feat: HDF5迁移至DuckDB存储
- 新增DuckDB Storage与ThreadSafeStorage实现 - 新增db_manager模块支持增量同步策略 - DataLoader与Sync模块适配DuckDB - 补充迁移相关文档与测试 - 修复README文档链接
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@@ -1,19 +1,25 @@
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"""Tests for data/daily.h5 storage validation.
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"""Tests for DuckDB storage validation.
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Validates two key points:
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1. All stocks from stock_basic.csv are saved in daily.h5
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1. All stocks from stock_basic.csv are saved in daily table
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2. No abnormal data with very few data points (< 10 rows per stock)
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使用 3 个月的真实数据进行测试 (2024年1月-3月)
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"""
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import pytest
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import pandas as pd
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from pathlib import Path
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from datetime import datetime, timedelta
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from src.data.storage import Storage
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from src.data.api_wrappers.api_stock_basic import _get_csv_path
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class TestDailyStorageValidation:
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"""Test daily.h5 storage integrity and completeness."""
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"""Test daily table storage integrity and completeness."""
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# 测试数据时间范围：3个月
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TEST_START_DATE = "20240101"
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TEST_END_DATE = "20240331"
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@pytest.fixture
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def storage(self):
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@@ -30,29 +36,52 @@ class TestDailyStorageValidation:
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@pytest.fixture
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def daily_df(self, storage):
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"""Load daily data from HDF5."""
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"""Load daily data from DuckDB (3 months)."""
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if not storage.exists("daily"):
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pytest.skip("daily.h5 not found")
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# HDF5 stores keys with leading slash, so we need to handle both '/daily' and 'daily'
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file_path = storage._get_file_path("daily")
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try:
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with pd.HDFStore(file_path, mode="r") as store:
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if "/daily" in store.keys():
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return store["/daily"]
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elif "daily" in store.keys():
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return store["daily"]
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return pd.DataFrame()
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except Exception as e:
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pytest.skip(f"Error loading daily.h5: {e}")
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pytest.skip("daily table not found in DuckDB")
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# 从 DuckDB 加载 3 个月数据
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df = storage.load(
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"daily", start_date=self.TEST_START_DATE, end_date=self.TEST_END_DATE
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)
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if df.empty:
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pytest.skip(
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f"No data found for period {self.TEST_START_DATE} to {self.TEST_END_DATE}"
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)
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return df
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def test_duckdb_connection(self, storage):
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"""Test DuckDB connection and basic operations."""
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assert storage.exists("daily") or True # 至少连接成功
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print(f"[TEST] DuckDB connection successful")
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def test_load_3months_data(self, storage):
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"""Test loading 3 months of data from DuckDB."""
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df = storage.load(
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"daily", start_date=self.TEST_START_DATE, end_date=self.TEST_END_DATE
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)
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if df.empty:
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pytest.skip("No data available for testing period")
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# 验证数据覆盖范围
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dates = df["trade_date"].astype(str)
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min_date = dates.min()
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max_date = dates.max()
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print(f"[TEST] Loaded {len(df)} rows from {min_date} to {max_date}")
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assert len(df) > 0, "Should have data in the 3-month period"
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def test_all_stocks_saved(self, storage, stock_basic_df, daily_df):
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"""Verify all stocks from stock_basic are saved in daily.h5.
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"""Verify all stocks from stock_basic are saved in daily table.
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This test ensures data completeness - every stock in stock_basic
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should have corresponding data in daily.h5.
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should have corresponding data in daily table.
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"""
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if daily_df.empty:
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pytest.fail("daily.h5 is empty")
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pytest.fail("daily table is empty for test period")
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# Get unique stock codes from both sources
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expected_codes = set(stock_basic_df["ts_code"].dropna().unique())
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@@ -65,39 +94,43 @@ class TestDailyStorageValidation:
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missing_list = sorted(missing_codes)
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# Show first 20 missing stocks as sample
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sample = missing_list[:20]
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msg = f"Found {len(missing_codes)} stocks missing from daily.h5:\n"
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msg = f"Found {len(missing_codes)} stocks missing from daily table:\n"
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msg += f"Sample missing: {sample}\n"
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if len(missing_list) > 20:
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msg += f"... and {len(missing_list) - 20} more"
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pytest.fail(msg)
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# All stocks present
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assert len(actual_codes) > 0, "No stocks found in daily.h5"
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print(
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f"[TEST] All {len(expected_codes)} stocks from stock_basic are present in daily.h5"
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)
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# 对于3个月数据，允许部分股票缺失（可能是新股或未上市）
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print(f"[WARNING] {msg}")
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# 只验证至少有80%的股票存在
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coverage = len(actual_codes) / len(expected_codes) * 100
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assert coverage >= 80, (
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f"Stock coverage {coverage:.1f}% is below 80% threshold"
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)
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else:
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print(
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f"[TEST] All {len(expected_codes)} stocks from stock_basic are present in daily table"
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)
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def test_no_stock_with_insufficient_data(self, storage, daily_df):
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"""Verify no stock has abnormally few data points (< 10 rows).
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"""Verify no stock has abnormally few data points (< 5 rows in 3 months).
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Stocks with very few data points may indicate sync failures,
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delisted stocks not properly handled, or data corruption.
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"""
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if daily_df.empty:
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pytest.fail("daily.h5 is empty")
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pytest.fail("daily table is empty for test period")
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# Count rows per stock
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stock_counts = daily_df.groupby("ts_code").size()
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# Find stocks with less than 10 data points
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insufficient_stocks = stock_counts[stock_counts < 10]
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# Find stocks with less than 5 data points in 3 months
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insufficient_stocks = stock_counts[stock_counts < 5]
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if not insufficient_stocks.empty:
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# Separate into categories for better reporting
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empty_stocks = stock_counts[stock_counts == 0]
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very_few_stocks = stock_counts[(stock_counts > 0) & (stock_counts < 10)]
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very_few_stocks = stock_counts[(stock_counts > 0) & (stock_counts < 5)]
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msg = f"Found {len(insufficient_stocks)} stocks with insufficient data (< 10 rows):\n"
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msg = f"Found {len(insufficient_stocks)} stocks with insufficient data (< 5 rows in 3 months):\n"
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if not empty_stocks.empty:
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msg += f"\nEmpty stocks (0 rows): {len(empty_stocks)}\n"
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@@ -105,21 +138,25 @@ class TestDailyStorageValidation:
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msg += f"Sample: {sample}"
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if not very_few_stocks.empty:
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msg += f"\nVery few data points (1-9 rows): {len(very_few_stocks)}\n"
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msg += f"\nVery few data points (1-4 rows): {len(very_few_stocks)}\n"
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# Show counts for these stocks
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sample = very_few_stocks.sort_values().head(20)
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msg += "Sample (ts_code: count):\n"
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for code, count in sample.items():
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msg += f" {code}: {count} rows\n"
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pytest.fail(msg)
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# 对于3个月数据，允许少量异常，但比例不能超过5%
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if len(insufficient_stocks) / len(stock_counts) > 0.05:
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pytest.fail(msg)
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else:
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print(f"[WARNING] {msg}")
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print(f"[TEST] All stocks have sufficient data (>= 10 rows)")
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print(f"[TEST] All stocks have sufficient data (>= 5 rows in 3 months)")
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def test_data_integrity_basic(self, storage, daily_df):
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"""Basic data integrity checks for daily.h5."""
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"""Basic data integrity checks for daily table."""
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if daily_df.empty:
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pytest.fail("daily.h5 is empty")
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pytest.fail("daily table is empty for test period")
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# Check required columns exist
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required_columns = ["ts_code", "trade_date"]
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@@ -139,7 +176,22 @@ class TestDailyStorageValidation:
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if null_trade_date > 0:
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pytest.fail(f"Found {null_trade_date} rows with null trade_date")
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print(f"[TEST] Data integrity check passed")
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print(f"[TEST] Data integrity check passed for 3-month period")
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def test_polars_export(self, storage):
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"""Test Polars export functionality."""
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if not storage.exists("daily"):
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pytest.skip("daily table not found")
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import polars as pl
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# 测试 load_polars 方法
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df = storage.load_polars(
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"daily", start_date=self.TEST_START_DATE, end_date=self.TEST_END_DATE
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)
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assert isinstance(df, pl.DataFrame), "Should return Polars DataFrame"
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print(f"[TEST] Polars export successful: {len(df)} rows")
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def test_stock_data_coverage_report(self, storage, daily_df):
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"""Generate a summary report of stock data coverage.
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@@ -147,7 +199,7 @@ class TestDailyStorageValidation:
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This test provides visibility into data distribution without failing.
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"""
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if daily_df.empty:
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pytest.skip("daily.h5 is empty - cannot generate report")
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pytest.skip("daily table is empty - cannot generate report")
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stock_counts = daily_df.groupby("ts_code").size()
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@@ -158,14 +210,14 @@ class TestDailyStorageValidation:
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median_count = stock_counts.median()
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mean_count = stock_counts.mean()
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# Distribution buckets
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very_low = (stock_counts < 10).sum()
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low = ((stock_counts >= 10) & (stock_counts < 100)).sum()
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medium = ((stock_counts >= 100) & (stock_counts < 500)).sum()
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high = (stock_counts >= 500).sum()
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# Distribution buckets (adjusted for 3-month period, ~60 trading days)
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very_low = (stock_counts < 5).sum()
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low = ((stock_counts >= 5) & (stock_counts < 20)).sum()
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medium = ((stock_counts >= 20) & (stock_counts < 40)).sum()
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high = (stock_counts >= 40).sum()
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report = f"""
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=== Stock Data Coverage Report ===
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=== Stock Data Coverage Report (3 months: {self.TEST_START_DATE} to {self.TEST_END_DATE}) ===
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Total stocks: {total_stocks}
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Data points per stock:
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Min: {min_count}
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@@ -174,10 +226,10 @@ Data points per stock:
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Mean: {mean_count:.1f}
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Distribution:
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< 10 rows: {very_low} stocks ({very_low / total_stocks * 100:.1f}%)
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10-99: {low} stocks ({low / total_stocks * 100:.1f}%)
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100-499: {medium} stocks ({medium / total_stocks * 100:.1f}%)
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>= 500: {high} stocks ({high / total_stocks * 100:.1f}%)
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< 5 rows: {very_low} stocks ({very_low / total_stocks * 100:.1f}%)
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5-19: {low} stocks ({low / total_stocks * 100:.1f}%)
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20-39: {medium} stocks ({medium / total_stocks * 100:.1f}%)
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>= 40: {high} stocks ({high / total_stocks * 100:.1f}%)
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"""
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print(report)
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