Simon
273aee2772
- cross_sectional_zscore / composite_score / build_factor_panels 从 local_engine 迁入 quant/composite.py；新增统一入口 build_score_panel(daily, factor_specs) - local_engine re-export 保持旧引用兼容；selection/engine 的评分面板构建均指向 composite —— 选股与回测的复合分实现收敛于一处 - 回归：quant/eval/research/selection 一致性/qlib 引擎测试全过；全量 pytest 通过
293 lines
11 KiB
Python
293 lines
11 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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- 调仓日 t 的选股只使用 <=t 的因子值与收盘价
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- 成交发生在 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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- 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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"""
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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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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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ResearchSpec,
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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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TRADING_DAYS = 252
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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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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 rebalance_dates(index: pd.Index, rebalance: str, start: date) -> list[pd.Timestamp]:
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"""按频率取首个交易日（>= start）。"""
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periods = index.to_period("M" if rebalance == "monthly" else "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 [ts for ts in order if ts.date() >= start]
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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。"""
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def __init__(self, spec: ResearchSpec, score: pd.DataFrame, close: pd.DataFrame) -> 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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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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rebal = {
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d
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for d in rebalance_dates(self.close.index, self.spec.rebalance, self.spec.period[0])
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if d.date() <= end_date
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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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def _value(d: pd.Timestamp) -> float:
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total = cash
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for s, qty in shares.items():
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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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for d in dates:
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if d in rebal:
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cash = self._rebalance(
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d, cash, shares, entry_date, entry_price, trades, positions, notional
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)
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equity_rows[d] = _value(d)
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equity = pd.Series(equity_rows).sort_index()
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return self._to_result(equity, trades, positions, notional)
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# ---- 调仓（t 收盘执行，自 t+1 生效） ----
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def _rebalance(self, d, cash, shares, entry_date, entry_price, trades, positions, notional):
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close_d = self.close.loc[d]
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prev_d = self.prev_close.loc[d]
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sold_notional = 0.0
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# 1) 卖出：跌停或无价（停牌）持仓保留，其余卖出
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for s in [s for s in shares if shares[s] > 0]:
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c, p = close_d[s], prev_d[s]
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if _nan(c):
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continue # 停牌无价：保留
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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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continue # 跌停无法卖出：保留到下一调仓
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qty = shares[s]
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proceeds = qty * float(c) * (1 - self.costs.slippage_rate)
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fee = proceeds * (self.costs.commission_rate + self.costs.stamp_tax_rate)
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cash += proceeds - fee
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sold_notional += proceeds
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trades.append(
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Trade(
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entry_date=entry_date[s],
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exit_date=d.date(),
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symbol=s,
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entry_price=entry_price[s],
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exit_price=float(c),
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return_pct=(float(c) / entry_price[s] - 1.0) * 100,
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)
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)
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shares[s] = 0.0
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entry_date.pop(s, None)
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entry_price.pop(s, None)
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# 2) 买入：取得分最高且可买的 TopN（涨停 / 无价剔除）
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score_d = self.score.loc[d].dropna()
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top = score_d.sort_values(ascending=False).index.tolist()
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targets: list[str] = []
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for s in top:
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if len(targets) >= self.spec.selection.top_n:
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break
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c, p = close_d[s], prev_d[s]
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if _nan(c) or _nan(p) or p <= 0:
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continue
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if c / p >= _limit_up_ratio(s):
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continue # 涨停不可追买
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targets.append(s)
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if targets:
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budget = cash / len(targets)
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for s in targets:
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c = float(close_d[s])
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price_in = c * (1 + self.costs.slippage_rate)
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invest = budget * (1 - self.costs.commission_rate)
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shares[s] = invest / price_in
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entry_date[s] = d.date()
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entry_price[s] = price_in
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notional.append(budget)
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cash -= budget * len(targets)
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# 3) 记录调仓后仓位
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total = cash + sum(
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float(self.close.at[d, s] * qty)
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for s, qty in shares.items()
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if qty > 0 and not _nan(self.close.at[d, s])
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)
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if total > 0:
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for s, qty in shares.items():
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if qty > 0 and not _nan(self.close.at[d, s]):
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positions.append(
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Position(
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date=d.date(), symbol=s, weight=float(qty * self.close.at[d, s] / total)
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)
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)
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return cash
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# ---- 指标 ----
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def _to_result(self, equity, trades, positions, notional) -> BacktestResult:
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start, end = equity.index[0].date(), equity.index[-1].date()
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init = float(self.spec.initial_capital)
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final = float(equity.iloc[-1])
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rets = equity.pct_change().dropna()
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n = len(rets)
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total_ret = (final / init - 1.0) * 100 if init else 0.0
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annual = (
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((final / init) ** (TRADING_DAYS / max(n, 1)) - 1.0) * 100
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if final > 0 and init > 0
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else -100.0
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)
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mean_r, std_r = (float(rets.mean()), float(rets.std(ddof=1))) if n else (0.0, 0.0)
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sharpe = mean_r / std_r * math.sqrt(TRADING_DAYS) if std_r and mean_r else 0.0
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vol = std_r * math.sqrt(TRADING_DAYS) * 100
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dd = (equity / equity.cummax() - 1.0).min() * 100
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wins = [t for t in trades if t.return_pct > 0]
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win_rate = len(wins) / len(trades) * 100 if trades else 0.0
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avg_turn = (sum(notional) / len(notional) / ((init + final) / 2)) * 100 if notional else 0.0
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eq_pts = [CurvePoint(date=d.date(), value=round(float(v), 2)) for d, v in equity.items()]
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dd_series = (equity / equity.cummax() - 1.0) * 100
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drawdown = [
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CurvePoint(date=d.date(), value=round(float(v), 3)) for d, v in dd_series.items()
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]
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monthly: list[MonthlyReturn] = []
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yearly: list[YearlyReturn] = []
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if len(equity) > 1:
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m = equity.resample("ME").last().pct_change().dropna()
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monthly = [
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MonthlyReturn(
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year=int(d.year), month=int(d.month), return_pct=round(float(v) * 100, 3)
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)
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for d, v in m.items()
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]
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y = equity.resample("YE").last().pct_change().dropna()
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yearly = [
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YearlyReturn(year=int(d.year), return_pct=round(float(v) * 100, 3))
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for d, v in y.items()
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]
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summary = BacktestSummary(
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start=start,
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end=end,
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initial_capital=round(init, 2),
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final_equity=round(final, 2),
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total_return_pct=round(total_ret, 3),
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annual_return_pct=round(annual, 3),
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sharpe=round(sharpe, 3),
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max_drawdown_pct=round(float(dd), 3),
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volatility_pct=round(vol, 3),
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win_rate_pct=round(win_rate, 2),
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total_trades=len(trades),
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avg_turnover_pct=round(avg_turn, 2),
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)
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return BacktestResult(
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summary=summary,
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equity_curve=eq_pts,
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drawdown=drawdown,
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monthly_returns=monthly,
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yearly_returns=yearly,
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positions=positions,
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trades=trades,
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turnover_pct=round(sum(notional) / max(init, 1) * 100, 2),
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unimplemented=list(_DEFAULT_UNIMPLEMENTED),
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config_snapshot=self.spec.model_dump(mode="json"),
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)
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def run_spec_factor_test(
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daily: pd.DataFrame,
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spec: ResearchSpec,
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horizon_days: int = 21,
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) -> tuple[FactorTestReport, dict[str, pd.DataFrame]]:
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"""单因子测试：因子面板 + 未来 horizon 收益 → FactorTestReport。"""
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assert spec.type == "factor_test"
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factor_name = spec.factors[0].name
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panels = build_factor_panels(daily, spec.factors)
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panel = panels[0][1]
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close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index()
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close.index = pd.to_datetime(close.index)
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forward = close.shift(-horizon_days) / close - 1.0
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report = run_factor_test(panel, forward, factor_name=factor_name)
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return report, {factor_name: panel}
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def _nan(v) -> bool:
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try:
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return bool(math.isnan(float(v)))
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except (TypeError, ValueError):
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return False
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