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a67e5588d0
v1.20.1 把 BacktestResult.performance 扩大为 dict[str, float | str] （为塞 diagnostic_warning 字符串），破坏 6 处下游消费方类型契约， CI mypy job 转红。重构为更干净的设计： - 诊断信息走独立的 BacktestResult.diagnostic 字段（performance.py PerformanceAnalyzer.diagnostic 属性 → engine 透出 → to_dict 含该字段 → CLI 显示），performance 字典回归 dict[str, float]（保留别名键）。 - optimizer.py 补 npt.NDArray 标注，消除 3 个既有 ndarray type-arg 错误。 测试：932 passed；mypy backtest/+portfolio/ 零错误；ruff 全绿。
437 lines
14 KiB
Python
437 lines
14 KiB
Python
"""回测 CLI 命令。"""
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from __future__ import annotations
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import importlib.util
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from pathlib import Path
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from typing import Any
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import click
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@click.command()
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@click.argument("market")
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@click.argument("code")
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@click.option("--strategy", "strategy_str", default=None, help="DSL 策略表达式 (P1)")
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@click.option("--strategy-file", "strategy_file", default=None, help="Python 策略文件路径")
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@click.option(
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"--combo-strategies",
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"combo_strategies",
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default=None,
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help="多因子组合：逗号分隔的策略文件路径（如 strats/a.py,strats/b.py,strats/c.py）",
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)
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@click.option(
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"--combo-mode",
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"combo_mode",
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default="MAJORITY",
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type=click.Choice(["AND", "OR", "MAJORITY"], case_sensitive=False),
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help="多因子信号合并模式（默认 MAJORITY）",
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)
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@click.option("--cash", default=100000.0, type=float, help="初始资金")
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@click.option("--commission", default=0.0003, type=float, help="佣金率")
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@click.option(
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"--execution",
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default="next_open",
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type=click.Choice(["next_open", "next_close"]),
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help="成交价规则",
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)
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@click.option("--period", default="DAILY", help="K线周期")
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@click.option("--adjust", default="NONE", help="复权: NONE/QFQ/HFQ")
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@click.option("--count", default=500, type=int, help="K线数量")
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@click.option("--indicators", default=None, help="预计算指标（逗号分隔）")
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@click.option(
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"--chanlun-level",
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"chanlun_level",
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default=None,
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help="自动计算缠论分析并注入策略（如 DAILY/30MIN）",
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)
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@click.option("--table", "use_table", is_flag=True, help="表格输出")
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@click.option("--output", "output_fmt", type=click.Choice(["json", "table", "csv"]), default="json")
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def backtest(
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market: str,
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code: str,
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strategy_str: str | None,
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strategy_file: str | None,
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combo_strategies: str | None,
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combo_mode: str,
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cash: float,
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commission: float,
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execution: str,
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period: str,
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adjust: str,
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count: int,
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indicators: str | None,
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chanlun_level: str | None,
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use_table: bool,
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output_fmt: str,
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) -> None:
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"""回测引擎：执行策略并返回绩效报告。
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示例：
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easy-tdx backtest SZ 000001 --strategy-file my_strategy.py
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easy-tdx backtest SH 600519 --strategy-file ma_cross.py --table
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easy-tdx backtest SZ 000001 --strategy-file my_strategy.py --indicators MACD,KDJ
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easy-tdx backtest SZ 000001 --strategy-file chanlun_strategy.py --chanlun-level DAILY
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easy-tdx backtest SZ 000001 \
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--combo-strategies strategies/macd_cross.py,strategies/rsi_reversal.py \
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--combo-mode MAJORITY --table
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"""
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from ..backtest.engine import BacktestEngine
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from ..cli.conn import get_mac_client
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from ..cli.parsers import parse_adjust, parse_market, parse_period
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from ..indicator import compute_indicators
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# 1. 加载策略（单策略 or 多因子组合）
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is_combo = combo_strategies is not None
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if is_combo:
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assert combo_strategies is not None # narrowed by is_combo
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combo_classes = _load_combo_strategies(combo_strategies)
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else:
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strategy_cls = _load_strategy(strategy_str, strategy_file)
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if strategy_cls is None:
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click.echo("错误: 必须指定 --strategy-file / --combo-strategies / --strategy", err=True)
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raise SystemExit(1)
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# 2. 获取数据
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mkt = parse_market(market)
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with get_mac_client() as client:
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df = client.get_stock_kline(
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mkt,
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code,
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period=parse_period(period),
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start=0,
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count=count,
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adjust=parse_adjust(adjust),
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)
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# 3. 预计算指标
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if indicators:
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indicator_list = [ind.strip() for ind in indicators.split(",")]
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df = compute_indicators(df, indicator_list)
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# 4. 创建引擎并运行
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if is_combo:
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from ..backtest.combo import CombinationRunner
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runner = CombinationRunner(
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strategy_classes=combo_classes,
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df=df,
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cash=cash,
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commission=commission,
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execution=execution,
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)
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result = runner.run_combination(
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indices=list(range(len(combo_classes))),
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mode=combo_mode.upper(),
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)
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else:
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assert strategy_cls is not None # guarded above by SystemExit
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engine = BacktestEngine(
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strategy=strategy_cls,
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cash=cash,
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commission=commission,
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execution=execution,
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chanlun_level=chanlun_level,
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)
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result = engine.run(df)
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# 5. 输出结果
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fmt = "table" if use_table else output_fmt
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if fmt == "json":
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click.echo(result.to_json())
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elif fmt == "table":
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_print_table(result)
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else:
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click.echo(result.to_json())
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def _load_strategy(strategy_str: str | None, strategy_file: str | None) -> type | None:
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"""加载策略类。
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优先从 Python 文件加载，其次从 DSL 表达式加载（未实现）。
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Args:
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strategy_str: DSL 策略表达式
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strategy_file: Python 策略文件路径
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Returns:
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Strategy 子类
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"""
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if strategy_file:
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return _load_strategy_from_file(strategy_file)
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if strategy_str:
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click.echo("错误: DSL 策略表达式尚未实现", err=True)
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return None
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return None
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def _load_strategy_from_file(path: str) -> type:
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"""从 Python 文件加载 Strategy 子类。
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Args:
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path: Python 文件路径
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Returns:
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Strategy 子类
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"""
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from ..backtest.strategy import Strategy
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file_path = Path(path)
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if not file_path.exists():
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click.echo(f"错误: 文件不存在: {path}", err=True)
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raise SystemExit(1)
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spec = importlib.util.spec_from_file_location("strategy_module", file_path)
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if spec is None or spec.loader is None:
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click.echo(f"错误: 无法加载文件: {path}", err=True)
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raise SystemExit(1)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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# 查找 Strategy 子类
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strategy_classes = []
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for name in dir(module):
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obj = getattr(module, name)
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try:
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if isinstance(obj, type) and issubclass(obj, Strategy) and obj is not Strategy:
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strategy_classes.append(obj)
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except TypeError:
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pass
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if not strategy_classes:
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click.echo(f"错误: 文件中未找到 Strategy 子类: {path}", err=True)
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raise SystemExit(1)
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if len(strategy_classes) > 1:
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click.echo(f"警告: 文件包含多个 Strategy 子类，使用第一个: {path}", err=True)
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return strategy_classes[0]
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def _load_combo_strategies(combo_strategies: str) -> list[type]:
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"""从逗号分隔的路径列表加载多个策略类。
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Args:
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combo_strategies: 逗号分隔的策略文件路径
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Returns:
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Strategy 子类列表
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"""
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paths = [p.strip() for p in combo_strategies.split(",") if p.strip()]
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if len(paths) < 2:
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click.echo("错误: --combo-strategies 至少需要 2 个策略文件", err=True)
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raise SystemExit(1)
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classes: list[type] = []
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for p in paths:
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cls = _load_strategy_from_file(p)
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classes.append(cls)
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names = [c.__name__ for c in classes]
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click.echo(f"[*] 多因子组合 ({len(classes)} 因子): {' + '.join(names)}")
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return classes
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def _print_table(result: Any) -> None:
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"""以表格形式输出回测结果。"""
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perf = result.performance
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config = result.config
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click.echo("=== 回测绩效概要 ===")
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click.echo(f"总收益率: {perf.get('total_return', 0):.2%}")
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click.echo(f"年化收益: {perf.get('annual_return', 0):.2%}")
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click.echo(f"最大回撤: {perf.get('max_drawdown', 0):.2%}")
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click.echo(f"夏普比率: {perf.get('sharpe', 0):.2f}")
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click.echo(f"胜率: {perf.get('win_rate', 0):.2%}")
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click.echo(f"交易次数: {perf.get('total_trades', 0)}")
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click.echo()
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if getattr(result, "diagnostic", None):
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click.echo(f"⚠ 诊断: {result.diagnostic}")
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click.echo()
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click.echo("=== 配置参数 ===")
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click.echo(f"初始资金: {config.get('cash', 0):.2f}")
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click.echo(f"佣金率: {config.get('commission', 0):.4f}")
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click.echo(f"成交规则: {config.get('execution', 'next_open')}")
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if config.get("chanlun_level"):
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click.echo(f"缠论级别: {config.get('chanlun_level')}")
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click.echo()
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if config.get("future_leak_warning"):
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click.echo("!!! 警告: 策略可能存在未来函数（使用未来数据）")
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click.echo()
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if not result.trades.empty:
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click.echo("=== 最近交易记录 ===")
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recent_trades = result.trades.tail(10)
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for idx, trade in recent_trades.iterrows():
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direction = "买入" if trade["direction"] == "BUY" else "卖出"
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status = "拒绝" if trade["rejected"] else "成交"
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click.echo(
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f" [{trade['datetime']}] {direction} "
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f"数量={trade['size']:.0f} 价格={trade['price']:.2f} "
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f"盈亏={trade['pnl']:.2f} [{status}]"
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)
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else:
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click.echo("无交易记录")
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# ── portfolio 多标的组合回测命令 ─────────────────────────────────────────────
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@click.command()
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@click.option(
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"--stocks",
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required=True,
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help="股票列表：逗号分隔的 市场:代码（如 SZ:000001,SH:600519,SH:600036）",
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)
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@click.option("--strategy-file", "strategy_file", required=True, help="Python 策略文件路径")
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@click.option("--cash", default=200_000.0, type=float, help="总资金（默认 20 万）")
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@click.option("--commission", default=0.0003, type=float, help="佣金率")
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@click.option(
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"--execution",
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default="next_open",
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type=click.Choice(["next_open", "next_close"]),
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help="成交价规则",
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)
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@click.option("--period", default="DAILY", help="K线周期")
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@click.option("--adjust", default="NONE", help="复权: NONE/QFQ/HFQ")
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@click.option("--count", default=500, type=int, help="K线数量")
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@click.option(
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"--allocation",
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default="equal",
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type=click.Choice(["equal"], case_sensitive=False),
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help="资金分配方式（默认 equal 均等分配）",
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)
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@click.option(
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"--chanlun-level",
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"chanlun_level",
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default=None,
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help="自动计算缠论分析并注入策略（如 DAILY/30MIN）",
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)
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@click.option("--table", "use_table", is_flag=True, help="表格输出")
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@click.option("--output", "output_fmt", type=click.Choice(["json", "table", "csv"]), default="json")
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def portfolio(
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stocks: str,
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strategy_file: str,
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cash: float,
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commission: float,
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execution: str,
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period: str,
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adjust: str,
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count: int,
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allocation: str,
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chanlun_level: str | None,
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use_table: bool,
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output_fmt: str,
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) -> None:
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"""多标的组合回测：共享资金池，独立产生信号，统一管理仓位。
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对多只股票同时回测，按均等比例分配资金，汇总组合整体绩效。
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示例：
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easy-tdx portfolio --stocks SZ:000001,SH:600519 --strategy-file ma_cross.py
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easy-tdx portfolio --stocks SZ:000001,SH:600519,SH:600036 \\
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--strategy-file my_strategy.py --cash 500000 --table
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easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
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--strategy-file chanlun_strat.py --chanlun-level DAILY
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"""
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import json
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from ..cli.conn import get_mac_client
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from ..cli.parsers import parse_adjust, parse_market, parse_period
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from .portfolio_engine import PortfolioBacktestEngine, StockData
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# 1. 加载策略
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strategy_cls = _load_strategy_from_file(strategy_file)
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strategy_name = strategy_cls.__name__
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# 2. 解析股票列表
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stock_list = []
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for item in stocks.split(","):
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item = item.strip()
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if ":" not in item:
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click.echo(f"错误: 股票格式应为 市场:代码，如 SZ:000001，收到: {item}", err=True)
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raise SystemExit(1)
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mkt_str, code = item.split(":", 1)
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stock_list.append((mkt_str.strip().upper(), code.strip()))
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if not stock_list:
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click.echo("错误: 未指定股票", err=True)
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raise SystemExit(1)
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click.echo(f"策略: {strategy_name} | 标的: {len(stock_list)} 只 | 资金: {cash:,.0f}", err=True)
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# 3. 获取数据
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stock_data_list: list[StockData] = []
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with get_mac_client() as client:
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for mkt_str, code in stock_list:
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mkt = parse_market(mkt_str)
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df = client.get_stock_kline(
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mkt,
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code,
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period=parse_period(period),
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start=0,
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count=count,
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adjust=parse_adjust(adjust),
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)
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stock_data_list.append(StockData(code=code, market=mkt_str, df=df))
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# 4. 创建引擎并运行
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engine = PortfolioBacktestEngine(
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strategy=strategy_cls,
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stocks=stock_data_list,
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total_cash=cash,
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allocation=allocation,
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commission=commission,
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execution=execution,
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chanlun_level=chanlun_level,
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)
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result = engine.run()
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# 5. 输出结果
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fmt = "table" if use_table else output_fmt
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if fmt == "table":
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_print_portfolio_table(result)
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else:
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click.echo(json.dumps(result.to_dict(), ensure_ascii=False, indent=2))
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def _print_portfolio_table(result: Any) -> None:
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"""以表格形式输出组合回测结果。"""
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perf = result.total_performance
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click.echo("=== 组合回测绩效概要 ===")
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click.echo(f"标的数量: {perf.get('total_stocks', 0)}")
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click.echo(f"总资金: {perf.get('total_cash', 0):,.0f}")
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click.echo(f"组合收益率: {perf.get('total_return', 0):.2%}")
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click.echo(f"组合年化: {perf.get('annual_return', 0):.2%}")
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click.echo()
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click.echo("── 各标的详情 ──")
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for key, stock_result in result.individual_results.items():
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sp = stock_result.performance
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alloc = result.equity_allocation.get(key, 0)
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click.echo(
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f" {key}: 收益={sp.get('total_return', 0):.2%} "
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f"夏普={sp.get('sharpe', 0):.2f} "
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f"回撤={sp.get('max_drawdown', 0):.2%} "
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f"分配={alloc:.0%} "
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f"交易={sp.get('total_trades', 0)}"
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)
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click.echo()
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