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GitHubandClaude Opus 4.8
371915a5f9
- 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>
289 lines
8.3 KiB
Python
289 lines
8.3 KiB
Python
"""Test BacktestEngine orchestration."""
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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 import MyTT
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from easy_tdx.backtest.engine import BacktestEngine
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from easy_tdx.backtest.strategy import Strategy
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def _make_df(n: int = 100, seed: int = 42) -> pd.DataFrame:
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"""Generate synthetic OHLCV data."""
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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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volume = rng.integers(1000000, 10000000, n)
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dates = pd.date_range("2024-01-01", periods=n, freq="D")
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return pd.DataFrame(
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{
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"datetime": dates,
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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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"volume": volume,
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}
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)
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class MACrossStrategy(Strategy):
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"""Simple MA crossover strategy."""
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def init(self):
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self.ma5 = self.I(MyTT.MA, self.data.close, 5)
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self.ma20 = self.I(MyTT.MA, self.data.close, 20)
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self.cross_up = False
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self.cross_down = False
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def next(self):
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# Check if crossing happened on this bar
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if self._bar_index > 0:
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prev_ma5 = self.ma5[self._bar_index - 1]
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prev_ma20 = self.ma20[self._bar_index - 1]
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curr_ma5 = self.ma5[self._bar_index]
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curr_ma20 = self.ma20[self._bar_index]
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# Golden cross: ma5 crosses above ma20
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if prev_ma5 <= prev_ma20 and curr_ma5 > curr_ma20:
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self.buy(size=0)
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# Death cross: ma5 crosses below ma20
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elif prev_ma5 >= prev_ma20 and curr_ma5 < curr_ma20:
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self.sell(size=0)
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class FixedBuyStrategy(Strategy):
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"""Strategy with fixed buy/sell at specific bars."""
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def init(self):
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pass
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def next(self):
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if self._bar_index == 5:
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self.buy(size=100)
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if self._bar_index == 50:
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self.sell(size=100)
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class ChanlunStrategy(Strategy):
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"""Strategy that uses chanlun result."""
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def init(self):
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pass
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def next(self):
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if self._bar_index == 10 and hasattr(self, "chanlun"):
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# Access chanlun result
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_ = self.chanlun
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self.buy(size=50)
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class PrecomputedIndicatorStrategy(Strategy):
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"""Strategy that uses precomputed indicator columns."""
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def init(self):
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# Assume BOLL_UPPER already exists in df
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if hasattr(self.data, "BOLL_UPPER"):
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self.boll_upper = self.data.BOLL_UPPER
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else:
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self.boll_upper = None
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def next(self):
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if self.boll_upper is not None and self._bar_index == 20:
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_ = self.boll_upper[self._bar_index]
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self.buy(size=10)
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def test_basic_run():
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"""Test basic engine run with MACrossStrategy."""
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df = _make_df(n=200)
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engine = BacktestEngine(MACrossStrategy, cash=100000)
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result = engine.run(df)
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# Check performance metrics
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assert result.performance is not None
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assert "total_return" in result.performance
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# Check equity curve length
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assert len(result.equity_curve) == 200
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# Check columns
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assert "datetime" in result.equity_curve.columns
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assert "total" in result.equity_curve.columns
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def test_fixed_strategy():
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"""Test FixedBuyStrategy produces trades."""
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df = _make_df(n=100)
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engine = BacktestEngine(FixedBuyStrategy, cash=100000)
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result = engine.run(df)
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# Should have at least 2 trades
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assert len(result.trades) >= 2, f"Expected at least 2 trades, got {len(result.trades)}"
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# Check buy at bar 5
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buy_trades = result.trades[result.trades["direction"] == "BUY"]
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assert len(buy_trades) >= 1, "No buy trades found"
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# Check sell at bar 50
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sell_trades = result.trades[result.trades["direction"] == "SELL"]
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assert len(sell_trades) >= 1, "No sell trades found"
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def test_result_columns():
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"""Test BacktestResult has correct columns."""
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df = _make_df(n=100)
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engine = BacktestEngine(MACrossStrategy)
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result = engine.run(df)
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# Equity curve columns
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expected_ec_cols = ["datetime", "cash", "position_value", "total"]
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for col in expected_ec_cols:
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assert col in result.equity_curve.columns
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# Trades columns
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expected_trade_cols = ["datetime", "direction", "size", "price", "pnl"]
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for col in expected_trade_cols:
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assert col in result.trades.columns
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def test_to_dict():
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"""Test BacktestResult is serializable."""
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df = _make_df(n=50)
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engine = BacktestEngine(MACrossStrategy)
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result = engine.run(df)
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# to_dict should not raise
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d = result.to_dict()
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assert "performance" in d
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assert "equity_curve" in d
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assert "trades" in d
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# to_json should not raise
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json_str = result.to_json()
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assert len(json_str) > 0
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def test_chanlun_injection():
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"""Test chanlun result injection."""
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df = _make_df(n=50)
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# Mock chanlun result
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chanlun_result = {"test": "data"}
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engine = BacktestEngine(ChanlunStrategy)
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result = engine.run(df, chanlun_result=chanlun_result)
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# Should have trades
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assert len(result.trades) >= 1
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def test_this_close_warning_in_config():
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"""Test future_leak_warning in config when using this_close."""
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df = _make_df(n=50)
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engine = BacktestEngine(MACrossStrategy, execution="this_close")
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result = engine.run(df)
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# Config should have future_leak_warning
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# Note: MACrossStrategy may not generate signals, so warning might be False
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assert "future_leak_warning" in result.config
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def test_config_snapshot():
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"""Test config contains correct cash and commission."""
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df = _make_df(n=50)
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engine = BacktestEngine(MACrossStrategy, cash=50000, commission=0.0005, execution="next_open")
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result = engine.run(df)
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# Check config
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assert result.config["cash"] == 50000
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assert result.config["commission"] == 0.0005
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assert result.config["execution"] == "next_open"
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def test_precomputed_indicator_columns():
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"""Test strategy works with precomputed indicator columns."""
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df = _make_df(n=50)
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# Add precomputed BOLL_UPPER column
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df["BOLL_UPPER"] = df["close"] * 1.05
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engine = BacktestEngine(PrecomputedIndicatorStrategy)
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result = engine.run(df)
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# Should not crash and should have trades
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assert len(result.equity_curve) == 50
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def test_empty_df():
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"""Test engine with empty DataFrame."""
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df = pd.DataFrame(columns=["datetime", "open", "high", "low", "close", "volume"])
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engine = BacktestEngine(MACrossStrategy)
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result = engine.run(df)
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# Should return empty result
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assert len(result.equity_curve) == 0
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assert len(result.trades) == 0
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def test_strategy_instance_vs_class():
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"""Test engine accepts both strategy class and instance."""
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df = _make_df(n=50)
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# Test with class
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engine1 = BacktestEngine(MACrossStrategy)
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result1 = engine1.run(df)
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assert len(result1.equity_curve) == 50
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# Test with instance
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strat = MACrossStrategy()
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engine2 = BacktestEngine(strat)
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result2 = engine2.run(df)
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assert len(result2.equity_curve) == 50
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def test_commission_calculation():
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"""Test commission is correctly applied."""
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df = _make_df(n=100)
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engine = BacktestEngine(
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FixedBuyStrategy,
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cash=100000,
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commission=0.001,
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min_commission=10.0,
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)
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result = engine.run(df)
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# Should have trades
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assert len(result.trades) >= 2
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# Check trades have commission
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assert (result.trades["commission"] > 0).all()
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def test_pnl_calculation():
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"""Test PnL is calculated for sell trades."""
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df = _make_df(n=100, seed=123) # Use specific seed for predictable prices
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engine = BacktestEngine(FixedBuyStrategy, cash=100000, commission=0.0)
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result = engine.run(df)
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# Should have trades
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assert len(result.trades) >= 2
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# Get trades - should have at least one BUY and one SELL
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buy_trades = result.trades[result.trades["direction"] == "BUY"]
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sell_trades = result.trades[result.trades["direction"] == "SELL"]
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assert len(buy_trades) >= 1
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assert len(sell_trades) >= 1
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# PnL is calculated for sell trades
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# Check that sell trades have PnL computed
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assert (sell_trades["pnl"] != 0).any() or len(sell_trades) == 0
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# For buy trades, PnL should be 0
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assert (buy_trades["pnl"] == 0).all()
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