mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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lytem28
b6cf0495e1
Unify strategy execution across backtest, screener, and monitoring; isolate backtest workloads in spawn workers; and add shared matrix caching plus valid-bar indicator acceleration.
202 lines
8.3 KiB
Python
202 lines
8.3 KiB
Python
"""稳健性指标测试 — Sortino + 蒙特卡罗回撤分位 + per-trade 明细。
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被测新增:
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- BacktestEngine._sortino_ratio(returns, periods_per_year): 下行波动调整收益比
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- BacktestEngine._mc_drawdown_percentiles(pnls, n_sims): 自助重抽样估计最大回撤分布
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- _calc_stats / _calc_portfolio_stats 输出新增 sortino / mc_maxdd_p50 / mc_maxdd_p95 /
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median_pnl / best / worst / avg_holding_days 字段
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"""
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from __future__ import annotations
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from datetime import date
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import numpy as np
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from app.backtest.engine import BacktestEngine, SimulationOptions, TradeRecord
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# ---------------------------------------------------------------
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# Sortino
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# ---------------------------------------------------------------
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def test_sortino_all_losses_is_exact():
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"""全亏损序列: mean/downside_dev * sqrt(252) 可手算校验。"""
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r = np.array([-0.1, -0.1])
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# mean=-0.1; neg=[-0.1,-0.1]; downside_dev=sqrt(mean(0.01,0.01))=0.1
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# sortino = -0.1/0.1 * sqrt(252) = -sqrt(252)
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got = BacktestEngine._sortino_ratio(r)
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assert abs(got - (-np.sqrt(252))) < 1e-6
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def test_sortino_no_downside_returns_none():
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"""无负收益 → 下行波动为 0, Sortino 未定义, 约定返回 None (不虚报 inf/0)。"""
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r = np.array([0.05, 0.10, 0.02])
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assert BacktestEngine._sortino_ratio(r) is None
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def test_sortino_exceeds_sharpe_when_downside_is_tamer():
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"""下行波动小于总波动时, Sortino 应高于 Sharpe (只惩罚下行的优势)。"""
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# 大涨小跌: 上行贡献总波动但不进下行 → sortino > sharpe
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r = np.array([0.20, -0.02, 0.20, -0.02])
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mean = float(np.mean(r))
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sharpe = mean / float(np.std(r)) * np.sqrt(252)
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sortino = BacktestEngine._sortino_ratio(r)
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assert sortino is not None
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assert sortino > sharpe
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def test_sortino_too_few_points():
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assert BacktestEngine._sortino_ratio(np.array([0.1])) == 0.0
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assert BacktestEngine._sortino_ratio(np.array([])) == 0.0
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# ---------------------------------------------------------------
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# 蒙特卡罗最大回撤分位
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# ---------------------------------------------------------------
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# 固定种子 (42) + 固定输入下的快照值; 一旦有人改种子或算法, 立即红。
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_MC_INPUT = np.array([0.05, -0.03, 0.08, -0.06, 0.02, -0.04, 0.10, -0.05])
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_MC_P50 = -0.0976
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_MC_P95 = -0.2108
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def test_mc_drawdown_is_deterministic_snapshot():
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"""固定种子 → 结果既跨调用一致, 又等于钉死的快照值 (防有人把种子改成系统熵)。"""
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a = BacktestEngine._mc_drawdown_percentiles(_MC_INPUT)
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b = BacktestEngine._mc_drawdown_percentiles(_MC_INPUT)
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assert a == b
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assert a["mc_maxdd_p50"] == _MC_P50
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assert a["mc_maxdd_p95"] == _MC_P95
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def test_mc_drawdown_p95_strictly_worse_and_negative():
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"""含亏损输入: 中位场景必有回撤 (p50<0), 且 P95 严格差于 P50 (非恒真的 <=)。"""
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r = BacktestEngine._mc_drawdown_percentiles(_MC_INPUT)
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assert r["mc_maxdd_p50"] < 0.0
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assert r["mc_maxdd_p95"] < r["mc_maxdd_p50"]
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def test_mc_drawdown_ignores_non_finite():
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"""含 nan/inf 的收益应被剔除, 结果与纯净输入完全一致 (不污染分位/序列化)。"""
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dirty = np.concatenate([_MC_INPUT, [np.nan, np.inf, -np.inf]])
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assert BacktestEngine._mc_drawdown_percentiles(dirty) == BacktestEngine._mc_drawdown_percentiles(_MC_INPUT)
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def test_mc_drawdown_clips_sub_minus_100pct_pnl():
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"""防御: 单笔 pnl <= -100% 会让 (1+pnl)<=0 使 cumprod 符号翻转; clip 后分位仍有限。"""
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pnls = np.array([0.05, -1.5, 0.08, -0.06, 0.02, -0.04]) # -1.5 = -150%, 现实不会有
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r = BacktestEngine._mc_drawdown_percentiles(pnls)
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assert r["mc_maxdd_p50"] is not None
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for v in (r["mc_maxdd_p50"], r["mc_maxdd_p95"]):
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assert v == v # 非 nan
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assert -1.0 <= v <= 0.0 # 回撤有界在 (-100%, 0], 未因符号翻转失真
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def test_mc_drawdown_all_positive_has_zero_drawdown():
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"""全正收益: 任何重排都无回撤 → 分位均为 0。"""
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pnls = np.array([0.01, 0.02, 0.03, 0.04, 0.05])
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r = BacktestEngine._mc_drawdown_percentiles(pnls)
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assert r["mc_maxdd_p50"] == 0.0
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assert r["mc_maxdd_p95"] == 0.0
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def test_mc_drawdown_too_few_trades():
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r = BacktestEngine._mc_drawdown_percentiles(np.array([0.1, -0.1]))
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assert r["mc_maxdd_p50"] is None
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assert r["mc_maxdd_p95"] is None
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# ---------------------------------------------------------------
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# 集成: stats 输出新字段
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# ---------------------------------------------------------------
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def _trades(pnls: list[float], durations: list[int]) -> list[TradeRecord]:
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out = []
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for p, d in zip(pnls, durations, strict=True):
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out.append(TradeRecord(
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symbol="A", entry_date=date(2024, 1, 1), exit_date=date(2024, 1, 1 + d),
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entry_price=10.0, exit_price=10.0 * (1 + p), pnl_pct=p, duration=d,
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exit_reason="signal",
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))
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return out
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def test_calc_stats_emits_robustness_fields():
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trades = _trades([0.10, -0.05, 0.08, -0.06], [3, 2, 5, 4])
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stats = BacktestEngine._calc_stats(trades, 100_000, date(2024, 1, 1), date(2024, 6, 1))
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for k in ("sortino", "mc_maxdd_p50", "mc_maxdd_p95", "median_pnl", "best", "worst", "avg_holding_days"):
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assert k in stats, f"缺字段 {k}"
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assert stats["best"] == round(0.10, 4)
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assert stats["worst"] == round(-0.06, 4)
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assert stats["median_pnl"] == round(float(np.median([0.10, -0.05, 0.08, -0.06])), 4)
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assert stats["avg_holding_days"] == round(float(np.mean([3, 2, 5, 4])), 1)
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def test_calc_stats_empty_trades_safe():
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"""空交易不应因新字段计算崩溃。"""
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stats = BacktestEngine._calc_stats([], 100_000, date(2024, 1, 1), date(2024, 6, 1))
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assert stats["n_trades"] == 0
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def test_portfolio_stats_emits_robustness_fields():
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"""portfolio 分支同样输出 sortino / mc / per-trade 字段。"""
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equity_curve = [
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{"date": "2024-01-01", "value": 100_000.0, "exposure": 0.0},
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{"date": "2024-01-02", "value": 103_000.0, "exposure": 0.5},
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{"date": "2024-01-03", "value": 101_000.0, "exposure": 0.5},
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{"date": "2024-01-04", "value": 105_000.0, "exposure": 0.5},
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]
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trades = _trades([0.06, -0.02, 0.04], [2, 1, 3])
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stats = BacktestEngine._calc_portfolio_stats(equity_curve, trades, 100_000)
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for k in ("sortino", "mc_maxdd_p50", "mc_maxdd_p95", "median_pnl", "best", "worst", "avg_holding_days"):
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assert k in stats, f"缺字段 {k}"
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def test_independent_candidate_stats_emits_sortino_and_mc():
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"""full 模式主路径 (_calc_independent_candidate_result) 必须输出 sortino / mc 字段。
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这是前端 full 模式指标卡的真实数据来源, 若漏拼字典展开会导致 UI 显示空值。
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"""
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# 构造足量交易 (>=3) 以触发 mc; 用引擎产出真实结果而非直接调私有函数
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trades = _trades([0.10, -0.05, 0.08, -0.06, 0.03], [2, 1, 3, 2, 4])
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result = BacktestEngine._calc_independent_candidate_result(
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trades, n_candidates=5, execution_stats={},
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)
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for k in ("sortino", "mc_maxdd_p50", "mc_maxdd_p95"):
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assert k in result.stats, f"independent 分支缺字段 {k}"
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# mc 应为有效数值 (n=5>=3)
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assert result.stats["mc_maxdd_p50"] is not None
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def test_lightweight_candidate_stats_do_not_call_monte_carlo(monkeypatch):
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trades = _trades([0.10, -0.05, 0.08, -0.06, 0.03], [2, 1, 3, 2, 4])
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def unexpected(_pnls):
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raise AssertionError("Monte Carlo should not run")
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monkeypatch.setattr(BacktestEngine, "_mc_drawdown_percentiles", unexpected)
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result = BacktestEngine._calc_independent_candidate_result(
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trades,
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n_candidates=5,
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execution_stats={},
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options=SimulationOptions(
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include_monte_carlo=False,
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include_curves=False,
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include_trades=False,
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include_per_symbol_stats=False,
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include_return_distribution=False,
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),
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)
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assert "mc_maxdd_p50" not in result.stats
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assert result.equity_curve == []
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assert result.drawdown_curve == []
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assert result.trades == []
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assert result.per_symbol_stats == []
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def test_calc_stats_all_wins_reports_sortino_none():
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"""全盈利交易在 stats 集成层: 无下行波动 → sortino 序列化为 None (非 0)。"""
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trades = _trades([0.10, 0.05, 0.08], [3, 2, 4])
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stats = BacktestEngine._calc_stats(trades, 100_000, date(2024, 1, 1), date(2024, 6, 1))
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assert stats["sortino"] is None
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