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）。
581 lines
24 KiB
Python
581 lines
24 KiB
Python
"""组合回测引擎（2026-09 重构）：多选股策略 + 持仓天数区间 + 日/周/月调仓。
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与 `local_engine.TopKBacktestRunner`（单策略、固定 m/y、全卖全买）并存：
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旧 runner 继续服务 `/api/backtests`（ResearchSpec）与因子测试相关的既有路径，
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本模块服务新的「回测组合」产品。两者产出**同一种 BacktestResult**，前端可视化无需改动。
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执行模型（用户确认的语义）：
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- **打分 = 并集 + Borda 秩和**：每个选股策略各自对「自己的股票池 ∩ 条件」内的股票
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按复合因子分排名；合并取并集，综合分 = Σ(1 / 该策略内名次)，未进入某策略排名的
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股票在该策略贡献 0。好处是不假设不同策略的因子分值可比、能容纳各策略股票池不同。
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- **调仓时机 daily/weekly/monthly**：决定「重新打分 + 调向目标」的节奏。
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- **持仓天数区间 [Tmin, Tmax]**：
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* 每个交易日都检查 Tmax —— 持有超过 Tmax 的个股**强制了结**（安全阀，
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即便调仓是月频也不能让个股远超 Tmax）；
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* 仅在调仓日：把「掉出 TopN 且已持 ≥ Tmin」的卖出（Tmin 防频繁换手），
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再从 TopN 里补买到 N 只（等权目标，只买不主动减持以尊重 Tmin）。
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- 成交仍在调仓日收盘（与旧引擎同一时序纪律，无未来函数）；涨跌停/停牌沿用旧近似。
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复用 local_engine 的纯工具（涨跌停幅度、NaN 判定、调仓日集合），其余记账逻辑
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为本模块自包含 —— 刻意不继承旧 runner，避免改动那条已验证的路径。
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass
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from datetime import date
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import pandas as pd
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from app.domain.entities.combo import BacktestCombo, ComboRunSpec, SelectionStrategyRef
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from app.domain.entities.research import (
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ActionRecord,
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BacktestResult,
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BacktestSummary,
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ConditionSpec,
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CostSpec,
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CurvePoint,
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FactorSpec,
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MonthlyReturn,
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Position,
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RankedPick,
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SymbolCurve,
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Trade,
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UniverseSpec,
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YearlyReturn,
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)
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from app.quant.composite import build_score_panel
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from app.quant.local_engine import _limit_up_ratio, _nan, rebalance_dates
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TRADING_DAYS = 252
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# ---------- 多策略打分：Borda 秩和 ----------
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def _ref_to_specs(ref: SelectionStrategyRef) -> tuple[UniverseSpec, list[FactorSpec], list[ConditionSpec]]:
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"""把归档快照里的 dict 还原成强类型 spec（喂给既有因子/条件求值器）。"""
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universe = UniverseSpec.model_validate(ref.universe)
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factors = [FactorSpec.model_validate(f) for f in ref.factors]
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conditions = [ConditionSpec.model_validate(c) for c in ref.conditions]
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return universe, factors, conditions
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def borda_combine(panels: list[pd.DataFrame]) -> pd.DataFrame:
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"""把多张「复合分面板」按 Borda 秩和合并成一张综合分面板。
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每张面板先按日降序排名（1 = 最高分），综合分 = Σ(1/名次)；某面板里 NaN（该股
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不在该策略可评分集）贡献 0。index/columns 取所有面板的并集。纯函数，便于单测。
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"""
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if not panels:
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return pd.DataFrame()
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dates = sorted({d for p in panels for d in p.index})
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symbols = sorted({s for p in panels for s in p.columns})
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borda = pd.DataFrame(0.0, index=dates, columns=symbols)
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for panel in panels:
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ranks = panel.rank(axis=1, ascending=False, na_option="keep")
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contrib = (1.0 / ranks).fillna(0.0)
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borda = borda.add(contrib.reindex(index=borda.index, columns=borda.columns), fill_value=0.0)
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return borda
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def combine_strategy_scores(
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daily: pd.DataFrame,
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strategies: list[SelectionStrategyRef],
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eligibility_fns: list,
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) -> tuple[pd.DataFrame, object]:
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"""多策略 → (综合分面板, 合并合格集闭包)。
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综合分面板 index=trade_date, columns=symbol，值为 Borda 秩和（越大越优先）。
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合并合格集闭包 `combined(as_of) -> set[symbol] | None`：各策略合格集的并集；
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全部策略都不过滤时返回 None（= 不过滤，交给面板的 dropna 处理）。
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"""
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# 每个策略一张「复合 zscore 面板」（已按方向加权求和）
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panels: list[pd.DataFrame] = []
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for ref in strategies:
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_universe, factors, _conditions = _ref_to_specs(ref)
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panels.append(build_score_panel(daily, factors))
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borda = borda_combine(panels)
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def combined(as_of: date) -> set[str] | None:
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sets = []
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any_filter = False
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for fn in eligibility_fns:
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if fn is None:
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continue
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s = fn(as_of)
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if s is not None:
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sets.append(s)
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any_filter = True
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if not any_filter:
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return None
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# 并集：任一策略认为合格即合格（Borda 会给没被某策略覆盖的股票较低分，自然靠后）
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out: set[str] = set()
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for s in sets:
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out |= s
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return out
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return borda, combined
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# ---------- 持仓区间回测 runner ----------
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@dataclass
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class _Holding:
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qty: float
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entry_date: date
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entry_price: float
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entry_ts: object = None # pd.Timestamp：按「交易日」计持仓天数用（自然日会跨周末失真）
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_UNIMPLEMENTED_BASE = [
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"涨跌停按收盘价相对上一有效收盘近似判定（未建模开盘一字 / 集合竞价路径）",
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"成交假设发生在调仓日收盘（未建模盘中价格路径与流动性冲击）",
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(
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"调仓日为「增量调仓」：只卖出超 Tmax / 掉出 TopN 且满 Tmin 的仓位，"
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"并从 TopN 补买至 N 只；**不主动减持超重仓位**以尊重 Tmin，权重会随行情漂移"
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"（非严格等权，买入侧按等权目标分配可用现金）"
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),
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"Tmax 强制卖出每个交易日检查；Tmin 保护与 TopN 重排仅在调仓日执行",
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(
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"多策略打分采用 Borda 秩和（各策略 1/名次 求和）：不假设不同策略的因子分值可比，"
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"但极端情况下某策略覆盖极少股票会使其秩和贡献偏大"
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),
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]
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class HoldingBandRunner:
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"""持仓天数区间 + 日/周/月调仓的组合回测 runner。"""
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def __init__(
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self,
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*,
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combo: BacktestCombo,
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costs: CostSpec,
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score: pd.DataFrame,
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close: pd.DataFrame,
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eligibility_fn=None,
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) -> None:
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self.combo = combo
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self.costs = costs
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close = close.copy()
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close.index = pd.to_datetime(close.index)
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self.close = close.sort_index()
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self.score = score.reindex(self.close.index).sort_index()
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self.eligibility_fn = eligibility_fn
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self.selection_history: list[RankedPick] = []
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self.signal_history: list[ActionRecord] = []
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self.traded_symbols: list[str] = []
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self._traded: set[str] = set()
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self._no_prev_close: set[str] = set()
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# ---- 主循环 ----
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def run(self) -> BacktestResult:
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start, end = self.spec_period()
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dates = [d for d in self.close.index if start <= d.date() <= end]
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if not dates:
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raise ValueError(f"回测区间 {start}~{end} 内没有任何行情数据，无法回测")
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cadence = set(
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rebalance_dates(self.close.index, self.combo.rebalance_freq, start, end)
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)
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cash = float(self.combo.initial_capital)
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holdings: dict[str, _Holding] = {}
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equity_rows: dict[pd.Timestamp, float] = {}
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trades: list[Trade] = []
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positions: list[Position] = []
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notional: list[float] = []
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cum: dict[str, float] = {}
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curve_rows: dict[str, list[CurvePoint]] = {}
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n = self.combo.hold_count
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tmin = self.combo.hold_min_days
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tmax = self.combo.hold_max_days
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# 交易日位置索引：持仓天数按「交易日」计（Tmin/Tmax 的自然单位），避免跨周末失真
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self._tday_pos = {ts: i for i, ts in enumerate(self.close.index)}
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def _equity(d: pd.Timestamp) -> float:
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total = cash
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for s, h in holdings.items():
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px = self.close.at[d, s] if d in self.close.index else None
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if px is None or (isinstance(px, float) and math.isnan(px)):
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continue
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total += h.qty * float(px)
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return total
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for d in dates:
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day = d.date()
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# 1) 用昨日持仓结算当日个股收益（与组合净值同一时序口径）
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self._accrue(d, holdings, cum, curve_rows)
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# 2) Tmax 安全阀：**每个交易日**强制了结超期仓位（不只调仓日）
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if tmax is not None:
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cash = self._force_exit_over_max(d, day, holdings, cash, trades, tmax)
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# 3) 调仓日：重新打分 + 增量调向目标 N 只
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if d in cadence:
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topn = self._top_n(d, n)
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cash = self._rebalance_to_target(
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d, day, topn, holdings, cash, trades, positions, notional, tmin, n
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)
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equity_rows[d] = _equity(d)
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self._mark_curve(d, holdings, cum, curve_rows)
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equity = pd.Series(equity_rows).sort_index()
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return self._to_result(equity, trades, positions, notional, cum, curve_rows)
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def spec_period(self) -> tuple[date, date]:
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return self.combo.period
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# ---- 打分 / 选股 ----
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def _top_n(self, d: pd.Timestamp, n: int) -> list[str]:
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score_d = self.score.loc[d].dropna()
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elig = self.eligibility_fn(d.date()) if self.eligibility_fn else None
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if elig is not None:
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score_d = score_d[score_d.index.isin(elig)]
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ranked = score_d.sort_values(ascending=False)
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top = ranked.head(n).index.tolist()
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day = d.date()
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for r, sym in enumerate(top, start=1):
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self.selection_history.append(
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RankedPick(date=day, symbol=sym, rank=r, score=round(float(ranked[sym]), 6))
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)
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return top
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# ---- Tmax 强制了结（每日） ----
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def _force_exit_over_max(self, d, day, holdings, cash, trades, tmax) -> float:
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prev_d = self.prev_close_at(d)
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close_d = self.close.loc[d]
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for s in [s for s in list(holdings)]:
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h = holdings[s]
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held = self._held_trading_days(h.entry_ts, d)
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if held <= tmax:
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continue
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c = close_d.get(s)
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p = prev_d.get(s) if prev_d is not None else None
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if _nan(c):
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self.signal_history.append(
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ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
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reject_reason=f"持有 {held} 天超 Tmax={tmax}，但当日无行情，顺延")
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)
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continue
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if not _nan(p) and p > 0 and c / p <= 1.0 - (_limit_up_ratio(s) - 1.0):
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self.signal_history.append(
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ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
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reject_reason=f"持有 {held} 天超 Tmax={tmax}，但跌停无法卖出，顺延")
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)
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continue
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cash = self._sell(s, h, float(c), day, cash, trades, holdings)
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return cash
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# ---- 调仓日：增量调向目标 ----
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def _rebalance_to_target(self, d, day, topn, holdings, cash, trades, positions, notional, tmin, n) -> float:
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close_d = self.close.loc[d]
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prev_d = self.prev_close_at(d)
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topn_set = set(topn)
|
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|
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# a) 卖出：掉出 TopN 且已满 Tmin 的（Tmin 保护：未满 Tmin 即使掉出也暂留）
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for s in [s for s in list(holdings)]:
|
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if s in topn_set:
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continue
|
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h = holdings[s]
|
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held = self._held_trading_days(h.entry_ts, d)
|
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if held < tmin:
|
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self.signal_history.append(
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ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
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reject_reason=f"掉出 TopN 但仅持 {held} 天 < Tmin={tmin}，暂留")
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)
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continue
|
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c = close_d.get(s)
|
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p = prev_d.get(s) if prev_d is not None else None
|
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if _nan(c):
|
||
self.signal_history.append(
|
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ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
|
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reject_reason="掉出 TopN，但当日无行情，保留到下一调仓")
|
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)
|
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continue
|
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if not _nan(p) and p > 0 and c / p <= 1.0 - (_limit_up_ratio(s) - 1.0):
|
||
self.signal_history.append(
|
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ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
|
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reject_reason="掉出 TopN，但跌停无法卖出，保留到下一调仓")
|
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)
|
||
continue
|
||
cash = self._sell(s, h, float(c), day, cash, trades, holdings)
|
||
|
||
# b) 补买：从 TopN 里挑尚未持有的，按等权目标用可用现金买入，直到 N 只或现金耗尽
|
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current = [s for s in topn if s in holdings and holdings[s].qty > 0]
|
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need = [s for s in topn if s not in holdings or holdings[s].qty <= 0]
|
||
slots_left = max(0, n - len(current))
|
||
buys = need[:slots_left]
|
||
if not buys:
|
||
self._record_positions(d, day, holdings, positions)
|
||
return cash
|
||
|
||
# 等权目标：每只 ≈ 当前权益 / N；单只预算 = min(目标, 可用现金均分)
|
||
equity_now = cash + sum(
|
||
holdings[s].qty * float(self.close.at[d, s])
|
||
for s in holdings
|
||
if holdings[s].qty > 0 and not _nan(self.close.at[d, s])
|
||
)
|
||
target_each = equity_now / n if n > 0 else 0.0
|
||
per_budget = min(target_each, cash / len(buys)) if buys else 0.0
|
||
|
||
for s in buys:
|
||
c = close_d.get(s)
|
||
p = prev_d.get(s) if prev_d is not None else None
|
||
if _nan(c):
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason="无行情（停牌），无法买入")
|
||
)
|
||
continue
|
||
if not _nan(p) and p > 0 and c / p >= _limit_up_ratio(s):
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason="涨停，无法追买")
|
||
)
|
||
continue
|
||
budget = min(per_budget, cash)
|
||
if budget <= 1e-9:
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason="可用现金不足，未成交")
|
||
)
|
||
continue
|
||
ok, spent = self._buy(s, budget, d, float(c), day, holdings, notional)
|
||
if not ok:
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason="预算不足以覆盖最低佣金，未成交")
|
||
)
|
||
continue
|
||
cash -= spent
|
||
|
||
self._record_positions(d, day, holdings, positions)
|
||
return cash
|
||
|
||
# ---- 买卖原子操作 ----
|
||
|
||
def _sell(self, s, h, close_price, day, cash, trades, holdings) -> float:
|
||
proceeds = h.qty * close_price * (1 - self.costs.slippage_rate)
|
||
commission = max(proceeds * self.costs.commission_rate, self.costs.min_commission)
|
||
fee = commission + proceeds * self.costs.stamp_tax_rate
|
||
cash += proceeds - fee
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="SELL", filled=True, price=close_price)
|
||
)
|
||
trades.append(
|
||
Trade(
|
||
entry_date=h.entry_date, exit_date=day, symbol=s,
|
||
entry_price=h.entry_price, exit_price=close_price,
|
||
return_pct=(close_price / h.entry_price - 1.0) * 100,
|
||
)
|
||
)
|
||
holdings.pop(s, None)
|
||
return cash
|
||
|
||
def _buy(self, s, budget, d, close_price, day, holdings, notional) -> tuple[bool, float]:
|
||
price_in = close_price * (1 + self.costs.slippage_rate)
|
||
commission = max(budget * self.costs.commission_rate, self.costs.min_commission)
|
||
invest = budget - commission
|
||
if invest <= 0:
|
||
return False, 0.0
|
||
qty = invest / price_in
|
||
prev = self.prev_close_at(d)
|
||
pv = prev.get(s) if prev is not None else float("nan")
|
||
if _nan(pv) or pv <= 0:
|
||
self._no_prev_close.add(s)
|
||
holdings[s] = _Holding(qty=qty, entry_date=day, entry_price=price_in, entry_ts=d)
|
||
notional.append(budget)
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="BUY", filled=True, price=round(price_in, 4))
|
||
)
|
||
if s not in self._traded:
|
||
self._traded.add(s)
|
||
self.traded_symbols.append(s)
|
||
return True, budget
|
||
|
||
# ---- 辅助 ----
|
||
|
||
def _held_trading_days(self, entry_ts, current_ts) -> int:
|
||
"""从入场到当前经过的**交易日**数（不含入场当日）。"""
|
||
e = self._tday_pos.get(entry_ts)
|
||
c = self._tday_pos.get(current_ts)
|
||
if e is None or c is None:
|
||
return 0
|
||
return max(0, c - e)
|
||
|
||
def prev_close_at(self, d: pd.Timestamp):
|
||
"""d 的前一有效收盘行（用于涨跌停判定）；不存在返回 None。"""
|
||
idx = self.close.index
|
||
pos = idx.get_loc(d) if d in idx else None
|
||
if pos is None or pos == 0:
|
||
return None
|
||
prev_ts = idx[pos - 1]
|
||
return self.close.loc[prev_ts]
|
||
|
||
def _accrue(self, d, holdings, cum, curve_rows) -> None:
|
||
prev = self.prev_close_at(d)
|
||
if prev is None:
|
||
return
|
||
close_d = self.close.loc[d]
|
||
for s in holdings:
|
||
c, p = close_d.get(s), prev.get(s)
|
||
if _nan(c) or _nan(p) or p <= 0:
|
||
continue
|
||
cum[s] = cum.get(s, 1.0) * (float(c) / float(p))
|
||
curve_rows.setdefault(s, []).append(
|
||
CurvePoint(date=d.date(), value=round((cum[s] - 1.0) * 100, 4))
|
||
)
|
||
|
||
def _mark_curve(self, d, holdings, cum, curve_rows) -> None:
|
||
day = d.date()
|
||
for s in holdings:
|
||
pts = curve_rows.setdefault(s, [])
|
||
if pts and pts[-1].date == day:
|
||
continue
|
||
pts.append(CurvePoint(date=day, value=round((cum.get(s, 1.0) - 1.0) * 100, 4)))
|
||
|
||
def _record_positions(self, d, day, holdings, positions) -> None:
|
||
total = sum(
|
||
h.qty * float(self.close.at[d, s])
|
||
for s, h in holdings.items()
|
||
if h.qty > 0 and not _nan(self.close.at[d, s])
|
||
)
|
||
if total <= 0:
|
||
return
|
||
for s, h in holdings.items():
|
||
if h.qty > 0 and not _nan(self.close.at[d, s]):
|
||
positions.append(
|
||
Position(date=day, symbol=s, weight=float(h.qty * self.close.at[d, s] / total))
|
||
)
|
||
|
||
# ---- 结果装配（与旧 runner 同构，保证前端可视化不变） ----
|
||
|
||
def _to_result(self, equity, trades, positions, notional, cum, curve_rows) -> BacktestResult:
|
||
start, end = equity.index[0].date(), equity.index[-1].date()
|
||
init = float(self.combo.initial_capital)
|
||
final = float(equity.iloc[-1])
|
||
rets = equity.pct_change().dropna()
|
||
nn = len(rets)
|
||
total_ret = (final / init - 1.0) * 100 if init else 0.0
|
||
annual = (
|
||
((final / init) ** (TRADING_DAYS / max(nn, 1)) - 1.0) * 100
|
||
if final > 0 and init > 0 else -100.0
|
||
)
|
||
mean_r = float(rets.mean()) if nn else 0.0
|
||
std_r = float(rets.std(ddof=1)) if nn else 0.0
|
||
sharpe = mean_r / std_r * math.sqrt(TRADING_DAYS) if std_r and mean_r else 0.0
|
||
vol = std_r * math.sqrt(TRADING_DAYS) * 100
|
||
dd = (equity / equity.cummax() - 1.0).min() * 100
|
||
wins = [t for t in trades if t.return_pct > 0]
|
||
win_rate = len(wins) / len(trades) * 100 if trades else 0.0
|
||
avg_turn = (sum(notional) / len(notional) / ((init + final) / 2)) * 100 if notional else 0.0
|
||
|
||
eq_pts = [CurvePoint(date=d.date(), value=round(float(v), 2)) for d, v in equity.items()]
|
||
dd_series = (equity / equity.cummax() - 1.0) * 100
|
||
drawdown = [CurvePoint(date=d.date(), value=round(float(v), 3)) for d, v in dd_series.items()]
|
||
|
||
monthly: list[MonthlyReturn] = []
|
||
yearly: list[YearlyReturn] = []
|
||
if len(equity) > 1:
|
||
m = equity.resample("ME").last().pct_change().dropna()
|
||
monthly = [
|
||
MonthlyReturn(year=int(d.year), month=int(d.month), return_pct=round(float(v) * 100, 3))
|
||
for d, v in m.items()
|
||
]
|
||
y = equity.resample("YE").last().pct_change().dropna()
|
||
yearly = [
|
||
YearlyReturn(year=int(d.year), return_pct=round(float(v) * 100, 3))
|
||
for d, v in y.items()
|
||
]
|
||
|
||
summary = BacktestSummary(
|
||
start=start, end=end, initial_capital=round(init, 2), final_equity=round(final, 2),
|
||
total_return_pct=round(total_ret, 3), annual_return_pct=round(annual, 3),
|
||
sharpe=round(sharpe, 3), max_drawdown_pct=round(float(dd), 3),
|
||
volatility_pct=round(vol, 3), win_rate_pct=round(win_rate, 2),
|
||
total_trades=len(trades), avg_turnover_pct=round(avg_turn, 2),
|
||
)
|
||
curves = self._symbol_curves(curve_rows, cum)
|
||
return BacktestResult(
|
||
summary=summary, equity_curve=eq_pts, drawdown=drawdown,
|
||
monthly_returns=monthly, yearly_returns=yearly,
|
||
positions=positions, trades=trades,
|
||
selection_history=self.selection_history,
|
||
signal_history=self.signal_history,
|
||
fills=[a for a in self.signal_history if a.filled],
|
||
symbol_curves=curves,
|
||
turnover_pct=round(sum(notional) / max(init, 1) * 100, 2),
|
||
unimplemented=self._unimplemented(),
|
||
config_snapshot={}, # 由服务层填入 ComboRunSpec（含策略+成本快照）
|
||
)
|
||
|
||
def _symbol_curves(self, curve_rows, cum) -> list[SymbolCurve]:
|
||
marks: dict[str, list[ActionRecord]] = {}
|
||
for a in self.signal_history:
|
||
if a.filled and a.symbol:
|
||
marks.setdefault(a.symbol, []).append(a)
|
||
out: list[SymbolCurve] = []
|
||
for s, points in curve_rows.items():
|
||
if not points:
|
||
continue
|
||
out.append(SymbolCurve(
|
||
symbol=s, points=points, marks=marks.get(s, []),
|
||
final_return_pct=round((cum.get(s, 1.0) - 1.0) * 100, 4),
|
||
))
|
||
out.sort(key=lambda c: abs(c.final_return_pct), reverse=True)
|
||
return out
|
||
|
||
def _unimplemented(self) -> list[str]:
|
||
notes = list(_UNIMPLEMENTED_BASE)
|
||
if self._no_prev_close:
|
||
notes.append(
|
||
f"有 {len(self._no_prev_close)} 只标的成交时缺少上一有效收盘价，"
|
||
"无法判定涨停（数据窗口起点或长期停牌后复牌），按可买处理"
|
||
)
|
||
c = self.combo
|
||
notes.append(
|
||
f"组合参数：N={c.hold_count}、持仓区间 [{c.hold_min_days}, "
|
||
f"{c.hold_max_days if c.hold_max_days is not None else '∞'}] 天、"
|
||
f"调仓 {c.rebalance_freq}、引用 {len(c.strategy_ids)} 个选股策略"
|
||
)
|
||
return notes
|
||
|
||
|
||
def run_combo_backtest(
|
||
*,
|
||
combo: BacktestCombo,
|
||
strategies: list[SelectionStrategyRef],
|
||
costs: CostSpec,
|
||
price_adjustment: str,
|
||
daily: pd.DataFrame,
|
||
eligibility_fns: list,
|
||
) -> BacktestResult:
|
||
"""组合回测入口：多策略 Borda 打分 → 持仓区间 runner → BacktestResult。
|
||
|
||
`eligibility_fns` 与 `strategies` 一一对应（每个策略一个「as_of→合格集」闭包，
|
||
可为 None 表示该策略无额外过滤）；由服务层用既有 selection 求值器装配。
|
||
返回结果的 config_snapshot 由调用方填入 ComboRunSpec（含策略+成本快照）以保证可复现。
|
||
"""
|
||
score, combined_elig = combine_strategy_scores(daily, strategies, eligibility_fns)
|
||
close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index()
|
||
runner = HoldingBandRunner(
|
||
combo=combo, costs=costs, score=score, close=close, eligibility_fn=combined_elig,
|
||
)
|
||
result = runner.run()
|
||
# 固化可复现规格（AGENT.md §21）：组合参数 + 当时各策略定义 + 当时成本/复权
|
||
result.config_snapshot = ComboRunSpec(
|
||
combo=combo, strategies=strategies, costs=costs, price_adjustment=price_adjustment,
|
||
).model_dump(mode="json")
|
||
return result
|