claude_dev
0dd81d709f
①后端tears序列化:sanguo_factor/tears_data.py新模块,alphalens已算分层序列(日度IC/月度聚合/十分组累计净值/多空Q10−Q1净值+最大回撤/去重叠年化/因子秩自相关)在分析时序列化为{factor}_tears.json,与tearsheet同源;FactorReport加tears_paths,GET /task/{id}/tears/{factor}端点(token header),_persist_factor同步落DB
②前端tears页:TearsPanel.vue按设计稿tab①——指标条7格+月度IC柱(红正绿负)/累计IC线双轴+月度IC热力图(年×月,CSS格)+分组累计净值Q1/Q5/Q10+多空净值(琥珀+面积+○最大回撤标注)+十分组年化(±5%虚线)+IC衰减(1/5/10D),1/5/10D全页联动;Result.vue的iframe→原生渲染;旧任务404自动回退iframe旧alphalens报告
③加入对比(设计稿tab②纯前端):factorCompare store(localStorage持久化,2~6个)+排行榜行内「＋对比/✓已选」列+详情页死按钮做实(选中青色态)+全局底部托盘CompareTray(chips可删/清空/对比N因子→)+对比页Compare.vue(指标并排·行最优青色高亮/累计IC叠加多线/月度IC序列Pearson相关性矩阵前端算/十分组小倍数SVG)
测试:tears_data纯函数5+全链真实alphalens6(合成因子IC>0/分层单调/JSON可序列化)+analyzer写盘/容错2+端点401/404/200共3;factor+api+orchestrator 317全绿;npm run build绿
379 lines
16 KiB
Python
379 lines
16 KiB
Python
"""Test analyzer module - Factor analysis with alphalens."""
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import sys
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import os
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_VNPY_SRC = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0"))
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sys.path.insert(0, _VNPY_SRC)
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from unittest.mock import Mock, patch, MagicMock
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import tempfile
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from datetime import datetime
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from types import SimpleNamespace
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from zoneinfo import ZoneInfo
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_SH = ZoneInfo("Asia/Shanghai")
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def _fake_bars(symbol, days, price=100.0):
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"""read_db_daily 假 bars。analyzer 会单独调 read_db_daily 取 close 建 prices，
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且 prices 日期须与 factor 日期对齐(aware, Asia/Shanghai)——不喂 bars 会触发
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DBG empty 守卫直接 continue，tears/IC 永不被调到(2026-08-15 前这批测试在
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任何环境都没跑过：Mac 缺依赖 skip/容器缺 pytest/CI 只跑 data_platform)。"""
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return [
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SimpleNamespace(datetime=datetime(2024, 1, d), vt_symbol=symbol, close_price=price)
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for d in days
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]
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def test_run_factor_analysis_returns_report():
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"""Test that run_factor_analysis returns FactorReport with correct structure."""
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from pathlib import Path
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from sanguo_factor.analyzer import run_factor_analysis, FactorReport
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with tempfile.TemporaryDirectory() as tmpdir:
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output_dir = str(Path(tmpdir) / "factor_analysis")
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# Patch imports to avoid ImportError when alphalens missing
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MockSession, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns"), \
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patch("sanguo_factor.analyzer.create_full_tear_sheet"):
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# Mock the AlphaLabSession
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mock_session_instance = Mock()
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MockSession.return_value = mock_session_instance
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mock_session_instance.load_symbols = Mock()
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mock_session_instance.compute_factors = Mock(return_value=Mock(to_pandas=Mock(return_value=Mock(
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set_index=Mock(return_value=Mock(__getitem__=Mock(return_value=Mock())))),
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pivot=Mock(return_value=Mock())
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)))
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# Mock get_factor to avoid registry call
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with patch("sanguo_factor.registry.get_factor", return_value={"expression": "ts_mean(close, 5)"}):
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# Run the analysis
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result = run_factor_analysis(
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symbols=["600000"],
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factor_names=["ma5"],
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start="2024-01-01",
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end="2024-06-30",
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cfg=Mock(),
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output_dir=output_dir
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)
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# Verify the result is a FactorReport with expected structure
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assert isinstance(result, FactorReport)
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assert result.factor_names == ["ma5"]
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assert result.output_dir == output_dir
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assert isinstance(result.ic_summary, dict)
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def test_run_factor_analysis_calls_load_symbols():
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"""Test that run_factor_analysis handles missing alphalens gracefully."""
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from pathlib import Path
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from sanguo_factor.analyzer import run_factor_analysis
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with tempfile.TemporaryDirectory() as tmpdir:
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output_dir = str(Path(tmpdir) / "factor_analysis")
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# Patch module-level variables to simulate missing alphalens
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with patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns", None), \
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patch("sanguo_factor.analyzer.create_full_tear_sheet", None):
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# alphalens missing - should return skeleton report with error
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result = run_factor_analysis(
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symbols=["600000", "000001"],
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factor_names=["ma5"],
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start="2024-01-01",
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end="2024-06-30",
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cfg=Mock(),
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output_dir=output_dir
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)
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# Verify skeleton report returned
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assert "error" in result.ic_summary
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def test_run_factor_analysis_adds_features():
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"""Test that run_factor_analysis returns correct structure when alphalens missing."""
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from pathlib import Path
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from sanguo_factor.analyzer import run_factor_analysis
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with tempfile.TemporaryDirectory() as tmpdir:
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output_dir = str(Path(tmpdir) / "factor_analysis")
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# Patch module-level variables to simulate missing alphalens
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with patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns", None), \
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patch("sanguo_factor.analyzer.create_full_tear_sheet", None):
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# alphalens missing - verify structure
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result = run_factor_analysis(
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symbols=["600000"],
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factor_names=["ma5"],
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start="2024-01-01",
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end="2024-06-30",
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cfg=Mock(),
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output_dir=output_dir
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)
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# Verify factor_names preserved even when alphalens missing
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assert result.factor_names == ["ma5"]
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assert result.output_dir == output_dir
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def test_run_factor_analysis_calls_tears(tmp_path):
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"""Test that run_factor_analysis calls alphalens tears pipeline.
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Requires polars - runs in container, skips locally.
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"""
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import pytest
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pytest.importorskip("polars")
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pytest.importorskip("alphalens")
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from pathlib import Path
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from sanguo_factor.analyzer import run_factor_analysis
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
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patch("sanguo_factor.analyzer.create_full_tear_sheet") as MT, \
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patch("sanguo_data.datareader.read_db_daily",
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return_value=_fake_bars("600000", (2, 3, 4, 5, 8))):
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import polars as pl
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# 非空 + aware 日期(生产 compute_factors 会 localize;naive 会被 prices 对齐
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# isin 过滤成空触发 DBG 守卫)
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_days = (2, 3, 4, 5, 8)
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MS.return_value.compute_factors.return_value = pl.DataFrame({
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"datetime": [datetime(2024, 1, d, tzinfo=_SH) for d in _days],
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"vt_symbol": ["600000"] * len(_days),
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"ma5": [0.5] * len(_days),
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})
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MC.return_value = MagicMock()
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report = run_factor_analysis(
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["600000"], ["ma5"], "2024-01-01", "2024-06-30",
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cfg=MagicMock(), output_dir=str(tmp_path)
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)
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assert report.factor_names == ["ma5"]
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MC.assert_called_once()
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MT.assert_called_once()
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def test_run_factor_analysis_extracts_ic_values(tmp_path):
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"""Test that run_factor_analysis extracts IC values from alphalens.
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Requires polars/alphalens - runs in container, skips locally.
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"""
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import pytest
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pytest.importorskip("polars")
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pytest.importorskip("alphalens")
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import pandas as pd
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from datetime import datetime
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from sanguo_factor.analyzer import run_factor_analysis
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# Create mock factor_data with MultiIndex (datetime, asset) and IC columns
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dates = pd.date_range("2024-01-01", periods=10, freq="D", tz="Asia/Shanghai")
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assets = ["AAPL", "GOOGL"]
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index = pd.MultiIndex.from_product([dates, assets], names=["datetime", "asset"])
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# Mock factor_data with forward returns
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mock_factor_data = pd.DataFrame(index=index)
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mock_factor_data["factor"] = [0.5] * 20 # Factor values
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mock_factor_data["1D"] = [0.01] * 20 # 1-day forward returns
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mock_factor_data["5D"] = [0.05] * 20 # 5-day forward returns
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mock_factor_data["10D"] = [0.10] * 20 # 10-day forward returns
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# Mock IC DataFrame returned by factor_information_coefficient
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mock_ic_df = pd.DataFrame({
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"1D": [0.05, 0.03, 0.07, 0.04, 0.06, 0.05, 0.04, 0.06, 0.05, 0.04],
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"5D": [0.08, 0.06, 0.09, 0.07, 0.08, 0.07, 0.08, 0.06, 0.07, 0.08],
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"10D": [0.12, 0.10, 0.13, 0.11, 0.12, 0.11, 0.12, 0.10, 0.11, 0.12]
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}, index=dates)
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# Mock polars DataFrame（datetime 用带时区 ISO 串，与 _fake_bars localize 后对齐）
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mock_pl_df = MagicMock()
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mock_pl_df.to_pandas.return_value = pd.DataFrame({
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"datetime": [d.isoformat() for d in dates for _ in assets],
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"vt_symbol": assets * len(dates),
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"ma5": [0.5] * 20,
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"close": [100.0] * 20
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})
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
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patch("sanguo_factor.analyzer.create_full_tear_sheet") as MT, \
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patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
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patch("sanguo_data.datareader.read_db_daily",
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return_value=_fake_bars("AAPL", range(1, 11)) + _fake_bars("GOOGL", range(1, 11))):
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# Mock compute_factors to return polars DataFrame
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MS.return_value.compute_factors.return_value = mock_pl_df
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# Mock get_clean_factor_and_forward_returns to return our factor_data
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MC.return_value = mock_factor_data
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# Mock IC function to return our IC DataFrame
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MIC.return_value = mock_ic_df
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report = run_factor_analysis(
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["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
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cfg=MagicMock(), output_dir=str(tmp_path)
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)
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# Verify IC values were extracted
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assert "ma5" in report.ic_summary
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assert "ic" in report.ic_summary["ma5"]
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# Check IC structure contains expected periods
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ic_data = report.ic_summary["ma5"]["ic"]
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assert "1D" in ic_data
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assert "5D" in ic_data
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assert "10D" in ic_data
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# Verify IC statistics are computed
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assert "mean" in ic_data["1D"]
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assert "icir" in ic_data["1D"]
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assert "std" in ic_data["1D"]
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# Verify approximate values (mean should be around 0.05 for 1D)
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assert abs(ic_data["1D"]["mean"] - 0.05) < 0.01 # Allow small rounding errors
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def test_run_factor_analysis_writes_tears_json(tmp_path):
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"""tears JSON(方案A)写盘 + FactorReport.tears_paths 记录 + factor/generated_at 补齐."""
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import pytest
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pytest.importorskip("polars")
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pytest.importorskip("alphalens")
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import json
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import pandas as pd
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from sanguo_factor.analyzer import run_factor_analysis
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dates = pd.date_range("2024-01-01", periods=6, freq="D", tz="Asia/Shanghai")
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mock_factor_data = pd.DataFrame(index=pd.MultiIndex.from_product(
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[dates, ["AAPL"]], names=["datetime", "asset"]))
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mock_factor_data["factor"] = [0.5] * 6
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mock_factor_data["1D"] = [0.01] * 6
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mock_factor_data["5D"] = [0.05] * 6
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mock_factor_data["10D"] = [0.10] * 6
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mock_pl_df = MagicMock()
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mock_pl_df.to_pandas.return_value = pd.DataFrame({
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"datetime": [d.isoformat() for d in dates],
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"vt_symbol": ["AAPL"] * 6,
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"ma5": [0.5] * 6,
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})
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
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patch("sanguo_factor.analyzer.create_full_tear_sheet"), \
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patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
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patch("sanguo_factor.tears_data.build_tears_data",
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return_value={"factor_autocorr": 0.9, "periods": {"1D": {"ic_mean": 0.1}}}) as MB, \
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patch("sanguo_data.datareader.read_db_daily",
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return_value=_fake_bars("AAPL", range(1, 7))):
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MS.return_value.compute_factors.return_value = mock_pl_df
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MC.return_value = mock_factor_data
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MIC.return_value = pd.DataFrame(
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{"1D": [0.05, 0.04, 0.06, 0.05, 0.04, 0.06]}, index=dates)
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report = run_factor_analysis(
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["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
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cfg=MagicMock(), output_dir=str(tmp_path)
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)
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MB.assert_called_once()
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assert "ma5" in report.tears_paths
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assert report.tears_paths["ma5"].endswith("ma5_tears.json")
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assert os.path.exists(report.tears_paths["ma5"])
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with open(report.tears_paths["ma5"], encoding="utf-8") as f:
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data = json.load(f)
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assert data["factor"] == "ma5"
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assert data["periods"]["1D"]["ic_mean"] == 0.1
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assert data["generated_at"]
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def test_run_factor_analysis_tears_json_error_not_fatal(tmp_path):
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"""build_tears_data 抛错 → 只标注 tears_json_error,IC 结果保留."""
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import pytest
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pytest.importorskip("polars")
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pytest.importorskip("alphalens")
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import pandas as pd
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from sanguo_factor.analyzer import run_factor_analysis
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dates = pd.date_range("2024-01-01", periods=4, freq="D", tz="Asia/Shanghai")
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mock_factor_data = pd.DataFrame(index=pd.MultiIndex.from_product(
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[dates, ["AAPL"]], names=["datetime", "asset"]))
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for c in ("factor", "1D", "5D", "10D"):
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mock_factor_data[c] = [0.5] * 4
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mock_pl_df = MagicMock()
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mock_pl_df.to_pandas.return_value = pd.DataFrame({
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"datetime": [d.isoformat() for d in dates],
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"vt_symbol": ["AAPL"] * 4,
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"ma5": [0.5] * 4,
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})
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
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patch("sanguo_factor.analyzer.create_full_tear_sheet"), \
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patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
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patch("sanguo_factor.tears_data.build_tears_data",
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side_effect=RuntimeError("boom")), \
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patch("sanguo_data.datareader.read_db_daily",
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return_value=_fake_bars("AAPL", range(1, 5))):
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MS.return_value.compute_factors.return_value = mock_pl_df
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MC.return_value = mock_factor_data
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MIC.return_value = pd.DataFrame({"1D": [0.05, 0.04, 0.06, 0.05]}, index=dates)
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report = run_factor_analysis(
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["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
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cfg=MagicMock(), output_dir=str(tmp_path)
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)
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assert report.ic_summary["ma5"]["status"] == "success"
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assert "tears_json_error" in report.ic_summary["ma5"]
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assert report.tears_paths == {}
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def test_run_factor_analysis_ic_extraction_fails_gracefully(tmp_path):
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"""Test that IC extraction failures don't crash the pipeline.
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Requires polars/alphalens - runs in container, skips locally.
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"""
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import pytest
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pytest.importorskip("polars")
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pytest.importorskip("alphalens")
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from sanguo_factor.analyzer import run_factor_analysis
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import pandas as pd
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# Mock polars DataFrame（datetime 带时区，与 _fake_bars 对齐防 DBG 空守卫）
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mock_pl_df = MagicMock()
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mock_pl_df.to_pandas.return_value = pd.DataFrame({
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"datetime": ["2024-01-01T00:00:00+08:00"],
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"vt_symbol": ["AAPL"],
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"ma5": [0.5],
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"close": [100.0]
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})
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
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patch("sanguo_factor.analyzer.create_full_tear_sheet") as MT, \
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patch("sanguo_factor.analyzer.factor_information_coefficient") as MIC, \
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patch("sanguo_data.datareader.read_db_daily",
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return_value=_fake_bars("AAPL", (1, 2))):
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MS.return_value.compute_factors.return_value = mock_pl_df
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# Mock get_clean_factor_and_forward_returns to return valid data
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mock_factor_data = MagicMock()
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MC.return_value = mock_factor_data
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# Mock IC function to raise an exception
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MIC.side_effect = Exception("IC calculation failed")
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report = run_factor_analysis(
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["AAPL"], ["ma5"], "2024-01-01", "2024-01-10",
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cfg=MagicMock(), output_dir=str(tmp_path)
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)
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# Verify IC error is captured but status/report still exist
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assert "ma5" in report.ic_summary
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assert "ic" in report.ic_summary["ma5"]
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assert "error" in report.ic_summary["ma5"]["ic"]
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# Status and report should still be present
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assert "status" in report.ic_summary["ma5"]
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