mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
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Justin Gu
d32c5e17b4
回测是 numpy/pandas 的 CPU 密集计算并持有 GIL，多线程无加速，必须用 多进程。照搬项目里已跑通的 screen/scanner.py 进程池模板。 后端: - OptimizeAllBacktestRequest 新增 workers 字段（默认 1=串行，0-32） - 抽出模块顶层函数 _optimize_one_strategy（可被 ProcessPoolExecutor pickle），策略类在子进程内构造，避开跨进程 pickle 问题 - _run_optimize_all 在 workers>=2 时用进程池并行，否则走原串行逻辑 - 顺带修复 backtest_schemas.py 的 E501 历史遗留 前端: - OptimizeView 配置区新增「一键寻优并发」选择器：自动检测 CPU 核数 + 串行/4/8/16 进程下拉，默认串行，标注推荐档 min(CPU, 8) - types.ts/api.ts 透传 workers 字段 实测 8 进程 vs 串行提速 4-6x；端到端冒烟验证并行与串行结果完全一致
415 lines
16 KiB
Python
415 lines
16 KiB
Python
"""回测 Web API 的请求/响应模型与结果序列化。
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与 ``easy_tdx.web.schemas`` 分离，避免主 schemas 文件膨胀。结果序列化复用
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主 schemas 的 numpy/datetime 清洗思路，但递归处理嵌套结构（BacktestResult
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的 performance 是 dict、equity_curve 是 list[dict]、config 是 dict）。
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"""
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from __future__ import annotations
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from typing import Any, Literal
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from pydantic import BaseModel, Field, model_validator
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__all__ = [
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"BacktestRequest",
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"BacktestResultResponse",
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"StrategySchemaResponse",
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"TaskSubmitResponse",
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"TaskStateResponse",
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"OptimizeAllBacktestRequest",
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"OptimizeAllResult",
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"OptimizeAllRankEntry",
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"SavedStrategy",
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"SavedStrategyCreate",
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"SavedStrategyListResponse",
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"MultiStrategyItem",
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"MultiStrategyBacktestRequest",
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"serialize_result",
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]
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# ── 请求模型 ───────────────────────────────────────────────────────────────────
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class BacktestRequest(BaseModel):
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"""单标的回测请求。
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两种数据来源（二选一）：
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- ``ohlcv``: 直接内联 OHLCV 记录（前端已有数据时）。
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- ``symbol`` + ``category`` + ``count``: 指定标的，由后端取行情。
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两者都给则以内联数据为准。
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"""
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strategy: str = Field(..., description="策略名（见 /backtest/strategies）")
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params: dict[str, Any] = Field(default_factory=dict, description="策略参数")
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cash: float = Field(default=1_000_000.0, gt=0, description="初始资金")
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commission: float = Field(default=0.0003, ge=0, le=0.01, description="佣金费率")
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min_commission: float = Field(default=5.0, ge=0, description="单笔最低佣金")
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stamp_tax: float = Field(default=0.001, ge=0, le=0.01, description="印花税（卖出）")
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slippage: float = Field(default=0.0, ge=0, le=0.05, description="滑点费率")
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execution: Literal["next_open", "next_close"] = Field(
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default="next_open", description="成交模式"
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)
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# 数据来源 A：内联 OHLCV（上限与 symbol 路径的 count 上限对齐，防 DoS）
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ohlcv: list[dict[str, Any]] | None = Field(
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default=None,
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max_length=2000,
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description="内联 K 线记录（含 datetime/open/high/low/close/vol/amount，最多 2000 条）",
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)
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# 数据来源 B：按标的取行情
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symbol: str | None = Field(
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default=None,
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pattern=r"^(SZ|SH|BJ):\d{6}$",
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description="标的代码，格式 市场:代码，如 SZ:000001",
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)
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category: Literal["DAY", "WEEK", "MONTH", "MIN_5", "MIN_15", "MIN_30", "MIN_60"] = Field(
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default="DAY", description="K 线周期"
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)
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count: int = Field(default=250, ge=20, le=2000, description="K 线根数")
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@model_validator(mode="after")
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def _check_data_source(self) -> BacktestRequest:
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if self.ohlcv is None and self.symbol is None:
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raise ValueError("必须提供 ohlcv（内联数据）或 symbol（标的代码）之一")
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return self
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class PortfolioBacktestRequest(BaseModel):
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"""组合（多标的）回测请求。
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与单标的 BacktestRequest 的区别：用 ``stocks`` 列表替代单个 ``symbol``，
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资金按 equal 模式均分到各标的。日期范围过滤由前端完成（取满 800 根后过滤）。
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"""
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strategy: str = Field(..., description="策略名")
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params: dict[str, Any] = Field(default_factory=dict, description="策略参数")
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cash: float = Field(default=1_000_000.0, gt=0, description="组合总资金")
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commission: float = Field(default=0.0003, ge=0, le=0.01)
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min_commission: float = Field(default=5.0, ge=0)
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stamp_tax: float = Field(default=0.001, ge=0, le=0.01)
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slippage: float = Field(default=0.0, ge=0, le=0.05)
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execution: Literal["next_open", "next_close"] = Field(default="next_open")
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stocks: list[str] = Field(
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...,
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min_length=1,
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max_length=20,
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description='标的列表，格式 "市场:代码"，如 ["SZ:000001","SH:600519"]',
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)
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category: Literal["DAY", "WEEK", "MONTH", "MIN_5", "MIN_15", "MIN_30", "MIN_60"] = Field(
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default="DAY"
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)
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start_date: str | None = Field(default=None, description="开始日期 YYYY-MM-DD（可选过滤）")
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end_date: str | None = Field(default=None, description="结束日期 YYYY-MM-DD（可选过滤）")
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@model_validator(mode="after")
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def _check_stocks_format(self) -> PortfolioBacktestRequest:
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for s in self.stocks:
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if not s.startswith(("SZ:", "SH:", "BJ:")) or len(s) != 9:
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raise ValueError(f"标的格式应为 '市场:6位代码'，得到 {s!r}")
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return self
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class OptimizeBacktestRequest(BaseModel):
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"""参数网格寻优请求。
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在单个标的上，对策略的 1-2 个参数做网格搜索。数据来源与单标的回测一致
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（ohlcv 内联或 symbol 取行情）。param_grid 指定寻优参数及其取值列表，
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网格大小（各取值数乘积）上限 200。
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"""
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strategy: str = Field(..., description="策略名")
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cash: float = Field(default=1_000_000.0, gt=0)
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commission: float = Field(default=0.0003, ge=0, le=0.01)
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slippage: float = Field(default=0.0, ge=0, le=0.05)
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execution: Literal["next_open", "next_close"] = Field(default="next_open")
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param_grid: dict[str, list[int | float | str]] = Field(
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...,
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min_length=1,
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max_length=2,
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description='参数取值网格，如 {"fast":[5,10,20], "slow":[15,20,30]}',
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)
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# 数据来源 A：内联 OHLCV
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ohlcv: list[dict[str, Any]] | None = Field(default=None, max_length=2000)
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# 数据来源 B：按标的取行情
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symbol: str | None = Field(default=None, pattern=r"^(SZ|SH|BJ):\d{6}$")
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category: Literal["DAY", "WEEK", "MONTH", "MIN_5", "MIN_15", "MIN_30", "MIN_60"] = Field(
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default="DAY"
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)
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count: int = Field(default=250, ge=20, le=800)
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start_date: str | None = Field(default=None)
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end_date: str | None = Field(default=None)
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@model_validator(mode="after")
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def _check_data_source(self) -> OptimizeBacktestRequest:
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if self.ohlcv is None and self.symbol is None:
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raise ValueError("必须提供 ohlcv 或 symbol 之一")
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return self
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class OptimizeAllBacktestRequest(BaseModel):
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"""一键寻优所有策略请求。
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在单个标的上，对所有策略的预设参数网格（见
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``easy_tdx.backtest.strategies.presets.STRATEGY_PRESETS``）依次做网格寻优，
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取各策略最优点汇总成全局排名，找出最佳策略 + 参数组合。数据来源与单标的
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回测一致（ohlcv 内联或 symbol 取行情）。
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"""
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cash: float = Field(default=1_000_000.0, gt=0)
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commission: float = Field(default=0.0003, ge=0, le=0.01)
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slippage: float = Field(default=0.0, ge=0, le=0.05)
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execution: Literal["next_open", "next_close"] = Field(default="next_open")
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workers: int = Field(
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default=1,
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ge=0,
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le=32,
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description="一键寻优并发工作进程数：0 或 1=串行（默认）；2+=ProcessPoolExecutor "
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"并行（CPU-bound，线程无加速）。推荐 min(cpu_count, 8)。",
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)
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# 数据来源 A：内联 OHLCV
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ohlcv: list[dict[str, Any]] | None = Field(default=None, max_length=2000)
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# 数据来源 B：按标的取行情
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symbol: str | None = Field(default=None, pattern=r"^(SZ|SH|BJ):\d{6}$")
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category: Literal["DAY", "WEEK", "MONTH", "MIN_5", "MIN_15", "MIN_30", "MIN_60"] = Field(
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default="DAY"
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)
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count: int = Field(default=250, ge=20, le=800)
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start_date: str | None = Field(default=None)
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end_date: str | None = Field(default=None)
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@model_validator(mode="after")
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def _check_data_source(self) -> OptimizeAllBacktestRequest:
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if self.ohlcv is None and self.symbol is None:
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raise ValueError("必须提供 ohlcv 或 symbol 之一")
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return self
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# ── 响应模型 ───────────────────────────────────────────────────────────────────
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class StrategySchemaResponse(BaseModel):
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"""策略 schema 列表响应（/backtest/strategies）。"""
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strategies: list[dict[str, Any]]
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count: int
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class BacktestResultResponse(BaseModel):
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"""回测结果响应（已清洗为 JSON 原生类型）。"""
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performance: dict[str, Any]
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equity_curve: list[dict[str, Any]]
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trades: list[dict[str, Any]]
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positions: list[dict[str, Any]]
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config: dict[str, Any]
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class TaskSubmitResponse(BaseModel):
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"""后台任务提交响应。"""
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task_id: str
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status: Literal["pending", "running"]
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class TaskStateResponse(BaseModel):
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"""后台任务状态响应。"""
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task_id: str
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status: Literal["pending", "running", "done", "failed"]
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result: dict[str, Any] | None = None
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error: str | None = None
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description: str = ""
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elapsed: float = 0.0
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class TaskSummary(BaseModel):
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"""任务摘要（列表用，不含完整 result）。"""
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task_id: str
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status: Literal["pending", "running", "done", "failed"]
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description: str = ""
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created_at: float = 0.0
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elapsed: float = 0.0
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class TaskListResponse(BaseModel):
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"""任务摘要列表响应。"""
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tasks: list[TaskSummary]
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count: int
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class OptimizeAllRankEntry(BaseModel):
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"""一键寻优全局排名单行：某策略的最优点摘要。"""
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strategy: str
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strategy_label: str
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params: dict[str, Any]
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total_return: float = 0.0
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sharpe: float = 0.0
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max_drawdown: float = 0.0
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total_trades: int = 0
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win_rate: float = 0.0
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profit_factor: float = 0.0
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grid_points: int = 0 # 该策略本轮寻优的网格点数
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class OptimizeAllResult(BaseModel):
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"""一键寻优所有策略的结果：全局排名 + 最佳 + 各策略最优点。"""
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ranking: list[OptimizeAllRankEntry] # 按 total_return 降序
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best: OptimizeAllRankEntry | None = None
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per_strategy: dict[str, OptimizeAllRankEntry] = {} # 策略名 → 最优点
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total_grid_points: int = 0 # 所有策略网格点合计
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# ── 已保存策略（策略库 / StrategyLibrary）───────────────────────────────────────
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class SavedStrategyCreate(BaseModel):
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"""新建一条已保存策略的请求体。
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前端在单标的/组合回测结果区点「保存策略」时提交。``strategy`` + ``params``
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是回测引擎可直接消费的最小复现形态；``context`` 记录当时测的标的/日期，
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``snapshot`` 记录保存时的关键绩效指标（"为什么觉得它好"）。
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"""
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name: str = Field(..., min_length=1, max_length=120, description="策略名称（用户自拟）")
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kind: Literal["single", "portfolio", "multi"] = Field(
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..., description="来源：单标的/组合/多策略组合"
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)
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strategy: str = Field(..., description="策略名（注册表 key，如 ma_cross）")
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strategy_label: str = Field(default="", description="策略展示名")
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params: dict[str, Any] = Field(default_factory=dict)
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context: dict[str, Any] = Field(
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default_factory=dict,
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description="标的上下文：single 存 symbol/category/start_date/end_date；"
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"portfolio 存 stocks 列表；multi 存 items(MultiStrategyItem[]) + cash/execution",
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)
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trade_config: dict[str, Any] = Field(
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default_factory=dict, description="资金与成本配置（cash/commission/...）"
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)
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snapshot: dict[str, Any] = Field(
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default_factory=dict, description="保存时的成绩快照（total_return/sharpe/...）"
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)
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tags: list[str] = Field(default_factory=list)
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notes: str = Field(default="", max_length=2000)
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class SavedStrategy(BaseModel):
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"""一条已保存策略（响应模型，含 id 与时间戳）。"""
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id: str
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name: str
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kind: Literal["single", "portfolio", "multi"]
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strategy: str
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strategy_label: str = ""
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params: dict[str, Any] = {}
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context: dict[str, Any] = {}
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trade_config: dict[str, Any] = {}
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snapshot: dict[str, Any] = {}
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tags: list[str] = []
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notes: str = ""
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created_at: str = ""
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updated_at: str = ""
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app_version: str = ""
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class SavedStrategyListResponse(BaseModel):
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"""策略库列表响应。"""
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strategies: list[SavedStrategy]
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count: int
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# ── 多策略组合回测（资金分仓 / 并行制）──────────────────────────────────────────
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class MultiStrategyItem(BaseModel):
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"""多策略组合回测的单条策略槽位。
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每条 = 一个策略 + 它的参数 + 它要跑的原标的 + 日期范围。资金由请求体的
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``cash`` 统一给出，引擎按策略数均分到各条。
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"""
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strategy: str = Field(..., description="策略名（注册表 key，如 ma_cross）")
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strategy_label: str = Field(default="", description="策略展示名（用于结果 key）")
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params: dict[str, Any] = Field(default_factory=dict)
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symbol: str = Field(
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...,
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pattern=r"^(SZ|SH|BJ):\d{6}$",
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description='标的完整代码，格式 "市场:6位代码"，如 "SH:601088"',
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)
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category: Literal["DAY", "WEEK", "MONTH", "MIN_5", "MIN_15", "MIN_30", "MIN_60"] = Field(
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default="DAY"
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)
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start_date: str | None = Field(default=None, description="开始日期 YYYY-MM-DD（可选过滤）")
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end_date: str | None = Field(default=None, description="结束日期 YYYY-MM-DD（可选过滤）")
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class MultiStrategyBacktestRequest(BaseModel):
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"""多策略组合回测请求（资金分仓）。
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勾选 N 个策略，各跑在各自原标的上，总资金按策略数均分。响应该请求的后台任务
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结果是 ``MultiStrategyResult``（结构同 ``PortfolioResult``，前端复用组合页图表）。
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"""
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items: list[MultiStrategyItem] = Field(
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..., min_length=1, max_length=20, description="策略槽位列表（1~20 条）"
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)
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cash: float = Field(default=1_000_000.0, gt=0, description="组合总资金（均分给各策略）")
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commission: float = Field(default=0.0003, ge=0, le=0.01)
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min_commission: float = Field(default=5.0, ge=0)
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stamp_tax: float = Field(default=0.001, ge=0, le=0.01)
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slippage: float = Field(default=0.0, ge=0, le=0.05)
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execution: Literal["next_open", "next_close"] = Field(default="next_open")
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# ── 结果序列化 ─────────────────────────────────────────────────────────────────
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def _clean_value(v: Any) -> Any:
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"""把单个值转为 JSON 原生类型（递归处理容器）。"""
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# bool 必须先于 int 判断（bool 是 int 子类）
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if isinstance(v, bool):
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return v
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if isinstance(v, int | str):
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return v
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if isinstance(v, float):
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# NaN/Inf 无法 JSON 序列化，转 None
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import math
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||
return None if math.isnan(v) or math.isinf(v) else v
|
||
if v is None:
|
||
return v
|
||
if hasattr(v, "isoformat"):
|
||
return v.isoformat()
|
||
if hasattr(v, "item"):
|
||
# numpy scalar → Python native（递归清洗，处理 numpy NaN）
|
||
return _clean_value(v.item())
|
||
if isinstance(v, dict):
|
||
return {str(k): _clean_value(val) for k, val in v.items()}
|
||
if isinstance(v, list | tuple):
|
||
return [_clean_value(item) for item in v]
|
||
# 兜底：不可序列化的对象转字符串
|
||
return str(v)
|
||
|
||
|
||
def serialize_result(result: Any) -> dict[str, Any]:
|
||
"""把 BacktestResult（或其 to_dict()）清洗为纯 JSON 兼容字典。
|
||
|
||
递归处理 performance / equity_curve / trades / positions / config，
|
||
把 numpy scalar 转 Python 原生、NaN/Inf 转 None（JSON 不支持）、
|
||
datetime 转 ISO 字符串。
|
||
"""
|
||
if hasattr(result, "to_dict"):
|
||
result = result.to_dict()
|
||
return {str(k): _clean_value(v) for k, v in result.items()}
|