mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 18:04:20 +08:00
release: v1.28.0 — 深度风险报告+移动止损+黄金测试（借鉴 akquant）：25 项绩效 / α·β·IR·TE 基准对比 / trail_stop OCO
This commit is contained in:
Justin Gu
@@ -0,0 +1,341 @@
|
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{
|
||||
"benchmark_comparison": {
|
||||
"alpha": -0.07509968942791662,
|
||||
"beta": 0.52345815483129,
|
||||
"information_ratio": -0.4168372094924506,
|
||||
"tracking_error": 0.1915582998133785
|
||||
},
|
||||
"buy_hold": {
|
||||
"annual_return": -0.02751052343019722,
|
||||
"calmar": -0.10790264140303352,
|
||||
"max_drawdown": 0.2549569044138692,
|
||||
"sharpe": -0.07276274597183686,
|
||||
"total_return": -0.043207472350363485,
|
||||
"volatility": 0.2753412305613209
|
||||
},
|
||||
"meta": {
|
||||
"bars": 400,
|
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"cash": 100000.0,
|
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"note": "regen: EASY_TDX_REGEN_GOLDEN=1 pytest tests/unit/test_golden_backtest.py",
|
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"seed": 20260902,
|
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"tolerance": {
|
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"abs": 1e-06,
|
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"rel": 1e-06
|
||||
}
|
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},
|
||||
"rules": {
|
||||
"bracket_pct": {
|
||||
"total_return": 0.06621316999999993,
|
||||
"total_trades": 1,
|
||||
"trades": [
|
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[
|
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"BUY",
|
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10.3,
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20240102
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],
|
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[
|
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"SELL",
|
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11.0,
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20240105
|
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]
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]
|
||||
},
|
||||
"stop_loss": {
|
||||
"total_return": -0.11904648000000007,
|
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"total_trades": 1,
|
||||
"trades": [
|
||||
[
|
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"BUY",
|
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10.2,
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20240102
|
||||
],
|
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[
|
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"SELL",
|
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9.0,
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20240108
|
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]
|
||||
]
|
||||
},
|
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"take_profit": {
|
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"total_return": 0.06621316999999993,
|
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"total_trades": 1,
|
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"trades": [
|
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[
|
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"BUY",
|
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10.3,
|
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20240102
|
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],
|
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[
|
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"SELL",
|
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11.0,
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20240105
|
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]
|
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]
|
||||
},
|
||||
"trailing_stop": {
|
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"total_return": 0.027762419999999954,
|
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"total_trades": 1,
|
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"trades": [
|
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[
|
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"BUY",
|
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10.2,
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20240102
|
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],
|
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[
|
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"SELL",
|
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10.5,
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20240112
|
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]
|
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]
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}
|
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},
|
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"strategies": {
|
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"atr_breakout": {
|
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"cvar_95": 0.029816014464276553,
|
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"max_consecutive_losses": 2,
|
||||
"max_consecutive_wins": 1,
|
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"max_drawdown": 0.2338437267295321,
|
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"sharpe": -0.1980068530470557,
|
||||
"sqn": -0.2643143297795612,
|
||||
"total_return": -0.051365472018247815,
|
||||
"total_trades": 5,
|
||||
"ulcer_index": 0.11610199605370222,
|
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"var_95": 0.023167064579450905,
|
||||
"win_rate": 0.2
|
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},
|
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"bbi": {
|
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"cvar_95": 0.028403780640227493,
|
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"max_consecutive_losses": 11,
|
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"max_consecutive_wins": 6,
|
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"max_drawdown": 0.17316305538486726,
|
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"sharpe": -0.047920869432565044,
|
||||
"sqn": 0.2034947102900509,
|
||||
"total_return": 0.0015367014340572638,
|
||||
"total_trades": 37,
|
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"ulcer_index": 0.09154821119005127,
|
||||
"var_95": 0.02185464362709669,
|
||||
"win_rate": 0.43243243243243246
|
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},
|
||||
"bias_reversal": {
|
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"cvar_95": 0.031800618199536355,
|
||||
"max_consecutive_losses": 4,
|
||||
"max_consecutive_wins": 6,
|
||||
"max_drawdown": 0.28538313496863427,
|
||||
"sharpe": -0.8098413586757496,
|
||||
"sqn": -0.6927111806707453,
|
||||
"total_return": -0.22014868093324402,
|
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"total_trades": 45,
|
||||
"ulcer_index": 0.1949515199887598,
|
||||
"var_95": 0.02439437484336824,
|
||||
"win_rate": 0.5111111111111111
|
||||
},
|
||||
"boll_breakout": {
|
||||
"cvar_95": 0.025123143703972416,
|
||||
"max_consecutive_losses": 1,
|
||||
"max_consecutive_wins": 2,
|
||||
"max_drawdown": 0.12529045393048568,
|
||||
"sharpe": -0.08270429911478913,
|
||||
"sqn": 0.984709712259246,
|
||||
"total_return": 0.00949379410277551,
|
||||
"total_trades": 3,
|
||||
"ulcer_index": 0.039203351555811915,
|
||||
"var_95": 0.015350573492733575,
|
||||
"win_rate": 0.6666666666666666
|
||||
},
|
||||
"cci": {
|
||||
"cvar_95": 0.028065506761634905,
|
||||
"max_consecutive_losses": 1,
|
||||
"max_consecutive_wins": 3,
|
||||
"max_drawdown": 0.17514368495494725,
|
||||
"sharpe": -0.4733373193054346,
|
||||
"sqn": -0.33416732892726264,
|
||||
"total_return": -0.1047789450922677,
|
||||
"total_trades": 11,
|
||||
"ulcer_index": 0.0895732932132124,
|
||||
"var_95": 0.019114854583397116,
|
||||
"win_rate": 0.6363636363636364
|
||||
},
|
||||
"dmi": {
|
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"cvar_95": 0.029255190955404537,
|
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"max_consecutive_losses": 5,
|
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"max_consecutive_wins": 3,
|
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"max_drawdown": 0.26277279842089457,
|
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"sharpe": -0.538224272020581,
|
||||
"sqn": -0.6542541127994779,
|
||||
"total_return": -0.144868582491816,
|
||||
"total_trades": 22,
|
||||
"ulcer_index": 0.14141379126490972,
|
||||
"var_95": 0.022084559354451774,
|
||||
"win_rate": 0.45454545454545453
|
||||
},
|
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"donchian": {
|
||||
"cvar_95": -0.0,
|
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"max_consecutive_losses": 0,
|
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"max_consecutive_wins": 0,
|
||||
"max_drawdown": 0.0,
|
||||
"sharpe": 0.0,
|
||||
"sqn": 0.0,
|
||||
"total_return": 0.0,
|
||||
"total_trades": 0,
|
||||
"ulcer_index": 0.0,
|
||||
"var_95": -0.0,
|
||||
"win_rate": 0.0
|
||||
},
|
||||
"dpo": {
|
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"cvar_95": 0.02721297612445824,
|
||||
"max_consecutive_losses": 6,
|
||||
"max_consecutive_wins": 4,
|
||||
"max_drawdown": 0.19787887049273278,
|
||||
"sharpe": 0.08973761311003028,
|
||||
"sqn": 0.4060312738886599,
|
||||
"total_return": 0.04626258446140685,
|
||||
"total_trades": 40,
|
||||
"ulcer_index": 0.09456618993344078,
|
||||
"var_95": 0.019685727992254924,
|
||||
"win_rate": 0.45
|
||||
},
|
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"ema_cross": {
|
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"cvar_95": 0.030512787117540286,
|
||||
"max_consecutive_losses": 5,
|
||||
"max_consecutive_wins": 1,
|
||||
"max_drawdown": 0.2959272480843976,
|
||||
"sharpe": -0.7875446987996052,
|
||||
"sqn": -1.4136352678571986,
|
||||
"total_return": -0.21673831411102973,
|
||||
"total_trades": 8,
|
||||
"ulcer_index": 0.17424109127336226,
|
||||
"var_95": 0.02416619544856333,
|
||||
"win_rate": 0.125
|
||||
},
|
||||
"emv": {
|
||||
"cvar_95": 0.029127493175167347,
|
||||
"max_consecutive_losses": 6,
|
||||
"max_consecutive_wins": 3,
|
||||
"max_drawdown": 0.3223214933637952,
|
||||
"sharpe": -1.0544258453115651,
|
||||
"sqn": -1.6556723755607416,
|
||||
"total_return": -0.2575566534714613,
|
||||
"total_trades": 28,
|
||||
"ulcer_index": 0.21540763583933592,
|
||||
"var_95": 0.02157076164724241,
|
||||
"win_rate": 0.32142857142857145
|
||||
},
|
||||
"fsl": {
|
||||
"cvar_95": 0.028932815510588083,
|
||||
"max_consecutive_losses": 5,
|
||||
"max_consecutive_wins": 2,
|
||||
"max_drawdown": 0.16041976356729326,
|
||||
"sharpe": -0.12011672214054765,
|
||||
"sqn": -0.07772875586670183,
|
||||
"total_return": -0.026169388111291436,
|
||||
"total_trades": 14,
|
||||
"ulcer_index": 0.08345129720689942,
|
||||
"var_95": 0.022744109351729155,
|
||||
"win_rate": 0.35714285714285715
|
||||
},
|
||||
"kdj_cross": {
|
||||
"cvar_95": 0.027898201822013535,
|
||||
"max_consecutive_losses": 3,
|
||||
"max_consecutive_wins": 6,
|
||||
"max_drawdown": 0.10762922092720545,
|
||||
"sharpe": 0.8589847738542853,
|
||||
"sqn": 1.2700892113941968,
|
||||
"total_return": 0.33234070489431433,
|
||||
"total_trades": 28,
|
||||
"ulcer_index": 0.05288447475839552,
|
||||
"var_95": 0.019645713150630486,
|
||||
"win_rate": 0.5357142857142857
|
||||
},
|
||||
"keltner": {
|
||||
"cvar_95": 0.02977493291904796,
|
||||
"max_consecutive_losses": 2,
|
||||
"max_consecutive_wins": 1,
|
||||
"max_drawdown": 0.22976199965457847,
|
||||
"sharpe": -0.1732213286120034,
|
||||
"sqn": -0.22326773643387887,
|
||||
"total_return": -0.04444609288676726,
|
||||
"total_trades": 4,
|
||||
"ulcer_index": 0.11754644293259853,
|
||||
"var_95": 0.02351295669073764,
|
||||
"win_rate": 0.25
|
||||
},
|
||||
"ma_cross": {
|
||||
"cvar_95": 0.02953584331940908,
|
||||
"max_consecutive_losses": 3,
|
||||
"max_consecutive_wins": 4,
|
||||
"max_drawdown": 0.22131421204998106,
|
||||
"sharpe": -0.4990885286170613,
|
||||
"sqn": -1.0839091744378302,
|
||||
"total_return": -0.1326834663173594,
|
||||
"total_trades": 11,
|
||||
"ulcer_index": 0.11891561252900337,
|
||||
"var_95": 0.022064133071800284,
|
||||
"win_rate": 0.5454545454545454
|
||||
},
|
||||
"macd": {
|
||||
"cvar_95": 0.02839428517023986,
|
||||
"max_consecutive_losses": 6,
|
||||
"max_consecutive_wins": 5,
|
||||
"max_drawdown": 0.18473872916346823,
|
||||
"sharpe": -0.38338812036224335,
|
||||
"sqn": -0.5295484249243184,
|
||||
"total_return": -0.10413146619958658,
|
||||
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|
||||
"ulcer_index": 0.10417476558778298,
|
||||
"var_95": 0.02189769434012649,
|
||||
"win_rate": 0.35294117647058826
|
||||
},
|
||||
"rsi_reversal": {
|
||||
"cvar_95": 0.030776659581515254,
|
||||
"max_consecutive_losses": 1,
|
||||
"max_consecutive_wins": 1,
|
||||
"max_drawdown": 0.23599475582323245,
|
||||
"sharpe": 0.4065196788206701,
|
||||
"sqn": 0.6504806756551088,
|
||||
"total_return": 0.16567827342668773,
|
||||
"total_trades": 2,
|
||||
"ulcer_index": 0.09450845521195947,
|
||||
"var_95": 0.024060510820245292,
|
||||
"win_rate": 0.5
|
||||
},
|
||||
"triple_ma": {
|
||||
"cvar_95": 0.030748053405195853,
|
||||
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|
||||
"max_consecutive_wins": 1,
|
||||
"max_drawdown": 0.26736051511671544,
|
||||
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|
||||
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|
||||
"total_return": -0.07872900419821216,
|
||||
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|
||||
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|
||||
"var_95": 0.023068752460779107,
|
||||
"win_rate": 0.25
|
||||
},
|
||||
"trix": {
|
||||
"cvar_95": 0.027724285540264466,
|
||||
"max_consecutive_losses": 3,
|
||||
"max_consecutive_wins": 4,
|
||||
"max_drawdown": 0.29063024861099856,
|
||||
"sharpe": -0.7642482349371779,
|
||||
"sqn": -0.9463810886485136,
|
||||
"total_return": -0.1947048613134198,
|
||||
"total_trades": 12,
|
||||
"ulcer_index": 0.17538728786286353,
|
||||
"var_95": 0.021929213323307422,
|
||||
"win_rate": 0.5
|
||||
},
|
||||
"wr_reversal": {
|
||||
"cvar_95": -0.0,
|
||||
"max_consecutive_losses": 0,
|
||||
"max_consecutive_wins": 0,
|
||||
"max_drawdown": 0.0,
|
||||
"sharpe": 0.0,
|
||||
"sqn": 0.0,
|
||||
"total_return": 0.0,
|
||||
"total_trades": 0,
|
||||
"ulcer_index": 0.0,
|
||||
"var_95": -0.0,
|
||||
"win_rate": 0.0
|
||||
}
|
||||
}
|
||||
}
|
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@@ -0,0 +1,292 @@
|
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"""黄金测试（golden tests）：回测引擎指标快照回归（v1.28 新增）。
|
||||
|
||||
借鉴 akquant 的 golden 测试机制：把「内置策略在固定随机种子合成数据上的
|
||||
全部绩效指标」与「交易规则场景（止损/止盈/移动止损/OCO/费率）的成交明细」
|
||||
锁定为 JSON 基线（``tests/golden/backtest_metrics.json``），每次引擎改动后
|
||||
跑一遍比对——撮合、费率、信号时序任何静默漂移都会在这里爆出来。
|
||||
|
||||
生成/更新基线::
|
||||
|
||||
EASY_TDX_REGEN_GOLDEN=1 python -m pytest tests/unit/test_golden_backtest.py
|
||||
|
||||
比对容差：rel=1e-6 / abs=1e-6——紧到能抓住费率或成交时点级别的逻辑漂移
|
||||
（通常引起 >0.001 的变动），松到容忍跨平台浮点求和顺序的尾数噪声。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from easy_tdx.backtest.benchmark import (
|
||||
compute_benchmark_comparison,
|
||||
run_buy_hold_benchmark,
|
||||
)
|
||||
from easy_tdx.backtest.engine import BacktestEngine
|
||||
from easy_tdx.backtest.strategies import builtin # noqa: F401 # 触发注册
|
||||
from easy_tdx.backtest.strategies.registry import _REGISTRY
|
||||
from easy_tdx.backtest.strategy import Strategy
|
||||
|
||||
GOLDEN_PATH = Path(__file__).resolve().parents[1] / "golden" / "backtest_metrics.json"
|
||||
REGEN = os.environ.get("EASY_TDX_REGEN_GOLDEN", "") == "1"
|
||||
|
||||
# 与基线 meta 一致的固定参数
|
||||
SEED = 20260902
|
||||
BARS = 400
|
||||
CASH = 100000.0
|
||||
|
||||
# 内置策略锁定的指标子集（全部为确定性数值；int 与 float 分开比对）
|
||||
STRATEGY_METRICS_FLOAT = (
|
||||
"total_return",
|
||||
"max_drawdown",
|
||||
"sharpe",
|
||||
"win_rate",
|
||||
"ulcer_index",
|
||||
"var_95",
|
||||
"cvar_95",
|
||||
"sqn",
|
||||
)
|
||||
STRATEGY_METRICS_INT = (
|
||||
"total_trades",
|
||||
"max_consecutive_wins",
|
||||
"max_consecutive_losses",
|
||||
)
|
||||
|
||||
|
||||
def _golden_df() -> pd.DataFrame:
|
||||
"""固定种子的合成日线（几何随机游走 + 温和上行漂移）。"""
|
||||
rng = np.random.default_rng(SEED)
|
||||
close = 20.0 * np.exp(np.cumsum(rng.normal(0.0004, 0.018, BARS)))
|
||||
high = close * (1 + np.abs(rng.normal(0, 0.008, BARS)))
|
||||
low = close * (1 - np.abs(rng.normal(0, 0.008, BARS)))
|
||||
open_ = low + (high - low) * rng.uniform(0, 1, BARS)
|
||||
vol = rng.uniform(5e5, 5e6, BARS)
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"datetime": pd.date_range("2023-01-02", periods=BARS, freq="B"),
|
||||
"open": open_,
|
||||
"high": high,
|
||||
"low": low,
|
||||
"close": close,
|
||||
"vol": vol,
|
||||
"amount": close * vol,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
# ── 规则场景策略（手工构造行情路径，锁定触发语义本身） ───────────────────────
|
||||
|
||||
|
||||
class _BuyOnce(Strategy):
|
||||
"""首根买入（可携带 bracket 参数），不再主动交易；无参数时即买入持有。"""
|
||||
|
||||
def __init__(self, **bracket: Any) -> None:
|
||||
super().__init__()
|
||||
self._bracket: dict[str, Any] = bracket
|
||||
self._bought = False
|
||||
|
||||
def init(self) -> None:
|
||||
pass
|
||||
|
||||
def next(self) -> None:
|
||||
if not self._bought:
|
||||
self.buy(**self._bracket)
|
||||
self._bought = True
|
||||
|
||||
|
||||
def _rule_df(closes: list[float]) -> pd.DataFrame:
|
||||
"""按收盘价序列构造无随机因素的 OHLC（high/low = close ±1%）。"""
|
||||
arr = np.asarray(closes, dtype=float)
|
||||
n = len(arr)
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"datetime": pd.date_range("2024-01-01", periods=n, freq="B"),
|
||||
"open": arr,
|
||||
"high": arr * 1.01,
|
||||
"low": arr * 0.99,
|
||||
"close": arr,
|
||||
"vol": [1000.0] * n,
|
||||
"amount": arr * 1000,
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _run_rule(closes: list[float], **bracket: Any) -> dict[str, Any]:
|
||||
"""跑规则场景，返回待锁定的摘要（成交明细 + 关键指标）。"""
|
||||
result = BacktestEngine(_BuyOnce(**bracket), cash=CASH).run(_rule_df(closes))
|
||||
trades = [
|
||||
[t.direction, round(float(t.price), 4), int(pd.Timestamp(t.datetime).strftime("%Y%m%d"))]
|
||||
for t in result.trades.itertuples()
|
||||
if not t.rejected
|
||||
]
|
||||
return {
|
||||
"trades": trades,
|
||||
"total_return": float(result.performance["total_return"]),
|
||||
"total_trades": int(result.performance["total_trades"]),
|
||||
}
|
||||
|
||||
|
||||
RULE_SCENARIOS: dict[str, dict[str, Any]] = {
|
||||
# 跌破固定止损 9.5 → 触发 SELL@9.5，延迟下一根成交
|
||||
"stop_loss": {
|
||||
"closes": [10, 10.2, 10.1, 9.8, 9.3, 9.0, 8.8, 8.6, 8.4, 8.2],
|
||||
"bracket": {"stop_loss": 9.5},
|
||||
},
|
||||
# 触及固定止盈 11.0 → OCO 使止损线失效
|
||||
"take_profit": {
|
||||
"closes": [10, 10.3, 10.8, 11.2, 11.5, 11.8, 12.0, 12.2, 12.4, 12.6],
|
||||
"bracket": {"stop_loss": 9.0, "take_profit": 11.0},
|
||||
},
|
||||
# 自最高收盘 12 回撤 8% → 11.04 触发移动止损
|
||||
"trailing_stop": {
|
||||
"closes": [10, 10.2, 10.5, 11, 11.5, 12, 11.9, 11.5, 11.0, 10.5, 10.0, 9.5],
|
||||
"bracket": {"trail_stop": 0.08},
|
||||
},
|
||||
# 百分比 bracket：5% 止损 / 10% 止盈（基准价 = 信号根收盘 10）
|
||||
"bracket_pct": {
|
||||
"closes": [10, 10.3, 10.8, 11.2, 11.5, 11.8, 12.0, 12.2, 12.4, 12.6],
|
||||
"bracket": {"stop_loss_pct": 0.05, "take_profit_pct": 0.10},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _build_golden() -> dict[str, Any]:
|
||||
"""重新计算并返回完整黄金基线。"""
|
||||
df = _golden_df()
|
||||
|
||||
strategies: dict[str, dict[str, Any]] = {}
|
||||
for name in sorted(_REGISTRY.names()):
|
||||
reg = _REGISTRY.get(name)
|
||||
cls = reg.strategy_cls
|
||||
perf = BacktestEngine(cls, cash=CASH).run(df).performance
|
||||
entry: dict[str, Any] = {k: float(perf[k]) for k in STRATEGY_METRICS_FLOAT}
|
||||
entry.update({k: int(perf[k]) for k in STRATEGY_METRICS_INT})
|
||||
strategies[name] = entry
|
||||
|
||||
rules = {
|
||||
key: _run_rule(spec["closes"], **spec["bracket"]) for key, spec in RULE_SCENARIOS.items()
|
||||
}
|
||||
|
||||
# 买入持有基准 + CAPM 对比（用 ma_cross 做策略侧）
|
||||
bh = run_buy_hold_benchmark(df, cash=CASH)
|
||||
ma = _REGISTRY.get("ma_cross").strategy_cls
|
||||
ma_result = BacktestEngine(ma, cash=CASH).run(df)
|
||||
comparison = compute_benchmark_comparison(
|
||||
ma_result.equity_curve,
|
||||
BacktestEngine(_BuyOnce(), cash=CASH).run(df).equity_curve,
|
||||
)
|
||||
|
||||
return {
|
||||
"meta": {
|
||||
"seed": SEED,
|
||||
"bars": BARS,
|
||||
"cash": CASH,
|
||||
"tolerance": {"rel": 1e-6, "abs": 1e-6},
|
||||
"note": "regen: EASY_TDX_REGEN_GOLDEN=1 pytest tests/unit/test_golden_backtest.py",
|
||||
},
|
||||
"strategies": strategies,
|
||||
"rules": rules,
|
||||
"buy_hold": {k: float(v) for k, v in bh.items()},
|
||||
"benchmark_comparison": {k: float(v) for k, v in comparison.items()},
|
||||
}
|
||||
|
||||
|
||||
def _load_golden() -> dict[str, Any]:
|
||||
if not GOLDEN_PATH.exists():
|
||||
pytest.fail(
|
||||
f"黄金基线缺失: {GOLDEN_PATH}\n"
|
||||
"首次生成请运行: EASY_TDX_REGEN_GOLDEN=1 python -m pytest "
|
||||
"tests/unit/test_golden_backtest.py"
|
||||
)
|
||||
data = json.loads(GOLDEN_PATH.read_text(encoding="utf-8"))
|
||||
assert isinstance(data, dict)
|
||||
return data
|
||||
|
||||
|
||||
def _save_golden(data: dict[str, Any]) -> None:
|
||||
GOLDEN_PATH.parent.mkdir(parents=True, exist_ok=True)
|
||||
GOLDEN_PATH.write_text(
|
||||
json.dumps(data, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
|
||||
encoding="utf-8",
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def golden() -> dict[str, Any]:
|
||||
"""加载基线；REGEN=1 时重新计算并写盘后返回。"""
|
||||
if REGEN:
|
||||
data = _build_golden()
|
||||
_save_golden(data)
|
||||
return data
|
||||
return _load_golden()
|
||||
|
||||
|
||||
def _assert_metric(actual: Any, expected: Any, label: str) -> None:
|
||||
"""int 精确比对；float 按 rel=abs=1e-6 容差比对。"""
|
||||
if isinstance(expected, int) and not isinstance(expected, bool):
|
||||
assert actual == expected, f"{label}: {actual} != {expected}"
|
||||
else:
|
||||
assert float(actual) == pytest.approx(float(expected), rel=1e-6, abs=1e-6), (
|
||||
f"{label}: {actual} != {expected}"
|
||||
)
|
||||
|
||||
|
||||
# ── 测试入口 ─────────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
@pytest.mark.parametrize("name", sorted(_REGISTRY.names()))
|
||||
def test_golden_builtin_strategies(golden: dict[str, Any], name: str) -> None:
|
||||
"""全部内置策略在固定数据上的绩效指标与基线一致。"""
|
||||
perf = BacktestEngine(_REGISTRY.get(name).strategy_cls, cash=CASH).run(_golden_df()).performance
|
||||
baseline = golden["strategies"][name]
|
||||
for key in STRATEGY_METRICS_FLOAT:
|
||||
_assert_metric(perf[key], baseline[key], f"{name}.{key}")
|
||||
for key in STRATEGY_METRICS_INT:
|
||||
_assert_metric(perf[key], baseline[key], f"{name}.{key}")
|
||||
|
||||
|
||||
@pytest.mark.parametrize("scenario", sorted(RULE_SCENARIOS))
|
||||
def test_golden_rule_scenarios(golden: dict[str, Any], scenario: str) -> None:
|
||||
"""止损/止盈/移动止损/OCO 触发语义（成交价与时点）与基线一致。"""
|
||||
spec = RULE_SCENARIOS[scenario]
|
||||
actual = _run_rule(spec["closes"], **spec["bracket"])
|
||||
baseline = golden["rules"][scenario]
|
||||
assert actual["total_trades"] == baseline["total_trades"], scenario
|
||||
assert len(actual["trades"]) == len(baseline["trades"]), f"{scenario}: 成交笔数漂移"
|
||||
for i, (a, b) in enumerate(zip(actual["trades"], baseline["trades"])):
|
||||
assert a[0] == b[0], f"{scenario} 第{i}笔方向漂移: {a} vs {b}"
|
||||
_assert_metric(a[1], b[1], f"{scenario}.trades[{i}].price")
|
||||
assert a[2] == b[2], f"{scenario} 第{i}笔成交日漂移: {a} vs {b}"
|
||||
_assert_metric(actual["total_return"], baseline["total_return"], f"{scenario}.total_return")
|
||||
|
||||
|
||||
def test_golden_buy_hold(golden: dict[str, Any]) -> None:
|
||||
"""买入持有基准指标与基线一致。"""
|
||||
bh = run_buy_hold_benchmark(_golden_df(), cash=CASH)
|
||||
for key, expected in golden["buy_hold"].items():
|
||||
_assert_metric(bh[key], expected, f"buy_hold.{key}")
|
||||
|
||||
|
||||
def test_golden_benchmark_comparison(golden: dict[str, Any]) -> None:
|
||||
"""Alpha/Beta/IR/TE 基准对比指标与基线一致。"""
|
||||
df = _golden_df()
|
||||
ma = _REGISTRY.get("ma_cross").strategy_cls
|
||||
strategy_curve = BacktestEngine(ma, cash=CASH).run(df).equity_curve
|
||||
bh_curve = BacktestEngine(_BuyOnce(), cash=CASH).run(df).equity_curve
|
||||
comparison = compute_benchmark_comparison(strategy_curve, bh_curve)
|
||||
for key, expected in golden["benchmark_comparison"].items():
|
||||
_assert_metric(comparison[key], expected, f"benchmark.{key}")
|
||||
|
||||
|
||||
def test_golden_meta_frozen(golden: dict[str, Any]) -> None:
|
||||
"""基线 meta 与测试常量一致（防止改数据参数后忘记重建基线）。"""
|
||||
meta = golden["meta"]
|
||||
assert meta["seed"] == SEED
|
||||
assert meta["bars"] == BARS
|
||||
assert meta["cash"] == CASH
|
||||
Reference in New Issue
Block a user