release: v1.17.11 — Web UI 策略库（SQLite 持久化）+ 多策略资金分仓组合回测

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。

tests/unit/test_multi_strategy.py

@@ -0,0 +1,211 @@
+"""单元测试：多策略资金分仓组合回测引擎（MultiStrategyEngine）。
+
+覆盖：
+- 基本多策略回测（2~3 个策略，各跑各的 df，合并曲线）
+- 资金均分（1/N）
+- individual_results 的 key 格式 "{label}@{symbol}"
+- 合并净值曲线列结构 + 日期并集对齐
+- 空策略列表兜底
+- 同标的不同策略可区分
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from easy_tdx.backtest.multi_strategy_engine import (
+    MultiStrategyEngine,
+    StrategySlot,
+)
+from easy_tdx.backtest.strategy import Strategy
+
+
+class SimpleBuyStrategy(Strategy):
+    """简单策略：bar 5 买入，bar 30 卖出。"""
+
+    def init(self) -> None:
+        pass
+
+    def next(self) -> None:
+        if self._bar_index == 5 and self.position["size"] == 0:
+            self.buy(size=0)
+        elif self._bar_index == 30 and self.position["size"] > 0:
+            self.sell(size=0)
+
+
+class HoldStrategy(Strategy):
+    """从不交易的策略（净值曲线恒等于初始资金）。"""
+
+    def init(self) -> None:
+        pass
+
+    def next(self) -> None:
+        pass
+
+
+def _make_df(n: int = 100, seed: int = 42, start: str = "2024-01-01") -> pd.DataFrame:
+    """生成随机 OHLCV DataFrame（与 test_portfolio_engine 同构造方式）。"""
+    rng = np.random.default_rng(seed)
+    close = 100.0 + np.cumsum(rng.normal(0, 1, n))
+    high = close + rng.uniform(0, 1, n)
+    low = close - rng.uniform(0, 1, n)
+    open_ = low + rng.uniform(0, high - low, n)
+    vol = rng.integers(1_000_000, 10_000_000, n).astype(float)
+    return pd.DataFrame(
+        {
+            "datetime": pd.date_range(start, periods=n, freq="D"),
+            "open": open_,
+            "high": high,
+            "low": low,
+            "close": close,
+            "vol": vol,
+            "amount": vol * close,
+        }
+    )
+
+
+class TestMultiStrategyEngine:
+    def test_basic_run_two_strategies(self) -> None:
+        """两个策略各跑各的 df，应产出合并结果。"""
+        slots = [
+            StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), _make_df(100, seed=42)),
+            StrategySlot("RSI", "SZ:000001", SimpleBuyStrategy(), _make_df(100, seed=99)),
+        ]
+        engine = MultiStrategyEngine(slots, total_cash=1_000_000)
+        result = engine.run()
+
+        # individual_results 的 key 形如 "{label}@{symbol}"
+        assert set(result.individual_results.keys()) == {
+            "双均线@SH:601088",
+            "RSI@SZ:000001",
+        }
+        # 整体绩效含基本字段
+        assert "total_return" in result.total_performance
+        assert result.total_performance["total_stocks"] == 2
+        assert result.total_performance["total_cash"] == 1_000_000
+
+    def test_total_performance_has_full_metrics(self) -> None:
+        """组合整体绩效应含完整 19 项指标（夏普/回撤/胜率/盈亏比等），与单标的同口径。"""
+        slots = [
+            StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), _make_df(100, seed=42)),
+            StrategySlot("RSI", "SZ:000001", SimpleBuyStrategy(), _make_df(100, seed=99)),
+        ]
+        perf = MultiStrategyEngine(slots, total_cash=1_000_000).run().total_performance
+        # 关键指标都应在（来自 PerformanceAnalyzer）
+        for key in [
+            "total_return",
+            "annual_return",
+            "sharpe",
+            "sortino",
+            "calmar",
+            "max_drawdown",
+            "max_dd_duration",
+            "volatility",
+            "total_trades",
+            "win_trades",
+            "lose_trades",
+            "win_rate",
+            "profit_factor",
+            "avg_win",
+            "avg_loss",
+            "max_win",
+            "max_loss",
+        ]:
+            assert key in perf, f"缺少指标 {key}"
+        # max_drawdown 用正值约定（与单标的一致），介于 0~1
+        assert 0 <= perf["max_drawdown"] <= 1
+        # 合并净值曲线的 drawdown 也应是正值
+        result = MultiStrategyEngine(slots, total_cash=1_000_000).run()
+        assert (result.combined_equity["drawdown"] >= 0).all()
+
+    def test_capital_split_equal(self) -> None:
+        """资金按策略数均分：每个槽位 1/N。"""
+        slots = [
+            StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(50, seed=1)),
+            StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), _make_df(50, seed=2)),
+            StrategySlot("C", "SZ:000002", SimpleBuyStrategy(), _make_df(50, seed=3)),
+        ]
+        engine = MultiStrategyEngine(slots, total_cash=900_000)
+        allocs = engine._compute_allocations()  # noqa: SLF001 — 测试内部均分逻辑
+        assert len(allocs) == 3
+        assert all(v == 300_000 for v in allocs.values())
+        # equity_allocation 是占比，各 1/3
+        result = engine.run()
+        assert all(abs(v - 1 / 3) < 1e-9 for v in result.equity_allocation.values())
+
+    def test_combined_equity_has_expected_columns(self) -> None:
+        """合并净值曲线应有 datetime/total/drawdown/drawdown_pct 列。"""
+        slots = [
+            StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(60, seed=7)),
+        ]
+        engine = MultiStrategyEngine(slots, total_cash=500_000)
+        result = engine.run()
+        cols = set(result.combined_equity.columns)
+        assert {"datetime", "total", "drawdown", "drawdown_pct"} <= cols
+        assert len(result.combined_equity) > 0
+
+    def test_combined_equity_aligns_disjoint_dates(self) -> None:
+        """两个策略日期范围不同时，合并曲线应按并集对齐（ffill）。"""
+        # 策略 A 跑 2024-01 起 60 根，策略 B 跑 2024-03 起 60 根
+        df_a = _make_df(60, seed=1, start="2024-01-01")
+        df_b = _make_df(60, seed=2, start="2024-03-01")
+        slots = [
+            StrategySlot("A", "SH:601088", SimpleBuyStrategy(), df_a),
+            StrategySlot("B", "SZ:000001", SimpleBuyStrategy(), df_b),
+        ]
+        engine = MultiStrategyEngine(slots, total_cash=1_000_000)
+        result = engine.run()
+        # 合并曲线长度应至少覆盖两个范围的最晚结束日（并集）
+        assert len(result.combined_equity) >= 60
+
+    def test_empty_strategies_returns_empty_result(self) -> None:
+        """空策略列表应返回空结果，不抛异常。"""
+        engine = MultiStrategyEngine([], total_cash=1_000_000)
+        result = engine.run()
+        assert result.individual_results == {}
+        assert result.total_performance["total_return"] == 0.0
+        # combined_equity 为带表头的空 DataFrame
+        assert len(result.combined_equity) == 0
+        assert set(result.combined_equity.columns) == {
+            "datetime",
+            "total",
+            "drawdown",
+            "drawdown_pct",
+        }
+
+    def test_same_symbol_different_strategies_distinguished(self) -> None:
+        """同标的不同策略应能区分（key 含 label）。"""
+        df = _make_df(60, seed=5)
+        slots = [
+            StrategySlot("双均线", "SH:601088", SimpleBuyStrategy(), df.copy()),
+            StrategySlot("RSI", "SH:601088", HoldStrategy(), df.copy()),
+        ]
+        engine = MultiStrategyEngine(slots, total_cash=1_000_000)
+        result = engine.run()
+        # 两个 key 不同，都带同一 symbol
+        assert "双均线@SH:601088" in result.individual_results
+        assert "RSI@SH:601088" in result.individual_results
+
+    def test_hold_strategy_keeps_initial_capital(self) -> None:
+        """从不交易的策略，其净值曲线末值应等于初始分得资金。"""
+        slots = [
+            StrategySlot("Hold", "SH:601088", HoldStrategy(), _make_df(40, seed=1)),
+        ]
+        engine = MultiStrategyEngine(slots, total_cash=1_000_000)
+        result = engine.run()
+        ec = result.individual_results["Hold@SH:601088"].equity_curve
+        # 不交易 → 末值 ≈ 初始资金 1_000_000（单策略拿全部）
+        assert abs(ec["total"].iloc[-1] - 1_000_000) < 1.0
+
+    def test_to_dict_serializable(self) -> None:
+        """to_dict 应产出 JSON 兼容结构（含 individual_results / combined_equity）。"""
+        slots = [
+            StrategySlot("A", "SH:601088", SimpleBuyStrategy(), _make_df(50, seed=1)),
+        ]
+        result = MultiStrategyEngine(slots, total_cash=500_000).run()
+        d = result.to_dict()
+        assert "total_performance" in d
+        assert "individual_results" in d
+        assert "combined_equity" in d
+        assert isinstance(d["individual_results"]["A@SH:601088"], dict)