段，但未注入 FinancialRepository")
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getter = getattr(self._financial_repo, "list_announced_many", None)
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rows = list(getter(symbols, as_of)) if getter else []
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out: dict[str, Any] = {}
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for r in rows:
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out[r.symbol] = r
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return out
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def _to_ref(s: SelectionStrategy) -> SelectionStrategyRef:
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return SelectionStrategyRef(
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id=s.id,
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name=s.name,
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universe=s.universe.model_dump(),
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factors=[f.model_dump() for f in s.factors],
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conditions=[c.model_dump() for c in s.conditions],
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)
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def _stage(cb, name: str) -> None:
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if cb is not None:
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cb(name)
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# 让 ComboRunSpec 在模块导入时完成前向引用重建（entities/combo.py 末尾已 rebuild，此处兜底）
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ComboRunSpec.model_rebuild()
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