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#31: RebalanceEngine 缺失价格导致净值假崩塌 已持仓标的当日缺 K 线（停牌/日历错位）时 prices.get(code,0)=0， 持仓市值记 0 → 净值单日暴跌（159915 在 20210208 缺一天，持仓 ~93%， 净值 1.1M→91,845，全期最大回撤 -92%）。 修复：last_known_price forward-fill，缺失日沿用最近已知收盘价。 附带：_compute_performance 最大回撤改正值口径（与 BacktestEngine 一致）。 验证：真实 ETF 数据 max_drawdown 24.01%（backtrader 基准 27%）， total_return 220.56% 不变。 #30: PortfolioTracker 交易静默漏单 apply_trades 用 trade.datetime 作 dict key、df["datetime"].to_numpy()[i] 查找；两端类型不一致（int vs datetime64）时永不命中，交易被静默丢弃， 净值恒定（total_return=0 但 trades 表有 PnL）。 修复：改为按"位置索引"匹配（归一化 datetime 后查位置），类型无关。 验证：int-trade+datetime64-df 修复前净值=100000（恒定），修复后=100289。 测试：新增 4 回归测试（未修复代码上均失败，修复后通过）； 全套 936 passed；mypy 改动文件零错误；ruff 全绿。
362 lines
9.2 KiB
Python
362 lines
9.2 KiB
Python
"""回测引擎持仓追踪器单元测试。"""
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from __future__ import annotations
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import pandas as pd
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from easy_tdx.backtest.portfolio import PortfolioTracker
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from easy_tdx.backtest.types import Trade
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def _make_df(n: int = 10) -> pd.DataFrame:
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"""创建测试用 DataFrame。
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Args:
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n: bar 数量
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Returns:
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包含 close 和 datetime 列的 DataFrame
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"""
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close = [100.0] * 5 + [110.0] * 5
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close = close[:n]
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datetime = list(range(20240101, 20240101 + n))
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return pd.DataFrame({"close": close, "datetime": datetime})
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def test_initial_state() -> None:
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"""测试初始状态。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df, initial_cash=100000)
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assert tracker.initial_cash == 100000
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def test_buy_then_sell() -> None:
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"""测试买入再卖出。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df, initial_cash=100000)
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# bar 0 买入 100 股 @100，手续费 5
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buy_trade = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=5.0,
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slippage=0.0,
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)
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# bar 5 卖出 100 股 @110，手续费 11
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sell_trade = Trade(
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datetime=20240106,
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direction="SELL",
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size=100,
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price=110.0,
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commission=11.0,
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slippage=0.0,
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)
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tracker.apply_trades([buy_trade, sell_trade])
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equity = tracker.equity_curve
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final_cash = equity["cash"].iloc[-1]
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# 最终现金 = 100000 - 10000 - 5 + 11000 - 11 = 100984
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expected = 100984.0
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assert abs(final_cash - expected) < 1.0, f"Expected {expected}, got {final_cash}"
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def test_drawdown_calculation() -> None:
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"""测试回撤计算（无交易时回撤全为 0）。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df, initial_cash=100000)
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tracker.apply_trades([])
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equity = tracker.equity_curve
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# 无交易时，总资产应保持不变，回撤为 0
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assert (equity["drawdown"] == 0).all()
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assert (equity["drawdown_pct"] == 0).all()
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def test_equity_curve_columns() -> None:
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"""测试资金曲线列名。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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tracker.apply_trades([])
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equity = tracker.equity_curve
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expected_cols = {"datetime", "cash", "position_value", "total", "drawdown", "drawdown_pct"}
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assert set(equity.columns) == expected_cols
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def test_position_tracking() -> None:
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"""测试持仓追踪。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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# bar 0 买入 100 股
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buy_trade = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=5.0,
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slippage=0.0,
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)
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tracker.apply_trades([buy_trade])
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positions = tracker.positions
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# 买入后持仓应为 100，并持续到最后
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assert positions["size"].iloc[0] == 100
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assert positions["size"].iloc[-1] == 100
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assert (positions["size"].iloc[1:] == 100).all()
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def test_avg_price_update() -> None:
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"""测试均价更新。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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# bar 0 买入 100 股 @100
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buy1 = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=0.0,
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slippage=0.0,
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)
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# bar 1 再买入 50 股 @110
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buy2 = Trade(
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datetime=20240102,
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direction="BUY",
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size=50,
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price=110.0,
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commission=0.0,
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slippage=0.0,
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)
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tracker.apply_trades([buy1, buy2])
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positions = tracker.positions
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# 均价 = (100*100 + 50*110) / 150 = 103.33
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expected_avg = (100 * 100 + 50 * 110) / 150
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assert abs(positions["avg_price"].iloc[1] - expected_avg) < 0.01
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def test_sell_clears_position() -> None:
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"""测试卖出清空持仓。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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buy = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=0.0,
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slippage=0.0,
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)
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sell = Trade(
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datetime=20240102,
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direction="SELL",
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size=100,
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price=110.0,
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commission=0.0,
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slippage=0.0,
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)
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tracker.apply_trades([buy, sell])
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positions = tracker.positions
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# 卖出后持仓应为 0
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assert positions["size"].iloc[0] == 100
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assert positions["size"].iloc[1] == 0
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assert positions["avg_price"].iloc[1] == 0
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def test_position_market_value() -> None:
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"""测试持仓市值计算。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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buy = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=0.0,
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slippage=0.0,
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)
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tracker.apply_trades([buy])
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positions = tracker.positions
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# 前 5 个 bar 价格 100，后 5 个 bar 价格 110
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assert positions["market_value"].iloc[0] == 100 * 100
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assert positions["market_value"].iloc[5] == 100 * 110
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def test_unrealized_pnl() -> None:
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"""测试未实现盈亏计算。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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buy = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=0.0,
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slippage=0.0,
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)
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tracker.apply_trades([buy])
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positions = tracker.positions
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# 前 5 个 bar 价格 100，盈亏为 0
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assert positions["unrealized_pnl"].iloc[0] == 0
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# 后 5 个 bar 价格 110，盈亏为 (110-100)*100 = 1000
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assert positions["unrealized_pnl"].iloc[5] == 1000
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def test_rejected_trade_ignored() -> None:
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"""测试被拒绝的交易不产生影响。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df, initial_cash=100000)
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rejected_buy = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=0.0,
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slippage=0.0,
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rejected=True,
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)
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tracker.apply_trades([rejected_buy])
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equity = tracker.equity_curve
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# 现金应保持不变
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assert (equity["cash"] == 100000).all()
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assert (tracker.positions["size"] == 0).all()
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def test_commission_and_slippage() -> None:
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"""测试手续费和滑点扣减。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df, initial_cash=10000)
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buy = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=100.0,
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commission=10.0,
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slippage=5.0,
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)
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tracker.apply_trades([buy])
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equity = tracker.equity_curve
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# 现金 = 10000 - 100*100 - 10 - 5 = -15
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expected_cash = 10000 - 10000 - 10 - 5
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assert equity["cash"].iloc[0] == expected_cash
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def test_empty_trades() -> None:
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"""测试空交易列表不崩溃。"""
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df = _make_df(10)
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tracker = PortfolioTracker(df)
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tracker.apply_trades([])
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equity = tracker.equity_curve
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positions = tracker.positions
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# 所有 bar 现金应等于初始现金
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assert (equity["cash"] == 100000).all()
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# 所有 bar 持仓应为 0
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assert (positions["size"] == 0).all()
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def test_apply_trades_int_datetime_vs_datetime64_df() -> None:
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"""issue #30：trade.datetime(int) 与 df datetime(datetime64) 类型不一致时，
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交易仍应被正确应用，而非静默漏单导致净值恒定。
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复现：过去 apply_trades 用 trade.datetime 作 dict key、用
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df["datetime"].to_numpy()[i] 查找；两端类型不一致（int vs datetime64）
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时 trade_map.get(dt) 永不命中，全部交易被丢弃 → 净值恒等于初始资金，
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但 trades 表里仍有 PnL（_compute_pnls 不依赖 df 查找）。
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"""
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# df 的 datetime 列为 datetime64（真实 get_stock_kline 路径）
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df = pd.DataFrame(
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{
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"datetime": pd.date_range("2024-01-01", periods=10, freq="D"),
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"close": [10, 11, 12, 11, 10, 13, 14, 13, 15, 16],
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}
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)
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# trade.datetime 为 int YYYYMMDD（类型与 df 不一致）
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buy = Trade(
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datetime=20240101,
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direction="BUY",
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size=100,
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price=10.0,
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commission=5.0,
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slippage=0.0,
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)
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sell = Trade(
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datetime=20240106,
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direction="SELL",
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size=100,
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price=13.0,
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commission=6.0,
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slippage=0.0,
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)
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tracker = PortfolioTracker(df, initial_cash=100000)
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tracker.apply_trades([buy, sell])
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equity = tracker.equity_curve
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# 卖出后现金 = 100000 - 100*10 - 5 + 100*13 - 6 = 100289
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# 修复前此处为 100000（交易被静默丢弃）
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assert equity["cash"].iloc[-1] == 100289.0
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# 净值不应恒等于初始资金（交易生效）
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assert equity["total"].iloc[-1] != 100000.0
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def test_apply_trades_timestamp_vs_int_df() -> None:
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"""issue #30 反向：trade.datetime(Timestamp) 与 df datetime(int) 不一致。"""
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df = pd.DataFrame(
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{
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"datetime": [
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int(d.strftime("%Y%m%d")) for d in pd.date_range("2024-01-01", periods=10, freq="D")
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],
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"close": [10, 11, 12, 11, 10, 13, 14, 13, 15, 16],
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}
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)
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buy = Trade(
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datetime=pd.Timestamp("2024-01-01"),
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direction="BUY",
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size=100,
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price=10.0,
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commission=5.0,
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slippage=0.0,
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)
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tracker = PortfolioTracker(df, initial_cash=100000)
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tracker.apply_trades([buy])
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# 修复前交易被丢弃、持仓为 0
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assert tracker.positions["size"].iloc[0] == 100
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