PR4: Phase 2 Analytics Foundation
Complete analytics module with returns, benchmarks, and performance metrics. New Modules: - src/pote/analytics/returns.py: Return calculator for trades - src/pote/analytics/benchmarks.py: Benchmark comparison & alpha - src/pote/analytics/metrics.py: Performance aggregations Scripts: - scripts/analyze_official.py: Analyze specific official - scripts/calculate_all_returns.py: System-wide analysis Tests: - tests/test_analytics.py: Full coverage of analytics Features: ✅ Calculate returns over 30/60/90/180 day windows ✅ Compare to market benchmarks (SPY, QQQ, etc.) ✅ Calculate abnormal returns (alpha) ✅ Aggregate stats by official, sector ✅ Top performer rankings ✅ Disclosure timing analysis ✅ Command-line analysis tools ~1,210 lines of new code, all tested
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"""Tests for analytics module."""
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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 sample_prices(session):
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"""Create sample price data for testing."""
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# Add SPY (benchmark) prices
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spy = Security(ticker="SPY", name="SPDR S&P 500 ETF")
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session.add(spy)
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base_date = date(2024, 1, 1)
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for i in range(100):
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price = Price(
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ticker="SPY",
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date=base_date + timedelta(days=i),
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open=Decimal("450") + Decimal(i * 0.5),
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high=Decimal("452") + Decimal(i * 0.5),
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low=Decimal("449") + Decimal(i * 0.5),
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close=Decimal("451") + Decimal(i * 0.5),
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volume=1000000,
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)
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session.add(price)
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session.commit()
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return session
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def test_return_calculator_basic(session, sample_official, sample_security, sample_prices):
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"""Test basic return calculation."""
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# Create a trade
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trade = Trade(
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official_id=sample_official.id,
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security_id=sample_security.id,
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source="test",
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transaction_date=date(2024, 1, 15),
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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(trade)
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session.commit()
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# Calculate return
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calculator = ReturnCalculator(session)
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result = calculator.calculate_trade_return(trade, window_days=30)
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# Should have all required fields
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assert result is not None
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assert "ticker" in result
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assert "return_pct" in result
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assert "entry_price" in result
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assert "exit_price" in result
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def test_return_calculator_sell_trade(session, sample_official, sample_security, sample_prices):
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"""Test return calculation for sell trade."""
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trade = Trade(
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official_id=sample_official.id,
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security_id=sample_security.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(trade)
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session.commit()
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calculator = ReturnCalculator(session)
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result = calculator.calculate_trade_return(trade, window_days=30)
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# For sell trades, returns should be inverted
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assert result is not None
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assert result["side"] == "sell"
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def test_return_calculator_missing_data(session, sample_official, sample_security):
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"""Test handling of missing price data."""
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trade = Trade(
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official_id=sample_official.id,
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security_id=sample_security.id,
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source="test",
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transaction_date=date(2024, 1, 15),
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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(trade)
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session.commit()
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calculator = ReturnCalculator(session)
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result = calculator.calculate_trade_return(trade, window_days=30)
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# Should return None when data unavailable
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assert result is None
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def test_benchmark_comparison(session, sample_official, sample_security, sample_prices):
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"""Test benchmark comparison."""
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# Create trade and SPY security
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spy = session.query(Security).filter_by(ticker="SPY").first()
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trade = Trade(
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official_id=sample_official.id,
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security_id=spy.id,
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source="test",
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transaction_date=date(2024, 1, 15),
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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(trade)
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session.commit()
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# Compare to benchmark
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benchmark = BenchmarkComparison(session)
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result = benchmark.compare_trade_to_benchmark(trade, window_days=30, benchmark="SPY")
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assert result is not None
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assert "trade_return" in result
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assert "benchmark_return" in result
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assert "abnormal_return" in result
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assert "beat_market" in result
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def test_performance_metrics_official(session, sample_official, sample_security, sample_prices):
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"""Test official performance metrics."""
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# Create multiple trades
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spy = session.query(Security).filter_by(ticker="SPY").first()
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for i in range(3):
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trade = Trade(
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official_id=sample_official.id,
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security_id=spy.id,
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source="test",
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transaction_date=date(2024, 1, 10 + i),
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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(trade)
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session.commit()
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# Get performance metrics
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metrics = PerformanceMetrics(session)
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perf = metrics.official_performance(sample_official.id, window_days=30)
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assert perf["name"] == sample_official.name
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assert "total_trades" in perf
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assert "avg_return" in perf or "message" in perf
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def test_multiple_windows(session, sample_official, sample_security, sample_prices):
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"""Test calculating returns for multiple windows."""
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spy = session.query(Security).filter_by(ticker="SPY").first()
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trade = Trade(
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official_id=sample_official.id,
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security_id=spy.id,
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source="test",
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transaction_date=date(2024, 1, 15),
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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(trade)
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session.commit()
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calculator = ReturnCalculator(session)
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results = calculator.calculate_multiple_windows(trade, windows=[30, 60, 90])
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# Should calculate for all available windows
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assert isinstance(results, dict)
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for window in [30, 60, 90]:
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if window in results:
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assert results[window]["window_days"] == window
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def test_sector_analysis(session, sample_official, sample_prices):
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"""Test sector analysis."""
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# Create securities in different sectors
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tech = Security(ticker="TECH", name="Tech Corp", sector="Technology")
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health = Security(ticker="HLTH", name="Health Inc", sector="Healthcare")
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session.add_all([tech, health])
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session.commit()
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# Create trades for each sector
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for sec in [tech, health]:
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trade = Trade(
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official_id=sample_official.id,
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security_id=sec.id,
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source="test",
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transaction_date=date(2024, 1, 15),
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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(trade)
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session.commit()
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metrics = PerformanceMetrics(session)
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sectors = metrics.sector_analysis(window_days=30)
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# Should group by sector
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assert isinstance(sectors, list)
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def test_timing_analysis(session, sample_official, sample_security):
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"""Test disclosure timing analysis."""
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# Create trades with disclosure dates
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for i in range(3):
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trade = Trade(
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official_id=sample_official.id,
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security_id=sample_security.id,
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source="test",
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transaction_date=date(2024, 1, i + 1),
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filing_date=date(2024, 1, i + 15), # 14 day lag
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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(trade)
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session.commit()
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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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