feat(backtest): add BacktestEngine with vectorized execution pipeline

- Implement BacktestEngine orchestrator with 4-step pipeline: 1. Signal generation (Strategy) 2. Order simulation (OrderSimulator) 3. Portfolio tracking (PortfolioTracker) 4. Performance analysis (PerformanceAnalyzer) - Support both strategy class and instance initialization - Add PnL calculation for sell trades - Add JSON serialization with numpy/timestamp support - Include comprehensive test coverage (12 tests, all passing) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-09-12 22:44:17 +08:00 · 2026-06-09 18:11:20 +08:00
parent 94fabccef8
commit 371915a5f9
3 changed files with 564 additions and 1 deletions
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+"""Test BacktestEngine orchestration."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from easy_tdx import MyTT
+from easy_tdx.backtest.engine import BacktestEngine
+from easy_tdx.backtest.strategy import Strategy
+
+
+def _make_df(n: int = 100, seed: int = 42) -> pd.DataFrame:
+    """Generate synthetic OHLCV data."""
+    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)
+    volume = rng.integers(1000000, 10000000, n)
+
+    dates = pd.date_range("2024-01-01", periods=n, freq="D")
+    return pd.DataFrame(
+        {
+            "datetime": dates,
+            "open": open_,
+            "high": high,
+            "low": low,
+            "close": close,
+            "volume": volume,
+        }
+    )
+
+
+class MACrossStrategy(Strategy):
+    """Simple MA crossover strategy."""
+
+    def init(self):
+        self.ma5 = self.I(MyTT.MA, self.data.close, 5)
+        self.ma20 = self.I(MyTT.MA, self.data.close, 20)
+        self.cross_up = False
+        self.cross_down = False
+
+    def next(self):
+        # Check if crossing happened on this bar
+        if self._bar_index > 0:
+            prev_ma5 = self.ma5[self._bar_index - 1]
+            prev_ma20 = self.ma20[self._bar_index - 1]
+            curr_ma5 = self.ma5[self._bar_index]
+            curr_ma20 = self.ma20[self._bar_index]
+
+            # Golden cross: ma5 crosses above ma20
+            if prev_ma5 <= prev_ma20 and curr_ma5 > curr_ma20:
+                self.buy(size=0)
+            # Death cross: ma5 crosses below ma20
+            elif prev_ma5 >= prev_ma20 and curr_ma5 < curr_ma20:
+                self.sell(size=0)
+
+
+class FixedBuyStrategy(Strategy):
+    """Strategy with fixed buy/sell at specific bars."""
+
+    def init(self):
+        pass
+
+    def next(self):
+        if self._bar_index == 5:
+            self.buy(size=100)
+        if self._bar_index == 50:
+            self.sell(size=100)
+
+
+class ChanlunStrategy(Strategy):
+    """Strategy that uses chanlun result."""
+
+    def init(self):
+        pass
+
+    def next(self):
+        if self._bar_index == 10 and hasattr(self, "chanlun"):
+            # Access chanlun result
+            _ = self.chanlun
+            self.buy(size=50)
+
+
+class PrecomputedIndicatorStrategy(Strategy):
+    """Strategy that uses precomputed indicator columns."""
+
+    def init(self):
+        # Assume BOLL_UPPER already exists in df
+        if hasattr(self.data, "BOLL_UPPER"):
+            self.boll_upper = self.data.BOLL_UPPER
+        else:
+            self.boll_upper = None
+
+    def next(self):
+        if self.boll_upper is not None and self._bar_index == 20:
+            _ = self.boll_upper[self._bar_index]
+            self.buy(size=10)
+
+
+def test_basic_run():
+    """Test basic engine run with MACrossStrategy."""
+    df = _make_df(n=200)
+    engine = BacktestEngine(MACrossStrategy, cash=100000)
+    result = engine.run(df)
+
+    # Check performance metrics
+    assert result.performance is not None
+    assert "total_return" in result.performance
+
+    # Check equity curve length
+    assert len(result.equity_curve) == 200
+
+    # Check columns
+    assert "datetime" in result.equity_curve.columns
+    assert "total" in result.equity_curve.columns
+
+
+def test_fixed_strategy():
+    """Test FixedBuyStrategy produces trades."""
+    df = _make_df(n=100)
+    engine = BacktestEngine(FixedBuyStrategy, cash=100000)
+    result = engine.run(df)
+
+    # Should have at least 2 trades
+    assert len(result.trades) >= 2, f"Expected at least 2 trades, got {len(result.trades)}"
+
+    # Check buy at bar 5
+    buy_trades = result.trades[result.trades["direction"] == "BUY"]
+    assert len(buy_trades) >= 1, "No buy trades found"
+
+    # Check sell at bar 50
+    sell_trades = result.trades[result.trades["direction"] == "SELL"]
+    assert len(sell_trades) >= 1, "No sell trades found"
+
+
+def test_result_columns():
+    """Test BacktestResult has correct columns."""
+    df = _make_df(n=100)
+    engine = BacktestEngine(MACrossStrategy)
+    result = engine.run(df)
+
+    # Equity curve columns
+    expected_ec_cols = ["datetime", "cash", "position_value", "total"]
+    for col in expected_ec_cols:
+        assert col in result.equity_curve.columns
+
+    # Trades columns
+    expected_trade_cols = ["datetime", "direction", "size", "price", "pnl"]
+    for col in expected_trade_cols:
+        assert col in result.trades.columns
+
+
+def test_to_dict():
+    """Test BacktestResult is serializable."""
+    df = _make_df(n=50)
+    engine = BacktestEngine(MACrossStrategy)
+    result = engine.run(df)
+
+    # to_dict should not raise
+    d = result.to_dict()
+    assert "performance" in d
+    assert "equity_curve" in d
+    assert "trades" in d
+
+    # to_json should not raise
+    json_str = result.to_json()
+    assert len(json_str) > 0
+
+
+def test_chanlun_injection():
+    """Test chanlun result injection."""
+    df = _make_df(n=50)
+
+    # Mock chanlun result
+    chanlun_result = {"test": "data"}
+
+    engine = BacktestEngine(ChanlunStrategy)
+    result = engine.run(df, chanlun_result=chanlun_result)
+
+    # Should have trades
+    assert len(result.trades) >= 1
+
+
+def test_this_close_warning_in_config():
+    """Test future_leak_warning in config when using this_close."""
+    df = _make_df(n=50)
+    engine = BacktestEngine(MACrossStrategy, execution="this_close")
+    result = engine.run(df)
+
+    # Config should have future_leak_warning
+    # Note: MACrossStrategy may not generate signals, so warning might be False
+    assert "future_leak_warning" in result.config
+
+
+def test_config_snapshot():
+    """Test config contains correct cash and commission."""
+    df = _make_df(n=50)
+    engine = BacktestEngine(MACrossStrategy, cash=50000, commission=0.0005, execution="next_open")
+    result = engine.run(df)
+
+    # Check config
+    assert result.config["cash"] == 50000
+    assert result.config["commission"] == 0.0005
+    assert result.config["execution"] == "next_open"
+
+
+def test_precomputed_indicator_columns():
+    """Test strategy works with precomputed indicator columns."""
+    df = _make_df(n=50)
+    # Add precomputed BOLL_UPPER column
+    df["BOLL_UPPER"] = df["close"] * 1.05
+
+    engine = BacktestEngine(PrecomputedIndicatorStrategy)
+    result = engine.run(df)
+
+    # Should not crash and should have trades
+    assert len(result.equity_curve) == 50
+
+
+def test_empty_df():
+    """Test engine with empty DataFrame."""
+    df = pd.DataFrame(columns=["datetime", "open", "high", "low", "close", "volume"])
+    engine = BacktestEngine(MACrossStrategy)
+    result = engine.run(df)
+
+    # Should return empty result
+    assert len(result.equity_curve) == 0
+    assert len(result.trades) == 0
+
+
+def test_strategy_instance_vs_class():
+    """Test engine accepts both strategy class and instance."""
+    df = _make_df(n=50)
+
+    # Test with class
+    engine1 = BacktestEngine(MACrossStrategy)
+    result1 = engine1.run(df)
+    assert len(result1.equity_curve) == 50
+
+    # Test with instance
+    strat = MACrossStrategy()
+    engine2 = BacktestEngine(strat)
+    result2 = engine2.run(df)
+    assert len(result2.equity_curve) == 50
+
+
+def test_commission_calculation():
+    """Test commission is correctly applied."""
+    df = _make_df(n=100)
+    engine = BacktestEngine(
+        FixedBuyStrategy,
+        cash=100000,
+        commission=0.001,
+        min_commission=10.0,
+    )
+    result = engine.run(df)
+
+    # Should have trades
+    assert len(result.trades) >= 2
+
+    # Check trades have commission
+    assert (result.trades["commission"] > 0).all()
+
+
+def test_pnl_calculation():
+    """Test PnL is calculated for sell trades."""
+    df = _make_df(n=100, seed=123)  # Use specific seed for predictable prices
+    engine = BacktestEngine(FixedBuyStrategy, cash=100000, commission=0.0)
+    result = engine.run(df)
+
+    # Should have trades
+    assert len(result.trades) >= 2
+
+    # Get trades - should have at least one BUY and one SELL
+    buy_trades = result.trades[result.trades["direction"] == "BUY"]
+    sell_trades = result.trades[result.trades["direction"] == "SELL"]
+
+    assert len(buy_trades) >= 1
+    assert len(sell_trades) >= 1
+
+    # PnL is calculated for sell trades
+    # Check that sell trades have PnL computed
+    assert (sell_trades["pnl"] != 0).any() or len(sell_trades) == 0
+
+    # For buy trades, PnL should be 0
+    assert (buy_trades["pnl"] == 0).all()