feat: add portfolio optimization and congress tracking (TDD)
Portfolio optimization (3 endpoints): - POST /portfolio/optimize - HRP optimal weights via scipy clustering - POST /portfolio/correlation - pairwise correlation matrix - POST /portfolio/risk-parity - inverse-volatility risk parity weights Congress tracking (2 endpoints): - GET /regulators/congress/trades - congress member stock trades - GET /regulators/congress/bills?query= - search congress bills Implementation: - portfolio_service.py: HRP with scipy fallback to inverse-vol - congress_service.py: multi-provider fallback pattern - 51 new tests (14 portfolio unit, 20 portfolio route, 12 congress unit, 7 congress route) - All 312 tests passing
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Yaojia Wang
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tests/test_portfolio_service.py
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263
tests/test_portfolio_service.py
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"""Tests for portfolio optimization service (TDD - RED phase first)."""
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from unittest.mock import AsyncMock, patch
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import pytest
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# --- HRP Optimization ---
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_hrp_optimize_happy_path(mock_fetch):
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"""HRP returns weights that sum to ~1.0 for valid symbols."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame(
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{
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"AAPL": [150.0, 151.0, 149.0, 152.0, 153.0],
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"MSFT": [300.0, 302.0, 298.0, 305.0, 307.0],
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"GOOGL": [2800.0, 2820.0, 2790.0, 2830.0, 2850.0],
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}
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)
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import portfolio_service
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result = await portfolio_service.optimize_hrp(
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["AAPL", "MSFT", "GOOGL"], days=365
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)
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assert result["method"] == "hrp"
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assert set(result["weights"].keys()) == {"AAPL", "MSFT", "GOOGL"}
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total = sum(result["weights"].values())
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assert abs(total - 1.0) < 0.01
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_hrp_optimize_single_symbol(mock_fetch):
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"""Single symbol gets weight of 1.0."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame(
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{"AAPL": [150.0, 151.0, 149.0, 152.0, 153.0]}
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)
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import portfolio_service
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result = await portfolio_service.optimize_hrp(["AAPL"], days=365)
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assert result["weights"]["AAPL"] == pytest.approx(1.0, abs=0.01)
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_hrp_optimize_no_data_raises(mock_fetch):
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"""Raises ValueError when no price data is available."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame()
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import portfolio_service
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with pytest.raises(ValueError, match="No price data"):
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await portfolio_service.optimize_hrp(["AAPL", "MSFT"], days=365)
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@pytest.mark.asyncio
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async def test_hrp_optimize_empty_symbols_raises():
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"""Raises ValueError for empty symbol list."""
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import portfolio_service
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with pytest.raises(ValueError, match="symbols"):
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await portfolio_service.optimize_hrp([], days=365)
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# --- Correlation Matrix ---
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_correlation_matrix_happy_path(mock_fetch):
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"""Correlation matrix has 1.0 on diagonal and valid shape."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame(
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{
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"AAPL": [150.0, 151.0, 149.0, 152.0, 153.0],
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"MSFT": [300.0, 302.0, 298.0, 305.0, 307.0],
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"GOOGL": [2800.0, 2820.0, 2790.0, 2830.0, 2850.0],
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}
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)
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import portfolio_service
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result = await portfolio_service.compute_correlation(
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["AAPL", "MSFT", "GOOGL"], days=365
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)
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assert result["symbols"] == ["AAPL", "MSFT", "GOOGL"]
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matrix = result["matrix"]
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assert len(matrix) == 3
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assert len(matrix[0]) == 3
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# Diagonal should be 1.0
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for i in range(3):
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assert abs(matrix[i][i] - 1.0) < 0.01
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_correlation_matrix_two_symbols(mock_fetch):
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"""Two-symbol correlation is symmetric."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame(
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{
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"AAPL": [150.0, 151.0, 149.0, 152.0, 153.0],
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"MSFT": [300.0, 302.0, 298.0, 305.0, 307.0],
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}
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)
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import portfolio_service
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result = await portfolio_service.compute_correlation(["AAPL", "MSFT"], days=365)
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matrix = result["matrix"]
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# Symmetric: matrix[0][1] == matrix[1][0]
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assert abs(matrix[0][1] - matrix[1][0]) < 0.001
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_correlation_no_data_raises(mock_fetch):
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"""Raises ValueError when no data is returned."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame()
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import portfolio_service
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with pytest.raises(ValueError, match="No price data"):
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await portfolio_service.compute_correlation(["AAPL", "MSFT"], days=365)
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@pytest.mark.asyncio
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async def test_correlation_empty_symbols_raises():
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"""Raises ValueError for empty symbol list."""
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import portfolio_service
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with pytest.raises(ValueError, match="symbols"):
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await portfolio_service.compute_correlation([], days=365)
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# --- Risk Parity ---
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_risk_parity_happy_path(mock_fetch):
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"""Risk parity returns weights and risk_contributions summing to ~1.0."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame(
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{
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"AAPL": [150.0, 151.0, 149.0, 152.0, 153.0],
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"MSFT": [300.0, 302.0, 298.0, 305.0, 307.0],
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"GOOGL": [2800.0, 2820.0, 2790.0, 2830.0, 2850.0],
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}
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)
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import portfolio_service
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result = await portfolio_service.compute_risk_parity(
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["AAPL", "MSFT", "GOOGL"], days=365
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)
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assert result["method"] == "risk_parity"
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assert set(result["weights"].keys()) == {"AAPL", "MSFT", "GOOGL"}
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assert set(result["risk_contributions"].keys()) == {"AAPL", "MSFT", "GOOGL"}
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total_w = sum(result["weights"].values())
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assert abs(total_w - 1.0) < 0.01
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_risk_parity_single_symbol(mock_fetch):
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"""Single symbol gets weight 1.0 and risk_contribution 1.0."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame(
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{"AAPL": [150.0, 151.0, 149.0, 152.0, 153.0]}
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)
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import portfolio_service
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result = await portfolio_service.compute_risk_parity(["AAPL"], days=365)
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assert result["weights"]["AAPL"] == pytest.approx(1.0, abs=0.01)
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assert result["risk_contributions"]["AAPL"] == pytest.approx(1.0, abs=0.01)
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical_prices", new_callable=AsyncMock)
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async def test_risk_parity_no_data_raises(mock_fetch):
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"""Raises ValueError when no price data is available."""
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import pandas as pd
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mock_fetch.return_value = pd.DataFrame()
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import portfolio_service
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with pytest.raises(ValueError, match="No price data"):
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await portfolio_service.compute_risk_parity(["AAPL", "MSFT"], days=365)
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@pytest.mark.asyncio
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async def test_risk_parity_empty_symbols_raises():
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"""Raises ValueError for empty symbol list."""
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import portfolio_service
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with pytest.raises(ValueError, match="symbols"):
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await portfolio_service.compute_risk_parity([], days=365)
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# --- fetch_historical_prices helper ---
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical")
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async def test_fetch_historical_prices_returns_dataframe(mock_fetch_hist):
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"""fetch_historical_prices assembles a price DataFrame from OBBject results."""
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import pandas as pd
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from unittest.mock import MagicMock
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mock_result = MagicMock()
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mock_result.results = [
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MagicMock(date="2024-01-01", close=150.0),
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MagicMock(date="2024-01-02", close=151.0),
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]
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mock_fetch_hist.return_value = mock_result
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import portfolio_service
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df = await portfolio_service.fetch_historical_prices(["AAPL"], days=30)
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assert isinstance(df, pd.DataFrame)
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assert "AAPL" in df.columns
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@pytest.mark.asyncio
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@patch("portfolio_service.fetch_historical")
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async def test_fetch_historical_prices_skips_none(mock_fetch_hist):
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"""fetch_historical_prices returns empty DataFrame when all fetches fail."""
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import pandas as pd
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mock_fetch_hist.return_value = None
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import portfolio_service
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df = await portfolio_service.fetch_historical_prices(["AAPL", "MSFT"], days=30)
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assert isinstance(df, pd.DataFrame)
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assert df.empty
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