mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 13:24:18 +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。
335 lines
12 KiB
Python
335 lines
12 KiB
Python
"""适配性评估（fitness）+ 一条龙评估（benchmark）测试。"""
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from __future__ import annotations
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import json
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from typing import Any
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import numpy as np
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import pandas as pd
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import pytest
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from easy_tdx.backtest.benchmark import evaluate_strategy, run_buy_hold_benchmark
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from easy_tdx.backtest.fitness import FitnessEngine, rolling_fitness_scores
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from easy_tdx.backtest.strategy import Strategy
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class _BuyFirstBar(Strategy):
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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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class _CycleTrader(Strategy):
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"""每 10 根切换一次持仓（买卖交替），保证各段有完整回合（total_trades>0）。"""
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def init(self) -> None:
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self._count = 0
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self._holding = False
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def next(self) -> None:
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self._count += 1
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if self._count % 10 == 0:
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if self._holding:
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self.sell()
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self._holding = False
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else:
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self.buy()
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self._holding = True
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def _df(n: int = 500, drift: float = 0.004) -> pd.DataFrame:
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rng = np.random.default_rng(3)
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dates = pd.date_range("2018-01-01", periods=n, freq="B")
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close = 10.0 * np.cumprod(1.0 + drift + rng.normal(0, 0.006, n))
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return pd.DataFrame(
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{
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"datetime": dates,
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"open": close * 0.999,
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"high": close * 1.01,
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"low": close * 0.99,
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"close": close,
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"vol": 1000.0,
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}
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)
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# ── FitnessEngine ─────────────────────────────────────────────────────────────
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def test_fitness_three_segments_and_checks():
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rep = FitnessEngine(_CycleTrader).evaluate(_df(600))
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assert [s.name for s in rep.segments] == ["train", "valid", "test"]
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assert len(rep.checks) == 8
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names = {c.name for c in rep.checks}
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assert names == {
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"train_profitable",
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"valid_profitable",
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"test_profitable",
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"sign_consistent",
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"drawdown_bounded",
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"train_enough_trades",
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"test_active",
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"oos_sharpe_positive",
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}
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# 上涨行情 + 买入持有 → 大部分检查通过 → 高适配
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assert rep.pass_ratio >= 0.75
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assert rep.high_fitness
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def test_fitness_checks_carry_values():
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rep = FitnessEngine(_CycleTrader).evaluate(_df(600))
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for c in rep.checks:
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assert c.detail # 每条检查附实际值（可解释性）
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assert isinstance(c.passed, bool)
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def test_fitness_losing_market_fails():
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rep = FitnessEngine(_BuyFirstBar).evaluate(_df(600, drift=-0.002))
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assert rep.pass_ratio < 0.75
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assert not rep.high_fitness
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# 三段全亏 → sign_consistent 通过（同号），但盈利检查全挂
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by_name = {c.name: c.passed for c in rep.checks}
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assert by_name["train_profitable"] is False
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assert by_name["valid_profitable"] is False
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assert by_name["test_profitable"] is False
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def test_fitness_insufficient_data_returns_empty():
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rep = FitnessEngine(_BuyFirstBar).evaluate(_df(60)) # valid 段 = 12 根 < 20 → 空报告
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assert rep.segments == []
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assert rep.checks == []
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assert rep.high_fitness is False
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def test_fitness_invalid_split_raises():
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with pytest.raises(ValueError, match="split"):
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FitnessEngine(_BuyFirstBar, split=(0.5, 0.2, 0.2))
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def test_fitness_prefix_no_lookahead():
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"""evaluate_prefix 只用前缀：末段测试段终点必须早于 end_index。"""
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df = _df(600)
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rep = FitnessEngine(_BuyFirstBar).evaluate_prefix(df, 400)
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assert [s.name for s in rep.segments] == ["train", "valid", "test"]
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# 前缀评估的测试段末日期 < 第 400 根的日期
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dt_col = "datetime"
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cutoff = pd.Timestamp(df[dt_col].iloc[399]).strftime("%Y-%m-%d")
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assert rep.segments[-1].end <= cutoff
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def test_rolling_fitness_scores_series():
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df = _df(700)
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scores = rolling_fitness_scores(df, _BuyFirstBar, step=100, min_prefix=300)
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assert len(scores) >= 3
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assert all(s["index"] < 700 for s in scores)
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assert all(0.0 <= s["pass_ratio"] <= 1.0 for s in scores)
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# 时间升序
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idxs = [s["index"] for s in scores]
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assert idxs == sorted(idxs)
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def test_fitness_report_serializable():
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rep = FitnessEngine(_BuyFirstBar).evaluate(_df(500))
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d = rep.to_dict()
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json.dumps(d, default=str)
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assert d["total_checks"] == 8
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assert "high_fitness" in d
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# ── benchmark（一条龙评估）────────────────────────────────────────────────────
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def test_buy_hold_benchmark_matches_trend():
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df = _df(300, drift=0.002)
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bh = run_buy_hold_benchmark(df)
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total = df["close"].iloc[-1] / df["close"].iloc[0] - 1
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assert bh["total_return"] == pytest.approx(total, rel=0.05) # 扣少量费用
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def test_evaluate_strategy_full_report_structure():
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report = evaluate_strategy(_BuyFirstBar, _df(500))
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for key in (
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"performance",
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"score",
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"grade",
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"walkforward",
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"fitness",
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"benchmark",
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"config",
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):
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assert key in report
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# 绩效 19 项
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assert "total_return" in report["performance"]
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assert "sharpe" in report["performance"]
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# 评分/评级结构
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assert 0 <= report["score"]["total"] <= 100
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assert report["grade"]["grade"] in ("S", "A", "B", "C", "D")
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# WF
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assert report["walkforward"]["n_windows"] == 7
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# 适配性
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assert report["fitness"]["total_checks"] == 8
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# 基准
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assert "buy_hold" in report["benchmark"]
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assert "excess_return" in report["benchmark"]
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def test_evaluate_strategy_excess_return_sign():
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"""上涨行情 + 买入持有策略 ≈ 基准本身，excess_return 接近 0（扣费差异）。"""
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report = evaluate_strategy(_BuyFirstBar, _df(400))
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excess = report["benchmark"]["excess_return"]
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assert abs(excess) < 0.05
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def test_evaluate_strategy_serializable():
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report = evaluate_strategy(_BuyFirstBar, _df(300), n_windows=3)
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text = json.dumps(report, default=str)
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assert "excess_return" in text
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def test_evaluate_strategy_auto_fees_for_etf():
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report = evaluate_strategy(_BuyFirstBar, _df(300), symbol="SH:510300", auto_fees=True)
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assert report["config"]["symbol"] == "SH:510300"
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assert report["config"]["auto_fees"] is True
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def test_evaluate_portfolio_auto_fees_fitness_per_stock_symbol():
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"""组合体检逐段应按各标的品种解析费率（auto_fees 与主回测同口径）。
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回归：evaluate_portfolio 的 per-stock FitnessEngine 漏传 symbol，
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auto_fees 不生效——ETF 组合的三段体检被按股票口径错收印花税（卖方 0.001），
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与组合回测主路径（PortfolioBacktestEngine 逐标的 resolve_fee_model）不一致。
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期望：报告里的三段体检与「正确传入 symbol 的独立体检」逐段一致。
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"""
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from easy_tdx.backtest.benchmark import evaluate_portfolio
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from easy_tdx.backtest.portfolio_engine import StockData
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df = _df(240, drift=0.002)
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stocks = [StockData("159915", "SZ", df)] # ETF：法定免印花税
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report = evaluate_portfolio(
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_CycleTrader, stocks, total_cash=100_000, auto_fees=True, n_windows=3
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)
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expected = FitnessEngine(symbol="SZ159915", strategy=_CycleTrader, auto_fees=True).evaluate(df)
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actual_returns = [seg["total_return"] for seg in report["fitness"]["segments"]]
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expected_returns = [seg.total_return for seg in expected.segments]
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assert len(actual_returns) == 3
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for actual, exp in zip(actual_returns, expected_returns):
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assert actual == pytest.approx(exp, abs=1e-9)
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# ── evaluate_portfolio（v1.31 组合级一条龙）───────────────────────────────────
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def _stocks_for_portfolio() -> list[Any]:
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from easy_tdx.backtest.portfolio_engine import StockData
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return [
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StockData("000001", "SZ", _df(400, drift=0.002)),
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StockData("600000", "SH", _df(400, drift=0.003)),
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]
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def test_evaluate_portfolio_full_report_structure():
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"""组合一条龙报告与单标的 evaluate_strategy 同构（前端面板可复用）。"""
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from easy_tdx.backtest.benchmark import evaluate_portfolio
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report = evaluate_portfolio(_CycleTrader(), _stocks_for_portfolio(), total_cash=500_000)
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for key in ("performance", "score", "grade", "walkforward", "fitness", "benchmark", "config"):
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assert key in report
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# 组合绩效：完整指标 + 组合字段
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assert "sqn" in report["performance"]
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assert "max_consecutive_losses" in report["performance"]
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assert report["performance"]["total_stocks"] == 2
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# 评分/评级
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assert 0 <= report["score"]["total"] <= 100
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assert report["score"]["wf_provided"] is True
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assert report["grade"]["grade"] in ("S", "A", "B", "C", "D")
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assert report["grade"]["scenario"] == "portfolio"
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# 组合 WF
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assert len(report["walkforward"]["windows"]) > 0
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# 适配性（跨标的聚合）
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assert report["fitness"]["total_checks"] == 8
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assert "只标的通过" in report["fitness"]["checks"][0]["detail"]
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# 基准
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assert "buy_hold" in report["benchmark"]
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assert "excess_return" in report["benchmark"]
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# config 记录标的清单
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assert report["config"]["stocks"] == ["SZ000001", "SH600000"]
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def test_evaluate_portfolio_buy_hold_excess_near_zero():
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"""首根买入持有策略 ≈ 等权买入持有基准，excess_return 接近 0（扣费差异）。"""
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from easy_tdx.backtest.benchmark import evaluate_portfolio
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report = evaluate_portfolio(_BuyFirstBar(), _stocks_for_portfolio(), total_cash=500_000)
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assert abs(report["benchmark"]["excess_return"]) < 0.05
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def test_evaluate_portfolio_serializable():
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from easy_tdx.backtest.benchmark import evaluate_portfolio
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report = evaluate_portfolio(
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_CycleTrader(), _stocks_for_portfolio(), total_cash=500_000, n_windows=3
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)
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text = json.dumps(report, default=str)
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assert "excess_return" in text
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# ── evaluate_multi（v1.31.1：多策略组合一条龙）────────────────────────────────
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def _slots_for_multi() -> list[Any]:
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from easy_tdx.backtest.multi_strategy_engine import StrategySlot
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return [
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StrategySlot(
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label="动量", symbol="SZ:000001", strategy=_CycleTrader(), df=_df(400, drift=0.002)
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),
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StrategySlot(
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label="反转", symbol="SH:600000", strategy=_CycleTrader(), df=_df(400, drift=0.003)
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),
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]
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def test_evaluate_multi_full_report_structure():
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"""多策略组合一条龙报告与 evaluate_portfolio 同构（前端面板可复用）。"""
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from easy_tdx.backtest.benchmark import evaluate_multi
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report = evaluate_multi(_slots_for_multi(), total_cash=500_000)
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for key in ("performance", "score", "grade", "walkforward", "fitness", "benchmark", "config"):
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assert key in report
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assert "sqn" in report["performance"]
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assert "max_consecutive_losses" in report["performance"]
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assert report["performance"]["total_stocks"] == 2
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assert 0 <= report["score"]["total"] <= 100
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assert report["score"]["wf_provided"] is True
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assert report["grade"]["grade"] in ("S", "A", "B", "C", "D")
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assert report["grade"]["scenario"] == "portfolio"
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assert len(report["walkforward"]["windows"]) > 0
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assert report["fitness"]["total_checks"] == 8
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assert "只标的通过" in report["fitness"]["checks"][0]["detail"]
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assert "buy_hold" in report["benchmark"]
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assert report["config"]["slots"] == ["动量@SZ:000001", "反转@SH:600000"]
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def test_evaluate_multi_buy_hold_excess_near_zero():
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"""各槽位换成买入持有后，组合收益 ≈ 等权买入持有基准（excess 近 0）。"""
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from easy_tdx.backtest.benchmark import evaluate_multi
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from easy_tdx.backtest.multi_strategy_engine import StrategySlot
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bh_slots = [
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StrategySlot(label=s.label, symbol=s.symbol, strategy=_BuyFirstBar(), df=s.df)
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for s in _slots_for_multi()
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]
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report = evaluate_multi(bh_slots, total_cash=500_000)
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assert abs(report["benchmark"]["excess_return"]) < 0.05
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def test_evaluate_multi_serializable():
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from easy_tdx.backtest.benchmark import evaluate_multi
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report = evaluate_multi(_slots_for_multi(), total_cash=500_000, n_windows=3)
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text = json.dumps(report, default=str)
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assert "excess_return" in text
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