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组合回测（一策略×多标的）此前只能看 4 个数字，本轮把单标的的整条 分析链路在组合端补齐（WebUI/REST 双端）： - portfolio_engine：合并净值+汇总成交喂 PerformanceAnalyzer，输出 完整 25 项指标（SQN/最大连胜连亏/Ulcer/VaR/CVaR 等）+ 组合层 trades（symbol 列）；修复假年化与回撤口径（负值+固定分母 → 逐点峰值，与单标的/多策略一致） - walkforward：新增 PortfolioWalkForwardEngine，按标的日期并集切窗、 每窗独立开仓、合成组合窗内净值，复用 WalkForwardResult 结构 - benchmark：新增 evaluate_portfolio 一条龙（组合回测+组合WF+ 跨标的多数口径适配性体检+综合评分+组合评级+等权买入持有基准对比）， 报告结构与单标的 evaluate_strategy 同构 - performance：FIFO 持仓天数配对支持 symbol 分组 - Web：新增 POST /backtest/portfolio/wf/run/async 与 /backtest/portfolio/evaluate/run/async；组合回测响应附带 grade（组合净值口径）与 score；新增 _normalize_bars_dt 修复 按标的取数路径的字符串日期/遗留 date 列崩溃（E2E 揭露） - 前端：组合页新增附加分析勾选区与组合绩效指标/WF/一条龙/成交明细 区块；buildPortfolioAiPrompt 组合版 Prompt；抽通用 AiInterpretModal（回测页迁移共用，行为不变）；TradeTable 支持 showSymbol；EvaluatePanel 支持 gradeOverride - 测试：后端 +17 例（pytest 1603 绿）、aiPrompt 组合版 2 例、 Playwright 组合页 E2E 2 例（9/9 绿）
302 lines
11 KiB
Python
302 lines
11 KiB
Python
"""单元测试：多标的组合回测引擎."""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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import pytest
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from easy_tdx.backtest.portfolio_engine import (
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PortfolioBacktestEngine,
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StockData,
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)
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from easy_tdx.backtest.strategy import Strategy
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class SimpleBuyStrategy(Strategy):
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"""简单策略：bar 5 买入，bar 30 卖出."""
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def init(self) -> None:
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pass
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def next(self) -> None:
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if self._bar_index == 5 and self.position["size"] == 0:
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self.buy(size=0)
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elif self._bar_index == 30 and self.position["size"] > 0:
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self.sell(size=0)
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def _make_df(n: int = 100, seed: int = 42) -> pd.DataFrame:
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"""生成随机 OHLCV DataFrame."""
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rng = np.random.default_rng(seed)
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close = 100.0 + np.cumsum(rng.normal(0, 1, n))
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high = close + rng.uniform(0, 1, n)
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low = close - rng.uniform(0, 1, n)
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open_ = low + rng.uniform(0, high - low, n)
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vol = rng.integers(1000000, 10000000, n).astype(float)
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return pd.DataFrame(
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{
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"datetime": pd.date_range("2024-01-01", periods=n, freq="D"),
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"open": open_,
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"high": high,
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"low": low,
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"close": close,
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"vol": vol,
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"amount": vol * close,
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}
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)
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class TestPortfolioBacktest:
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"""测试组合回测引擎."""
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def test_basic_portfolio_run(self) -> None:
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"""基本组合回测应正常完成."""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("600000", "SH", _make_df(100, seed=99)),
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]
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=stocks,
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total_cash=200000,
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)
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result = engine.run()
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assert result.total_performance is not None
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assert "total_return" in result.total_performance
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assert len(result.individual_results) == 2
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assert result.total_performance["total_stocks"] == 2
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def test_equal_allocation(self) -> None:
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"""均等分配：每只标的资金应为总资金/标的数."""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("000002", "SZ", _make_df(100, seed=99)),
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]
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=stocks,
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total_cash=100000,
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allocation="equal",
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)
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result = engine.run()
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# 每只标的分配 50000
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assert result.equity_allocation["SZ000001"] == 0.5
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assert result.equity_allocation["SZ000002"] == 0.5
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def test_empty_stocks(self) -> None:
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"""空标的列表应返回零绩效."""
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=[],
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total_cash=100000,
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)
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result = engine.run()
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assert result.total_performance["total_return"] == 0.0
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assert len(result.individual_results) == 0
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def test_to_dict_serializable(self) -> None:
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"""结果应可序列化为字典."""
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stocks = [StockData("000001", "SZ", _make_df(100))]
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=stocks,
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total_cash=100000,
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)
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result = engine.run()
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d = result.to_dict()
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assert "total_performance" in d
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assert "individual_results" in d
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assert "equity_allocation" in d
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assert "combined_equity" in d
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class TestStrategyInstanceParams:
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"""测试策略实例（带参数）的透传——Phase 3 引擎改造的核心."""
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def test_strategy_instance_params_passed_through(self) -> None:
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"""传策略实例时，参数应透传到每个标的（而非用默认值）."""
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from easy_tdx.backtest.strategies import get_registry
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entry = get_registry().get("ma_cross")
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strategy_instance = entry.build({"fast": 10, "slow": 30})
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stocks = [
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StockData("000001", "SZ", _make_df(120, seed=1)),
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StockData("000002", "SZ", _make_df(120, seed=2)),
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]
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engine = PortfolioBacktestEngine(
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strategy=strategy_instance,
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stocks=stocks,
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total_cash=200000,
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)
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result = engine.run()
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assert len(result.individual_results) == 2
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for res in result.individual_results.values():
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assert res.performance is not None
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class TestCombinedEquity:
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"""测试组合净值曲线生成."""
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def test_combined_equity_generated(self) -> None:
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"""组合净值曲线应生成且含 total/drawdown/drawdown_pct 列."""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("600000", "SH", _make_df(100, seed=99)),
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]
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=stocks,
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total_cash=200000,
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)
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result = engine.run()
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assert len(result.combined_equity) > 0
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cols = set(result.combined_equity.columns)
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assert {"datetime", "total", "drawdown", "drawdown_pct"} <= cols
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assert result.combined_equity["total"].iloc[0] > 0
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def test_combined_equity_date_alignment(self) -> None:
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"""日期范围不同的标的应正确对齐（forward-fill）."""
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stocks = [
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StockData("000001", "SZ", _make_df(80, seed=1)),
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StockData("000002", "SZ", _make_df(100, seed=2)),
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]
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=stocks,
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total_cash=200000,
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)
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result = engine.run()
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assert len(result.combined_equity) >= 100
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def test_combined_equity_empty(self) -> None:
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"""空标的列表应返回空净值曲线（带表头）."""
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engine = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy,
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stocks=[],
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total_cash=100000,
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)
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result = engine.run()
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assert len(result.combined_equity) == 0
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assert set(result.combined_equity.columns) == {
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"datetime",
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"total",
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"drawdown",
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"drawdown_pct",
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}
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class TestPortfolioFullMetrics:
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"""v1.31：组合级完整绩效指标（合并净值 + 汇总成交喂 PerformanceAnalyzer）。"""
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def test_total_performance_has_full_metrics(self) -> None:
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"""组合整体绩效应含与单标的同口径的完整指标（SQN/连胜连亏等）。"""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("600000", "SH", _make_df(100, seed=99)),
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]
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result = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy, stocks=stocks, total_cash=200000
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).run()
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perf = result.total_performance
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# 单标的 PerformanceAnalyzer 的全部关键键 + 组合字段
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for key in (
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"total_return",
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"annual_return",
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"max_drawdown",
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"sharpe",
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"sortino",
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"calmar",
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"volatility",
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"win_rate",
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"profit_factor",
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"sqn",
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"max_consecutive_wins",
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"max_consecutive_losses",
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"total_stocks",
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"total_cash",
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):
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assert key in perf, f"缺少指标 {key}"
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assert perf["total_stocks"] == 2
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assert perf["total_cash"] == 200000
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def test_annual_return_is_annualized(self) -> None:
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"""年化收益应基于时间长度换算，不再等于总收益（旧版直接赋值的简化）。"""
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stocks = [StockData("000001", "SZ", _make_df(400, seed=42))]
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result = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy, stocks=stocks, total_cash=100000
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).run()
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perf = result.total_performance
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assert perf["annual_return"] != perf["total_return"]
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def test_drawdown_pct_positive_and_relative_to_peak(self) -> None:
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"""drawdown/drawdown_pct 应为正值且相对逐点峰值（与单标的/多策略口径一致）。"""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("600000", "SH", _make_df(100, seed=7)),
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]
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result = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy, stocks=stocks, total_cash=200000
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).run()
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ce = result.combined_equity
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assert (ce["drawdown_pct"] >= 0).all()
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assert (ce["drawdown"] >= 0).all()
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# 回撤比例 = 回撤额 / 当时峰值
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peak = ce["total"].cummax()
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expected = (peak - ce["total"]) / peak.where(peak != 0, 1.0)
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np.testing.assert_allclose(ce["drawdown_pct"], expected, rtol=1e-9)
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def test_combined_trades_have_symbol_column(self) -> None:
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"""组合层汇总成交应附 symbol 列（FIFO 按标的分组 + 前端明细表用）。"""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("600000", "SH", _make_df(100, seed=99)),
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]
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result = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy, stocks=stocks, total_cash=200000
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).run()
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assert "symbol" in result.trades.columns
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assert set(result.trades["symbol"]) == {"SZ000001", "SH600000"}
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# 每个标的的成交数 == 该标的独立回测的成交数
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for key, res in result.individual_results.items():
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n = (result.trades["symbol"] == key).sum()
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assert n == len(res.trades)
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def test_total_return_matches_capital_weighted(self) -> None:
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"""组合 total_return 应等于各标的资金加权收益（合并曲线首值=总资金）。"""
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stocks = [
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StockData("000001", "SZ", _make_df(100, seed=42)),
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StockData("600000", "SH", _make_df(100, seed=99)),
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]
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result = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy, stocks=stocks, total_cash=200000
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).run()
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weighted = sum(
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0.5 * res.performance.get("total_return", 0.0)
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for res in result.individual_results.values()
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)
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assert result.total_performance["total_return"] == pytest.approx(weighted, abs=1e-9)
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def test_to_dict_contains_trades(self) -> None:
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"""to_dict 应包含组合层成交表（REST/AI 解读消费）。"""
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stocks = [StockData("000001", "SZ", _make_df(100, seed=42))]
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result = PortfolioBacktestEngine(
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strategy=SimpleBuyStrategy, stocks=stocks, total_cash=100000
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).run()
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d = result.to_dict()
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assert "trades" in d
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assert isinstance(d["trades"], list)
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