mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 16:54:20 +08:00
Justin Gu
05dc9a74af
新增两层能力： 1. 策略库：单标的/组合回测结果可保存到本地 SQLite 单文件 (~/.easy_tdx/strategies.db)，策略库页可载入回填、重跑、删除。 2. 多策略组合回测：勾选 N 个单标的策略，各拿 1/N 资金、各跑原标的， 净值曲线按日期并集对齐求和，组合结果含 19 项完整绩效指标 + 持仓表。 后端：strategy_store.py(SQLite CRUD) + multi_strategy_engine.py(资金分仓引擎) + routers/strategies.py + /backtest/multi-strategy/run/async。 前端：StrategiesView.vue + 保存策略按钮 + 复用组合页图表组件。 895 单测全绿（+24 新增），ruff/mypy strict/前端 vue-tsc 全通过。
246 lines
9.9 KiB
Python
246 lines
9.9 KiB
Python
"""多策略组合回测引擎（资金分仓 / 并行制）。
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与 :class:`~easy_tdx.backtest.portfolio_engine.PortfolioBacktestEngine` 的区别：
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- 后者是「**一个**策略 × **多只**股票」，资金按股票均分。
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- 本引擎是「**多个**策略 × **各自**原标的」，资金按策略均分，每个策略独立回测，
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各自的净值曲线按日期对齐后求和，得到组合整体净值曲线。
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典型场景：用户在策略库勾选若干「好策略」，各跑在它保存时的标的上，看综合表现。
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用法::
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engine = MultiStrategyEngine(
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strategies=[
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StrategySlot(label="双均线交叉", symbol="SH:601088", strategy=strat_a, df=df_a),
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StrategySlot(label="RSI反转", symbol="SZ:000001", strategy=strat_b, df=df_b),
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],
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total_cash=1_000_000,
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)
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result = engine.run()
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print(result.total_performance)
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输出结构与 :class:`~easy_tdx.backtest.portfolio_engine.PortfolioResult` 一致，便于
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前端复用组合页的净值曲线 / 对比表 / 叠加图组件。``individual_results`` 的 key 形如
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``"双均线交叉@SH:601088"``（既能区分同标的不同策略，又一眼看清跑哪个票）。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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import pandas as pd
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from easy_tdx.backtest.engine import BacktestEngine
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from easy_tdx.backtest.strategy import Strategy
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from easy_tdx.backtest.types import BacktestResult
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@dataclass
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class StrategySlot:
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"""单个策略槽位：一个已构造的策略实例 + 它要跑的标的标识与 K 线。
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Attributes:
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label: 策略展示名（如 "双均线交叉"），用于拼 individual_results 的 key。
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symbol: 标的完整代码（如 "SH:601088"），仅用于标识与展示。
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strategy: 已构造（带参数）的策略实例。
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df: 该标的的 K 线 DataFrame。
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"""
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label: str
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symbol: str
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strategy: Strategy
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df: pd.DataFrame
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@dataclass
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class MultiStrategyResult:
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"""多策略组合回测结果（字段语义与 PortfolioResult 对齐，便于前端复用）。
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Attributes:
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total_performance: 组合整体绩效（资金加权收益率 + 策略数 + 总资金）。
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individual_results: 每个策略槽位的独立回测结果，key 形如 "{label}@{symbol}"。
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equity_allocation: 每个槽位的资金分配比例（均分时各 1/N）。
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combined_equity: 组合整体净值曲线（各槽位按日期并集 ffill 对齐后求和），
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列: datetime / total / drawdown / drawdown_pct。
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"""
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total_performance: dict[str, float]
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individual_results: dict[str, BacktestResult]
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equity_allocation: dict[str, float]
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combined_equity: pd.DataFrame
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def to_dict(self) -> dict[str, Any]:
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return {
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"total_performance": self.total_performance,
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"individual_results": {k: v.to_dict() for k, v in self.individual_results.items()},
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"equity_allocation": self.equity_allocation,
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"combined_equity": self.combined_equity.to_dict(orient="records"),
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}
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class MultiStrategyEngine:
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"""多策略资金分仓组合回测引擎。
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把总资金按策略数均分，每个策略在各自的 K 线上独立回测（各跑各的），
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再把各净值曲线按日期对齐求和，得到组合整体净值。资金分配方式固定为
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"equal"（均分）——多策略组合的目标是"看综合表现"，均分是最直接的基线。
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参数与 :class:`~easy_tdx.backtest.portfolio_engine.PortfolioBacktestEngine`
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对齐（``strategy``/``stocks`` 换成 ``strategies``），便于复用资金/成本配置。
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"""
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def __init__(
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self,
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strategies: list[StrategySlot],
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total_cash: float = 1_000_000.0,
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commission: float = 0.0003,
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min_commission: float = 5.0,
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stamp_tax: float = 0.001,
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slippage: float = 0.0,
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execution: str = "next_open",
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) -> None:
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self._strategies = strategies
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self._total_cash = total_cash
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self._commission = commission
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self._min_commission = min_commission
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self._stamp_tax = stamp_tax
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self._slippage = slippage
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self._execution = execution
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def _compute_allocations(self) -> dict[str, float]:
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"""资金均分：每个策略槽位拿 total_cash / N。"""
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n = len(self._strategies)
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if n == 0:
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return {}
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per = self._total_cash / n
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return {self._key(s): per for s in self._strategies}
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@staticmethod
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def _key(s: StrategySlot) -> str:
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"""individual_results / allocation 的统一 key："{label}@{symbol}"。"""
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return f"{s.label}@{s.symbol}"
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def run(self) -> MultiStrategyResult:
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"""逐策略独立回测，再汇总成组合整体绩效与合并净值曲线。"""
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allocations = self._compute_allocations()
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individual_results: dict[str, BacktestResult] = {}
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for slot in self._strategies:
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key = self._key(slot)
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cash = allocations.get(key, 0)
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engine = BacktestEngine(
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strategy=slot.strategy,
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cash=cash,
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commission=self._commission,
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min_commission=self._min_commission,
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stamp_tax=self._stamp_tax,
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slippage=self._slippage,
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execution=self._execution,
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)
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individual_results[key] = engine.run(slot.df)
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total_alloc = sum(allocations.values())
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equity_pct = {k: v / total_alloc if total_alloc > 0 else 0 for k, v in allocations.items()}
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combined_equity = self._build_combined_equity(individual_results, allocations)
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total_perf = self._aggregate_performance(individual_results, allocations, combined_equity)
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return MultiStrategyResult(
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total_performance=total_perf,
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individual_results=individual_results,
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equity_allocation=equity_pct,
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combined_equity=combined_equity,
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)
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def _aggregate_performance(
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self,
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results: dict[str, BacktestResult],
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allocations: dict[str, float],
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combined_equity: pd.DataFrame,
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) -> dict[str, float]:
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"""组合整体绩效：基于合并净值曲线 + 汇总成交算完整 19 项指标。
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与 PortfolioBacktestEngine 仅给 4 个字段不同，这里把合并净值曲线和所有
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槽位的成交汇总，喂给 PerformanceAnalyzer，得到与单标的回测同口径的完整
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指标（夏普/回撤/胜率/盈亏比等），便于前端复用 MetricTable 展示。
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"""
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from easy_tdx.backtest.performance import PerformanceAnalyzer
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total_cash = sum(allocations.values())
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base: dict[str, float] = {
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"total_stocks": float(len(results)), # 字段名沿用 PortfolioResult
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"total_cash": total_cash,
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}
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if not results or len(combined_equity) < 2:
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base.update({"total_return": 0.0, "annual_return": 0.0})
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return base
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# 汇总所有槽位的成交（concat 成一张表，PerformanceAnalyzer 据此算
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# 胜率/盈亏比/平均盈亏等交易类指标）。所有策略均无成交时给空表兜底。
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trade_frames = [r.trades for r in results.values() if len(r.trades) > 0]
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all_trades = (
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pd.concat(trade_frames, ignore_index=True)
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if trade_frames
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else pd.DataFrame(columns=["direction", "pnl", "rejected"])
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)
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analyzer = PerformanceAnalyzer(equity_curve=combined_equity, trades=all_trades)
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metrics = analyzer.compute()
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metrics["total_stocks"] = float(len(results))
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metrics["total_cash"] = total_cash
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return metrics
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def _build_combined_equity(
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self,
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results: dict[str, BacktestResult],
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allocations: dict[str, float],
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) -> pd.DataFrame:
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"""把各策略独立净值曲线按日期并集 ffill 对齐后求和。
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算法与 ``PortfolioBacktestEngine._build_combined_equity`` 一致：
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各策略回测日期范围可能不同（取数差异、停牌），取 datetime 并集，
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每个策略的 total 列 forward-fill 对齐到并集后求和得组合总净值，
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再算回撤。
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"""
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del allocations # 资金分配不参与曲线形状（各策略独立 full cash 回测，
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# 合并的是 normalized 的净值贡献；保持签名与 Portfolio 版一致便于对照）
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empty = pd.DataFrame(columns=["datetime", "total", "drawdown", "drawdown_pct"])
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if not results:
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return empty
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series_list: list[pd.Series] = []
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for key, result in results.items():
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ec = result.equity_curve
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if len(ec) == 0:
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continue
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dt = ec["datetime"]
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if dt.dtype.kind in "iu": # int YYYYMMDD
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dt = pd.to_datetime(dt.astype(str), format="%Y%m%d")
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elif dt.dtype != "datetime64[ns]":
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dt = pd.to_datetime(dt)
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s = pd.Series(ec["total"].to_numpy(), index=dt, name=key)
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series_list.append(s)
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if not series_list:
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return empty
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aligned = pd.concat(series_list, axis=1).sort_index()
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aligned = aligned.ffill().fillna(0)
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total = aligned.sum(axis=1)
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# 回撤：用正值约定（peak - total），与单标的 PortfolioTracker.equity_curve
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# 及 PerformanceAnalyzer 一致；EquityChart 也按正值展示（前端取负向下画）。
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peak = total.cummax()
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drawdown = peak - total
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initial = peak.iloc[0] if len(peak) > 0 and peak.iloc[0] != 0 else 1.0
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drawdown_pct = drawdown / initial
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return pd.DataFrame(
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{
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"datetime": total.index,
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"total": total.to_numpy(),
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"drawdown": drawdown.to_numpy(),
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"drawdown_pct": drawdown_pct.to_numpy(),
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}
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).reset_index(drop=True)
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