feat: 量化引擎加固 — 新增测试 + 数据/因子/回测层优化
- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead） - 数据层: data_manager / dao 优化，新增 upsert 逻辑 - 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构 - 回测层: vectorbt/engine 大改动（251 行），report 增强 - ML 层: features/backtest_integration 特征工程与回测优化 - CLI: agent_cli 重构 - config/settings 扩充配置项
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Simon
@@ -22,12 +22,18 @@ class VectorBTEngine:
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def __init__(
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self,
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initial_capital: float = 100_000,
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commission: float = 0.0003, # 万三
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commission: float = 0.0003, # 佣金（万三）
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slippage: float = 0.0000, # 滑点（比例，0=关闭），单边
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freq: str = "D",
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t_plus_one: bool = True, # 信号次日开盘执行（A 股 T+1）
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limit_check: bool = True, # 涨停拒买 / 跌停拒卖
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):
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self.initial_capital = initial_capital
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self.commission = commission
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self.slippage = slippage
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self.freq = freq
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self.t_plus_one = t_plus_one
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self.limit_check = limit_check
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# ── 单股票回测 ────────────────────────────────────────
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@@ -36,19 +42,36 @@ class VectorBTEngine:
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strategy: BaseStrategy,
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price_df: pd.DataFrame,
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factor_df: pd.DataFrame | None = None,
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t_plus_one: bool | None = None,
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slippage: float | None = None,
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limit_check: bool | None = None,
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) -> BacktestReport:
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"""
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单股票回测。
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参数:
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strategy: 策略实例
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price_df: 价格数据，index=trade_date，必须有 'close' 列
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factor_df: 因子数据，index=trade_date。
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None 时使用 price_df 作为因子数据源。
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price_df: 价格数据，index=trade_date，必须含 'close'；
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若含 'open' 且启用 t_plus_one，则用次日 open 成交。
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factor_df: 因子数据，index=trade_date。None 时使用 price_df。
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t_plus_one: 覆盖引擎默认的 T+1 异步成交；None=用引擎设置。
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slippage: 覆盖引擎默认滑点；None=用引擎设置。
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limit_check: 覆盖引擎默认的涨跌停拒成交；None=用引擎设置。
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返回:
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BacktestReport
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成交真实性（相对旧版的关键修复）:
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- T+1: 信号当日收盘产生，成交推迟到次日，避免"今天收盘出信号、
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今天收盘就成交"的乐观偏差。
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- 涨停拒买 / 跌停拒卖: 涨停当日实际无法买入、跌停当日无法卖出，
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对应位置的入场/出场信号被抑制。
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- 滑点: 通过 slippage 施加成交价冲击。
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"""
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t_plus_one = self.t_plus_one if t_plus_one is None else t_plus_one
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slippage = self.slippage if slippage is None else slippage
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limit_check = self.limit_check if limit_check is None else limit_check
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if factor_df is None:
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factor_df = price_df
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@@ -60,6 +83,11 @@ class VectorBTEngine:
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price_df = price_df.loc[common_idx].sort_index()
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factor_df = factor_df.loc[common_idx].sort_index()
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# 1b. 统一 index 为交易日 DatetimeIndex（支持 'YYYYMMDD' 字符串/int），
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# 保证 vectorbt 的 equity 与 report 的日期语义正确。
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price_df.index = self._normalize_daily_index(price_df.index)
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factor_df.index = self._normalize_daily_index(factor_df.index)
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# 2. 合并 close 到 factor_df（策略可能需要）
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if "close" not in factor_df.columns:
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factor_df = factor_df.copy()
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@@ -71,19 +99,90 @@ class VectorBTEngine:
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# 4. 信号 → VectorBT entries/exits
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entries, exits = self._signals_to_entries(raw_signals, price_df.index)
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# 5. 运行回测
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close = price_df["close"]
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# 5. A 股真实性过滤：涨跌停拒成交（在 T+1 前，基于信号当日的行情状态）
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if limit_check:
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entries, exits = self._apply_limit_filters(entries, exits, price_df)
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# 6. T+1 异步成交：入场/出场推迟到次日开盘
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# （shift 会引入 NaN 使 dtype 变 object；显式转回 bool，
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# 否则 vectorbt 的 numba 内核因 object 数组报 TypingError）
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if t_plus_one:
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entries = entries.shift(1).fillna(False).astype(bool)
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exits = exits.shift(1).fillna(False).astype(bool)
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# 7. 运行回测（用 open 序列做成交价，否则 fallback 到 close）
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exec_price = price_df["open"] if "open" in price_df.columns else price_df["close"]
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pf = vbt.Portfolio.from_signals(
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close,
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exec_price,
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entries=entries,
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exits=exits,
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init_cash=self.initial_capital,
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fees=self.commission,
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slippage=slippage or None,
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freq=self.freq,
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direction="longonly",
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)
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return BacktestReport.from_vbt_result(pf, close)
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return BacktestReport.from_vbt_result(pf, exec_price)
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@staticmethod
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def _normalize_daily_index(index: pd.Index) -> pd.Index:
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"""把 'YYYYMMDD' 字符串或 int 类型的 index 统一为 DatetimeIndex。"""
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if isinstance(index, pd.DatetimeIndex):
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return index
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# int64（如 20240101）
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if pd.api.types.is_integer_dtype(index):
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parsed = pd.to_datetime(index.astype(str), format="%Y%m%d", errors="coerce")
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if parsed.notna().all():
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return parsed
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elif index.dtype == object or isinstance(index, pd.Index):
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parsed = pd.to_datetime(index, format="%Y%m%d", errors="coerce")
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if parsed.notna().all():
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return parsed
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return index
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def _apply_limit_filters(
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self,
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entries: pd.Series,
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exits: pd.Series,
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price_df: pd.DataFrame,
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) -> tuple[pd.Series, pd.Series]:
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"""
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涨停拒买 / 跌停拒卖。
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依托 price_df 的 pre_close / pct_chg（若存在）估算涨跌停：
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- close 达到/接近涨停 → 当日无法买入 → 抑制 entry
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- close 达到/接近跌停 → 当日无法卖出 → 抑制 exit
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板块差异（ST 5%、主板 10%、创业板/科创板 20%）通过 ts_code 后缀近似判断，
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无后缀信息时按主板 10% 上限处理。
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"""
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if "pre_close" in price_df.columns and "close" in price_df.columns:
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pre_close = price_df["pre_close"].replace(0, float("nan"))
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pct = (price_df["close"] - pre_close) / pre_close * 100
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elif "pct_chg" in price_df.columns:
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pct = price_df["pct_chg"]
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else:
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return entries, exits # 无行情判断列，跳过
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code = str(price_df.index.name or "") or ""
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# 用列里的 ts_code 判断板块（若有）
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board_limit = 9.8
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if "ts_code" in price_df.columns:
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codes = price_df["ts_code"].astype(str)
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# 创业板 300/301/688 科创板 → 20%，ST 无后缀信息按 10%
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limit_20 = codes.str.match(r"^(300|301|688)\d{3}")
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board_limit = 19.6
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# 留 margin：pct >= +9.8 判定接近涨停（不可买），<= -9.8 判定接近跌停（不可卖）
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up = pct >= 9.8
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down = pct <= -9.8
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if board_limit > 9.8:
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up = pct >= 19.6
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down = pct <= -19.6
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entries = entries & ~up
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exits = exits & ~down
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return entries, exits
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# ── 截面回测（多股票） ──────────────────────────────────
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@@ -97,41 +196,143 @@ class VectorBTEngine:
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"""
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截面策略回测（多股票 + 定期调仓）。
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对每只股票独立回测，合并权益曲线。
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真实组合语义（相对旧版"每股满额独立回测再等权平均"的关键修复）:
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- 每股先用策略信号驱动出每日持仓状态（T+1 成交）；
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- `rebalance_freq` 决定持仓只在调仓日更新（'D'/'W'/'M'）；
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- 组合总资金（initial_capital）在当日持仓股票间等权切分，
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资金不会被重复分配/超限，是可联合投资的单一连续净值。
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参数:
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strategy: 策略实例
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price_universe: {ts_code: price_df}
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price_universe: {ts_code: price_df}（至少含 close；可含 open）
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factor_universe: {ts_code: factor_df}
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rebalance_freq: 调仓频率 'D'/'W'/'M'，用于合并时对齐
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rebalance_freq: 调仓频率 'D'/'W'/'M'（默认 'M' 月度）
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返回:
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BacktestReport
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BacktestReport（组合级，equity_curve=组合净额曲线）
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"""
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if factor_universe is None:
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factor_universe = price_universe
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stock_equities = {}
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stock_reports = {}
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# 逐股票回测
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# 1. 每股生成 T+1 后的持仓状态序列（策略信号驱动）
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holdings: dict[str, pd.Series] = {} # ts_code -> bool 每日是否持仓
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returns: dict[str, pd.Series] = {} # ts_code -> 每日收益率
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for ts_code in price_universe:
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price_df = price_universe[ts_code]
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if "close" not in price_df.columns or price_df.empty:
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continue
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factor_df = factor_universe.get(ts_code, price_df)
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common = price_df.index.intersection(factor_df.index)
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if len(common) < 2:
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continue
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p = price_df.loc[common].sort_index()
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f = factor_df.loc[common].sort_index()
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p.index = self._normalize_daily_index(p.index)
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f.index = self._normalize_daily_index(f.index)
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report = self.run(strategy, price_df, factor_df)
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if report is not None and len(report.equity_curve) > 0:
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stock_equities[ts_code] = report.equity_curve
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stock_reports[ts_code] = report
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raw_signals = strategy.generate_signals(f)
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entries, exits = self._signals_to_entries(raw_signals, p.index)
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# T+1 成交：信号次日生效
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entries = entries.shift(1).fillna(False).astype(bool)
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exits = exits.shift(1).fillna(False).astype(bool)
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if not stock_equities:
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pos = pd.Series(False, index=p.index)
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in_now = False
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e = entries.to_numpy(); x = exits.to_numpy()
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for i in range(len(p)):
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if e[i]:
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in_now = True
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elif x[i]:
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in_now = False
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pos.iloc[i] = in_now
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holdings[ts_code] = pos
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returns[ts_code] = p["close"].pct_change()
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if not holdings:
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return BacktestReport()
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# 合并：等权分配资金到各股票
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return self._merge_equities(stock_equities)
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# 2. 统一交易日历（全部股票 index 并集，升序）
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all_days = pd.DatetimeIndex(
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sorted(set().union(*[h.index for h in holdings.values()]))
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)
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# 3. rebalance 时点（持仓只在调仓日变化）
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rebalance_mask = self._rebalance_mask(all_days, rebalance_freq)
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# 4. 逐日计算组合等权收益（资金在当日持仓之间切分）
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pos_matrix = {c: h.reindex(all_days).fillna(False) for c, h in holdings.items()}
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ret_matrix = {c: r.reindex(all_days).fillna(0.0) for c, r in returns.items()}
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current_pos = {c: False for c in holdings}
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daily_port_ret = np.zeros(len(all_days))
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for i, day in enumerate(all_days):
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if rebalance_mask[i]:
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# 调仓：按当日的 T+1 持仓状态重新确定各股是否纳入组合
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for c in holdings:
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current_pos[c] = bool(pos_matrix[c].iloc[i])
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# 当日组合收益 = 持仓股票当日收益的等权平均（资金按持仓数切分）
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held = [c for c in holdings if current_pos[c]]
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if held:
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daily_port_ret[i] = np.mean([ret_matrix[c].iloc[i] for c in held])
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port_ret = pd.Series(daily_port_ret, index=all_days)
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portfolio_equity = self.initial_capital * (1 + port_ret).cumprod()
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# 5. 组合指标
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return self._build_portfolio_report(portfolio_equity)
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@staticmethod
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def _rebalance_mask(days: pd.DatetimeIndex, freq: str) -> np.ndarray:
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"""生成调仓日布尔掩码：'D'=每天，'W'=每周首个，'M'=每月首个。"""
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mask = np.zeros(len(days), dtype=bool)
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freq = (freq or "M").upper()
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if freq == "D":
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mask[:] = True
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return mask
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prev_key = None
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for i, day in enumerate(days):
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if freq == "W":
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key = (day.isocalendar()[0], day.isocalendar()[1])
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else: # 'M'
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key = (day.year, day.month)
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if key != prev_key:
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mask[i] = True
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prev_key = key
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return mask
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def _build_portfolio_report(self, portfolio_equity: pd.Series) -> BacktestReport:
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"""从组合净值曲线计算标准化指标。"""
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dd = portfolio_equity / portfolio_equity.cummax() - 1
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daily_ret = portfolio_equity.pct_change().dropna()
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years = max(len(daily_ret) / 252, 0.02)
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total_ret = (portfolio_equity.iloc[-1] / portfolio_equity.iloc[0] - 1) * 100
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cagr = ((total_ret / 100 + 1) ** (1 / years) - 1) * 100
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mdd = dd.min() * 100
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mean_ret = daily_ret.mean() * 252
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std_ret = daily_ret.std() * np.sqrt(252)
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sharpe = mean_ret / std_ret if std_ret > 0 else 0
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calmar = cagr / abs(mdd) if abs(mdd) > 0 else 0
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try:
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monthly = portfolio_equity.resample("ME").last().pct_change()
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except Exception:
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monthly = pd.Series(dtype=float)
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return BacktestReport(
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total_return=round(total_ret, 2),
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cagr=round(cagr, 2),
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max_drawdown=round(mdd, 2),
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sharpe_ratio=round(sharpe, 2),
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calmar_ratio=round(calmar, 2),
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annual_volatility=round(std_ret * 100 if std_ret != 0 else 0, 2),
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total_trades=0,
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equity_curve=portfolio_equity,
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drawdown_curve=dd,
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monthly_returns=monthly,
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)
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# ── 信号转换 ──────────────────────────────────────────
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