mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 15:44:18 +08:00
Justin Gu
e374a0da28
对 v1.21→v1.32.5 的 249 文件 4.2 万行改动做六路专项审查，本轮落地全部发现： 回测正确性：组合收益 fillna(0) 虚增、轮动停牌日过期价成交、单标的 WF 逐窗指标 被预热区稀释（三件套均带先红后绿回归）；worst_drawdown 方向、grading 容错、 组合体检品种费率、寻优端点费率透传。 安全：LLM api_url 仅 http/https 且禁 userinfo（封死 file:// 读取与 Key 外送链）、 错误响应不回显原始 body、响应体 2MB 上限、配置原子写、坏配置字段级防御。 数据：涨跌停价整数分币舍入（67/318/90 个价位错 1 分漏判清零）、交易时段/采样/ provisional 统一沪时区、warehouse 增量缺口自动全量重拉、provisional 定点转正、 baostock 真故障抛错 + W/M 去 tradestatus（实测服务端报错，周月兜底此前从未工作） + 指数 vol 股→手（实测锚定）、ccpm 结构变更抛错。 Web API：缓存键补 count/vipdoc、NaN 清洗先于缓存、count>800 分页取全量、 submit 透传真实状态、pending 不再被淘汰成幽灵、watchlist/server 入参约束。 公式：FILTER 去副作用、0-1 值域误判收严、递归深度上限、REF 负移位显式禁止。 前端：4 处请求竞态序号守卫、Sparkline viewBox、北交所 market=2 映射、 空数据缓存死角、AI 弹窗卸载中止轮询、量能/资金日历口径修正。 CLI/CI：warehouse sync 失败 exit 1、参数校验干净报错、release 真实发布 SHA256、 CI 超时与缓存、spec 补 baostock 前提。 约 60 条回归测试先红后绿；pytest 1820 全过，ruff/mypy/vue-tsc/node --test 全绿。
879 lines
30 KiB
Python
879 lines
30 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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"--auto-fees",
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"auto_fees",
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is_flag=True,
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help="按标的品种自动解析费率（ETF/可转债免印花税等；显式 --commission 优先）",
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)
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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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@click.option(
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"--wf",
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"walk_forward",
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is_flag=True,
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help="附加 Walk-Forward 样本外验证（默认 7 窗，每窗独立开仓）",
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)
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@click.option("--wf-windows", "wf_windows", default=7, type=int, help="Walk-Forward 窗口数")
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@click.option(
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"--evaluate",
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"full_evaluate",
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is_flag=True,
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help="一条龙评估：回测+WF+适配性+综合评分+S-D评级+买入持有基准对比（覆盖常规输出）",
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)
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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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auto_fees: bool,
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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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walk_forward: bool,
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wf_windows: int,
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full_evaluate: bool,
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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 300308 --strategy-file ma_cross.py --wf --wf-windows 7
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easy-tdx backtest SZ 300308 --strategy-file ma_cross.py --evaluate
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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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# 组合模式暂不支持的分析旗标：显式告警而非静默吞掉（审查修复）
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if is_combo and walk_forward:
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click.echo(
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"警告: --wf（Walk-Forward 样本外验证）暂不支持 --combo-strategies 组合模式，已忽略",
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err=True,
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)
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if is_combo and full_evaluate:
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click.echo(
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"警告: --evaluate（一条龙评估）暂不支持 --combo-strategies 组合模式，已忽略", err=True
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)
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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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# 一条龙评估：覆盖常规输出（含回测本身，无需重复跑）
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if full_evaluate:
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import json as _json
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from ..backtest.benchmark import evaluate_strategy
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report = evaluate_strategy(
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strategy=strategy_cls,
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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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symbol=f"{market}:{code}",
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auto_fees=auto_fees,
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n_windows=wf_windows,
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)
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click.echo(_json.dumps(report, ensure_ascii=False, default=str))
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return
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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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symbol=f"{market}:{code}",
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auto_fees=auto_fees,
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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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# 6. 附加 Walk-Forward 样本外验证（--wf）
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if walk_forward and not is_combo:
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import json as _json
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from ..backtest.walkforward import WalkForwardEngine
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assert strategy_cls is not None
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wf = WalkForwardEngine(
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strategy=strategy_cls,
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n_windows=wf_windows,
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cash=cash,
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commission=commission,
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execution=execution,
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symbol=f"{market}:{code}",
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auto_fees=auto_fees,
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)
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wf_report = {"walkforward": wf.run(df).to_dict()}
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click.echo(_json.dumps(wf_report, ensure_ascii=False, default=str))
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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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# 深度风险指标（v1.28 新增；老结果缺键时跳过，不输出 0 假值）
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if perf.get("ulcer_index") is not None:
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click.echo(f"Ulcer 指数: {perf.get('ulcer_index', 0):.4f}")
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click.echo(f"日 VaR(95%): {perf.get('var_95', 0):.2%}")
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click.echo(f"日 CVaR(95%): {perf.get('cvar_95', 0):.2%}")
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click.echo(f"SQN 系统质量: {perf.get('sqn', 0):.2f}")
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click.echo(
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f"最大连胜/连亏: {perf.get('max_consecutive_wins', 0)} / "
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f"{perf.get('max_consecutive_losses', 0)}"
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)
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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),
|
||
help="资金分配方式（默认 equal 均等分配）",
|
||
)
|
||
@click.option(
|
||
"--chanlun-level",
|
||
"chanlun_level",
|
||
default=None,
|
||
help="自动计算缠论分析并注入策略（如 DAILY/30MIN）",
|
||
)
|
||
@click.option(
|
||
"--auto-fees",
|
||
"auto_fees",
|
||
is_flag=True,
|
||
help="按标的品种自动解析费率（ETF/可转债免印花税等；显式 --commission 优先）",
|
||
)
|
||
@click.option("--wf", "walk_forward", is_flag=True, help="附加组合级 Walk-Forward 样本外验证")
|
||
@click.option("--wf-windows", "wf_windows", default=7, type=int, help="Walk-Forward 窗口数")
|
||
@click.option(
|
||
"--evaluate",
|
||
"full_evaluate",
|
||
is_flag=True,
|
||
help="组合级一条龙评估：组合回测+组合WF+适配性+综合评分+组合评级+等权买入持有基准对比（覆盖常规输出）",
|
||
)
|
||
@click.option("--table", "use_table", is_flag=True, help="表格输出")
|
||
@click.option("--output", "output_fmt", type=click.Choice(["json", "table", "csv"]), default="json")
|
||
def portfolio(
|
||
stocks: str,
|
||
strategy_file: str,
|
||
cash: float,
|
||
commission: float,
|
||
execution: str,
|
||
period: str,
|
||
adjust: str,
|
||
count: int,
|
||
allocation: str,
|
||
chanlun_level: str | None,
|
||
auto_fees: bool,
|
||
walk_forward: bool,
|
||
wf_windows: int,
|
||
full_evaluate: bool,
|
||
use_table: bool,
|
||
output_fmt: str,
|
||
) -> None:
|
||
"""多标的组合回测：共享资金池，独立产生信号，统一管理仓位。
|
||
|
||
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。
|
||
|
||
示例：
|
||
|
||
easy-tdx portfolio --stocks SZ:000001,SH:600519 --strategy-file ma_cross.py
|
||
|
||
easy-tdx portfolio --stocks SZ:000001,SH:600519,SH:600036 \\
|
||
--strategy-file my_strategy.py --cash 500000 --table
|
||
|
||
easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
|
||
--strategy-file chanlun_strat.py --chanlun-level DAILY
|
||
|
||
easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
|
||
--strategy-file ma_cross.py --wf --wf-windows 7
|
||
|
||
easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
|
||
--strategy-file ma_cross.py --evaluate
|
||
"""
|
||
import json
|
||
|
||
from ..cli.conn import get_mac_client
|
||
from ..cli.parsers import parse_adjust, parse_market, parse_period
|
||
from .portfolio_engine import PortfolioBacktestEngine, StockData
|
||
|
||
# 1. 加载策略
|
||
strategy_cls = _load_strategy_from_file(strategy_file)
|
||
strategy_name = strategy_cls.__name__
|
||
|
||
# 2. 解析股票列表
|
||
stock_list = []
|
||
for item in stocks.split(","):
|
||
item = item.strip()
|
||
if ":" not in item:
|
||
click.echo(f"错误: 股票格式应为 市场:代码，如 SZ:000001，收到: {item}", err=True)
|
||
raise SystemExit(1)
|
||
mkt_str, code = item.split(":", 1)
|
||
stock_list.append((mkt_str.strip().upper(), code.strip()))
|
||
|
||
if not stock_list:
|
||
click.echo("错误: 未指定股票", err=True)
|
||
raise SystemExit(1)
|
||
|
||
click.echo(f"策略: {strategy_name} | 标的: {len(stock_list)} 只 | 资金: {cash:,.0f}", err=True)
|
||
|
||
# 3. 获取数据
|
||
stock_data_list: list[StockData] = []
|
||
with get_mac_client() as client:
|
||
for mkt_str, code in stock_list:
|
||
mkt = parse_market(mkt_str)
|
||
df = client.get_stock_kline(
|
||
mkt,
|
||
code,
|
||
period=parse_period(period),
|
||
start=0,
|
||
count=count,
|
||
adjust=parse_adjust(adjust),
|
||
)
|
||
stock_data_list.append(StockData(code=code, market=mkt_str, df=df))
|
||
|
||
# 4. 组合级一条龙评估：覆盖常规输出（含组合回测本身，无需重复跑）
|
||
if full_evaluate:
|
||
from .benchmark import evaluate_portfolio
|
||
|
||
report = evaluate_portfolio(
|
||
strategy=strategy_cls,
|
||
stocks=stock_data_list,
|
||
total_cash=cash,
|
||
commission=commission,
|
||
execution=execution,
|
||
chanlun_level=chanlun_level,
|
||
auto_fees=auto_fees,
|
||
n_windows=wf_windows,
|
||
)
|
||
click.echo(json.dumps(report, ensure_ascii=False, default=str))
|
||
return
|
||
|
||
# 5. 组合级 Walk-Forward 样本外验证（--wf）
|
||
if walk_forward:
|
||
from .walkforward import PortfolioWalkForwardEngine
|
||
|
||
wf = PortfolioWalkForwardEngine(
|
||
strategy=strategy_cls,
|
||
stocks=stock_data_list,
|
||
n_windows=wf_windows,
|
||
total_cash=cash,
|
||
commission=commission,
|
||
execution=execution,
|
||
chanlun_level=chanlun_level,
|
||
auto_fees=auto_fees,
|
||
)
|
||
click.echo(json.dumps({"walkforward": wf.run().to_dict()}, ensure_ascii=False, default=str))
|
||
return
|
||
|
||
# 6. 常规组合回测
|
||
engine = PortfolioBacktestEngine(
|
||
strategy=strategy_cls,
|
||
stocks=stock_data_list,
|
||
total_cash=cash,
|
||
allocation=allocation,
|
||
commission=commission,
|
||
execution=execution,
|
||
chanlun_level=chanlun_level,
|
||
auto_fees=auto_fees,
|
||
)
|
||
result = engine.run()
|
||
|
||
# 7. 输出结果
|
||
fmt = "table" if use_table else output_fmt
|
||
if fmt == "table":
|
||
_print_portfolio_table(result)
|
||
else:
|
||
click.echo(json.dumps(result.to_dict(), ensure_ascii=False, indent=2))
|
||
|
||
|
||
def _print_portfolio_table(result: Any) -> None:
|
||
"""以表格形式输出组合回测结果。"""
|
||
perf = result.total_performance
|
||
|
||
click.echo("=== 组合回测绩效概要 ===")
|
||
click.echo(f"标的数量: {perf.get('total_stocks', 0)}")
|
||
click.echo(f"总资金: {perf.get('total_cash', 0):,.0f}")
|
||
click.echo(f"组合收益率: {perf.get('total_return', 0):.2%}")
|
||
click.echo(f"组合年化: {perf.get('annual_return', 0):.2%}")
|
||
click.echo()
|
||
|
||
click.echo("── 各标的详情 ──")
|
||
for key, stock_result in result.individual_results.items():
|
||
sp = stock_result.performance
|
||
alloc = result.equity_allocation.get(key, 0)
|
||
click.echo(
|
||
f" {key}: 收益={sp.get('total_return', 0):.2%} "
|
||
f"夏普={sp.get('sharpe', 0):.2f} "
|
||
f"回撤={sp.get('max_drawdown', 0):.2%} "
|
||
f"分配={alloc:.0%} "
|
||
f"交易={sp.get('total_trades', 0)}"
|
||
)
|
||
click.echo()
|
||
|
||
|
||
# ── strategies 内置策略列表命令 ──────────────────────────────────────────────
|
||
|
||
|
||
@click.command("strategies")
|
||
@click.option("--output", "output_fmt", type=click.Choice(["json", "table"]), default="table")
|
||
def strategies(output_fmt: str) -> None:
|
||
"""列出内置策略注册表：名称、参数定义与预设寻优网格。
|
||
|
||
策略名可直接用于 optimize --strategy / Web API /backtest/run 的 strategy 字段。
|
||
|
||
示例：
|
||
|
||
easy-tdx strategies
|
||
|
||
easy-tdx strategies --output json
|
||
"""
|
||
import json
|
||
|
||
from .strategies import get_registry
|
||
|
||
entries = get_registry().all()
|
||
|
||
if output_fmt == "json":
|
||
click.echo(json.dumps([e.to_schema() for e in entries], ensure_ascii=False, indent=2))
|
||
return
|
||
|
||
click.echo(f"=== 内置策略（{len(entries)} 个）===\n")
|
||
for entry in entries:
|
||
schema = entry.to_schema()
|
||
params_desc = ", ".join(f"{p['name']}={p['default']}" for p in schema["params"])
|
||
grid = schema.get("preset_grid") or {}
|
||
points = 1
|
||
for vals in grid.values():
|
||
points *= len(vals)
|
||
grid_desc = " × ".join(f"{k}:{len(v)}" for k, v in grid.items()) if grid else "无"
|
||
click.echo(f" {entry.name} — {entry.label}")
|
||
click.echo(f" 参数: {params_desc or '无'}")
|
||
click.echo(f" 预设网格: {grid_desc}（{points} 点）")
|
||
if entry.description:
|
||
click.echo(f" 说明: {entry.description}")
|
||
click.echo()
|
||
|
||
|
||
# ── optimize 参数网格寻优命令 ────────────────────────────────────────────────
|
||
|
||
|
||
def _coerce_param_value(raw: str) -> Any:
|
||
"""把字符串参数值尽量转为 int/float，失败保留字符串。"""
|
||
try:
|
||
return int(raw)
|
||
except ValueError:
|
||
pass
|
||
try:
|
||
return float(raw)
|
||
except ValueError:
|
||
return raw
|
||
|
||
|
||
def _parse_param_grid(pairs: tuple[str, ...]) -> dict[str, list[Any]]:
|
||
"""解析 --param fast=5,10,15 形式的自定义网格。"""
|
||
grid: dict[str, list[Any]] = {}
|
||
for item in pairs:
|
||
name, sep, raw = item.partition("=")
|
||
values = [_coerce_param_value(v.strip()) for v in raw.split(",") if v.strip()]
|
||
if not sep or not name.strip() or not values:
|
||
click.echo(f"错误: --param 格式应为 参数名=值1,值2，收到: {item}", err=True)
|
||
raise SystemExit(1)
|
||
grid[name.strip()] = values
|
||
return grid
|
||
|
||
|
||
@click.command()
|
||
@click.argument("market")
|
||
@click.argument("code")
|
||
@click.option(
|
||
"--strategy",
|
||
"strategy_name",
|
||
default=None,
|
||
help="注册表策略名（见 strategies 命令；网格取 --param 或该策略预设）",
|
||
)
|
||
@click.option(
|
||
"--all",
|
||
"optimize_all",
|
||
is_flag=True,
|
||
help="一键寻优所有内置策略：逐策略按预设网格寻优，输出全局排名",
|
||
)
|
||
@click.option(
|
||
"--param",
|
||
"param_pairs",
|
||
multiple=True,
|
||
help="自定义参数网格，如 --param fast=5,10,15 --param slow=20,60（覆盖预设）",
|
||
)
|
||
@click.option("--cash", default=1_000_000.0, type=float, help="初始资金")
|
||
@click.option("--commission", default=0.0003, type=float, help="佣金率")
|
||
@click.option("--slippage", default=0.0, type=float, help="滑点")
|
||
@click.option(
|
||
"--execution",
|
||
default="next_open",
|
||
type=click.Choice(["next_open", "next_close"]),
|
||
help="成交价规则",
|
||
)
|
||
@click.option(
|
||
"--workers",
|
||
default=1,
|
||
type=int,
|
||
help="并行进程数：1=串行+指标缓存（默认）；2+=进程级并行",
|
||
)
|
||
@click.option("--period", default="DAILY", help="K线周期")
|
||
@click.option("--adjust", default="NONE", help="复权: NONE/QFQ/HFQ")
|
||
@click.option("--count", default=500, type=int, help="K线数量")
|
||
@click.option("--top", default=15, type=int, help="表格输出显示前 N 行")
|
||
@click.option("--table", "use_table", is_flag=True, help="表格输出")
|
||
@click.option("--output", "output_fmt", type=click.Choice(["json", "table"]), default="json")
|
||
def optimize(
|
||
market: str,
|
||
code: str,
|
||
strategy_name: str | None,
|
||
optimize_all: bool,
|
||
param_pairs: tuple[str, ...],
|
||
cash: float,
|
||
commission: float,
|
||
slippage: float,
|
||
execution: str,
|
||
workers: int,
|
||
period: str,
|
||
adjust: str,
|
||
count: int,
|
||
top: int,
|
||
use_table: bool,
|
||
output_fmt: str,
|
||
) -> None:
|
||
"""参数网格寻优：单策略网格搜索，或 --all 一键寻优所有内置策略。
|
||
|
||
示例：
|
||
|
||
easy-tdx optimize SZ 000001 --strategy ma_cross
|
||
|
||
easy-tdx optimize SZ 000001 --strategy ma_cross \\
|
||
--param fast=5,10,15 --param slow=20,60
|
||
|
||
easy-tdx optimize SZ 000001 --all --workers 4 --table
|
||
"""
|
||
from ..cli.conn import get_mac_client
|
||
from ..cli.parsers import parse_adjust, parse_market, parse_period
|
||
from .strategies import get_registry
|
||
from .strategies.presets import get_preset
|
||
|
||
registry = get_registry()
|
||
|
||
# 1. 校验模式与策略（联网取数之前，快速失败）
|
||
if optimize_all == (strategy_name is not None):
|
||
click.echo("错误: --all 与 --strategy 二选一", err=True)
|
||
raise SystemExit(1)
|
||
|
||
custom_grid = _parse_param_grid(param_pairs) if param_pairs else None
|
||
if optimize_all and custom_grid is not None:
|
||
# --all 逐策略使用各自预设网格，--param 无处安放：显式告警而非静默忽略（审查修复）
|
||
click.echo(
|
||
"警告: --param 在 --all 模式下被忽略（一键寻优逐策略使用各自预设网格；"
|
||
"如需自定义网格请指定 --strategy 单策略寻优）",
|
||
err=True,
|
||
)
|
||
if not optimize_all:
|
||
assert strategy_name is not None
|
||
try:
|
||
entry = registry.get(strategy_name)
|
||
except KeyError as exc:
|
||
click.echo(f"错误: {exc}", err=True)
|
||
raise SystemExit(1) from None
|
||
if custom_grid is not None:
|
||
declared = {p.name for p in entry.params}
|
||
unknown = set(custom_grid) - declared
|
||
if unknown:
|
||
click.echo(
|
||
f"错误: 未知参数 {sorted(unknown)}，"
|
||
f"'{strategy_name}' 可用参数: {sorted(declared)}",
|
||
err=True,
|
||
)
|
||
raise SystemExit(1)
|
||
else:
|
||
custom_grid = get_preset(strategy_name)
|
||
if not custom_grid:
|
||
first_param = entry.params[0].name if entry.params else "参数名"
|
||
click.echo(
|
||
f"错误: 策略 '{strategy_name}' 未登记预设网格，请用 --param 指定（如 "
|
||
f"--param {first_param}=5,10,20）",
|
||
err=True,
|
||
)
|
||
raise SystemExit(1)
|
||
|
||
# 2. 获取数据
|
||
mkt = parse_market(market)
|
||
with get_mac_client() as client:
|
||
df = client.get_stock_kline(
|
||
mkt,
|
||
code,
|
||
period=parse_period(period),
|
||
start=0,
|
||
count=count,
|
||
adjust=parse_adjust(adjust),
|
||
)
|
||
|
||
# 3. 寻优
|
||
fmt = "table" if use_table else output_fmt
|
||
import json
|
||
|
||
if optimize_all:
|
||
from .optimizer import optimize_all_strategies
|
||
|
||
report = optimize_all_strategies(
|
||
df,
|
||
cash=cash,
|
||
commission=commission,
|
||
slippage=slippage,
|
||
execution=execution,
|
||
workers=workers,
|
||
)
|
||
if fmt == "table":
|
||
_print_optimize_all_table(report, top)
|
||
else:
|
||
click.echo(json.dumps(report, ensure_ascii=False, default=str))
|
||
return
|
||
|
||
from .optimizer import ParamGridOptimizer
|
||
|
||
assert strategy_name is not None and custom_grid is not None
|
||
optimizer = ParamGridOptimizer(
|
||
strategy_name=strategy_name,
|
||
param_grid=custom_grid,
|
||
df=df,
|
||
cash=cash,
|
||
commission=commission,
|
||
slippage=slippage,
|
||
execution=execution,
|
||
workers=workers,
|
||
)
|
||
result = optimizer.run()
|
||
if fmt == "table":
|
||
_print_optimize_table(result.to_dict(), top)
|
||
else:
|
||
click.echo(json.dumps(result.to_dict(), ensure_ascii=False, default=str))
|
||
|
||
|
||
def _fmt_grid_point(params: dict[str, Any]) -> str:
|
||
"""把参数字典格式化为 fast=10, slow=20 形式。"""
|
||
return ", ".join(f"{k}={v}" for k, v in params.items())
|
||
|
||
|
||
def _print_optimize_table(report: dict[str, Any], top: int) -> None:
|
||
"""以表格形式输出单策略寻优结果。"""
|
||
results = report.get("results") or []
|
||
best = report.get("best")
|
||
click.echo(f"=== 参数寻优: {report.get('strategy')}（{len(results)} 个有效网格点）===\n")
|
||
click.echo(
|
||
f"{'排名':<4} {'参数':<28} {'总收益率':>8} {'夏普':>6} {'最大回撤':>8} "
|
||
f"{'交易':>4} {'胜率':>6}"
|
||
)
|
||
for i, r in enumerate(results[:top], 1):
|
||
click.echo(
|
||
f"{i:<4} {_fmt_grid_point(r['params']):<28} {r['total_return']:>8.2%} "
|
||
f"{r['sharpe']:>6.2f} {r['max_drawdown']:>8.2%} "
|
||
f"{r['total_trades']:>4} {r['win_rate']:>6.1%}"
|
||
)
|
||
if best:
|
||
click.echo(f"\n最佳参数: {_fmt_grid_point(best['params'])}")
|
||
if len(results) > top:
|
||
click.echo(f"（仅显示前 {top} 行，完整结果用 --output json）")
|
||
|
||
|
||
def _print_optimize_all_table(report: dict[str, Any], top: int) -> None:
|
||
"""以表格形式输出 --all 全策略寻优排名。"""
|
||
ranking = report.get("ranking") or []
|
||
click.echo(
|
||
f"=== 一键寻优所有策略（{len(ranking)} 个策略，"
|
||
f"共 {report.get('total_grid_points', 0)} 网格点）===\n"
|
||
)
|
||
click.echo(
|
||
f"{'排名':<4} {'策略':<20} {'最佳参数':<28} {'总收益率':>8} {'夏普':>6} "
|
||
f"{'最大回撤':>8} {'交易':>4} {'胜率':>6}"
|
||
)
|
||
for i, r in enumerate(ranking[:top], 1):
|
||
label = f"{r['strategy']} {r.get('strategy_label', '')}"
|
||
click.echo(
|
||
f"{i:<4} {label:<20} {_fmt_grid_point(r['params']):<28} "
|
||
f"{r['total_return']:>8.2%} {r['sharpe']:>6.2f} "
|
||
f"{r['max_drawdown']:>8.2%} {r['total_trades']:>4} {r['win_rate']:>6.1%}"
|
||
)
|
||
if report.get("skipped"):
|
||
click.echo(f"\n跳过（未注册）: {', '.join(report['skipped'])}")
|
||
if ranking:
|
||
click.echo(f"\n全局最优: {ranking[0]['strategy']} {_fmt_grid_point(ranking[0]['params'])}")
|
||
if len(ranking) > top:
|
||
click.echo(f"（仅显示前 {top} 行，完整结果用 --output json）")
|