b4e6a7c340
Critical Fixes: - Fixed Price model query to use security_id join with Security - Added Security import to returns.py module - Fixed all test fixtures to use test_db_session correctly - Added AAPL price data to sample_prices fixture New Tests: - tests/test_analytics_integration.py: 10 comprehensive integration tests * Real-world scenarios with synthetic price data * Return calculations, benchmark comparisons, performance metrics * Edge cases: missing data, sell trades, disclosure timing Documentation: - LOCAL_TEST_GUIDE.md: Complete guide for local testing * How to test before deploying * Current data status (live vs fixtures) * Multiple options for getting real data * Common issues and fixes Test Results: ✅ All 55 tests passing ✅ Analytics fully functional ✅ Ready for deployment Live Data Status: ❌ House Stock Watcher API still down (external issue) ✅ Manual CSV import works ✅ yfinance for prices works ✅ Can use system NOW with manual data
332 lines
11 KiB
Python
332 lines
11 KiB
Python
"""Integration tests for analytics with real-ish data."""
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import pytest
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from datetime import date, timedelta
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from decimal import Decimal
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from pote.analytics.returns import ReturnCalculator
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from pote.analytics.benchmarks import BenchmarkComparison
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from pote.analytics.metrics import PerformanceMetrics
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from pote.db.models import Official, Security, Trade, Price
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@pytest.fixture
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def full_test_data(test_db_session):
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"""Create complete test dataset with prices."""
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session = test_db_session
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# Create officials
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pelosi = Official(
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name="Nancy Pelosi",
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chamber="House",
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party="Democrat",
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state="CA",
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)
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tuberville = Official(
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name="Tommy Tuberville",
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chamber="Senate",
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party="Republican",
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state="AL",
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)
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session.add_all([pelosi, tuberville])
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session.flush()
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# Create securities
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nvda = Security(ticker="NVDA", name="NVIDIA Corporation", sector="Technology")
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spy = Security(ticker="SPY", name="SPDR S&P 500 ETF", sector="Financial")
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session.add_all([nvda, spy])
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session.flush()
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# Create price data for NVDA (upward trend)
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base_date = date(2024, 1, 1)
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nvda_base_price = Decimal("495.00")
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for i in range(120):
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current_date = base_date + timedelta(days=i)
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# Simulate upward trend: +0.5% per day on average
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price_change = Decimal(i) * Decimal("2.50") # ~50% gain over 120 days
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current_price = nvda_base_price + price_change
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price = Price(
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security_id=nvda.id,
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date=current_date,
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open=current_price - Decimal("5.00"),
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high=current_price + Decimal("5.00"),
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low=current_price - Decimal("8.00"),
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close=current_price,
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volume=50000000,
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)
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session.add(price)
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# Create price data for SPY (slower upward trend - ~10% over 120 days)
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spy_base_price = Decimal("450.00")
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for i in range(120):
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current_date = base_date + timedelta(days=i)
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price_change = Decimal(i) * Decimal("0.35")
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current_price = spy_base_price + price_change
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price = Price(
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security_id=spy.id,
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date=current_date,
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open=current_price - Decimal("2.00"),
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high=current_price + Decimal("2.00"),
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low=current_price - Decimal("3.00"),
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close=current_price,
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volume=100000000,
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)
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session.add(price)
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# Create trades
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# Pelosi buys NVDA early (should show good returns)
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trade1 = Trade(
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official_id=pelosi.id,
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security_id=nvda.id,
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source="test",
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transaction_date=date(2024, 1, 15), # Buy at ~495
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filing_date=date(2024, 2, 1),
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side="buy",
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value_min=Decimal("15001"),
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value_max=Decimal("50000"),
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)
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# Tuberville buys NVDA later (still good but less alpha)
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trade2 = Trade(
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official_id=tuberville.id,
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security_id=nvda.id,
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source="test",
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transaction_date=date(2024, 2, 1), # Buy at ~540
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filing_date=date(2024, 2, 15),
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side="buy",
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value_min=Decimal("50001"),
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value_max=Decimal("100000"),
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)
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session.add_all([trade1, trade2])
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session.commit()
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return {
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"officials": [pelosi, tuberville],
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"securities": [nvda, spy],
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"trades": [trade1, trade2],
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}
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def test_return_calculation_with_real_data(test_db_session, full_test_data):
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session = test_db_session
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"""Test return calculation with realistic price data."""
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calculator = ReturnCalculator(session)
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# Get Pelosi's NVDA trade
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trade = full_test_data["trades"][0]
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# Calculate 90-day return
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result = calculator.calculate_trade_return(trade, window_days=90)
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assert result is not None, "Should calculate return with available data"
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assert result["ticker"] == "NVDA"
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assert result["window_days"] == 90
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assert result["return_pct"] > 0, "NVDA should have positive return"
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# Entry around day 15, exit around day 105
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# Expected return: (720 - 532.5) / 532.5 = ~35%
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assert 30 < float(result["return_pct"]) < 50, f"Expected ~35% return, got {result['return_pct']}"
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print(f"\n✅ NVDA 90-day return: {result['return_pct']:.2f}%")
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print(f" Entry: ${result['entry_price']} on {result['transaction_date']}")
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print(f" Exit: ${result['exit_price']} on {result['exit_date']}")
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def test_benchmark_comparison_with_real_data(test_db_session, full_test_data):
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session = test_db_session
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"""Test benchmark comparison with SPY."""
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benchmark = BenchmarkComparison(session)
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# Get Pelosi's trade
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trade = full_test_data["trades"][0]
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# Compare to SPY
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result = benchmark.compare_trade_to_benchmark(trade, window_days=90, benchmark="SPY")
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assert result is not None
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assert result["ticker"] == "NVDA"
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assert result["benchmark"] == "SPY"
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# NVDA should beat SPY significantly
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assert result["beat_market"] is True
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assert float(result["abnormal_return"]) > 10, "NVDA should have strong alpha vs SPY"
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print(f"\n✅ Benchmark Comparison:")
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print(f" NVDA Return: {result['trade_return']:.2f}%")
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print(f" SPY Return: {result['benchmark_return']:.2f}%")
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print(f" Alpha: {result['abnormal_return']:+.2f}%")
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def test_official_performance_summary(test_db_session, full_test_data):
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session = test_db_session
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"""Test official performance aggregation."""
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metrics = PerformanceMetrics(session)
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pelosi = full_test_data["officials"][0]
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# Get performance summary
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perf = metrics.official_performance(pelosi.id, window_days=90)
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assert perf["name"] == "Nancy Pelosi"
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assert perf["total_trades"] >= 1
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if perf.get("trades_analyzed", 0) > 0:
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assert "avg_return" in perf
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assert "avg_alpha" in perf
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assert "win_rate" in perf
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assert perf["win_rate"] >= 0 and perf["win_rate"] <= 1
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print(f"\n✅ {perf['name']} Performance:")
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print(f" Total Trades: {perf['total_trades']}")
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print(f" Average Return: {perf['avg_return']:.2f}%")
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print(f" Alpha: {perf['avg_alpha']:+.2f}%")
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print(f" Win Rate: {perf['win_rate']:.1%}")
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def test_multiple_windows(test_db_session, full_test_data):
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session = test_db_session
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"""Test calculating multiple time windows."""
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calculator = ReturnCalculator(session)
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trade = full_test_data["trades"][0]
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# Calculate for 30, 60, 90 days
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results = calculator.calculate_multiple_windows(trade, windows=[30, 60, 90])
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assert len(results) == 3, "Should calculate all three windows"
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# Returns should generally increase with longer windows (given upward trend)
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if 30 in results and 90 in results:
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print(f"\n✅ Multiple Windows:")
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for window in [30, 60, 90]:
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if window in results:
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print(f" {window:3d} days: {results[window]['return_pct']:+7.2f}%")
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def test_top_performers(test_db_session, full_test_data):
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session = test_db_session
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"""Test top performer ranking."""
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metrics = PerformanceMetrics(session)
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top = metrics.top_performers(window_days=90, limit=5)
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assert isinstance(top, list)
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assert len(top) > 0
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print(f"\n✅ Top Performers:")
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for i, perf in enumerate(top, 1):
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if perf.get("trades_analyzed", 0) > 0:
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print(f" {i}. {perf['name']:20s} | Alpha: {perf['avg_alpha']:+6.2f}%")
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def test_system_statistics(test_db_session, full_test_data):
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session = test_db_session
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"""Test system-wide statistics."""
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metrics = PerformanceMetrics(session)
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stats = metrics.summary_statistics(window_days=90)
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assert stats["total_officials"] >= 2
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assert stats["total_trades"] >= 2
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assert stats["total_securities"] >= 2
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print(f"\n✅ System Statistics:")
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print(f" Officials: {stats['total_officials']}")
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print(f" Trades: {stats['total_trades']}")
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print(f" Securities: {stats['total_securities']}")
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if stats.get("avg_alpha") is not None:
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print(f" Avg Alpha: {stats['avg_alpha']:+.2f}%")
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print(f" Beat Market: {stats['beat_market_rate']:.1%}")
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def test_disclosure_timing(test_db_session, full_test_data):
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session = test_db_session
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"""Test disclosure lag analysis."""
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metrics = PerformanceMetrics(session)
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timing = metrics.timing_analysis()
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assert "avg_disclosure_lag_days" in timing
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assert timing["avg_disclosure_lag_days"] > 0
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print(f"\n✅ Disclosure Timing:")
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print(f" Average Lag: {timing['avg_disclosure_lag_days']:.1f} days")
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print(f" Median Lag: {timing['median_disclosure_lag_days']} days")
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def test_sector_analysis(test_db_session, full_test_data):
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session = test_db_session
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"""Test sector-level analysis."""
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metrics = PerformanceMetrics(session)
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sectors = metrics.sector_analysis(window_days=90)
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assert isinstance(sectors, list)
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if sectors:
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print(f"\n✅ Sector Analysis:")
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for s in sectors:
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print(f" {s['sector']:15s} | {s['trade_count']} trades | Alpha: {s['avg_alpha']:+6.2f}%")
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def test_edge_case_missing_exit_price(test_db_session, full_test_data):
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session = test_db_session
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"""Test handling of trade with no exit price available."""
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calculator = ReturnCalculator(session)
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nvda = session.query(Security).filter_by(ticker="NVDA").first()
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pelosi = session.query(Official).filter_by(name="Nancy Pelosi").first()
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# Create trade with transaction date far in future (no exit price)
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future_trade = Trade(
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official_id=pelosi.id,
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security_id=nvda.id,
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source="test",
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transaction_date=date(2024, 12, 1), # No prices available this far out
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side="buy",
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value_min=Decimal("10000"),
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value_max=Decimal("50000"),
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)
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session.add(future_trade)
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session.commit()
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result = calculator.calculate_trade_return(future_trade, window_days=90)
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assert result is None, "Should return None when price data unavailable"
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print("\n✅ Correctly handles missing price data")
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def test_sell_trade_logic(test_db_session, full_test_data):
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session = test_db_session
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"""Test that sell trades have inverted return logic."""
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calculator = ReturnCalculator(session)
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nvda = session.query(Security).filter_by(ticker="NVDA").first()
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pelosi = session.query(Official).filter_by(name="Nancy Pelosi").first()
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# Create sell trade during uptrend (should show negative return)
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sell_trade = Trade(
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official_id=pelosi.id,
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security_id=nvda.id,
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source="test",
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transaction_date=date(2024, 1, 15),
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side="sell",
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value_min=Decimal("10000"),
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value_max=Decimal("50000"),
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)
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session.add(sell_trade)
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session.commit()
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result = calculator.calculate_trade_return(sell_trade, window_days=90)
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if result:
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# Selling during uptrend = negative return
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assert result["return_pct"] < 0, "Sell during uptrend should show negative return"
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print(f"\n✅ Sell trade return correctly inverted: {result['return_pct']:.2f}%")
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