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）。
788 lines
34 KiB
Python
788 lines
34 KiB
Python
"""LocalEngine —— 默认研究引擎（纯 pandas，AGENT.md §40 简单可替换优先）。
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无未来函数纪律：
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- 择股日 s 的选股只使用 <= s 的因子值、条件字段与收盘价
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- 成交发生在调仓日 t 收盘（价格 = close[t] ± 滑点）；t 当日组合收益用 t-1 收盘持仓结算，
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调仓在 t 收盘生效、自 t+1 起计收益 —— 不存在「当日买入当日计收益」的未来函数
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- 顺延买入（defer_buy）只在**之后的交易日**补成交，绝不回溯到择股日之前
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- 涨跌停 / 停牌约束按可达信息近似建模，未建模部分显式写入结果 unimplemented
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周期模型（本次扩展，见 ResearchSpec）：
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- 择股日集合 S：每 m 个月（selection_interval_months），锚定回测起始月
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- 调仓日集合 R：每 y 个月（rebalance_interval_months，缺省 = m）
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- m 未给 → S = R（每次调仓都重新择股，与历史行为一致）
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- 候选池 = S 日按因子分排序的 Top n（top_n）；实际持仓 = 池内前 x（hold_top_x）
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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.research import (
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ActionRecord,
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BacktestResult,
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BacktestSummary,
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CurvePoint,
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FactorTestReport,
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MonthlyReturn,
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Position,
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RankedPick,
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ResearchSpec,
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SymbolCurve,
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Trade,
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YearlyReturn,
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)
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from app.quant.composite import ( # noqa: F401 —— re-export（模块化后旧引用仍可用）
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build_factor_panels,
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composite_score,
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cross_sectional_zscore,
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)
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from app.quant.evaluation import run_factor_test
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from app.quant.portfolio import (
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allocate_with_max_position,
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equal_weight_budget,
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unimplemented_notes,
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)
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TRADING_DAYS = 252
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# 个股收益曲线数量上限：
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# None（默认）= 不截断，期内持有的每只都输出（「完整存档」；体积由归档侧的
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# 字节预算兜底，见 app/application/services/experiment_archive.py）；
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# 数字 = 按 |期末收益| 降序截断，且如实写入 unimplemented 说明。
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# 取值优先级：本模块变量（测试 monkeypatch 用）> config research.archive_curve_limit。
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_MAX_SYMBOL_CURVES: int | None = None
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def _resolved_curve_limit() -> int | None:
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"""当前生效的曲线数量上限（None = 完整输出）。"""
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if _MAX_SYMBOL_CURVES is not None:
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return _MAX_SYMBOL_CURVES
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from app.core.config import get_settings
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return get_settings().research_archive_curve_limit
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# 恒定的未建模说明（AGENT.md §24：未实现项必须在结果中显式标注）
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_DEFAULT_UNIMPLEMENTED = [
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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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"调仓为「全部卖出 → 按目标等权重新买入」，未做权重漂移微调："
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"保留在目标名单中的股票也会产生一次完整买卖往返，交易成本估计偏保守"
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),
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(
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"股票池来自本地行情表（已含退市股：stock.status='D' 且带 delist_date，"
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"退市日之后自动退出池子）。残余偏差：库里仅有 2019-12 之后退市的标的，"
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"更早退市者无行情数据"
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),
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(
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"exclude_st 的名称口径见 config_snapshot.price_basis.name_basis：时点口径依赖 "
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"stock_name_history（sync namechange），未同步时回退最新名称快照，"
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"会漏掉「曾是高股息、后来才变 ST」的股息陷阱样本"
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),
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]
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def _limit_up_ratio(symbol: str) -> float:
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"""按板块近似涨跌停幅度。"""
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code = symbol[:3]
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if code in {"300", "301", "688"}:
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return 1.199
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if code.startswith(("8", "4", "92")):
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return 1.299
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return 1.099
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def _month_firsts(index: pd.Index) -> list[pd.Timestamp]:
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"""每个自然月的首个交易日（按 index 顺序）。"""
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periods = index.to_period("M")
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seen: dict = {}
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order: list[pd.Timestamp] = []
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for ts, per in zip(index, periods, strict=True):
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if per not in seen:
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seen[per] = ts
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order.append(ts)
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return order
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def _week_firsts(index: pd.Index) -> list[pd.Timestamp]:
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"""每个自然周的首个交易日。"""
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periods = index.to_period("W")
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seen: dict = {}
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order: list[pd.Timestamp] = []
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for ts, per in zip(index, periods, strict=True):
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if per not in seen:
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seen[per] = ts
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order.append(ts)
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return order
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def _month_seq(ts: pd.Timestamp) -> int:
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"""月序号（year*12+month），用于「每 m 个月」的锚定计算。"""
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return int(ts.year) * 12 + int(ts.month)
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def rebalance_dates(
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index: pd.Index,
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rebalance: str,
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start: date,
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end: date | None = None,
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every_months: int | None = None,
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) -> list[pd.Timestamp]:
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"""调仓/择股日集合（按频率取首个交易日，>= start）。
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every_months=n（n>0）：忽略 rebalance 频率，改用「每 n 个月」——
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锚定 **首个 >= start 的交易日所在月**（锚点月 t0），取月序号满足
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`(t - t0) % n == 0` 的月份的首个交易日。
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这样 2020-01-01 起、n=6 → 2020-01、2020-07、2021-01 …；
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起始日改为 2020-03-15（该月首个交易日 03-02 早于 start）→ **2020-03-16**
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（03 月内首个 >= start 的交易日）、2020-09、2021-03 …。
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⚠️ 刻意**不丢弃锚点月**：若把锚点月整体过滤掉，m=y=6 且起始日非月初时
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会白等 6 个月才首次建仓（净值在前期恒等于初始资金，指标明显失真）。
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every_months=None：沿用 weekly / monthly / **daily** 频率。
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- daily：区间内**每个交易日**都是调仓日（回测组合的「日频调仓」）。
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- weekly / monthly：原行为，保持向后兼容。
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"""
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if every_months and every_months > 0:
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firsts = _week_firsts(index) if rebalance == "weekly" else _month_firsts(index)
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# 锚点 = start 所在月内首个 >= start 的交易日（可能不是该月首个交易日）
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days = pd.DatetimeIndex(index)
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after_start = days[days >= pd.Timestamp(start)]
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if len(after_start) == 0:
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return []
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anchor_ts = after_start[0]
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anchor = _month_seq(anchor_ts)
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# 后续月份：月序号与锚点月相差整数倍 m，取该月首个交易日
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out = [anchor_ts] + [
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ts
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for ts in firsts
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if _month_seq(ts) > anchor
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and (_month_seq(ts) - anchor) % every_months == 0
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and ts.date() >= start
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and ts != anchor_ts
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]
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elif rebalance == "daily":
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# 日频：区间内每个交易日
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days = pd.DatetimeIndex(index)
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out = [ts for ts in days if ts.date() >= start]
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else:
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firsts = _week_firsts(index) if rebalance == "weekly" else _month_firsts(index)
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out = [ts for ts in firsts if ts.date() >= start]
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if end is not None:
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out = [ts for ts in out if ts.date() <= end]
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return out
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@dataclass
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class PendingBuy:
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"""顺延买单：调仓日买不进（涨停/停牌）时挂起，之后逐日重试。
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仅当 SelectionSpec.defer_buy=True 时产生；到下一次调仓日仍未成交则作废。
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`budget` 是调仓日按等权/上限为该标的预留的资金，成交时按 min(budget, 可用现金) 执行。
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"""
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symbol: str
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budget: float
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since: date
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@dataclass
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class EngineResult:
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equity: pd.Series # index=date -> equity
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trades: list[Trade]
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positions: list[Position]
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rebalance_notional: list[float]
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class TopKBacktestRunner:
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"""TopK 等权、固定调仓频率的低频回测（支持择股/调仓双周期与顺延买入）。"""
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def __init__(
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self,
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spec: ResearchSpec,
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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.spec = spec
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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.costs = spec.costs
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# 上一有效收盘（用于涨跌停与收益结算，处理停牌日）
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self.prev_close = self.close.ffill().shift(1)
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# 条件过滤（可选）：(as_of: date) -> set[symbol] | None
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# 由 Service 注入（复用 selection.eligible_symbols），保证回测与选股同一套求值逻辑
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self.eligibility_fn = eligibility_fn
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# M9-2：调仓意图与信号/成交记录（v3 §20.3/§22.3）
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self.selection_history: list[RankedPick] = []
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self.signal_history: list[ActionRecord] = []
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# 当前候选池（择股日刷新）：current_ranked 为全市场可评分排序，current_pool = 前 n
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self.current_ranked: list[str] = []
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self.current_pool: list[str] = []
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# 本次回测期内被持有过的股票（用于个股收益曲线）
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self.traded_symbols: list[str] = []
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self._traded: set[str] = set()
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# 无前收导致涨停无法判定、按可买处理并**实际成交**的标的集合（结果中如实标注）
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self._no_prev_close_symbols: set[str] = set()
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# ---- 主流程 ----
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def run(self) -> BacktestResult:
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end_date = self.spec.period[1]
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dates = [d for d in self.close.index if self.spec.period[0] <= d.date() <= end_date]
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if not dates:
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raise ValueError(
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f"回测区间 {self.spec.period[0]}~{end_date} 内没有任何行情数据，无法回测"
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)
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m = self.spec.effective_selection_months
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y = self.spec.effective_rebalance_months
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rebal = set(
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rebalance_dates(
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self.close.index, self.spec.rebalance, self.spec.period[0], end_date, every_months=y
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)
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)
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if m is None:
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# 未给 m：每次调仓都重新择股（与历史行为一致）
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select = set(rebal)
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else:
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select = set(
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rebalance_dates(
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self.close.index, self.spec.rebalance, self.spec.period[0], end_date,
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every_months=m,
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)
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)
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cash = float(self.spec.initial_capital)
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shares: dict[str, float] = {}
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entry_date: dict[str, date] = {}
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entry_price: dict[str, float] = {}
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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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pending: list[PendingBuy] = []
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# 个股收益曲线：cum = 该股「持仓期间」的累计净值（1.0 = 未涨未跌）
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cum: dict[str, float] = {}
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curve_rows: dict[str, list[CurvePoint]] = {}
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def _value(d: pd.Timestamp) -> float:
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total = cash
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for s, qty in shares.items():
|
||
if qty <= 0:
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continue
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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 # 无行情日不计该仓（停牌近似，见 unimplemented）
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total += float(qty * px)
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return total
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|
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for d in dates:
|
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# 1) 先用「上一交易日收盘持仓」结算当日个股收益（与组合净值同一时序口径：
|
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# 当日收益来自昨日持仓）→ 建仓当日不计收益、卖出当日仍有收益
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self._accrue_symbol_returns(d, shares, cum, curve_rows)
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# 2) 择股 / 调仓（成交发生在当日收盘）
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if d in select:
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self.current_ranked, self.current_pool = self._select(d)
|
||
if d in rebal:
|
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# 上一次调仓挂起的顺延单作废（只在两次调仓之间有效）
|
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pending = []
|
||
cash = self._rebalance(
|
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d, cash, shares, entry_date, entry_price, trades, positions, notional,
|
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pending,
|
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)
|
||
elif pending:
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cash = self._fill_pending(d, cash, shares, entry_date, entry_price, pending, notional)
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equity_rows[d] = _value(d)
|
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# 3) 建仓当日补「基准点」：成交在当日收盘、收益自次日起计；该点使 BUY 标注
|
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# 能精确落在曲线上，也让多段持仓的分段起点可见（见 _mark_curve_dates）
|
||
self._mark_curve_dates(d, shares, cum, curve_rows)
|
||
|
||
equity = pd.Series(equity_rows).sort_index()
|
||
return self._to_result(equity, trades, positions, notional, cum, curve_rows)
|
||
|
||
# ---- 择股（择股日 s：只用 <= s 的数据） ----
|
||
|
||
def _select(self, d: pd.Timestamp) -> tuple[list[str], list[str]]:
|
||
"""返回 (全市场可评分排序, 候选池 top n)，并记录 selection_history。"""
|
||
score_d = self.score.loc[d].dropna()
|
||
eligible = None
|
||
if self.eligibility_fn is not None:
|
||
eligible = self.eligibility_fn(d.date())
|
||
if eligible is not None:
|
||
score_d = score_d[score_d.index.isin(eligible)]
|
||
ranked = score_d.sort_values(ascending=False).index.tolist()
|
||
n = self.spec.selection.top_n
|
||
pool = ranked[:n]
|
||
day = d.date()
|
||
for rank, sym in enumerate(pool, start=1):
|
||
self.selection_history.append(
|
||
RankedPick(date=day, symbol=sym, rank=rank, score=round(float(score_d[sym]), 6))
|
||
)
|
||
return ranked, pool
|
||
|
||
# ---- 调仓（t 收盘执行，自 t+1 生效） ----
|
||
|
||
def _rebalance(
|
||
self, d, cash, shares, entry_date, entry_price, trades, positions, notional, pending
|
||
):
|
||
close_d = self.close.loc[d]
|
||
prev_d = self.prev_close.loc[d]
|
||
day = d.date()
|
||
|
||
# 1) 卖出：逐持仓记录 SELL 意图与实际成交（跌停/无价则保留并说明）
|
||
for s in [s for s in shares if shares[s] > 0]:
|
||
c, p = close_d[s], prev_d[s]
|
||
if _nan(c):
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
|
||
reject_reason="无行情（停牌），保留持仓")
|
||
)
|
||
continue # 停牌无价：保留
|
||
if not _nan(p) and p > 0 and c / p <= 1.0 - (_limit_up_ratio(s) - 1.0):
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=s, signal="SELL", filled=False,
|
||
reject_reason="跌停无法卖出，保留到下一调仓")
|
||
)
|
||
continue # 跌停无法卖出：保留到下一调仓
|
||
qty = shares[s]
|
||
proceeds = qty * float(c) * (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=float(c))
|
||
)
|
||
trades.append(
|
||
Trade(
|
||
entry_date=entry_date[s],
|
||
exit_date=day,
|
||
symbol=s,
|
||
entry_price=entry_price[s],
|
||
exit_price=float(c),
|
||
return_pct=(float(c) / entry_price[s] - 1.0) * 100,
|
||
)
|
||
)
|
||
shares[s] = 0.0
|
||
entry_date.pop(s, None)
|
||
entry_price.pop(s, None)
|
||
|
||
# 2) 买入意图：候选池（= selection_history 记录的那批）
|
||
picks = list(self.current_pool)
|
||
x = min(self.spec.selection.x, len(picks))
|
||
sel = self.spec.selection
|
||
if x < self.spec.selection.x:
|
||
self.signal_history.append(
|
||
ActionRecord(
|
||
date=day,
|
||
symbol="",
|
||
signal="BUY",
|
||
filled=False,
|
||
reject_reason=(
|
||
f"候选池仅 {len(picks)} 只（< 目标持仓 x={self.spec.selection.x}），"
|
||
"按池内数量持仓"
|
||
),
|
||
)
|
||
)
|
||
|
||
def _buyable(sym) -> tuple[bool, str | None]:
|
||
c, p = close_d[sym], prev_d[sym]
|
||
if _nan(c):
|
||
return False, "无行情（停牌），无法买入"
|
||
if _nan(p) or p <= 0:
|
||
# 无有效前收（数据窗口起点 / 长期停牌后复牌）：无法判定涨停 → 按可买处理。
|
||
# 这里不计数：_buyable 是纯探测函数（替补扫描会重复调用同一标的），
|
||
# 计数放在真实成交路径 `_execute_buy`，避免把探测次数报成买入次数。
|
||
return True, None
|
||
if c / p >= _limit_up_ratio(sym):
|
||
return False, "涨停，无法追买"
|
||
return True, None
|
||
|
||
# 目标名单：默认 = 池内前 x；allow_substitute=True 时从全市场排序继续往下找
|
||
targets: list[str] = []
|
||
if sel.allow_substitute:
|
||
for sym in self.current_ranked:
|
||
if len(targets) >= self.spec.selection.x:
|
||
break
|
||
ok, _ = _buyable(sym)
|
||
if ok:
|
||
targets.append(sym)
|
||
else:
|
||
targets = picks[:x]
|
||
|
||
pending_specs: list[tuple[str, str | None]] = []
|
||
spends: dict[str, float] = {}
|
||
if targets:
|
||
cap = self.spec.portfolio.max_position_pct
|
||
if cap is None:
|
||
# 默认等权：按「目标持仓数」均分可用现金（顺延未成交的部分留作现金）
|
||
budget = equal_weight_budget(cash, len(targets))
|
||
spends = {s: budget for s in targets}
|
||
else:
|
||
# Portfolio v1.1：按单股上限（相对当日组合市值）分配，超出部分留现金
|
||
equity_now = cash + sum(
|
||
float(self.close.at[d, s] * qty)
|
||
for s, qty in shares.items()
|
||
if qty > 0 and not _nan(self.close.at[d, s])
|
||
)
|
||
spends = allocate_with_max_position(cash, targets, equity_now, cap)
|
||
|
||
for s in targets:
|
||
budget = spends[s]
|
||
if budget <= 1e-9:
|
||
# 分配额过小（可用现金≈0 或上限约束）：不成交且无额度可顺延，如实留痕
|
||
self.signal_history.append(
|
||
ActionRecord(
|
||
date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason="分配额不足（可用现金≈0），未成交",
|
||
)
|
||
)
|
||
continue
|
||
ok, reason = _buyable(s)
|
||
if not ok:
|
||
if sel.defer_buy:
|
||
# 顺延：挂单到之后首个可成交交易日（本次不成交，资金留现金）
|
||
pending_specs.append((s, reason))
|
||
self.signal_history.append(
|
||
ActionRecord(
|
||
date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason=f"{reason}，顺延到之后首个可成交日买入",
|
||
)
|
||
)
|
||
else:
|
||
self.signal_history.append(
|
||
ActionRecord(
|
||
date=day, symbol=s, signal="BUY", filled=False,
|
||
reject_reason=reason or "不可买入",
|
||
)
|
||
)
|
||
continue
|
||
if not self._execute_buy(
|
||
s, budget, d, close_d[s], shares, entry_date, entry_price, notional
|
||
):
|
||
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 -= budget
|
||
|
||
# 替补模式下目标名单取自 n 名之外，池内被跳过的标的也要记录意图，
|
||
# 否则「信号有了却没买」无法解释（v3 §20.3 Signal↔Fill 透明化）。
|
||
# 非替补模式下 targets == picks[:x]，池内标的都已在上面留痕，无需再遍历。
|
||
if sel.allow_substitute:
|
||
for sym in picks:
|
||
if sym in set(targets):
|
||
continue
|
||
_ok, reason = _buyable(sym)
|
||
self.signal_history.append(
|
||
ActionRecord(date=day, symbol=sym, signal="BUY", filled=False,
|
||
reject_reason=reason or "资金不足（未成交）")
|
||
)
|
||
|
||
# 3) 记录调仓后仓位
|
||
total = cash + sum(
|
||
float(self.close.at[d, s] * qty)
|
||
for s, qty in shares.items()
|
||
if qty > 0 and not _nan(self.close.at[d, s])
|
||
)
|
||
if total > 0:
|
||
for s, qty in shares.items():
|
||
if qty > 0 and not _nan(self.close.at[d, s]):
|
||
positions.append(
|
||
Position(
|
||
date=day, symbol=s, weight=float(qty * self.close.at[d, s] / total)
|
||
)
|
||
)
|
||
# 顺延单登记：预留额度 = 调仓日的等权/上限分配额（不因后续价格变化而变）
|
||
for sym, _reason in pending_specs:
|
||
pending.append(PendingBuy(symbol=sym, budget=spends.get(sym, 0.0), since=day))
|
||
return cash
|
||
|
||
def _execute_buy(
|
||
self, s, budget, d, close_value, shares, entry_date, entry_price, notional
|
||
) -> bool:
|
||
"""按收盘价 + 滑点买入；佣金（含最低佣金）从投入资金中扣除。
|
||
|
||
现金支出恒为 budget：shares = (budget - 佣金) / (收盘价 × (1 + 滑点))。
|
||
返回是否成交（预算不足以覆盖最低佣金时不成交，调用方不得扣减现金）。
|
||
"""
|
||
c = float(close_value)
|
||
price_in = c * (1 + self.costs.slippage_rate)
|
||
commission = max(budget * self.costs.commission_rate, self.costs.min_commission)
|
||
invest = budget - commission
|
||
if invest <= 0:
|
||
return False
|
||
# 累加而非覆盖：避免「跌停/停牌未卖出而保留的旧仓位」被静默清零
|
||
shares[s] = shares.get(s, 0.0) + invest / price_in
|
||
entry_date[s] = d.date()
|
||
entry_price[s] = price_in
|
||
prev = self.prev_close.at[d, s] if d in self.prev_close.index else float("nan")
|
||
if _nan(prev) or prev <= 0:
|
||
self._no_prev_close_symbols.add(s) # 无前收→涨停不可判定，如实记入标注
|
||
notional.append(budget)
|
||
self.signal_history.append(
|
||
ActionRecord(date=d.date(), 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
|
||
|
||
# ---- 顺延买入（defer_buy）：之后逐日重试 ----
|
||
|
||
def _fill_pending(self, d, cash, shares, entry_date, entry_price, pending, notional):
|
||
close_d = self.close.loc[d]
|
||
prev_d = self.prev_close.loc[d]
|
||
remaining: list[PendingBuy] = []
|
||
for order in pending:
|
||
if order.symbol in shares and shares[order.symbol] > 0:
|
||
continue # 期间已通过其他路径持有 → 撤销该顺延单
|
||
c, p = close_d.get(order.symbol), prev_d.get(order.symbol)
|
||
if _nan(c) or _nan(p) or p <= 0:
|
||
remaining.append(order)
|
||
continue
|
||
if c / p >= _limit_up_ratio(order.symbol):
|
||
remaining.append(order) # 仍涨停 → 继续顺延
|
||
continue
|
||
budget = min(order.budget, cash)
|
||
if budget <= 1e-9:
|
||
remaining.append(order) # 无可用现金（理论上不会发生）
|
||
continue
|
||
if not self._execute_buy(
|
||
order.symbol, budget, d, c, shares, entry_date, entry_price, notional
|
||
):
|
||
remaining.append(order) # 预算不足：保留挂单（下日现金可能已变化）
|
||
continue
|
||
cash -= budget
|
||
pending[:] = remaining
|
||
return cash
|
||
|
||
# ---- 个股收益曲线 ----
|
||
|
||
def _accrue_symbol_returns(self, d, shares, cum, curve_rows) -> None:
|
||
"""逐日累计各持仓股的「持仓期收益」（以建仓日收盘为 0% 基准）。
|
||
|
||
口径：cum 以 1.0 起算，仅在该股**持有期间**按日复利（close/prev_close）。
|
||
本方法在当日调仓**之前**调用，因此：
|
||
- 建仓当日不计收益（成交发生在当日收盘）→ 不存在当日买入当日计收益的未来函数
|
||
- 卖出当日仍计收益（当日收益来自昨日持仓）
|
||
未持有期间不产生数据点（曲线不落点），多段持仓则以 cum 连乘衔接；
|
||
前端以买卖点标注区分各段持仓区间。
|
||
"""
|
||
prev_d = self.prev_close.loc[d]
|
||
close_d = self.close.loc[d]
|
||
for s, qty in shares.items():
|
||
if qty <= 0:
|
||
continue
|
||
c, p = close_d.get(s), prev_d.get(s)
|
||
if _nan(c) or _nan(p) or p <= 0:
|
||
continue # 停牌/无前收：无有效收益
|
||
cum[s] = cum.get(s, 1.0) * (float(c) / float(p))
|
||
# 只为「当日持有」的股票落点（未持有期间不落点，显著压缩结果体积）
|
||
for s, qty in shares.items():
|
||
if qty <= 0:
|
||
continue
|
||
curve_rows.setdefault(s, []).append(
|
||
CurvePoint(date=d.date(), value=round((cum.get(s, 1.0) - 1.0) * 100, 4))
|
||
)
|
||
|
||
def _mark_curve_dates(self, d, shares, cum, curve_rows) -> None:
|
||
"""为当日持有但尚未落点的股票补一个基准点（建仓当日 / 顺延成交当日）。
|
||
|
||
值为该股当前的 `cum`（新标的为 1.0 → 0%，复买标的延续上一段的累计值），
|
||
因此曲线总能在买卖点当日取到数值，前端标注不会因缺数据点而被丢弃。
|
||
"""
|
||
day = d.date()
|
||
for s, qty in shares.items():
|
||
if qty <= 0:
|
||
continue
|
||
points = curve_rows.setdefault(s, [])
|
||
if points and points[-1].date == day:
|
||
continue
|
||
points.append(
|
||
CurvePoint(date=day, value=round((cum.get(s, 1.0) - 1.0) * 100, 4))
|
||
)
|
||
|
||
# ---- 指标 ----
|
||
|
||
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.spec.initial_capital)
|
||
final = float(equity.iloc[-1])
|
||
rets = equity.pct_change().dropna()
|
||
n = len(rets)
|
||
total_ret = (final / init - 1.0) * 100 if init else 0.0
|
||
annual = (
|
||
((final / init) ** (TRADING_DAYS / max(n, 1)) - 1.0) * 100
|
||
if final > 0 and init > 0
|
||
else -100.0
|
||
)
|
||
mean_r, std_r = (float(rets.mean()), float(rets.std(ddof=1))) if n else (0.0, 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, curve_note = 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(curve_note),
|
||
config_snapshot=self.spec.model_dump(mode="json"),
|
||
)
|
||
|
||
def _symbol_curves(self, curve_rows, cum) -> tuple[list[SymbolCurve], str | None]:
|
||
"""按「期末收益绝对值」降序输出个股曲线（前端默认展示前若干只）。
|
||
|
||
返回 (曲线列表, 截断说明)。默认**不截断**（`config research.archive_curve_limit`
|
||
为 null）：期内持有的每只都输出，保证归档完整；体积由归档侧的字节预算兜底
|
||
（见 experiment_archive）。仅当配置了数字上限时才截断，并如实标注哪一部分
|
||
被丢弃、为什么（AGENT §24：不静默降级，绝不假装完整）。
|
||
"""
|
||
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)
|
||
note = None
|
||
limit = _resolved_curve_limit()
|
||
if limit is not None and len(out) > limit:
|
||
note = (
|
||
f"个股收益曲线仅输出收益绝对值最大的 {limit} 只"
|
||
f"（期内共持有 {len(out)} 只）：完整明细见 trades / signal_history"
|
||
)
|
||
out = out[:limit]
|
||
return out, note
|
||
|
||
def _unimplemented(self, curve_note: str | None = None) -> list[str]:
|
||
notes = list(_DEFAULT_UNIMPLEMENTED) + unimplemented_notes(self.spec.portfolio)
|
||
if self._no_prev_close_symbols:
|
||
notes.append(
|
||
f"有 {len(self._no_prev_close_symbols)} 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"
|
||
)
|
||
if curve_note:
|
||
notes.append(curve_note)
|
||
sel = self.spec.selection
|
||
m = self.spec.effective_selection_months
|
||
y = self.spec.effective_rebalance_months
|
||
if m is not None and y is not None and y < m:
|
||
notes.append(
|
||
f"调仓间隔 y={y} 个月 < 择股间隔 m={m} 个月：两次择股之间会复用同一候选池"
|
||
"（池子陈旧），并非每次调仓都重新择股"
|
||
)
|
||
if sel.defer_buy:
|
||
notes.append(
|
||
"顺延买入：调仓日涨停/停牌无法买入的标的挂单至之后首个可成交交易日，"
|
||
"按该日收盘价成交；到下一次调仓仍未成交则作废并留作现金"
|
||
)
|
||
if self.spec.price_adjustment == "none":
|
||
notes.append(
|
||
"行情口径为不复权：现金分红未计入收益，除权日的价格下移会被计为亏损。"
|
||
"股息类策略建议使用 price_adjustment=hfq（后复权）"
|
||
)
|
||
if not self.spec.conditions:
|
||
notes.append("未配置选股过滤条件（conditions），候选池仅由 universe + 因子排序决定")
|
||
return notes
|
||
|
||
|
||
def run_spec_factor_test(
|
||
daily: pd.DataFrame,
|
||
spec: ResearchSpec,
|
||
horizon_days: int = 21,
|
||
) -> tuple[FactorTestReport, dict[str, pd.DataFrame]]:
|
||
"""单因子测试：因子面板 + 未来 horizon 收益 → FactorTestReport。"""
|
||
assert spec.type == "factor_test"
|
||
factor_name = spec.factors[0].name
|
||
panels = build_factor_panels(daily, spec.factors)
|
||
panel = panels[0][1]
|
||
close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index()
|
||
close.index = pd.to_datetime(close.index)
|
||
forward = close.shift(-horizon_days) / close - 1.0
|
||
report = run_factor_test(panel, forward, factor_name=factor_name)
|
||
return report, {factor_name: panel}
|
||
|
||
|
||
def _nan(v) -> bool:
|
||
"""缺失判定：None / NaN / 不可转 float 一律视为「无有效值」。
|
||
|
||
注意 Series.get(key) 对不存在的键返回 None（而非 NaN），故必须把 None 判为缺失。
|
||
"""
|
||
if v is None:
|
||
return True
|
||
try:
|
||
return bool(math.isnan(float(v)))
|
||
except (TypeError, ValueError):
|
||
return True |