Simon
73d191b43a
- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead） - 数据层: data_manager / dao 优化，新增 upsert 逻辑 - 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构 - 回测层: vectorbt/engine 大改动（251 行），report 增强 - ML 层: features/backtest_integration 特征工程与回测优化 - CLI: agent_cli 重构 - config/settings 扩充配置项
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
"""
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Agent 层回归测试：RiskAgent 配置集中可用性 + Orchestrator 每日流程步骤隔离。
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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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 unittest.mock import patch
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from agents.risk_agent import RiskAgent
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from agents.orchestrator import AgentOrchestrator, TOTAL_STEPS
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def _flat_dm():
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"""给出平淡行情 → 风险 low。"""
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class _DM:
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def get_daily(self, code):
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idx = pd.date_range('2025-01-01', periods=300, freq='B').strftime('%Y%m%d')
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close = np.linspace(100, 100 + 0.03 * 300, 300) + np.random.default_rng(0).normal(0, 0.2, 300)
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return pd.DataFrame({'trade_date': idx, 'close': close})
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return _DM()
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def _hot_dm():
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class _DM:
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def get_daily(self, code):
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idx = pd.date_range('2025-01-01', periods=300, freq='B').strftime('%Y%m%d')
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close = 100 * np.exp(np.cumsum(np.random.default_rng(1).normal(0, 0.03, 300)))
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return pd.DataFrame({'trade_date': idx, 'close': close})
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return _DM()
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def test_risk_config_low_level():
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r = RiskAgent(dm=_flat_dm()).execute()
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assert r['risk_level'] in ('low', 'medium', 'high')
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assert r['target_exposure'] in (0.85, 0.60, 0.30)
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def test_risk_config_high_level():
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r = RiskAgent(dm=_hot_dm()).execute()
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assert r['risk_level'] in ('high', 'medium')
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class _FailAgent:
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def execute(self, **kw):
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raise RuntimeError("boom")
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class _OkAgent:
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def execute(self, **kw):
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return {"report_path": "/tmp/x.md", "risk_level": "low",
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"target_exposure": 0.8, "top_picks": [], "date": "20260601"}
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class _FakeDM:
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def get_stock_list(self):
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return pd.DataFrame(index=['000001.SZ', '600519.SH'])
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def sync_daily(self, code):
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return 5
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class _FakeSent:
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def get_scope_stocks(self):
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return ["000001.SZ", "600519.SH"]
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def compute(self, *a, **k):
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return pd.DataFrame({"news_sent_5": [0.1, 0.2]})
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def test_run_daily_steps_isolated():
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"""risk/selection 抛错被隔离，report/sentiment/sync 仍成功。"""
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with patch('database.dao.get_latest_trade_date', side_effect=lambda c: "20260530"):
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orch = AgentOrchestrator(dm=_FakeDM(), sent=_FakeSent())
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orch.agents = {"risk": _FailAgent(), "selection": _FailAgent(),
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"report": _OkAgent(), "research": _OkAgent()}
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res = orch.run_daily(date="20260601")
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assert "risk" in res and isinstance(res["risk"], dict) and "error" in res["risk"]
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assert "selection" in res and isinstance(res["selection"], dict) and "error" in res["selection"]
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# 失败不中断：report/sentiment 仍成功
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assert "report" in res and "error" not in res["report"]
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assert isinstance(res.get("sentiment"), pd.DataFrame)
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def test_orchestrator_total_steps_is_5():
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assert TOTAL_STEPS == 5 |