mirror of
https://ghfast.top/https://github.com/aeroxw/easy-tdx.git
synced 2026-09-12 19:14:16 +08:00
Justin Gu
06b2617ebc
- Add --cov and --cov-fail-under=50 to CI pytest command - Replace hardcoded avg_holding_days=5.0 with FIFO-based calculation from actual trade datetime pairs (handles int and Timestamp types) - Vectorize _datetime_to_int using pd.to_datetime().strftime() instead of Python for-loop (~100-200x faster on large arrays) - Add 3 new test cases: weighted holding days, no datetime fallback, only-buys edge case
433 lines
12 KiB
Python
433 lines
12 KiB
Python
"""单元测试：绩效分析器。
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测试 PerformanceAnalyzer 的各项指标计算。
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"""
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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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from easy_tdx.backtest.performance import PerformanceAnalyzer
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def _make_equity_curve(n: int = 252, total_return: float = 0.1) -> pd.DataFrame:
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"""创建测试用资金曲线。
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Args:
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n: bar 数量
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total_return: 总收益率（例如 0.1 表示 10%）
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Returns:
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包含 datetime, total, drawdown 的 DataFrame
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"""
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# 计算每日收益率
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daily_ret = (1 + total_return) ** (1 / n) - 1
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# 生成权益曲线
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initial = 100000
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total = initial * np.cumprod(np.full(n, 1 + daily_ret))
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# 计算回撤
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peak = np.maximum.accumulate(total)
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drawdown = peak - total
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drawdown_pct = np.divide(drawdown, peak, out=np.zeros_like(drawdown), where=(peak != 0))
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return pd.DataFrame(
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{
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"datetime": np.arange(n),
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"total": total,
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"drawdown": drawdown,
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"drawdown_pct": drawdown_pct,
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}
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)
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def _make_trades() -> pd.DataFrame:
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"""创建测试用交易记录。
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Returns:
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包含 datetime, direction, pnl, rejected 的 DataFrame
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4 条交易: BUY@20240101, SELL@20240106(pnl=500), BUY@20240110, SELL@20240115(pnl=-500)
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"""
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return pd.DataFrame(
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{
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"datetime": [20240101, 20240106, 20240110, 20240115],
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"direction": ["BUY", "SELL", "BUY", "SELL"],
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"pnl": [0, 500, 0, -500],
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"rejected": [False, False, False, False],
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}
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)
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def test_total_return() -> None:
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"""测试总收益率计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 总收益率应接近 0.1（10%）
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assert abs(metrics["total_return"] - 0.1) < 0.01
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def test_max_drawdown_never_exceeds_100_pct() -> None:
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"""测试最大回撤永远不超过 100%（从峰值的跌幅）。"""
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# 模拟先涨 5 倍再腰斩的资金曲线
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total = np.concatenate(
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[
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np.linspace(100000, 600000, 126), # 涨到 60 万
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np.linspace(600000, 300000, 126), # 跌到 30 万
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]
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)
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peak = np.maximum.accumulate(total)
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drawdown = peak - total
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drawdown_pct = np.divide(drawdown, peak, out=np.zeros_like(drawdown), where=(peak != 0))
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equity = pd.DataFrame(
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{
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"datetime": np.arange(252),
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"total": total,
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"drawdown": drawdown,
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"drawdown_pct": drawdown_pct,
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}
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)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 最大回撤 = 从峰值跌 50%（30万 / 60万），不应超过 1.0
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assert 0.0 <= metrics["max_drawdown"] <= 1.0, (
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f"max_drawdown = {metrics['max_drawdown']:.2%}, should be in [0, 100%]"
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)
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assert abs(metrics["max_drawdown"] - 0.5) < 0.01
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def test_max_drawdown_zero_when_monotonic() -> None:
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"""测试单调递增时最大回撤接近 0。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 单调递增时回撤应很小（浮点误差）
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assert metrics["max_drawdown"] < 0.01
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def test_sharpe_positive_for_profit() -> None:
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"""测试正收益时夏普比率为正。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 正收益时夏普比率应大于 0
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assert metrics["sharpe"] > 0
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def test_win_rate() -> None:
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"""测试胜率计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 1 赢 1 输，胜率应接近 0.5
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assert abs(metrics["win_rate"] - 0.5) < 0.01
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def test_total_trades() -> None:
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"""测试总交易次数。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 只有 SELL 交易才算完整交易
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assert metrics["total_trades"] == 2
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def test_empty_trades() -> None:
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"""测试空交易记录。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = pd.DataFrame({"direction": [], "pnl": [], "rejected": []})
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 空 trades 时交易相关指标应为 0
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assert metrics["total_trades"] == 0
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assert metrics["win_trades"] == 0
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assert metrics["lose_trades"] == 0
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assert metrics["win_rate"] == 0
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def test_all_keys_present() -> None:
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"""测试所有 19 个指标都存在。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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expected_keys = {
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"total_return",
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"annual_return",
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"max_drawdown",
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"max_dd_duration",
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"sharpe",
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"sortino",
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"calmar",
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"total_trades",
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"win_trades",
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"lose_trades",
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"rejected_trades",
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"win_rate",
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"profit_factor",
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"avg_win",
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"avg_loss",
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"max_win",
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"max_loss",
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"avg_holding_days",
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"volatility",
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}
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assert set(metrics.keys()) == expected_keys
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def test_empty_equity_curve() -> None:
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"""测试空资金曲线返回全零指标。"""
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equity = pd.DataFrame({"total": [], "drawdown": [], "drawdown_pct": []})
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 所有指标应为 0
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assert all(v == 0 for v in metrics.values())
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def test_single_point_equity_curve() -> None:
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"""测试只有一个点的资金曲线返回全零指标。"""
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equity = pd.DataFrame({"total": [100000], "drawdown": [0], "drawdown_pct": [0.0]})
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 所有指标应为 0（需要至少 2 个点才能计算收益率）
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assert all(v == 0 for v in metrics.values())
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def test_profit_factor() -> None:
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"""测试盈亏比计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 盈利 500，亏损 500，盈亏比应为 1.0
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assert abs(metrics["profit_factor"] - 1.0) < 0.01
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def test_avg_win_and_loss() -> None:
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"""测试平均盈亏计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 1 笔盈利 500，平均盈利应接近 500
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assert abs(metrics["avg_win"] - 500) < 0.01
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# 1 笔亏损 500，平均亏损应接近 -500
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assert abs(metrics["avg_loss"] - (-500)) < 0.01
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def test_max_win_and_loss() -> None:
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"""测试最大盈亏计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 最大盈利应接近 500
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assert abs(metrics["max_win"] - 500) < 0.01
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# 最大亏损应接近 -500
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assert abs(metrics["max_loss"] - (-500)) < 0.01
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def test_annual_return() -> None:
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"""测试年化收益率计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 252 天 10% 收益，年化收益率应接近 0.1
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assert abs(metrics["annual_return"] - 0.1) < 0.01
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def test_volatility() -> None:
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"""测试年化波动率计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 波动率应大于 0
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assert metrics["volatility"] > 0
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def test_rejected_trades() -> None:
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"""测试被拒绝交易计数。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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# 创建包含被拒绝交易的记录
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trades = pd.DataFrame(
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{
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"direction": ["BUY", "SELL", "SELL", "SELL"],
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"pnl": [0, 500, 0, -500],
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"rejected": [False, False, True, False],
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}
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)
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 应有 1 笔被拒绝的交易
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assert metrics["rejected_trades"] == 1
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def test_win_trades_and_lose_trades_count() -> None:
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"""测试盈亏交易计数。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 1 笔盈利，1 笔亏损
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assert metrics["win_trades"] == 1
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assert metrics["lose_trades"] == 1
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def test_avg_holding_days_fifo() -> None:
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"""测试平均持仓天数（FIFO 配对计算）。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# BUY@20240101 → SELL@20240106: 5 天
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# BUY@20240110 → SELL@20240115: 5 天
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# 平均 = (5 + 5) / 2 = 5.0
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assert metrics["avg_holding_days"] == 5.0
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def test_avg_holding_days_weighted() -> None:
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"""测试加权平均持仓天数（不同持仓期）。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = pd.DataFrame(
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{
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"datetime": [20240101, 20240111, 20240120, 20240123],
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"direction": ["BUY", "SELL", "BUY", "SELL"],
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"pnl": [0, 500, 0, -200],
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"rejected": [False, False, False, False],
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}
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)
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# BUY@20240101 → SELL@20240111: 10 天
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# BUY@20240120 → SELL@20240123: 3 天
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# 平均 = (10 + 3) / 2 = 6.5
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assert metrics["avg_holding_days"] == 6.5
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def test_avg_holding_days_no_datetime() -> None:
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"""测试 trades 没有 datetime 列时返回 0.0。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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# 不含 datetime 列的交易记录
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trades = pd.DataFrame(
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{
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"direction": ["BUY", "SELL"],
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"pnl": [0, 500],
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"rejected": [False, False],
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}
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)
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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assert metrics["avg_holding_days"] == 0.0
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def test_avg_holding_days_only_buys() -> None:
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"""测试只有买入没有卖出时返回 0.0。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = pd.DataFrame(
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{
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"datetime": [20240101, 20240105],
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"direction": ["BUY", "BUY"],
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"pnl": [0, 0],
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"rejected": [False, False],
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}
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)
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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assert metrics["avg_holding_days"] == 0.0
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def test_max_dd_duration() -> None:
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"""测试最大回撤持续时间计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 单调递增时最大回撤持续时间应为 0
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assert metrics["max_dd_duration"] == 0
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def test_sortino() -> None:
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"""测试索提诺比率计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 正收益时索提诺比率应大于 0
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assert metrics["sortino"] > 0
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def test_calmar() -> None:
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"""测试卡玛比率计算。"""
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equity = _make_equity_curve(n=252, total_return=0.1)
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trades = _make_trades()
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analyzer = PerformanceAnalyzer(equity, trades)
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metrics = analyzer.compute()
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# 卡玛比率 = annual_return / max_drawdown
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# 由于 max_drawdown 很小，calmar 会很大
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assert metrics["calmar"] > 0
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