Compare commits
8
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 ao gong
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4d84f6a26a | ||
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ao gong
|
c12c9ba335 | ||
 ageorge156
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8a30bf5ebc | ||
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ao gong
|
7c7f0c06a7 | ||
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ao gong
|
741d5f1ad3 | ||
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ao gong
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cbb56d2f52 | ||
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ao gong
|
524fc73852 | ||
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ao gong
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8ef8e3208e |
+15
-2
@@ -16,6 +16,8 @@ permissions:
|
||||
jobs:
|
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lite:
|
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runs-on: ubuntu-latest
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env:
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UV_PYTHON_DOWNLOADS: never
|
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steps:
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- uses: actions/checkout@524e936cd9e579adf00e308bfdf971aebc7de09e
|
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with:
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@@ -26,7 +28,18 @@ jobs:
|
||||
if git ls-files .DS_Store | grep -q .; then echo "跟踪 .DS_Store"; exit 1; fi
|
||||
if git grep -n -I -E 'sk-[A-Za-z0-9]{20,}|AKIA[0-9A-Z]{16}|ghp_[A-Za-z0-9]{36}|xox[baprs]-[A-Za-z0-9-]{10,}' HEAD | grep -q .; then echo "检出疑似凭证"; exit 1; fi
|
||||
echo "Gitea 合规校验通过"
|
||||
|
||||
- name: 验证并同步共享运行时
|
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run: |
|
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test "$(python3 --version)" = "Python 3.13.15"
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test "$(uv --version | cut -d' ' -f1-2)" = "uv 0.12.3"
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uv sync --locked --extra dev
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uv run --locked --no-sync python -c 'import sys; assert sys.version_info[:2] == (3, 13)'
|
||||
|
||||
- name: 架构模块契约测试
|
||||
run: python3 tests/governance/test_module_spec.py
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run: |
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uv run --locked --no-sync python tests/governance/test_module_spec.py
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uv run --locked --no-sync python tests/governance/test_ci_contract.py
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- name: Syntax check
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||||
run: git ls-files -z '*.py' | xargs -0 python3 -m py_compile
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run: git ls-files -z '*.py' | xargs -0 uv run --locked --no-sync python -m py_compile
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@@ -0,0 +1 @@
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3.13
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@@ -139,15 +139,22 @@ print(factor_backtest.benchmark_stats(benchmark_returns))
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# 下游稳定交付：显式提供代码版本、数据快照和时区，不在核心层写数据库。
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from quant_engine.artifact import build_research_run_artifact
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from quant_engine.risk import CovarianceSnapshot
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from quant_engine.data_adapter import prepare_asset_return_snapshot
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from quant_engine.risk import estimate_covariance_snapshot
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risk_date = factor_backtest.position_weights.index[-1].date()
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risk_snapshot = CovarianceSnapshot(
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snapshot_id="<covariance-snapshot-id>",
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market_snapshot = prepare_asset_return_snapshot(
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qtdb_daily_long,
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source="qtdb_pro.hq_daily",
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source_snapshot_id="<upstream-ingestion-snapshot-id>",
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adjustment="qfq",
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)
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risk_snapshot = estimate_covariance_snapshot(
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market_snapshot.returns,
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as_of_date=risk_date,
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covariance=daily_return_covariance,
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return_frequency="1d",
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periods_per_year=252,
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lookback_sessions=252,
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min_observations=120,
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data_snapshot_id=market_snapshot.data_snapshot_id,
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)
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artifact = build_research_run_artifact(
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@@ -158,7 +165,7 @@ artifact = build_research_run_artifact(
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strategy_version="1.0.0",
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engine_version="1.2.0",
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code_revision="<git-sha>",
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data_snapshot_id="<data-snapshot-id>",
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data_snapshot_id=market_snapshot.data_snapshot_id,
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calendar="CN-A",
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timezone="Asia/Shanghai",
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started_at="2026-08-21T10:00:00+08:00",
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@@ -0,0 +1,9 @@
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profile: lite
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runtime_contract: v1
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language: python
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python_version: "3.13"
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python_manager: uv
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python_root: "."
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local_test_command: "python3 tests/governance/test_module_spec.py"
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requires_database: false
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integration_profile: none
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@@ -33,6 +33,29 @@ component-risk 语义结合，但只保留本项目需要的轻量合同：协
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component risk 闭合到年化组合波动，percentage contribution 闭合到 1。未来日期、资产
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标签不完整和零方差组合都直接失败，不以默认值伪造结果。
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## 2026-08-21：协方差快照估计
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||||
| 项目 | 借鉴内容 | 当前决策 |
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||||
|---|---|---|
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||||
| [PyPortfolioOpt risk models](https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/pypfopt/risk_models.py) | 将收益输入、协方差估计器和组合优化解耦；sample / EWM / shrinkage 使用统一标签输出 | 借鉴可替换估计器边界，不引入完整包 |
|
||||
| [scikit-learn covariance](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/covariance/_shrunk_covariance.py) | 维护成熟的 Ledoit–Wolf / OAS shrinkage 实现 | 未来作为可选 adapter；不复制统计公式 |
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||||
| [Qlib structured risk model](https://github.com/microsoft/qlib/blob/main/qlib/model/riskmodel/structured.py) | PCA/FA 结构化协方差和固定随机状态 | 留作因子风险模型阶段，不进入当前 baseline |
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当前 `estimate_covariance_snapshot` 只编排 pandas 的 sample covariance：先按 `as_of_date`
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截断，再取固定 session 窗口，使用 complete-case 行并拒绝历史不足；禁止 pandas 默认的
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pairwise 样本集合产生含义不一致的矩阵。snapshot ID 对窗口数据、缺失掩码、上游数据
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快照身份和估计参数做 SHA-256，追加未来数据不会改变历史快照。
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|
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市场适配层现以 `AssetReturnSnapshot` 固化 simple-return 输入：上游 ingestion snapshot ID、
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数据源、价格字段、复权口径、规范化价格值和缺失掩码共同形成内容寻址 ID；不前向填充
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停牌/缺失价格。该 ID 同时传入协方差快照和研究运行工件，避免同一研究链出现两套数据
|
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身份。
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||||
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||||
可选 shrinkage adapter 的评估结论是“保留边界，暂不实现”：当前运行依赖没有声明
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||||
scikit-learn，本切片也不修改版本或锁文件。未来只有在依赖治理接受后，才以延迟导入
|
||||
直接调用 scikit-learn 的 `LedoitWolf` / `OAS`，并让估计器名称、库版本与参数进入
|
||||
snapshot identity；不复制成熟统计公式，也不让环境中偶然存在的包改变 baseline 行为。
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||||
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||||
## hikyuu 的定位
|
||||
|
||||
[hikyuu](https://github.com/fasiondog/hikyuu) 的 SG / MM / CN / PG 部件化思想、
|
||||
|
||||
@@ -24,12 +24,14 @@
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||||
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||||
## Verification
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||||
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||||
- `pytest -q`: 526 passed，9 个既有 SciPy warning；
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||||
- risk/artifact focused coverage 87.85%；
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||||
- `pytest -q`: 540 passed，9 个既有 SciPy warning；
|
||||
- data-adapter focused coverage 77%（包含未连接真实 ClickHouse 的 I/O 便捷函数）；
|
||||
- `mypy --strict src/`: 16 source files passed；
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||||
- changed-scope Ruff: passed；
|
||||
- no runtime dependency added；
|
||||
- no database, network, broker or filesystem write side effect in artifact builder。
|
||||
- 三仓隔离 ClickHouse 黄金链路通过：市场价格 → return snapshot → covariance → artifact →
|
||||
publisher → reader；使用随机 localhost 端口、tmpfs 和自动容器清理。
|
||||
|
||||
## Current risk contract
|
||||
|
||||
@@ -38,8 +40,18 @@
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||||
- `risk_snapshots` 按研究交易日映射，可只生成需要的风险观察日；
|
||||
- 使用成交后实际持仓，不包含现金风险资产；协方差资产标签必须与研究资产全集一致；
|
||||
- `covariance_as_of_date` 不得晚于 `trade_date`；无正组合方差时拒绝产物。
|
||||
- `estimate_covariance_snapshot` 从显式数据快照的日收益生成无前视、complete-case、
|
||||
SHA-256 可复现的 per-period sample covariance；不包含 I/O 或未来行。
|
||||
- `prepare_asset_return_snapshot` 从规范化长表行情生成不前向填充的 simple daily returns；
|
||||
显式 ingestion snapshot ID、源/字段/复权口径、价格值和缺失掩码共同形成
|
||||
`asset-returns-v1:<sha256>`，并把同一 ID 传给 covariance 与 run artifact。
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||||
- artifact builder fail closed：每个 `CovarianceSnapshot.data_snapshot_id` 必须与 run 级
|
||||
`data_snapshot_id` 完全一致，禁止把其他行情快照的风险分解静默发布到当前研究运行。
|
||||
- shrinkage 适配器本轮不实现：scikit-learn 尚非声明依赖，未来只允许薄适配
|
||||
`LedoitWolf` / `OAS`，不复制公式、不依赖环境偶然安装状态。
|
||||
|
||||
## Next action
|
||||
|
||||
保持 Draft PR #5，不绕过堆叠顺序合并；下游 `research_results` / `research_platform`
|
||||
已在各自 Draft 分支兼容 1.0.0 / 1.1.0，下一阶段以真实数据适配器提供可复现协方差快照。
|
||||
继续在现有 Draft 分支消费同一数据 lineage。下一阶段优先把 ingestion snapshot ID 从
|
||||
真实 ELT 元数据接入调用方，再在依赖治理通过后单独交付可选 shrinkage adapter。
|
||||
|
||||
+6
-3
@@ -7,7 +7,7 @@ name = "quant_engine"
|
||||
version = "0.1.0"
|
||||
description = "量化研究引擎 —— alpha 因子库 + 执行仿真 + 技术指标 + 数据适配 + 回测工具（v1.2.0 从 research_results 抽出）"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.11"
|
||||
requires-python = ">=3.13,<3.14"
|
||||
license = { text = "MIT" }
|
||||
authors = [
|
||||
{ name = "researchhub team" },
|
||||
@@ -39,7 +39,7 @@ where = ["src"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
target-version = "py311"
|
||||
target-version = "py313"
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = ["E", "F", "W", "I", "N", "UP", "B", "A", "C4", "PT", "RUF"]
|
||||
@@ -54,10 +54,13 @@ ignore = [
|
||||
]
|
||||
|
||||
[tool.mypy]
|
||||
python_version = "3.11"
|
||||
python_version = "3.13"
|
||||
strict = true
|
||||
ignore_missing_imports = true
|
||||
|
||||
[tool.uv]
|
||||
index-url = "https://mirrors.cloud.tencent.com/pypi/simple"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
addopts = "-v --tb=short"
|
||||
|
||||
@@ -390,6 +390,7 @@ def _risk_trade_date(value: object) -> date:
|
||||
def _build_risk(
|
||||
result: FactorBacktestResult,
|
||||
run_id: str,
|
||||
data_snapshot_id: str,
|
||||
risk_snapshots: Mapping[object, CovarianceSnapshot] | None,
|
||||
) -> pd.DataFrame:
|
||||
if risk_snapshots is None:
|
||||
@@ -414,6 +415,8 @@ def _build_risk(
|
||||
f"covariance as_of_date {snapshot.as_of_date} must not be after trade date "
|
||||
f"{trade_date}"
|
||||
)
|
||||
if snapshot.data_snapshot_id != data_snapshot_id:
|
||||
raise ValueError("covariance snapshot data lineage differs from research run")
|
||||
normalized[trade_date] = snapshot
|
||||
|
||||
weights_by_date = result.position_weights
|
||||
@@ -572,6 +575,6 @@ def build_research_run_artifact(
|
||||
_positions=_build_positions(result, normalized_run_id),
|
||||
_attribution=attribution,
|
||||
_attribution_daily=attribution_daily,
|
||||
_risk=_build_risk(result, normalized_run_id, risk_snapshots),
|
||||
_risk=_build_risk(result, normalized_run_id, normalized_snapshot, risk_snapshots),
|
||||
_performance=_build_performance(result, normalized_run_id, benchmark_returns),
|
||||
)
|
||||
|
||||
@@ -6,22 +6,27 @@
|
||||
- execution.py 需要**宽表**（date × stock_code）prices / volumes
|
||||
- Tushare 字段命名：`ts_code / vol(手) / amount(千元) / pct_chg`，且**无 vwap 字段**
|
||||
|
||||
本模块提供 6 个纯函数，让新模块直接吃 qtdb_pro 真实数据：
|
||||
本模块提供可组合的数据适配函数，让新模块直接吃 qtdb_pro 真实数据：
|
||||
1. `long_to_wide()` — 长表 → 宽表（date × stock_code）
|
||||
2. `wide_to_long()` — 宽表 → 长表
|
||||
3. `rename_tushare_columns()` — 列名映射（ts_code→stock_code, vol→volume 等）
|
||||
4. `add_vwap_proxy()` — vwap 代理（Tushare 无 vwap 字段）
|
||||
5. `apply_adj_factor()` — 复权（hq_daily × hq_adj_factor 前复权）
|
||||
6. `prepare_stock_series()` — 单股提取（alpha_factors 输入）
|
||||
7. `prepare_execution_inputs()` — execution 输入（prices + volumes 宽表）
|
||||
8. `load_qtdb_daily()` — 便捷加载（qtdb_pro.hq_daily + 可选复权）
|
||||
7. `prepare_asset_return_snapshot()` — 带稳定 lineage 的资产日收益
|
||||
8. `prepare_execution_inputs()` — execution 输入（prices + volumes 宽表）
|
||||
9. `load_qtdb_daily()` — 便捷加载（qtdb_pro.hq_daily + 可选复权）
|
||||
|
||||
全部纯 pandas/numpy，零新依赖，mypy strict 兼容。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
from collections.abc import Mapping, Sequence
|
||||
from dataclasses import dataclass
|
||||
from datetime import date
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
@@ -32,12 +37,14 @@ from quant_engine.logging import get_logger
|
||||
logger = get_logger(__name__)
|
||||
|
||||
__all__ = [
|
||||
"AssetReturnSnapshot",
|
||||
"long_to_wide",
|
||||
"wide_to_long",
|
||||
"rename_tushare_columns",
|
||||
"add_vwap_proxy",
|
||||
"apply_adj_factor",
|
||||
"prepare_stock_series",
|
||||
"prepare_asset_return_snapshot",
|
||||
"prepare_execution_inputs",
|
||||
"load_qtdb_daily",
|
||||
]
|
||||
@@ -57,6 +64,107 @@ TUSHARE_RENAME: dict[str, str] = {
|
||||
}
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True, init=False, eq=False)
|
||||
class AssetReturnSnapshot:
|
||||
"""Immutable-by-interface daily return matrix with reproducible lineage."""
|
||||
|
||||
data_snapshot_id: str
|
||||
source: str
|
||||
source_snapshot_id: str
|
||||
price_field: str
|
||||
adjustment: str
|
||||
return_method: str
|
||||
start_date: date
|
||||
end_date: date
|
||||
sessions: int
|
||||
assets: tuple[str, ...]
|
||||
_returns: pd.DataFrame
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
data_snapshot_id: str,
|
||||
source: str,
|
||||
source_snapshot_id: str,
|
||||
price_field: str,
|
||||
adjustment: str,
|
||||
return_method: str,
|
||||
start_date: date,
|
||||
end_date: date,
|
||||
assets: tuple[str, ...],
|
||||
returns: pd.DataFrame,
|
||||
) -> None:
|
||||
for value, name in (
|
||||
(data_snapshot_id, "data_snapshot_id"),
|
||||
(source, "source"),
|
||||
(source_snapshot_id, "source_snapshot_id"),
|
||||
(price_field, "price_field"),
|
||||
(adjustment, "adjustment"),
|
||||
(return_method, "return_method"),
|
||||
):
|
||||
if not isinstance(value, str) or not value.strip():
|
||||
raise ValueError(f"{name} must be non-empty")
|
||||
if returns.empty or not isinstance(returns.index, pd.DatetimeIndex):
|
||||
raise ValueError("returns must contain a DatetimeIndex and at least one session")
|
||||
if tuple(returns.columns) != assets:
|
||||
raise ValueError("assets must match returns columns")
|
||||
if start_date > end_date:
|
||||
raise ValueError("start_date must not be after end_date")
|
||||
|
||||
object.__setattr__(self, "data_snapshot_id", data_snapshot_id.strip())
|
||||
object.__setattr__(self, "source", source.strip())
|
||||
object.__setattr__(self, "source_snapshot_id", source_snapshot_id.strip())
|
||||
object.__setattr__(self, "price_field", price_field.strip())
|
||||
object.__setattr__(self, "adjustment", adjustment.strip())
|
||||
object.__setattr__(self, "return_method", return_method.strip())
|
||||
object.__setattr__(self, "start_date", start_date)
|
||||
object.__setattr__(self, "end_date", end_date)
|
||||
object.__setattr__(self, "sessions", len(returns))
|
||||
object.__setattr__(self, "assets", assets)
|
||||
object.__setattr__(self, "_returns", returns.copy(deep=True))
|
||||
|
||||
@property
|
||||
def returns(self) -> pd.DataFrame:
|
||||
"""Return an isolated copy so callers cannot mutate the snapshot."""
|
||||
return self._returns.copy(deep=True)
|
||||
|
||||
|
||||
def _non_empty(value: str, name: str) -> str:
|
||||
if not isinstance(value, str) or not value.strip():
|
||||
raise ValueError(f"{name} must be non-empty")
|
||||
return value.strip()
|
||||
|
||||
|
||||
def _asset_return_snapshot_id(
|
||||
prices: pd.DataFrame,
|
||||
*,
|
||||
source: str,
|
||||
source_snapshot_id: str,
|
||||
price_field: str,
|
||||
adjustment: str,
|
||||
) -> str:
|
||||
values = prices.to_numpy(dtype=float, copy=True)
|
||||
missing = np.isnan(values)
|
||||
normalized = np.where(missing, 0.0, values).astype("<f8", copy=False)
|
||||
metadata = {
|
||||
"adjustment": adjustment,
|
||||
"assets": [str(asset) for asset in prices.columns],
|
||||
"price_field": price_field,
|
||||
"return_method": "simple",
|
||||
"schema": "asset-returns-v1",
|
||||
"sessions": [timestamp.date().isoformat() for timestamp in prices.index],
|
||||
"shape": list(values.shape),
|
||||
"source": source,
|
||||
"source_snapshot_id": source_snapshot_id,
|
||||
}
|
||||
digest = hashlib.sha256(
|
||||
json.dumps(metadata, sort_keys=True, separators=(",", ":")).encode("utf-8")
|
||||
)
|
||||
digest.update(missing.astype(np.uint8, copy=False).tobytes(order="C"))
|
||||
digest.update(normalized.tobytes(order="C"))
|
||||
return f"asset-returns-v1:{digest.hexdigest()}"
|
||||
|
||||
|
||||
def long_to_wide(
|
||||
df: pd.DataFrame,
|
||||
value_col: str = "close",
|
||||
@@ -283,6 +391,98 @@ def prepare_stock_series(
|
||||
return series_map
|
||||
|
||||
|
||||
def prepare_asset_return_snapshot(
|
||||
df: pd.DataFrame,
|
||||
*,
|
||||
source: str,
|
||||
source_snapshot_id: str,
|
||||
price_col: str = "close",
|
||||
adjustment: str = "none",
|
||||
stock_col: str = "stock_code",
|
||||
date_col: str = "trade_date",
|
||||
) -> AssetReturnSnapshot:
|
||||
"""Build deterministic simple daily returns from a long market-price table.
|
||||
|
||||
``source_snapshot_id`` must identify the upstream ingestion snapshot. The
|
||||
resulting ID additionally fingerprints canonical price values and their
|
||||
missing mask, so changed contents cannot retain the same downstream identity.
|
||||
Missing prices are never forward-filled.
|
||||
"""
|
||||
normalized_source = _non_empty(source, "source")
|
||||
normalized_source_snapshot_id = _non_empty(
|
||||
source_snapshot_id,
|
||||
"source_snapshot_id",
|
||||
)
|
||||
normalized_price_col = _non_empty(price_col, "price_col")
|
||||
normalized_adjustment = _non_empty(adjustment, "adjustment")
|
||||
if not isinstance(df, pd.DataFrame):
|
||||
raise TypeError("df must be a pandas DataFrame")
|
||||
if df.empty:
|
||||
raise ValueError("df must contain market prices")
|
||||
required = {date_col, stock_col, normalized_price_col}
|
||||
missing_columns = sorted(required.difference(df.columns))
|
||||
if missing_columns:
|
||||
raise ValueError(f"prepare_asset_return_snapshot: missing columns={missing_columns}")
|
||||
|
||||
market = df[[date_col, stock_col, normalized_price_col]].copy()
|
||||
if any(not isinstance(asset, str) or not asset.strip() for asset in market[stock_col]):
|
||||
raise ValueError("asset labels must be non-empty strings")
|
||||
market[stock_col] = market[stock_col].str.strip()
|
||||
try:
|
||||
normalized_dates = pd.to_datetime(market[date_col], errors="raise")
|
||||
except (TypeError, ValueError) as error:
|
||||
raise ValueError("trade dates must be valid dates") from error
|
||||
if normalized_dates.isna().any():
|
||||
raise ValueError("trade dates must be valid dates")
|
||||
market[date_col] = normalized_dates.dt.normalize()
|
||||
if market.duplicated(subset=[date_col, stock_col]).any():
|
||||
raise ValueError("duplicate asset/session prices are not allowed")
|
||||
|
||||
try:
|
||||
market[normalized_price_col] = pd.to_numeric(
|
||||
market[normalized_price_col],
|
||||
errors="raise",
|
||||
)
|
||||
except (TypeError, ValueError) as error:
|
||||
raise ValueError("prices must be numeric") from error
|
||||
observed_prices = market[normalized_price_col].dropna().to_numpy(dtype=float)
|
||||
if observed_prices.size == 0 or not np.isfinite(observed_prices).all():
|
||||
raise ValueError("prices must contain positive finite observations")
|
||||
if (observed_prices <= 0.0).any():
|
||||
raise ValueError("prices must contain positive finite observations")
|
||||
|
||||
prices = market.pivot(
|
||||
index=date_col,
|
||||
columns=stock_col,
|
||||
values=normalized_price_col,
|
||||
).sort_index()
|
||||
prices = prices.reindex(sorted(str(asset) for asset in prices.columns), axis="columns")
|
||||
prices = prices.astype(float)
|
||||
if len(prices) < 2:
|
||||
raise ValueError("market prices must contain at least two sessions")
|
||||
returns = prices.pct_change(fill_method=None)
|
||||
assets = tuple(str(asset) for asset in prices.columns)
|
||||
snapshot_id = _asset_return_snapshot_id(
|
||||
prices,
|
||||
source=normalized_source,
|
||||
source_snapshot_id=normalized_source_snapshot_id,
|
||||
price_field=normalized_price_col,
|
||||
adjustment=normalized_adjustment,
|
||||
)
|
||||
return AssetReturnSnapshot(
|
||||
data_snapshot_id=snapshot_id,
|
||||
source=normalized_source,
|
||||
source_snapshot_id=normalized_source_snapshot_id,
|
||||
price_field=normalized_price_col,
|
||||
adjustment=normalized_adjustment,
|
||||
return_method="simple",
|
||||
start_date=prices.index[0].date(),
|
||||
end_date=prices.index[-1].date(),
|
||||
assets=assets,
|
||||
returns=returns,
|
||||
)
|
||||
|
||||
|
||||
def prepare_execution_inputs(
|
||||
df: pd.DataFrame,
|
||||
stock_col: str = "stock_code",
|
||||
|
||||
+205
-7
@@ -5,6 +5,8 @@
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from datetime import date
|
||||
from typing import Any
|
||||
@@ -17,6 +19,7 @@ __all__ = [
|
||||
"ComponentRiskResult",
|
||||
"CovarianceSnapshot",
|
||||
"component_var",
|
||||
"estimate_covariance_snapshot",
|
||||
"labeled_component_risk",
|
||||
"marginal_risk_contribution",
|
||||
"risk_contribution",
|
||||
@@ -32,6 +35,14 @@ class CovarianceSnapshot:
|
||||
_covariance: pd.DataFrame
|
||||
return_frequency: str
|
||||
periods_per_year: int
|
||||
method: str
|
||||
window_start_date: date | None
|
||||
window_end_date: date | None
|
||||
observations: int | None
|
||||
lookback_sessions: int | None
|
||||
missing_policy: str
|
||||
data_snapshot_id: str
|
||||
input_sha256: str
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -41,6 +52,14 @@ class CovarianceSnapshot:
|
||||
covariance: pd.DataFrame,
|
||||
return_frequency: str,
|
||||
periods_per_year: int,
|
||||
method: str = "provided",
|
||||
window_start_date: str | date | pd.Timestamp | None = None,
|
||||
window_end_date: str | date | pd.Timestamp | None = None,
|
||||
observations: int | None = None,
|
||||
lookback_sessions: int | None = None,
|
||||
missing_policy: str = "provided",
|
||||
data_snapshot_id: str = "",
|
||||
input_sha256: str = "",
|
||||
) -> None:
|
||||
if not isinstance(snapshot_id, str) or not snapshot_id.strip():
|
||||
raise ValueError("snapshot_id must be non-empty")
|
||||
@@ -54,18 +73,59 @@ class CovarianceSnapshot:
|
||||
raise TypeError("covariance must be a pandas DataFrame")
|
||||
if covariance.empty:
|
||||
raise ValueError("covariance must contain at least one asset")
|
||||
try:
|
||||
normalized_as_of = pd.Timestamp(as_of_date)
|
||||
except (TypeError, ValueError) as error:
|
||||
raise ValueError("as_of_date must be a valid date") from error
|
||||
if pd.isna(normalized_as_of):
|
||||
raise ValueError("as_of_date must be a valid date")
|
||||
if not isinstance(method, str) or not method.strip():
|
||||
raise ValueError("method must be non-empty")
|
||||
if not isinstance(missing_policy, str) or not missing_policy.strip():
|
||||
raise ValueError("missing_policy must be non-empty")
|
||||
for value, name in (
|
||||
(observations, "observations"),
|
||||
(lookback_sessions, "lookback_sessions"),
|
||||
):
|
||||
if value is not None and (
|
||||
isinstance(value, bool) or not isinstance(value, int) or value <= 0
|
||||
):
|
||||
raise ValueError(f"{name} must be a positive integer when provided")
|
||||
if input_sha256 and (
|
||||
len(input_sha256) != 64
|
||||
or any(character not in "0123456789abcdef" for character in input_sha256)
|
||||
):
|
||||
raise ValueError("input_sha256 must be a lowercase SHA-256 digest")
|
||||
|
||||
normalized_as_of = _normalized_date(as_of_date, "as_of_date")
|
||||
normalized_window_start = (
|
||||
None
|
||||
if window_start_date is None
|
||||
else _normalized_date(window_start_date, "window_start_date")
|
||||
)
|
||||
normalized_window_end = (
|
||||
None
|
||||
if window_end_date is None
|
||||
else _normalized_date(window_end_date, "window_end_date")
|
||||
)
|
||||
if (normalized_window_start is None) != (normalized_window_end is None):
|
||||
raise ValueError("window_start_date and window_end_date must be provided together")
|
||||
if (
|
||||
normalized_window_start is not None
|
||||
and normalized_window_end is not None
|
||||
and normalized_window_start > normalized_window_end
|
||||
):
|
||||
raise ValueError("window_start_date must not be after window_end_date")
|
||||
if normalized_window_end is not None and normalized_window_end > normalized_as_of:
|
||||
raise ValueError("window_end_date must not be after as_of_date")
|
||||
|
||||
object.__setattr__(self, "snapshot_id", snapshot_id.strip())
|
||||
object.__setattr__(self, "as_of_date", normalized_as_of.date())
|
||||
object.__setattr__(self, "as_of_date", normalized_as_of)
|
||||
object.__setattr__(self, "_covariance", covariance.copy(deep=True))
|
||||
object.__setattr__(self, "return_frequency", return_frequency.strip())
|
||||
object.__setattr__(self, "periods_per_year", periods_per_year)
|
||||
object.__setattr__(self, "method", method.strip())
|
||||
object.__setattr__(self, "window_start_date", normalized_window_start)
|
||||
object.__setattr__(self, "window_end_date", normalized_window_end)
|
||||
object.__setattr__(self, "observations", observations)
|
||||
object.__setattr__(self, "lookback_sessions", lookback_sessions)
|
||||
object.__setattr__(self, "missing_policy", missing_policy.strip())
|
||||
object.__setattr__(self, "data_snapshot_id", data_snapshot_id.strip())
|
||||
object.__setattr__(self, "input_sha256", input_sha256)
|
||||
|
||||
@property
|
||||
def covariance(self) -> pd.DataFrame:
|
||||
@@ -73,6 +133,144 @@ class CovarianceSnapshot:
|
||||
return self._covariance.copy(deep=True)
|
||||
|
||||
|
||||
def _normalized_date(value: object, name: str) -> date:
|
||||
try:
|
||||
timestamp = pd.Timestamp(value)
|
||||
except (TypeError, ValueError) as error:
|
||||
raise ValueError(f"{name} must be a valid date") from error
|
||||
if pd.isna(timestamp):
|
||||
raise ValueError(f"{name} must be a valid date")
|
||||
return date(int(timestamp.year), int(timestamp.month), int(timestamp.day))
|
||||
|
||||
|
||||
def _positive_integer(value: int, name: str, *, minimum: int = 1) -> int:
|
||||
if isinstance(value, bool) or not isinstance(value, int) or value < minimum:
|
||||
raise ValueError(f"{name} must be an integer of at least {minimum}")
|
||||
return value
|
||||
|
||||
|
||||
def _input_fingerprint(window: pd.DataFrame, session_dates: list[date]) -> str:
|
||||
values = window.to_numpy(dtype=float, copy=True)
|
||||
missing = np.isnan(values)
|
||||
normalized = np.where(missing, 0.0, values).astype("<f8", copy=False)
|
||||
metadata = {
|
||||
"assets": [str(asset) for asset in window.columns],
|
||||
"sessions": [session.isoformat() for session in session_dates],
|
||||
"shape": list(values.shape),
|
||||
}
|
||||
digest = hashlib.sha256(
|
||||
json.dumps(metadata, sort_keys=True, separators=(",", ":")).encode("utf-8")
|
||||
)
|
||||
digest.update(missing.astype(np.uint8, copy=False).tobytes(order="C"))
|
||||
digest.update(normalized.tobytes(order="C"))
|
||||
return digest.hexdigest()
|
||||
|
||||
|
||||
def estimate_covariance_snapshot(
|
||||
asset_returns: pd.DataFrame,
|
||||
*,
|
||||
as_of_date: str | date | pd.Timestamp,
|
||||
lookback_sessions: int,
|
||||
min_observations: int,
|
||||
data_snapshot_id: str,
|
||||
return_frequency: str = "1d",
|
||||
periods_per_year: int = 252,
|
||||
) -> CovarianceSnapshot:
|
||||
"""Estimate a deterministic per-period sample covariance without look-ahead.
|
||||
|
||||
The selected lookback window is truncated at ``as_of_date`` before any
|
||||
calculation. Rows containing a missing asset return are removed as complete
|
||||
cases, preventing pairwise sample sets from producing an ambiguous matrix.
|
||||
"""
|
||||
if not isinstance(asset_returns, pd.DataFrame):
|
||||
raise TypeError("asset_returns must be a pandas DataFrame")
|
||||
if asset_returns.empty or asset_returns.shape[1] == 0:
|
||||
raise ValueError("asset_returns must contain observations and assets")
|
||||
if not isinstance(asset_returns.index, pd.DatetimeIndex):
|
||||
raise TypeError("asset_returns index must be a DatetimeIndex")
|
||||
if not asset_returns.index.is_unique or not asset_returns.index.is_monotonic_increasing:
|
||||
raise ValueError("asset_returns index must be unique and strictly increasing")
|
||||
if not asset_returns.columns.is_unique:
|
||||
raise ValueError("asset_returns must contain unique asset labels")
|
||||
if any(not isinstance(asset, str) or not asset.strip() for asset in asset_returns.columns):
|
||||
raise ValueError("asset_returns asset labels must be non-empty strings")
|
||||
|
||||
lookback = _positive_integer(lookback_sessions, "lookback_sessions")
|
||||
minimum = _positive_integer(min_observations, "min_observations", minimum=2)
|
||||
if minimum > lookback:
|
||||
raise ValueError("min_observations must not exceed lookback_sessions")
|
||||
normalized_data_snapshot_id = data_snapshot_id.strip()
|
||||
if not normalized_data_snapshot_id:
|
||||
raise ValueError("data_snapshot_id must be non-empty")
|
||||
normalized_as_of = _normalized_date(as_of_date, "as_of_date")
|
||||
|
||||
returns = asset_returns.astype(float, copy=True)
|
||||
values = returns.to_numpy()
|
||||
if np.isinf(values).any():
|
||||
raise ValueError("asset_returns must not contain infinite values")
|
||||
session_dates = [
|
||||
_normalized_date(index_value, "asset_returns index") for index_value in returns.index
|
||||
]
|
||||
if len(set(session_dates)) != len(session_dates):
|
||||
raise ValueError("asset_returns must contain at most one observation per session date")
|
||||
historical_mask = [session <= normalized_as_of for session in session_dates]
|
||||
window = returns.loc[historical_mask].tail(lookback)
|
||||
if window.empty:
|
||||
raise ValueError("asset_returns contain no observations on or before as_of_date")
|
||||
window_dates = [
|
||||
_normalized_date(index_value, "asset_returns index") for index_value in window.index
|
||||
]
|
||||
complete = window.dropna(axis=0, how="any")
|
||||
if len(complete) < minimum:
|
||||
raise ValueError(
|
||||
f"complete observations must be at least {minimum}; received {len(complete)}"
|
||||
)
|
||||
|
||||
covariance = complete.cov(ddof=1)
|
||||
covariance_values = covariance.to_numpy()
|
||||
if not np.isfinite(covariance_values).all():
|
||||
raise ValueError("sample covariance must be finite")
|
||||
input_sha256 = _input_fingerprint(window, window_dates)
|
||||
identity = {
|
||||
"as_of_date": normalized_as_of.isoformat(),
|
||||
"assets": list(returns.columns),
|
||||
"data_snapshot_id": normalized_data_snapshot_id,
|
||||
"estimator": "sample-cov-v1",
|
||||
"input_sha256": input_sha256,
|
||||
"lookback_sessions": lookback,
|
||||
"min_observations": minimum,
|
||||
"missing_policy": "complete_case",
|
||||
"observations": len(complete),
|
||||
"periods_per_year": periods_per_year,
|
||||
"return_frequency": return_frequency,
|
||||
"window_end_date": window_dates[-1].isoformat(),
|
||||
"window_start_date": window_dates[0].isoformat(),
|
||||
}
|
||||
identity_bytes = json.dumps(
|
||||
identity,
|
||||
sort_keys=True,
|
||||
separators=(",", ":"),
|
||||
).encode("utf-8")
|
||||
digest = hashlib.sha256(identity_bytes)
|
||||
digest.update(covariance_values.astype("<f8", copy=False).tobytes(order="C"))
|
||||
snapshot_id = f"sample-cov-v1:{digest.hexdigest()}"
|
||||
return CovarianceSnapshot(
|
||||
snapshot_id=snapshot_id,
|
||||
as_of_date=normalized_as_of,
|
||||
covariance=covariance,
|
||||
return_frequency=return_frequency,
|
||||
periods_per_year=periods_per_year,
|
||||
method="sample",
|
||||
window_start_date=window_dates[0],
|
||||
window_end_date=window_dates[-1],
|
||||
observations=len(complete),
|
||||
lookback_sessions=lookback,
|
||||
missing_policy="complete_case",
|
||||
data_snapshot_id=normalized_data_snapshot_id,
|
||||
input_sha256=input_sha256,
|
||||
)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True, eq=False)
|
||||
class ComponentRiskResult:
|
||||
"""Label-preserving Euler decomposition of portfolio volatility."""
|
||||
|
||||
@@ -9,14 +9,22 @@ ROOT = Path(__file__).resolve().parents[2]
|
||||
|
||||
|
||||
class CiContractTests(unittest.TestCase):
|
||||
def test_ci_is_one_dependency_free_lite_gate(self) -> None:
|
||||
def test_ci_is_one_locked_shared_runtime_lite_gate(self) -> None:
|
||||
workflow = (ROOT / ".gitea/workflows/ci.yml").read_text(encoding="utf-8")
|
||||
jobs = workflow.split("jobs:", 1)[1]
|
||||
self.assertEqual(re.findall(r"(?m)^ ([a-z][a-z0-9_-]*):\s*$", jobs), ["lite"])
|
||||
self.assertIn("actions/checkout@524e936cd9e579adf00e308bfdf971aebc7de09e", workflow)
|
||||
self.assertIn("persist-credentials: false", workflow)
|
||||
self.assertIn("python3 tests/governance/test_module_spec.py", workflow)
|
||||
for forbidden in ("setup-python", "pip ", "curl ", "wget ", "docker pull"):
|
||||
self.assertIn("UV_PYTHON_DOWNLOADS: never", workflow)
|
||||
self.assertIn('test "$(python3 --version)" = "Python 3.13.15"', workflow)
|
||||
self.assertIn(
|
||||
'test "$(uv --version | cut -d\' \' -f1-2)" = "uv 0.12.3"',
|
||||
workflow,
|
||||
)
|
||||
self.assertIn("uv sync --locked --extra dev", workflow)
|
||||
self.assertIn("uv run --locked --no-sync python", workflow)
|
||||
self.assertIn("tests/governance/test_ci_contract.py", workflow)
|
||||
for forbidden in ("setup-python", "setup-uv", "pip ", "curl ", "wget ", "docker pull"):
|
||||
self.assertNotIn(forbidden, workflow)
|
||||
|
||||
|
||||
|
||||
@@ -164,6 +164,7 @@ def test_research_artifact_projects_annualized_risk_from_actual_positions() -> N
|
||||
covariance=covariance,
|
||||
return_frequency="1d",
|
||||
periods_per_year=252,
|
||||
data_snapshot_id="qtdb-pro-20260108-v1",
|
||||
)
|
||||
|
||||
artifact = _build(result, risk_snapshots={trade_date: snapshot})
|
||||
@@ -182,6 +183,31 @@ def test_research_artifact_projects_annualized_risk_from_actual_positions() -> N
|
||||
assert risk["risk_contribution"].sum() == pytest.approx(1.0)
|
||||
|
||||
|
||||
def test_research_artifact_rejects_risk_from_a_different_data_snapshot() -> None:
|
||||
result = _backtest_result()
|
||||
trade_date = result.position_weights.index[-1].date()
|
||||
covariance = pd.DataFrame(
|
||||
[[0.0001, 0.0], [0.0, 0.0004]],
|
||||
index=["A", "B"],
|
||||
columns=["A", "B"],
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="data lineage differs"):
|
||||
_build(
|
||||
result,
|
||||
risk_snapshots={
|
||||
trade_date: CovarianceSnapshot(
|
||||
snapshot_id="foreign-covariance",
|
||||
as_of_date="2026-01-07",
|
||||
covariance=covariance,
|
||||
return_frequency="1d",
|
||||
periods_per_year=252,
|
||||
data_snapshot_id="different-market-snapshot",
|
||||
)
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
def test_research_artifact_rejects_future_or_misaligned_risk_snapshots() -> None:
|
||||
result = _backtest_result()
|
||||
trade_date = result.position_weights.index[-1].date()
|
||||
@@ -201,6 +227,7 @@ def test_research_artifact_rejects_future_or_misaligned_risk_snapshots() -> None
|
||||
covariance=covariance,
|
||||
return_frequency="1d",
|
||||
periods_per_year=252,
|
||||
data_snapshot_id="qtdb-pro-20260108-v1",
|
||||
)
|
||||
},
|
||||
)
|
||||
@@ -215,6 +242,7 @@ def test_research_artifact_rejects_future_or_misaligned_risk_snapshots() -> None
|
||||
covariance=covariance.loc[["B"], ["B"]],
|
||||
return_frequency="1d",
|
||||
periods_per_year=252,
|
||||
data_snapshot_id="qtdb-pro-20260108-v1",
|
||||
)
|
||||
},
|
||||
)
|
||||
|
||||
+168
-3
@@ -7,10 +7,12 @@ import pandas as pd
|
||||
import pytest
|
||||
|
||||
from quant_engine.data_adapter import (
|
||||
AssetReturnSnapshot,
|
||||
add_vwap_proxy,
|
||||
apply_adj_factor,
|
||||
load_qtdb_daily,
|
||||
long_to_wide,
|
||||
prepare_asset_return_snapshot,
|
||||
prepare_execution_inputs,
|
||||
prepare_stock_series,
|
||||
rename_tushare_columns,
|
||||
@@ -298,13 +300,176 @@ def test_prepare_execution_inputs_missing_close_raises() -> None:
|
||||
|
||||
|
||||
def test_prepare_execution_inputs_missing_selected_price_raises() -> None:
|
||||
df = pd.DataFrame(
|
||||
{"stock_code": ["A"], "trade_date": ["2024-01-01"], "close": [10.0]}
|
||||
)
|
||||
df = pd.DataFrame({"stock_code": ["A"], "trade_date": ["2024-01-01"], "close": [10.0]})
|
||||
with pytest.raises(ValueError, match="缺 open"):
|
||||
prepare_execution_inputs(df, price_col="open")
|
||||
|
||||
|
||||
# ── prepare_asset_return_snapshot ────────────────────────────
|
||||
|
||||
|
||||
def _daily_prices() -> pd.DataFrame:
|
||||
return pd.DataFrame(
|
||||
{
|
||||
"stock_code": ["B", "A", "B", "A", "B", "A"],
|
||||
"trade_date": [
|
||||
"2024-01-02",
|
||||
"2024-01-01",
|
||||
"2024-01-01",
|
||||
"2024-01-03",
|
||||
"2024-01-03",
|
||||
"2024-01-02",
|
||||
],
|
||||
"close": [18.0, 10.0, 20.0, 12.1, 19.8, 11.0],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def test_prepare_asset_return_snapshot_is_stable_and_immutable_by_interface() -> None:
|
||||
snapshot = prepare_asset_return_snapshot(
|
||||
_daily_prices(),
|
||||
source="qtdb_pro.hq_daily",
|
||||
source_snapshot_id="hq-daily:2024-01-03:v1",
|
||||
adjustment="qfq",
|
||||
)
|
||||
|
||||
assert isinstance(snapshot, AssetReturnSnapshot)
|
||||
assert snapshot.data_snapshot_id.startswith("asset-returns-v1:")
|
||||
assert snapshot.source == "qtdb_pro.hq_daily"
|
||||
assert snapshot.source_snapshot_id == "hq-daily:2024-01-03:v1"
|
||||
assert snapshot.price_field == "close"
|
||||
assert snapshot.adjustment == "qfq"
|
||||
assert snapshot.return_method == "simple"
|
||||
assert snapshot.start_date.isoformat() == "2024-01-01"
|
||||
assert snapshot.end_date.isoformat() == "2024-01-03"
|
||||
assert snapshot.sessions == 3
|
||||
assert snapshot.assets == ("A", "B")
|
||||
|
||||
expected = pd.DataFrame(
|
||||
{
|
||||
"A": [np.nan, 0.1, 0.1],
|
||||
"B": [np.nan, -0.1, 0.1],
|
||||
},
|
||||
index=pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03"]),
|
||||
)
|
||||
expected.index.name = "trade_date"
|
||||
expected.columns.name = "stock_code"
|
||||
pd.testing.assert_frame_equal(snapshot.returns, expected)
|
||||
|
||||
exposed = snapshot.returns
|
||||
exposed.iloc[1, 0] = 999.0
|
||||
assert snapshot.returns.iloc[1, 0] == pytest.approx(0.1)
|
||||
|
||||
|
||||
def test_asset_return_snapshot_identity_is_order_independent_and_content_addressed() -> None:
|
||||
kwargs = {
|
||||
"source": "qtdb_pro.hq_daily",
|
||||
"source_snapshot_id": "hq-daily:2024-01-03:v1",
|
||||
"adjustment": "none",
|
||||
}
|
||||
baseline = prepare_asset_return_snapshot(_daily_prices(), **kwargs)
|
||||
shuffled = prepare_asset_return_snapshot(
|
||||
_daily_prices().sample(frac=1.0, random_state=7),
|
||||
**kwargs,
|
||||
)
|
||||
changed_prices = _daily_prices().copy()
|
||||
changed_prices.loc[changed_prices["close"] == 12.1, "close"] = 12.2
|
||||
changed_content = prepare_asset_return_snapshot(changed_prices, **kwargs)
|
||||
changed_source = prepare_asset_return_snapshot(
|
||||
_daily_prices(),
|
||||
source="qtdb_pro.hq_daily",
|
||||
source_snapshot_id="hq-daily:2024-01-03:v2",
|
||||
adjustment="none",
|
||||
)
|
||||
|
||||
assert shuffled.data_snapshot_id == baseline.data_snapshot_id
|
||||
assert changed_content.data_snapshot_id != baseline.data_snapshot_id
|
||||
assert changed_source.data_snapshot_id != baseline.data_snapshot_id
|
||||
|
||||
|
||||
def test_prepare_asset_return_snapshot_does_not_fill_missing_prices() -> None:
|
||||
prices = _daily_prices()
|
||||
prices.loc[
|
||||
(prices["stock_code"] == "A") & (prices["trade_date"] == "2024-01-02"),
|
||||
"close",
|
||||
] = np.nan
|
||||
|
||||
snapshot = prepare_asset_return_snapshot(
|
||||
prices,
|
||||
source="qtdb_pro.hq_daily",
|
||||
source_snapshot_id="hq-daily:missing-middle",
|
||||
)
|
||||
|
||||
assert pd.isna(snapshot.returns.loc[pd.Timestamp("2024-01-02"), "A"])
|
||||
assert pd.isna(snapshot.returns.loc[pd.Timestamp("2024-01-03"), "A"])
|
||||
|
||||
|
||||
def test_prepare_asset_return_snapshot_rejects_duplicate_sessions() -> None:
|
||||
duplicate = pd.concat([_daily_prices(), _daily_prices().iloc[[0]]], ignore_index=True)
|
||||
|
||||
with pytest.raises(ValueError, match="duplicate"):
|
||||
prepare_asset_return_snapshot(
|
||||
duplicate,
|
||||
source="qtdb_pro.hq_daily",
|
||||
source_snapshot_id="hq-daily:duplicate",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("invalid_price", [0.0, -1.0, np.inf])
|
||||
def test_prepare_asset_return_snapshot_rejects_invalid_prices(invalid_price: float) -> None:
|
||||
prices = _daily_prices()
|
||||
prices.loc[0, "close"] = invalid_price
|
||||
|
||||
with pytest.raises(ValueError, match="positive finite"):
|
||||
prepare_asset_return_snapshot(
|
||||
prices,
|
||||
source="qtdb_pro.hq_daily",
|
||||
source_snapshot_id="hq-daily:invalid-price",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("source", "source_snapshot_id", "adjustment"),
|
||||
[
|
||||
("", "source-1", "none"),
|
||||
("qtdb_pro.hq_daily", "", "none"),
|
||||
("qtdb_pro.hq_daily", "source-1", ""),
|
||||
],
|
||||
)
|
||||
def test_prepare_asset_return_snapshot_requires_explicit_identity_semantics(
|
||||
source: str,
|
||||
source_snapshot_id: str,
|
||||
adjustment: str,
|
||||
) -> None:
|
||||
with pytest.raises(ValueError, match="must be non-empty"):
|
||||
prepare_asset_return_snapshot(
|
||||
_daily_prices(),
|
||||
source=source,
|
||||
source_snapshot_id=source_snapshot_id,
|
||||
adjustment=adjustment,
|
||||
)
|
||||
|
||||
|
||||
def test_asset_return_snapshot_feeds_reproducible_covariance_lineage() -> None:
|
||||
from quant_engine.risk import estimate_covariance_snapshot
|
||||
|
||||
market_snapshot = prepare_asset_return_snapshot(
|
||||
_daily_prices(),
|
||||
source="qtdb_pro.hq_daily",
|
||||
source_snapshot_id="hq-daily:2024-01-03:v1",
|
||||
)
|
||||
covariance_snapshot = estimate_covariance_snapshot(
|
||||
market_snapshot.returns,
|
||||
as_of_date=market_snapshot.end_date,
|
||||
lookback_sessions=3,
|
||||
min_observations=2,
|
||||
data_snapshot_id=market_snapshot.data_snapshot_id,
|
||||
)
|
||||
|
||||
assert covariance_snapshot.data_snapshot_id == market_snapshot.data_snapshot_id
|
||||
assert covariance_snapshot.snapshot_id.startswith("sample-cov-v1:")
|
||||
|
||||
|
||||
# ── 端到端：长表 → 适配 → alpha158 + execution ──────────────
|
||||
|
||||
|
||||
|
||||
@@ -10,12 +10,114 @@ from quant_engine.risk import (
|
||||
ComponentRiskResult,
|
||||
CovarianceSnapshot,
|
||||
component_var,
|
||||
estimate_covariance_snapshot,
|
||||
labeled_component_risk,
|
||||
marginal_risk_contribution,
|
||||
risk_contribution,
|
||||
)
|
||||
|
||||
|
||||
def test_estimate_covariance_snapshot_is_complete_case_and_reproducible() -> None:
|
||||
dates = pd.date_range("2026-01-05", periods=6, freq="B")
|
||||
returns = pd.DataFrame(
|
||||
{
|
||||
"A": [0.01, 0.02, 0.03, 0.04, 0.05, 99.0],
|
||||
"B": [0.02, 0.01, np.nan, 0.03, 0.04, -99.0],
|
||||
},
|
||||
index=dates,
|
||||
)
|
||||
as_of = dates[4]
|
||||
|
||||
snapshot = estimate_covariance_snapshot(
|
||||
returns,
|
||||
as_of_date=as_of,
|
||||
lookback_sessions=4,
|
||||
min_observations=3,
|
||||
data_snapshot_id="market-returns-20260109-v1",
|
||||
return_frequency="1d",
|
||||
periods_per_year=252,
|
||||
)
|
||||
|
||||
expected_window = returns.loc[:as_of].tail(4)
|
||||
expected = expected_window.dropna(how="any").cov()
|
||||
pd.testing.assert_frame_equal(snapshot.covariance, expected)
|
||||
assert snapshot.snapshot_id.startswith("sample-cov-v1:")
|
||||
assert snapshot.as_of_date == as_of.date()
|
||||
assert snapshot.method == "sample"
|
||||
assert snapshot.window_start_date == expected_window.index[0].date()
|
||||
assert snapshot.window_end_date == as_of.date()
|
||||
assert snapshot.observations == 3
|
||||
assert snapshot.lookback_sessions == 4
|
||||
assert snapshot.missing_policy == "complete_case"
|
||||
assert snapshot.data_snapshot_id == "market-returns-20260109-v1"
|
||||
assert len(snapshot.input_sha256) == 64
|
||||
|
||||
future_changed = returns.copy()
|
||||
future_changed.loc[dates[-1], :] = [1_000_000.0, -1_000_000.0]
|
||||
repeated = estimate_covariance_snapshot(
|
||||
future_changed,
|
||||
as_of_date=as_of,
|
||||
lookback_sessions=4,
|
||||
min_observations=3,
|
||||
data_snapshot_id="market-returns-20260109-v1",
|
||||
return_frequency="1d",
|
||||
periods_per_year=252,
|
||||
)
|
||||
assert repeated.snapshot_id == snapshot.snapshot_id
|
||||
pd.testing.assert_frame_equal(repeated.covariance, snapshot.covariance)
|
||||
|
||||
|
||||
def test_covariance_snapshot_identity_captures_data_and_estimator_contract() -> None:
|
||||
dates = pd.date_range("2026-01-05", periods=4, freq="B")
|
||||
returns = pd.DataFrame(
|
||||
{"A": [0.01, 0.02, -0.01, 0.03], "B": [0.02, -0.01, 0.01, 0.04]},
|
||||
index=dates,
|
||||
)
|
||||
base = estimate_covariance_snapshot(
|
||||
returns,
|
||||
as_of_date=dates[-1],
|
||||
lookback_sessions=4,
|
||||
min_observations=3,
|
||||
data_snapshot_id="snapshot-a",
|
||||
)
|
||||
different_source = estimate_covariance_snapshot(
|
||||
returns,
|
||||
as_of_date=dates[-1],
|
||||
lookback_sessions=4,
|
||||
min_observations=3,
|
||||
data_snapshot_id="snapshot-b",
|
||||
)
|
||||
|
||||
assert base.snapshot_id != different_source.snapshot_id
|
||||
assert base.covariance.equals(different_source.covariance)
|
||||
|
||||
|
||||
def test_estimate_covariance_snapshot_rejects_ambiguous_or_insufficient_history() -> None:
|
||||
dates = pd.date_range("2026-01-05", periods=4, freq="B")
|
||||
returns = pd.DataFrame(
|
||||
{"A": [0.01, np.nan, 0.03, 0.04], "B": [0.02, 0.01, np.nan, 0.03]},
|
||||
index=dates,
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="complete observations"):
|
||||
estimate_covariance_snapshot(
|
||||
returns,
|
||||
as_of_date=dates[-1],
|
||||
lookback_sessions=4,
|
||||
min_observations=3,
|
||||
data_snapshot_id="snapshot-a",
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="strictly increasing"):
|
||||
estimate_covariance_snapshot(
|
||||
returns.iloc[::-1],
|
||||
as_of_date=dates[-1],
|
||||
lookback_sessions=4,
|
||||
min_observations=2,
|
||||
data_snapshot_id="snapshot-a",
|
||||
)
|
||||
|
||||
|
||||
def test_covariance_snapshot_is_validated_and_immutable_by_interface() -> None:
|
||||
covariance = pd.DataFrame(
|
||||
[[0.04, 0.01], [0.01, 0.09]],
|
||||
|
||||
@@ -0,0 +1,408 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = "==3.13.*"
|
||||
resolution-markers = [
|
||||
"sys_platform == 'win32'",
|
||||
"sys_platform == 'emscripten'",
|
||||
"sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ast-serialize"
|
||||
version = "0.8.0"
|
||||
source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple" }
|
||||
sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/e1/a9/11851c3e02a3fea2ddc9932d1fdc7d2edaeecc0d2e11bc5f2a7fde2b0934/ast_serialize-0.8.0.tar.gz", hash = "sha256:6c37c43e4004dfb42d321ddedc569dc17ff4259296f3af577c9ea46a809bc010" }
|
||||
wheels = [
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/4c/11/911210c3c78923273a9211a2b6cfc4c8aa723b30dab3e1c8d19afb983b40/ast_serialize-0.8.0-cp315-abi3.abi3t-macosx_10_12_x86_64.whl", hash = "sha256:86b8a1e6d90467345356098b040150e82fbc26d24a7a202224b13dc1f6264ca0" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/77/89/6282881c8587606638db153cbe21e1e0c4d1f3970dee1aa0610a1c62a026/ast_serialize-0.8.0-cp315-abi3.abi3t-macosx_11_0_arm64.whl", hash = "sha256:39e92ff8e8cb45947fe9007174b2950e1fb098e6abd00266a13cd3bcf6675068" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/97/78/a9f846a03a340ff3728c915f23338ca742742f3292700559cdb3ad999b1e/ast_serialize-0.8.0-cp315-abi3.abi3t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c85d8d18db5b2dfcb3b7e38a4d600ca35504c0ed8a6f75cd1c811e4ffe248a15" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/c0/15/aba6ef8a988a6eceb6f0359589aac509e29ae2dba67fd9bfd5af0c3f13e7/ast_serialize-0.8.0-cp315-abi3.abi3t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9830ff7e764f74d9eefb01170c61a9f0fd2c027dac5fcb72e064decd57d56371" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/94/29/3f63d696ea7c5b8abadcecc3505be51bd900daaccc522ed8322fa5b05a93/ast_serialize-0.8.0-cp315-abi3.abi3t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6479d9722a4cd21b578f5478074c41e6169f04811996ec881655560f703a5bba" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/e2/5d/0aac338604ff59df5774d4304307898982252f325ff7cafe31d52fedcb65/ast_serialize-0.8.0-cp315-abi3.abi3t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a63bed264e818cd83eec11feed0f50aa162542b91132ef58afebc857182763a5" },
|
||||
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||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/00/bd/bf5a4be6a3525676499f6dff307991739ff6fdcad1481b1aeb6745339f58/scipy-1.18.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c825cef2f49e46753726a7181a8e199804a912b29519ada542c6ebc654951899" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/bd/4e/3c45c33e00a77996c4b1cb707929f833ba7b1d522ee29f882512c330676d/scipy-1.18.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e3b417bf8c2c7c16e8f58ad91db17783ec911ac16e7b50eb6eab6e809b4f5b07" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/93/0e/e0348fbc0dbab65c114cf78957e7dfeb49f8e8b556b4d930cc12ff195e18/scipy-1.18.1-cp313-cp313-win_amd64.whl", hash = "sha256:559ed65f60c1af5a03f3912605a1b5114f522c7c32fb23c3376ae8f03219fe28" },
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/50/a8/6a77f5f267c555108f0a864b6db714363dab567a8266422a79a385f9232b/scipy-1.18.1-cp313-cp313-win_arm64.whl", hash = "sha256:cd479fc04dd9401e3b4f49e76518768ef99c4f517a98c284eb091fd725719adf" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
version = "1.17.0"
|
||||
source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple" }
|
||||
sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81" }
|
||||
wheels = [
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.16.0"
|
||||
source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple" }
|
||||
sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/f6/cc/6253133b5bb138fc3306cebfbda2c520f545d36b5be2c7255cc528bb45d6/typing_extensions-4.16.0.tar.gz", hash = "sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5" }
|
||||
wheels = [
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/49/d3/b8441a820a491ddfc024b0b0cf0393375b75ea13866d9c66727e54c2fc80/typing_extensions-4.16.0-py3-none-any.whl", hash = "sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2026.3"
|
||||
source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple" }
|
||||
sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/92/ff/5a28bdfd8c3ebec42564ac7d0e54ca3db65044a9314a97f9564fa7a1e926/tzdata-2026.3.tar.gz", hash = "sha256:4a1518b8993086a7982523e071643f3c0e5f213e75b21318e78bcabfff9d1415" }
|
||||
wheels = [
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/e5/6d/b53b99a9f2766d095985947a5782f1702cabb129a34f7a802d7197af832f/tzdata-2026.3-py2.py3-none-any.whl", hash = "sha256:dc096730c87af6cab1b171c9d532be840741ff5d459015e7f6947bd7d7e54931" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "win32-setctime"
|
||||
version = "1.2.0"
|
||||
source = { registry = "https://mirrors.cloud.tencent.com/pypi/simple" }
|
||||
sdist = { url = "https://mirrors.cloud.tencent.com/pypi/packages/b3/8f/705086c9d734d3b663af0e9bb3d4de6578d08f46b1b101c2442fd9aecaa2/win32_setctime-1.2.0.tar.gz", hash = "sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0" }
|
||||
wheels = [
|
||||
{ url = "https://mirrors.cloud.tencent.com/pypi/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390" },
|
||||
]
|
||||
Reference in New Issue
Block a user