#!/usr/bin/env python3
"""
StockData統合テスト - 実際のオブジェクト作成と属性アクセスをテスト
server.pyで発生する可能性のある AttributeError を事前に検出
"""
import pytest
from src.models import StockData
from src.server import server
class TestStockDataIntegration:
"""StockDataモデルの統合テスト"""
def test_stockdata_required_attributes_for_earnings_trading_screener(self):
"""earnings_trading_screenerで使用される属性の存在確認"""
# 実際のStockDataオブジェクトを作成
stock = StockData(
ticker="AAPL",
company_name="Apple Inc.",
sector="Technology",
industry="Consumer Electronics"
)
# server.pyの748行目で使用される属性をテスト
required_attributes = [
'ticker', 'company_name', 'sector', 'price', 'price_change',
'eps_surprise', 'revenue_surprise', 'volatility', 'performance_1m'
]
for attr in required_attributes:
assert hasattr(stock, attr), f"StockData lacks required attribute: {attr}"
# 属性アクセスが例外を発生させないことを確認
try:
# server.pyで行われる操作をシミュレート
ticker = stock.ticker
company = stock.company_name
sector = stock.sector
price = stock.price
change = stock.price_change
eps_surprise = stock.eps_surprise
revenue_surprise = stock.revenue_surprise
volatility = stock.volatility
performance_1m = stock.performance_1m # performance_4w から修正
except AttributeError as e:
pytest.fail(f"AttributeError accessing StockData attribute: {e}")
def test_stockdata_performance_attributes_comprehensive(self):
"""パフォーマンス関連の全属性をテスト"""
stock = StockData(
ticker="MSFT",
company_name="Microsoft Corp.",
sector="Technology",
industry="Software"
)
# パフォーマンス関連属性をすべてチェック
performance_attributes = [
'performance_1min', 'performance_2min', 'performance_3min',
'performance_5min', 'performance_10min', 'performance_15min',
'performance_30min', 'performance_1h', 'performance_2h',
'performance_4h', 'performance_1w', 'performance_1m',
'performance_3m', 'performance_6m', 'performance_ytd',
'performance_1y', 'performance_2y', 'performance_3y',
'performance_5y', 'performance_10y', 'performance_since_inception'
]
for attr in performance_attributes:
assert hasattr(stock, attr), f"StockData lacks performance attribute: {attr}"
# None値でもAttributeErrorが発生しないことを確認
value = getattr(stock, attr)
assert value is None or isinstance(value, (int, float))
def test_mock_stockdata_with_complete_attributes(self):
"""完全な属性を持つStockDataオブジェクトのテスト"""
# 完全なサンプルデータでStockDataを作成
complete_stock = StockData(
ticker="GOOGL",
company_name="Alphabet Inc.",
sector="Technology",
industry="Internet Content",
price=150.0,
price_change=2.5,
price_change_percent=1.67,
volume=25000000,
market_cap=1900000000000,
pe_ratio=28.5,
eps=5.89,
eps_surprise=0.15,
revenue_surprise=0.05,
volatility=0.25,
performance_1w=3.2,
performance_1m=5.8,
performance_3m=12.5,
performance_ytd=18.9,
performance_1y=22.1,
eps_qoq_growth=15.2,
sales_qoq_growth=8.7,
target_price=165.0,
rsi=58.5,
beta=1.05
)
# server.pyで使用される全ての属性をテスト
assert complete_stock.ticker == "GOOGL"
assert complete_stock.company_name == "Alphabet Inc."
assert complete_stock.price == 150.0
assert complete_stock.performance_1m == 5.8
assert complete_stock.eps_surprise == 0.15
assert complete_stock.revenue_surprise == 0.05
def test_earnings_trading_screener_output_format(self):
"""earnings_trading_screenerの出力フォーマットをテスト"""
# テスト用のStockDataリストを作成
test_stocks = [
StockData(
ticker="AAPL",
company_name="Apple Inc.",
sector="Technology",
industry="Consumer Electronics",
price=180.0,
price_change=2.5,
eps_surprise=0.12,
revenue_surprise=0.08,
volatility=0.22,
performance_1m=4.5
),
StockData(
ticker="MSFT",
company_name="Microsoft Corp.",
sector="Technology",
industry="Software",
price=420.0,
price_change=-1.2,
eps_surprise=0.18,
revenue_surprise=0.03,
volatility=0.19,
performance_1m=7.2
)
]
# 各StockDataオブジェクトから文字列を生成
for stock in test_stocks:
# server.pyで行われる処理をシミュレート
try:
ticker_line = f"Ticker: {stock.ticker}"
company_line = f"Company: {stock.company_name}"
sector_line = f"Sector: {stock.sector}"
price_line = f"Price: ${stock.price:.2f}" if stock.price else "Price: N/A"
change_line = f"Change: {stock.price_change:.2f}%" if stock.price_change else "Change: N/A"
eps_surprise_line = f"EPS Surprise: {stock.eps_surprise:.2f}%" if stock.eps_surprise else "EPS Surprise: N/A"
revenue_surprise_line = f"Revenue Surprise: {stock.revenue_surprise:.2f}%" if stock.revenue_surprise else "Revenue Surprise: N/A"
volatility_line = f"Volatility: {stock.volatility:.2f}" if stock.volatility else "Volatility: N/A"
performance_line = f"1M Performance: {stock.performance_1m:.2f}%" if stock.performance_1m else "1M Performance: N/A"
# 正常に文字列が生成されることを確認
assert ticker_line.startswith("Ticker:")
assert company_line.startswith("Company:")
assert price_line.startswith("Price:")
assert performance_line.startswith("1M Performance:")
except AttributeError as e:
pytest.fail(f"AttributeError in output format generation: {e}")
def test_finviz_128_columns_coverage(self):
"""Finvizの128カラムがStockDataモデルで網羅されているかテスト"""
# 重要な Finviz カラムのサンプル
key_finviz_columns = [
'ticker', 'company_name', 'sector', 'industry', 'country',
'price', 'price_change', 'price_change_percent', 'volume', 'avg_volume',
'market_cap', 'pe_ratio', 'eps', 'dividend_yield', 'beta',
'performance_1w', 'performance_1m', 'performance_3m', 'performance_ytd',
'sma_20', 'sma_50', 'sma_200', 'rsi', 'volatility',
'earnings_date', 'eps_surprise', 'revenue_surprise', 'target_price'
]
stock = StockData(ticker="TEST", company_name="Test Corp", sector="Test", industry="Test")
missing_attributes = []
for column in key_finviz_columns:
if not hasattr(stock, column):
missing_attributes.append(column)
if missing_attributes:
pytest.fail(f"StockData missing key Finviz columns: {missing_attributes}")
if __name__ == "__main__":
pytest.main([__file__, "-v"])
