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https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
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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。
584 lines
22 KiB
Python
584 lines
22 KiB
Python
"""一条龙策略评估（evaluate_strategy，v1.25 新增）。
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把「全样本回测 + Walk-Forward 样本外 + 适配性体检 + 综合评分 + S-D 评级 +
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基准对比（买入持有）」打包成一次调用、一份报告（借鉴 backtest-system 的
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``evaluate_engine()``「拉数 / 对齐 / 选模式 / 对比参考引擎」一条龙思路，
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落在 easy-tdx 的三通道输出上）。
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报告结构（全部 JSON 兼容，直接喂 REST / CLI / AI Agent）::
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{
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"performance": {...19 项绩效...},
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"score": {"total": 78.3, "components": {...}}, # 综合评分（含 WF）
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"grade": {"grade": "B", "score": 71.2, ...}, # S-D 评级（不看收益）
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"walkforward": {"consistency": 0.71, "windows": [...]},
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"fitness": {"pass_ratio": 0.875, "high_fitness": true, "checks": [...]},
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"benchmark": {
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"buy_hold": {"total_return": 0.32, ...},
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"excess_return": 0.18, # 策略 - 买入持有
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"alpha": 0.09, "beta": 0.72, # v1.28：CAPM 对比
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"information_ratio": 0.85, "tracking_error": 0.12 # v1.28：主动管理指标
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},
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"config": {...}
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}
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基准对比的语义：**同区间、同费率、同初始资金**下，「首根 K 线全仓买入、
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持有到末根」的买入持有收益。策略连买入持有都跑不赢时，报告的
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``excess_return`` 为负——这是一票否决级别的研发信号。
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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import numpy as np
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import pandas as pd
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from easy_tdx.backtest.engine import BacktestEngine
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from easy_tdx.backtest.fitness import FitnessCheck, FitnessEngine, FitnessReport, FitnessSegment
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from easy_tdx.backtest.grading import grade_performance, grade_portfolio_equity
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from easy_tdx.backtest.scoring import score_strategy
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from easy_tdx.backtest.strategy import Strategy
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from easy_tdx.backtest.types import to_json_native
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from easy_tdx.backtest.walkforward import (
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MultiStrategyWalkForwardEngine,
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PortfolioWalkForwardEngine,
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WalkForwardEngine,
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)
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if TYPE_CHECKING:
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import numpy.typing as npt
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from easy_tdx.backtest.types import BacktestResult
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NDArray = npt.NDArray[np.float64]
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else:
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NDArray = np.ndarray
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__all__ = [
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"evaluate_strategy",
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"evaluate_portfolio",
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"evaluate_multi",
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"run_buy_hold_benchmark",
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"compute_benchmark_comparison",
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]
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class _BuyAndHold(Strategy):
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"""买入持有基准：首根 K 线全仓买入，持有到末根。"""
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def init(self) -> None:
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self._bought = False
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def next(self) -> None:
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if not self._bought:
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self.buy()
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self._bought = True
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def _run_buy_hold_result(
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df: pd.DataFrame,
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cash: float = 100000.0,
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commission: float = 0.0003,
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min_commission: float = 5.0,
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stamp_tax: float = 0.001,
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slippage: float = 0.0,
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execution: str = "next_open",
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symbol: str | None = None,
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auto_fees: bool = False,
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) -> BacktestResult:
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"""买入持有基准完整回测（内部用，返回 BacktestResult 以取资金曲线）。"""
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engine = BacktestEngine(
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strategy=_BuyAndHold,
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cash=cash,
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commission=commission,
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min_commission=min_commission,
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stamp_tax=stamp_tax,
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slippage=slippage,
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execution=execution,
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symbol=symbol,
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auto_fees=auto_fees,
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)
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return engine.run(df)
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def run_buy_hold_benchmark(
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df: pd.DataFrame,
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cash: float = 100000.0,
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commission: float = 0.0003,
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min_commission: float = 5.0,
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stamp_tax: float = 0.001,
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slippage: float = 0.0,
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execution: str = "next_open",
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symbol: str | None = None,
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auto_fees: bool = False,
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) -> dict[str, Any]:
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"""买入持有基准回测（与策略回测同区间、同费率、同资金）。"""
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result = _run_buy_hold_result(
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df, cash, commission, min_commission, stamp_tax, slippage, execution, symbol, auto_fees
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)
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keys = (
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"total_return",
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"annual_return",
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"max_drawdown",
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"sharpe",
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"calmar",
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"volatility",
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)
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return dict(to_json_native({k: result.performance.get(k, 0.0) for k in keys}))
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def compute_benchmark_comparison(
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strategy_curve: pd.DataFrame,
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benchmark_curve: pd.DataFrame,
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annual_days: int = 252,
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) -> dict[str, float]:
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"""策略 vs 基准的 CAPM / 主动管理对比指标（v1.28 新增）。
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从两条资金曲线的日收益率序列计算：
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- ``beta``: 协方差/基准方差，策略对基准的敏感度（1 = 与基准同涨跌）
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- ``alpha``: 年化 CAPM α ≈ (策略日均收益 − β×基准日均收益) × 年化天数，
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简化版（无风险利率并入截距），>0 说明剔除基准影响后仍有超额
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- ``information_ratio``: 年化信息比率 = mean(策略−基准)/std(策略−基准)×√N，
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每 1 单位跟踪误差换来多少超额收益
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- ``tracking_error``: 年化跟踪误差 = std(策略−基准)×√N
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两条曲线按 bar 对齐（截取较短长度）；基准方差为 0（曲线恒定）时
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beta/alpha 记 0，IR 在差值恒正且无波动时沿用 999 上限约定。
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Args:
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strategy_curve: 策略资金曲线（含 total 列）
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benchmark_curve: 基准资金曲线（含 total 列）
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annual_days: 年化交易日数
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Returns:
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{alpha, beta, information_ratio, tracking_error}
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"""
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s_total = strategy_curve["total"].to_numpy(dtype=np.float64)
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b_total = benchmark_curve["total"].to_numpy(dtype=np.float64)
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n = min(len(s_total), len(b_total))
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if n < 3:
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return {"alpha": 0.0, "beta": 0.0, "information_ratio": 0.0, "tracking_error": 0.0}
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def _daily_ret(total: NDArray) -> NDArray:
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safe_prev = np.where(total[:-1] != 0, total[:-1], np.nan)
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ret = np.diff(total) / safe_prev
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return ret[np.isfinite(ret)]
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s_ret = _daily_ret(s_total[:n])
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b_ret = _daily_ret(b_total[:n])
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m = min(len(s_ret), len(b_ret))
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if m < 2:
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return {"alpha": 0.0, "beta": 0.0, "information_ratio": 0.0, "tracking_error": 0.0}
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s_ret, b_ret = s_ret[:m], b_ret[:m]
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b_var = float(np.var(b_ret))
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if b_var > 1e-18:
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beta = float(np.cov(s_ret, b_ret)[0, 1] / b_var)
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alpha = float((np.mean(s_ret) - beta * np.mean(b_ret)) * annual_days)
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else:
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beta = 0.0
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alpha = float(np.mean(s_ret) * annual_days)
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diff = s_ret - b_ret
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diff_std = float(np.std(diff))
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if diff_std > 1e-12:
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information_ratio = float(np.mean(diff) / diff_std * np.sqrt(annual_days))
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elif np.mean(diff) > 0:
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information_ratio = 999.0
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else:
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information_ratio = 0.0
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tracking_error = diff_std * np.sqrt(annual_days)
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return {
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"alpha": alpha,
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"beta": beta,
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"information_ratio": information_ratio,
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"tracking_error": tracking_error,
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}
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def evaluate_strategy(
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strategy: type[Strategy] | Strategy,
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df: pd.DataFrame,
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cash: float = 100000.0,
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commission: float = 0.0003,
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min_commission: float = 5.0,
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stamp_tax: float = 0.001,
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slippage: float = 0.0,
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execution: str = "next_open",
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symbol: str | None = None,
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auto_fees: bool = False,
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n_windows: int = 7,
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warmup_ratio: float = 0.3,
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context_bars: int = 60,
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split: tuple[float, float, float] = (0.6, 0.2, 0.2),
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) -> dict[str, Any]:
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"""一条龙策略评估：回测 + WF + 适配性 + 评分 + 评级 + 基准对比。
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Args:
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strategy: 策略类或实例。
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df: K 线（datetime/open/high/low/close，时间升序）。
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其余参数: 透传给各子引擎（回测 / WF / 适配性共用同口径费率与执行）。
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n_windows / warmup_ratio: Walk-Forward 切窗参数。
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split: 适配性三段占比。
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Returns:
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完整评估报告字典（结构见模块 docstring）。
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"""
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engine_kwargs: dict[str, Any] = {
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"cash": cash,
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"commission": commission,
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"min_commission": min_commission,
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"stamp_tax": stamp_tax,
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"slippage": slippage,
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"execution": execution,
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"symbol": symbol,
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"auto_fees": auto_fees,
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}
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# 1. 全样本回测
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bt = BacktestEngine(strategy=strategy, **engine_kwargs).run(df)
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perf = bt.performance
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# 2. Walk-Forward 样本外
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wf = WalkForwardEngine(
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strategy=strategy,
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n_windows=n_windows,
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warmup_ratio=warmup_ratio,
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context_bars=context_bars,
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**engine_kwargs,
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).run(df)
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# 3. 适配性体检
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fitness = FitnessEngine(
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strategy=strategy,
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split=split,
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context_bars=context_bars,
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**engine_kwargs,
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).evaluate(df)
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# 4. 综合评分（叠加 WF 一致性）+ S-D 评级
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score = score_strategy(perf, wf=wf)
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grade = grade_performance(perf)
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# 5. 基准对比（买入持有，同区间同费率）：超额收益 + Alpha/Beta/IR/TE
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bh_result = _run_buy_hold_result(df, **engine_kwargs)
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bh_keys = ("total_return", "annual_return", "max_drawdown", "sharpe", "calmar", "volatility")
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bh = dict(to_json_native({k: bh_result.performance.get(k, 0.0) for k in bh_keys}))
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comparison = compute_benchmark_comparison(bt.equity_curve, bh_result.equity_curve)
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return {
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"performance": to_json_native(dict(perf)),
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"score": score.to_dict(),
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"grade": grade.to_dict(),
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"walkforward": wf.to_dict(),
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"fitness": fitness.to_dict(),
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"benchmark": {
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"buy_hold": bh,
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"excess_return": float(perf.get("total_return", 0.0))
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- float(bh.get("total_return", 0.0)),
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**comparison,
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},
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"config": {
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"symbol": symbol,
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"auto_fees": auto_fees,
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"execution": execution,
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"n_windows": n_windows,
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"warmup_ratio": warmup_ratio,
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"split": list(split),
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},
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}
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def evaluate_portfolio(
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strategy: type[Strategy] | Strategy,
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stocks: list[Any],
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total_cash: float = 1_000_000.0,
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commission: float = 0.0003,
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min_commission: float = 5.0,
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stamp_tax: float = 0.001,
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slippage: float = 0.0,
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execution: str = "next_open",
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chanlun_level: str | None = None,
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auto_fees: bool = False,
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n_windows: int = 7,
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warmup_ratio: float = 0.3,
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context_bars: int = 60,
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split: tuple[float, float, float] = (0.6, 0.2, 0.2),
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) -> dict[str, Any]:
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"""一条龙组合评估：组合回测 + 组合 WF + 适配性体检 + 综合评分 + 组合评级
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+ 等权买入持有基准对比。
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与 :func:`evaluate_strategy`（单标的）同构的报告结构，前端 EvaluatePanel
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可直接复用；差异点：
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- ``performance`` 来自组合引擎（完整 25 项指标，含 SQN/最大连胜连亏）；
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- ``walkforward`` 来自 :class:`~easy_tdx.backtest.walkforward.PortfolioWalkForwardEngine`；
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- ``fitness`` 为**跨标的聚合**：逐标的跑三段体检，检查项按「≥60% 标的
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通过」的多数口径合成，段指标取截面均值——诚实反映组合整体适配性；
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- ``grade`` 用组合净值口径 :func:`~easy_tdx.backtest.grading.grade_portfolio_equity`；
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- ``benchmark`` 为**等权买入持有组合**（每只标的分 1/N 资金首根买入持有
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到末根，同费率同区间），α/β/信息比率/跟踪误差基于两条组合净值曲线。
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Args:
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strategy: 策略类或实例。
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stocks: :class:`~easy_tdx.backtest.portfolio_engine.StockData` 列表。
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其余参数: 透传给组合回测 / 组合 WF / 适配性（同口径费率与执行）。
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Returns:
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完整评估报告字典（结构同 evaluate_strategy，config 记录标的清单）。
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"""
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from easy_tdx.backtest.portfolio_engine import PortfolioBacktestEngine
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engine_kwargs: dict[str, Any] = {
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"total_cash": total_cash,
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"commission": commission,
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"min_commission": min_commission,
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"stamp_tax": stamp_tax,
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"slippage": slippage,
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"execution": execution,
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"chanlun_level": chanlun_level,
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"auto_fees": auto_fees,
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}
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# 1. 全样本组合回测（完整 25 项指标 + 合并净值曲线）
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bt = PortfolioBacktestEngine(strategy=strategy, stocks=stocks, **engine_kwargs).run()
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perf = bt.total_performance
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# 2. 组合 Walk-Forward 样本外
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wf = PortfolioWalkForwardEngine(
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strategy=strategy,
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stocks=stocks,
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n_windows=n_windows,
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warmup_ratio=warmup_ratio,
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context_bars=context_bars,
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**engine_kwargs,
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).run()
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# 3. 适配性体检：逐标的跑三段体检，跨标的多数口径聚合。
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# 每标的传入各自 symbol，使 auto_fees 按品种解析费率——与组合回测主路径
|
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# （PortfolioBacktestEngine 逐标的 resolve_fee_model）同口径。此前漏传
|
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# symbol：ETF/可转债组合的三段体检被按股票口径错收印花税。
|
||
fitness_kwargs: dict[str, Any] = {
|
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k: v for k, v in engine_kwargs.items() if k not in ("total_cash", "chanlun_level")
|
||
}
|
||
per_stock_fitness = [
|
||
FitnessEngine(
|
||
strategy=strategy,
|
||
split=split,
|
||
context_bars=context_bars,
|
||
symbol=f"{stock.market}{stock.code}",
|
||
**fitness_kwargs,
|
||
).evaluate(stock.df)
|
||
for stock in stocks
|
||
]
|
||
fitness = _aggregate_fitness(per_stock_fitness, split)
|
||
|
||
# 4. 综合评分（叠加组合 WF 一致性）+ 组合评级（净值曲线口径）
|
||
score = score_strategy(dict(perf), wf=wf)
|
||
grade = grade_portfolio_equity(bt.combined_equity.to_dict(orient="records"))
|
||
|
||
# 5. 基准对比：等权买入持有组合（每只标的 1/N 首根买入持有到末根，同费率）
|
||
bh_bt = PortfolioBacktestEngine(strategy=_BuyAndHold, stocks=stocks, **engine_kwargs).run()
|
||
bh_keys = ("total_return", "annual_return", "max_drawdown", "sharpe", "calmar", "volatility")
|
||
bh = dict(to_json_native({k: bh_bt.total_performance.get(k, 0.0) for k in bh_keys}))
|
||
comparison = compute_benchmark_comparison(bt.combined_equity, bh_bt.combined_equity)
|
||
|
||
return {
|
||
"performance": to_json_native(dict(perf)),
|
||
"score": score.to_dict(),
|
||
"grade": grade.to_dict(),
|
||
"walkforward": wf.to_dict(),
|
||
"fitness": fitness.to_dict(),
|
||
"benchmark": {
|
||
"buy_hold": bh,
|
||
"excess_return": float(perf.get("total_return", 0.0))
|
||
- float(bh.get("total_return", 0.0)),
|
||
**comparison,
|
||
},
|
||
"config": {
|
||
"stocks": [f"{s.market}{s.code}" for s in stocks],
|
||
"total_cash": total_cash,
|
||
"auto_fees": auto_fees,
|
||
"execution": execution,
|
||
"n_windows": n_windows,
|
||
"warmup_ratio": warmup_ratio,
|
||
"split": list(split),
|
||
},
|
||
}
|
||
|
||
|
||
def _aggregate_fitness(
|
||
reports: list[FitnessReport],
|
||
split: tuple[float, float, float],
|
||
pass_ratio_threshold: float = 0.6,
|
||
) -> FitnessReport:
|
||
"""把逐标的的适配性体检报告聚合为组合级报告（多数口径）。
|
||
|
||
- 检查项：同名检查项跨标的计通过率，≥ ``pass_ratio_threshold``（默认
|
||
60%）标的通过则组合级该项通过，detail 记「x/y 只标的通过」；
|
||
- 段摘要：段起止取各标的的最早/最晚，收益/夏普/胜率取截面均值，
|
||
最大回撤取最深（max），交易数取合计——回答「组合整体在三段的形态」。
|
||
"""
|
||
aggregated = FitnessReport(split=split)
|
||
valid = [r for r in reports if r.checks]
|
||
if not valid:
|
||
return aggregated
|
||
|
||
# 检查项：按首份报告的检查顺序（FitnessEngine 的 8 项固定顺序）
|
||
n = len(valid)
|
||
for check in valid[0].checks:
|
||
passed_n = sum(1 for r in valid for c in r.checks if c.name == check.name and c.passed)
|
||
aggregated.checks.append(
|
||
FitnessCheck(
|
||
name=check.name,
|
||
passed=passed_n >= max(1, int(np.ceil(pass_ratio_threshold * n))),
|
||
detail=f"{passed_n}/{n} 只标的通过（组合多数口径）",
|
||
)
|
||
)
|
||
|
||
# 段摘要：train/valid/test 逐段截面聚合
|
||
for seg in valid[0].segments:
|
||
same = [s for s in (r.segment_by_name(seg.name) for r in valid) if s is not None]
|
||
if not same:
|
||
continue
|
||
aggregated.segments.append(
|
||
FitnessSegment(
|
||
name=seg.name,
|
||
start=min(s.start for s in same),
|
||
end=max(s.end for s in same),
|
||
bars=int(round(float(np.mean([s.bars for s in same])))),
|
||
total_return=float(np.mean([s.total_return for s in same])),
|
||
sharpe=float(np.mean([s.sharpe for s in same])),
|
||
max_drawdown=float(max(s.max_drawdown for s in same)),
|
||
total_trades=int(sum(s.total_trades for s in same)),
|
||
win_rate=float(np.mean([s.win_rate for s in same])),
|
||
)
|
||
)
|
||
|
||
aggregated.pass_ratio = (
|
||
sum(1 for c in aggregated.checks if c.passed) / len(aggregated.checks)
|
||
if aggregated.checks
|
||
else 0.0
|
||
)
|
||
aggregated.high_fitness = aggregated.pass_ratio >= 0.75
|
||
return aggregated
|
||
|
||
|
||
def evaluate_multi(
|
||
strategies: list[Any],
|
||
total_cash: float = 1_000_000.0,
|
||
commission: float = 0.0003,
|
||
min_commission: float = 5.0,
|
||
stamp_tax: float = 0.001,
|
||
slippage: float = 0.0,
|
||
execution: str = "next_open",
|
||
n_windows: int = 7,
|
||
warmup_ratio: float = 0.3,
|
||
context_bars: int = 60,
|
||
split: tuple[float, float, float] = (0.6, 0.2, 0.2),
|
||
) -> dict[str, Any]:
|
||
"""多策略组合一条龙评估：组合回测 + 组合 WF + 跨槽位适配性体检 + 综合评分
|
||
+ 组合评级 + 等权买入持有基准对比。
|
||
|
||
与 :func:`evaluate_portfolio`（一个策略 × 多标的）同构，报告结构一致；
|
||
差异点仅在槽位划分——每个槽位是「一个策略 × 它自己的标的」
|
||
（:class:`~easy_tdx.backtest.multi_strategy_engine.StrategySlot`）：
|
||
|
||
- 全样本回测走 :class:`~easy_tdx.backtest.multi_strategy_engine.MultiStrategyEngine`；
|
||
- Walk-Forward 走
|
||
:class:`~easy_tdx.backtest.walkforward.MultiStrategyWalkForwardEngine`；
|
||
- 适配性体检逐槽位（各自策略 × 各自标的）跑三段后按多数口径聚合；
|
||
- 买入持有基准 = 各槽位标的的等权买入持有组合（策略换成 _BuyAndHold，
|
||
其余不变），α/β/信息比率/跟踪误差基于两条组合净值曲线。
|
||
|
||
Args:
|
||
strategies: StrategySlot 列表（每个槽位已绑定策略实例与 K 线）。
|
||
其余参数: 透传给组合回测 / 组合 WF / 适配性（同口径费率与执行）。
|
||
|
||
Returns:
|
||
完整评估报告字典（结构同 evaluate_strategy，config 记录槽位清单）。
|
||
"""
|
||
from easy_tdx.backtest.multi_strategy_engine import MultiStrategyEngine, StrategySlot
|
||
|
||
engine_kwargs: dict[str, Any] = {
|
||
"total_cash": total_cash,
|
||
"commission": commission,
|
||
"min_commission": min_commission,
|
||
"stamp_tax": stamp_tax,
|
||
"slippage": slippage,
|
||
"execution": execution,
|
||
}
|
||
|
||
# 1. 全样本组合回测（完整 25 项指标 + 合并净值曲线）
|
||
bt = MultiStrategyEngine(strategies=list(strategies), **engine_kwargs).run()
|
||
perf = bt.total_performance
|
||
|
||
# 2. 组合 Walk-Forward 样本外
|
||
wf = MultiStrategyWalkForwardEngine(
|
||
strategies=list(strategies),
|
||
n_windows=n_windows,
|
||
warmup_ratio=warmup_ratio,
|
||
context_bars=context_bars,
|
||
**engine_kwargs,
|
||
).run()
|
||
|
||
# 3. 适配性体检：逐槽位（各自策略 × 各自标的）跑三段体检，多数口径聚合
|
||
per_cash = total_cash / max(len(strategies), 1)
|
||
fitness_kwargs: dict[str, Any] = {
|
||
"commission": commission,
|
||
"min_commission": min_commission,
|
||
"stamp_tax": stamp_tax,
|
||
"slippage": slippage,
|
||
"execution": execution,
|
||
}
|
||
per_slot_fitness = [
|
||
FitnessEngine(
|
||
strategy=slot.strategy,
|
||
split=split,
|
||
context_bars=context_bars,
|
||
cash=per_cash,
|
||
**fitness_kwargs,
|
||
).evaluate(slot.df)
|
||
for slot in strategies
|
||
]
|
||
fitness = _aggregate_fitness(per_slot_fitness, split)
|
||
|
||
# 4. 综合评分（叠加组合 WF 一致性）+ 组合评级（净值曲线口径）
|
||
score = score_strategy(dict(perf), wf=wf)
|
||
grade = grade_portfolio_equity(bt.combined_equity.to_dict(orient="records"))
|
||
|
||
# 5. 基准对比：各槽位标的的等权买入持有组合（策略换成 _BuyAndHold，其余不变）
|
||
bh_slots = [
|
||
StrategySlot(label=s.label, symbol=s.symbol, strategy=_BuyAndHold(), df=s.df)
|
||
for s in strategies
|
||
]
|
||
bh_bt = MultiStrategyEngine(strategies=bh_slots, **engine_kwargs).run()
|
||
bh_keys = ("total_return", "annual_return", "max_drawdown", "sharpe", "calmar", "volatility")
|
||
bh = dict(to_json_native({k: bh_bt.total_performance.get(k, 0.0) for k in bh_keys}))
|
||
comparison = compute_benchmark_comparison(bt.combined_equity, bh_bt.combined_equity)
|
||
|
||
return {
|
||
"performance": to_json_native(dict(perf)),
|
||
"score": score.to_dict(),
|
||
"grade": grade.to_dict(),
|
||
"walkforward": wf.to_dict(),
|
||
"fitness": fitness.to_dict(),
|
||
"benchmark": {
|
||
"buy_hold": bh,
|
||
"excess_return": float(perf.get("total_return", 0.0))
|
||
- float(bh.get("total_return", 0.0)),
|
||
**comparison,
|
||
},
|
||
"config": {
|
||
"slots": [f"{s.label}@{s.symbol}" for s in strategies],
|
||
"total_cash": total_cash,
|
||
"execution": execution,
|
||
"n_windows": n_windows,
|
||
"warmup_ratio": warmup_ratio,
|
||
"split": list(split),
|
||
},
|
||
}
|