mirror of
https://ghfast.top/https://github.com/aeroxw/easy-tdx.git
synced 2026-09-12 22:44:17 +08:00
Justin Gu
f6ae69845d
- 成交价精简为 next_open/next_close，删除 this_close/worst/best 三种 非真实模式（this_close 有未来函数偏差，worst/best 为压力测试边界） - 初始资金默认统一为 1,000,000（原 10万/20万） - 新增 presets.py：18 策略各配 1-2 参数的预设寻优网格（笛卡尔积≤200） - registry.to_schema() 返回 preset_grid 字段供前端自动填充 - 新增 POST /backtest/optimize-all/run/async：逐策略预设网格寻优 + 全局排名（OptimizeAllResult：ranking/best/per_strategy/total_grid_points） - 新增 optimize-all 端到端单测 2 例（838 单测全绿）
729 lines
24 KiB
Python
729 lines
24 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.execution import TWAPExecution, VWAPExecution
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from easy_tdx.backtest.slippage import FixedSlippage, SquareRootSlippage
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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_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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class PositionAwareStrategy(Strategy):
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"""Strategy that checks position before trading (the common pattern)."""
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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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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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if prev_ma5 <= prev_ma20 and curr_ma5 > curr_ma20:
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if self.position["size"] == 0:
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self.buy(size=0)
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elif prev_ma5 >= prev_ma20 and curr_ma5 < curr_ma20:
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if self.position["size"] > 0:
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self.sell(size=0)
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def test_position_aware_buy_sell_alternation():
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"""Regression: strategy that checks position must produce alternating BUY/SELL."""
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df = _make_df(n=300, seed=42)
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engine = BacktestEngine(PositionAwareStrategy, cash=100000)
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result = engine.run(df)
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trades = result.trades[~result.trades["rejected"]]
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directions = trades["direction"].tolist()
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# Must have both BUYs and SELLs
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assert "BUY" in directions, "No BUY trades generated"
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assert "SELL" in directions, "No SELL trades generated — position feedback broken"
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# Trades must alternate: no two consecutive BUYs or SELLs
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for i in range(1, len(directions)):
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assert directions[i] != directions[i - 1], (
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f"Consecutive same-direction trades at index {i}: "
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f"{directions[i - 1]} -> {directions[i]}"
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)
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def test_position_aware_no_duplicate_buys():
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"""After a BUY, position['size'] > 0 so strategy should not buy again."""
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df = _make_df(n=300, seed=42)
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engine = BacktestEngine(PositionAwareStrategy, cash=100000)
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result = engine.run(df)
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buy_trades = result.trades[(result.trades["direction"] == "BUY") & (~result.trades["rejected"])]
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# Each BUY's size should be reasonable (not tiny leftover from exhausted cash)
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if len(buy_trades) > 1:
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# No consecutive buys where the second is tiny (cash leftover artifact)
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sizes = buy_trades["size"].tolist()
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for i in range(1, len(sizes)):
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# Second buy in a pair should not be tiny compared to first
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# (would indicate position wasn't tracked between bars)
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if i >= 1:
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prev_size = sizes[i - 1]
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cur_size = sizes[i]
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# Allow some variance but not orders-of-magnitude difference
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if prev_size > 0:
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assert cur_size > prev_size * 0.1, (
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f"Suspicious tiny buy {cur_size} after {prev_size} — "
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f"position feedback may be broken"
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)
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# ── Stop-Loss / Take-Profit ──────────────────────────────────────────────────
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def _make_flat_df(n: int = 30, base_price: float = 100.0) -> pd.DataFrame:
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"""Generate flat OHLCV data at constant price for SL/TP testing."""
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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": [base_price] * n,
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"high": [base_price + 2.0] * n,
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"low": [base_price - 2.0] * n,
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"close": [base_price] * n,
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"vol": [1000000] * n,
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"amount": [100000000] * n,
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}
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)
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class StopLossStrategy(Strategy):
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"""Strategy that buys with stop-loss."""
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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, stop_loss=95.0)
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class TakeProfitStrategy(Strategy):
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"""Strategy that buys with take-profit."""
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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, take_profit=110.0)
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class StopLossAndTakeProfitStrategy(Strategy):
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"""Strategy that buys with both stop-loss and take-profit."""
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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, stop_loss=95.0, take_profit=110.0)
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def test_stop_loss_triggers_sell():
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"""Test stop-loss triggers auto SELL when price drops below stop."""
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df = _make_flat_df(n=30)
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# Bar 12 drops low below stop_loss=95.0
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df.loc[12, "low"] = 93.0
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df.loc[12, "high"] = 96.0
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df.loc[12, "close"] = 94.0
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df.loc[12, "open"] = 97.0
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engine = BacktestEngine(StopLossStrategy, cash=100000)
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result = engine.run(df)
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trades = result.trades[~result.trades["rejected"]]
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sell_trades = trades[trades["direction"] == "SELL"]
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# Should have at least one SELL triggered by stop-loss
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assert len(sell_trades) >= 1, "Expected stop-loss sell"
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# Sell price should be at stop_loss price (95.0)
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assert sell_trades.iloc[0]["price"] == 95.0
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def test_take_profit_triggers_sell():
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"""Test take-profit triggers auto SELL when price rises above target.
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注意（审计 #4）：止盈信号延迟到下一根开盘成交（消除前视偏差）。
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当下一根开盘价低于触发价时（跳空回落），SELL 取更不利的实际开盘价。
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"""
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df = _make_flat_df(n=30)
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# Bar 12 rises above take_profit=110.0
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df.loc[12, "high"] = 112.0
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df.loc[12, "low"] = 108.0
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df.loc[12, "close"] = 111.0
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df.loc[12, "open"] = 109.0
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# Bar 13 开盘回落到 100（跳空），止盈延迟成交应取更不利的 100 而非触发价 110
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df.loc[13, "open"] = 100.0
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engine = BacktestEngine(TakeProfitStrategy, cash=100000)
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result = engine.run(df)
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trades = result.trades[~result.trades["rejected"]]
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sell_trades = trades[trades["direction"] == "SELL"]
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# Should have at least one SELL triggered by take-profit
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assert len(sell_trades) >= 1, "Expected take-profit sell"
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# 延迟到下一根（bar 13）开盘成交，跳空回落取更不利的实际价 100（非触发价 110）
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assert sell_trades.iloc[0]["price"] == 100.0
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def test_stop_loss_gap_down_fills_at_worse_price():
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"""SL 信号延迟到下一根开盘成交；若跳空下跌，取更不利的开盘价（审计 #4）。
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构造当根触及止损、但下一根开盘远低于止损价的跳空场景，
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断言实际成交价取更不利的开盘价，回测净值低于"触发价成交"基线。
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"""
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df = _make_flat_df(n=30)
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# Bar 12 触及 stop_loss=95（low=93）
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df.loc[12, "low"] = 93.0
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df.loc[12, "high"] = 96.0
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df.loc[12, "close"] = 94.0
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df.loc[12, "open"] = 97.0
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# Bar 13 跳空低开到 90（远低于止损价 95），应取 90 而非 95
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df.loc[13, "open"] = 90.0
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df.loc[13, "low"] = 89.0
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df.loc[13, "high"] = 91.0
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df.loc[13, "close"] = 90.5
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engine = BacktestEngine(StopLossStrategy, cash=100000)
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result = engine.run(df)
|
||
|
||
trades = result.trades[~result.trades["rejected"]]
|
||
sell_trades = trades[trades["direction"] == "SELL"]
|
||
assert len(sell_trades) >= 1, "Expected stop-loss sell"
|
||
# 跳空下跌：SELL 取 min(next_open=90, trigger=95) = 90（更不利）
|
||
assert sell_trades.iloc[0]["price"] == 90.0
|
||
|
||
|
||
def test_stop_loss_not_triggered_when_price_stays_above():
|
||
"""Test no SL sell when price never drops to stop level."""
|
||
df = _make_flat_df(n=30, base_price=100.0)
|
||
# low is always 98.0 (> stop_loss=95.0), so SL never triggers
|
||
|
||
engine = BacktestEngine(StopLossStrategy, cash=100000)
|
||
result = engine.run(df)
|
||
|
||
trades = result.trades[~result.trades["rejected"]]
|
||
sell_trades = trades[trades["direction"] == "SELL"]
|
||
|
||
# No SELL should be triggered by SL (low=98 > stop_loss=95)
|
||
assert len(sell_trades) == 0, "SL should not trigger when price stays above"
|
||
|
||
|
||
def test_stop_loss_takes_priority_over_strategy_sell():
|
||
"""SL-triggered sell prevents duplicate strategy sell."""
|
||
df = _make_flat_df(n=30)
|
||
|
||
class SLThenManualSell(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 5 and self.position["size"] == 0:
|
||
self.buy(size=0, stop_loss=95.0)
|
||
# Manual sell at bar 15 — but SL should have fired first
|
||
if self._bar_index == 15 and self.position["size"] > 0:
|
||
self.sell(size=0)
|
||
|
||
# Bar 10 triggers stop-loss
|
||
df.loc[10, "low"] = 93.0
|
||
df.loc[10, "close"] = 94.0
|
||
|
||
engine = BacktestEngine(SLThenManualSell, cash=100000)
|
||
result = engine.run(df)
|
||
|
||
trades = result.trades[~result.trades["rejected"]]
|
||
sell_trades = trades[trades["direction"] == "SELL"]
|
||
|
||
# Should have exactly 1 SELL (from SL, not the manual one at bar 15)
|
||
assert len(sell_trades) == 1, f"Expected 1 SL sell, got {len(sell_trades)}"
|
||
assert sell_trades.iloc[0]["price"] == 95.0
|
||
|
||
|
||
# ── Chanlun Auto-Bridge ──────────────────────────────────────────────────────
|
||
|
||
|
||
class ChanlunAwareStrategy(Strategy):
|
||
"""Strategy that buys when chanlun analysis has at least one bi."""
|
||
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self.chanlun is not None and self._bar_index == 15 and self.position["size"] == 0:
|
||
# Strategy uses chanlun result to make trading decisions
|
||
bis = self.chanlun.bis if hasattr(self.chanlun, "bis") else []
|
||
if len(bis) > 0:
|
||
self.buy(size=0)
|
||
|
||
|
||
def test_chanlun_auto_bridge():
|
||
"""Test chanlun_level auto-computes and injects analysis into strategy."""
|
||
df = _make_df(n=100)
|
||
engine = BacktestEngine(ChanlunAwareStrategy, cash=100000, chanlun_level="DAILY")
|
||
result = engine.run(df)
|
||
|
||
# Strategy should have received chanlun result (100 bars → at least some bis)
|
||
trades = result.trades[~result.trades["rejected"]]
|
||
buy_trades = trades[trades["direction"] == "BUY"]
|
||
|
||
# With 100 bars of random data, ChanlunAnalyser should produce bis,
|
||
# so the strategy should trigger a BUY at bar 15
|
||
assert len(buy_trades) >= 1, "Expected chanlun-aware BUY"
|
||
|
||
|
||
def test_chanlun_manual_result_overrides_auto():
|
||
"""Test explicit chanlun_result takes priority over chanlun_level."""
|
||
df = _make_df(n=50)
|
||
|
||
class CheckerStrategy(Strategy):
|
||
received: object = None
|
||
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 10:
|
||
CheckerStrategy.received = self.chanlun
|
||
self.buy(size=10)
|
||
|
||
# Pass explicit result — should NOT auto-compute
|
||
manual_result = {"manual": True}
|
||
engine = BacktestEngine(CheckerStrategy, cash=100000, chanlun_level="DAILY")
|
||
engine.run(df, chanlun_result=manual_result)
|
||
|
||
# Strategy should have received the manual result, not auto-computed one
|
||
assert CheckerStrategy.received == manual_result
|
||
|
||
|
||
# ── SlippageModel + ExecutionModel Integration ───────────────────────────────
|
||
|
||
|
||
class TestEngineSlippageModel:
|
||
"""BacktestEngine with SlippageModel integration."""
|
||
|
||
def test_engine_with_slippage_model(self) -> None:
|
||
"""Engine uses SlippageModel."""
|
||
|
||
class SimpleBuy(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 0:
|
||
self.buy(size=100)
|
||
|
||
df = _make_df(20)
|
||
engine = BacktestEngine(
|
||
SimpleBuy,
|
||
cash=100000,
|
||
slippage_model=FixedSlippage(per_share=0.05),
|
||
)
|
||
result = engine.run(df)
|
||
buy_trades = result.trades[result.trades["direction"] == "BUY"]
|
||
if len(buy_trades) > 0:
|
||
assert buy_trades.iloc[0]["slippage"] > 0
|
||
|
||
|
||
class TestEngineExecutionModel:
|
||
"""BacktestEngine with ExecutionModel integration."""
|
||
|
||
def test_engine_with_twap(self) -> None:
|
||
"""Engine uses TWAP execution."""
|
||
|
||
class SimpleBuy(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 0:
|
||
self.buy(size=300)
|
||
|
||
df = _make_df(20)
|
||
engine = BacktestEngine(
|
||
SimpleBuy,
|
||
cash=100000,
|
||
execution_model=TWAPExecution(n_bars=3),
|
||
)
|
||
result = engine.run(df)
|
||
buy_trades = result.trades[result.trades["direction"] == "BUY"]
|
||
assert len(buy_trades) >= 1
|
||
|
||
def test_execution_model_affects_equity(self) -> None:
|
||
"""ExecutionModel 路径的交易必须真正进入 PortfolioTracker。
|
||
|
||
回归 datetime 类型分歧 bug：ExecutionModel 曾把 Trade.datetime 转成
|
||
int(YYYYMMDD)，而 PortfolioTracker 用 df 原始 Timestamp 作为 trade_map
|
||
的 key，导致 ExecutionModel 路径（TWAP/VWAP/Limit）的交易全部被静默
|
||
跳过、权益曲线恒定、收益归零。
|
||
"""
|
||
|
||
class BuyAndHold(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 0:
|
||
self.buy(size=0) # 全仓
|
||
|
||
df = _make_df(30) # Timestamp datetime（真实行情场景）
|
||
engine = BacktestEngine(
|
||
BuyAndHold,
|
||
cash=100000,
|
||
execution_model=TWAPExecution(n_bars=3),
|
||
)
|
||
result = engine.run(df)
|
||
|
||
# 交易必须影响持仓与权益曲线（不能全程空仓 / 恒定）
|
||
assert result.positions["size"].max() > 0
|
||
assert result.equity_curve["total"].nunique() > 1
|
||
|
||
def test_execution_model_with_vol_column(self) -> None:
|
||
"""真实行情数据使用 vol 列名，VWAP/方根滑点应能读到成交量。
|
||
|
||
回归 volume 列名分歧 bug：回测代码曾只认 "volume"，真实数据列为
|
||
"vol"，导致滑点模型 volume 恒为 0、退化到百分比模式，VWAP 退化为等权。
|
||
"""
|
||
|
||
class BuyOnce(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 0:
|
||
self.buy(size=0)
|
||
|
||
df = _make_df(30).rename(columns={"volume": "vol"})
|
||
engine = BacktestEngine(
|
||
BuyOnce,
|
||
cash=100000,
|
||
execution_model=VWAPExecution(n_bars=3),
|
||
slippage_model=SquareRootSlippage(),
|
||
)
|
||
result = engine.run(df)
|
||
buy = result.trades[result.trades["direction"] == "BUY"]
|
||
assert len(buy) > 0
|
||
# volume 读到非 0 → 方根冲击未退化 → 滑点 > 0
|
||
assert (buy["slippage"] > 0).all()
|
||
|
||
def test_engine_backward_compatible(self) -> None:
|
||
"""No new params: behavior unchanged."""
|
||
|
||
class SimpleBuy(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 0:
|
||
self.buy(size=100)
|
||
|
||
df = _make_df(20)
|
||
engine = BacktestEngine(SimpleBuy, cash=100000)
|
||
result = engine.run(df)
|
||
assert len(result.trades) >= 1
|
||
|
||
def test_engine_accepts_date_column(self) -> None:
|
||
"""引擎应直接接受真实行情日线的 date 列（而非 datetime）。
|
||
|
||
get_security_bars 日线返回 date 列，引擎在 run() 入口由 date 派生
|
||
datetime，下游无感兼容。回归此前用户必须手动重命名才能跑日线回测的问题。
|
||
"""
|
||
|
||
class SimpleBuy(Strategy):
|
||
def init(self) -> None:
|
||
pass
|
||
|
||
def next(self) -> None:
|
||
if self._bar_index == 0:
|
||
self.buy(size=0)
|
||
|
||
# 仅 date 列、无 datetime 列 —— 模拟 get_security_bars 日线输出
|
||
df = _make_df(30).rename(columns={"datetime": "date", "volume": "vol"})
|
||
|
||
engine = BacktestEngine(SimpleBuy, cash=100000)
|
||
result = engine.run(df)
|
||
assert result.positions["size"].max() > 0
|