File size: 5,228 Bytes
ce4bc73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
from unittest.mock import patch
import pandas as pd
import pytest
from src.folio.utils import get_beta
# Test categories based on check_beta.py:
# 1. Money market funds (SPAXX**, FMPXX) - should have beta ≈ 0
# 2. Low volatility instruments (TLT, SHY, BIL) - should have beta near 0
# 3. Market correlated ETFs (MCHI, IEFA) - should have significant beta
# 4. High beta stocks (AAPL, GOOGL) - should have beta > 1
# 5. Market benchmark (SPY) - should have beta ≈ 1
@pytest.fixture
def mock_data_fetcher():
"""Create a mock DataFetcher that can be configured per test."""
with patch("src.folio.utils.data_fetcher") as mock:
yield mock
def create_price_data(returns_data, base_price=100.0):
"""Helper to create price data from a list of returns."""
prices = [base_price]
for ret in returns_data:
prices.append(prices[-1] * (1 + ret))
return pd.DataFrame({"Close": prices})
def test_money_market_fund(mock_data_fetcher):
"""Test that instruments with constant price return beta of 0."""
# Money market funds typically have constant or near-constant prices
constant_prices = pd.DataFrame({"Close": [1.00] * 100})
market_data = create_price_data([0.01] * 99) # Market with some movement
mock_data_fetcher.fetch_data.return_value = constant_prices
mock_data_fetcher.fetch_market_data.return_value = market_data
beta = get_beta("SPAXX")
assert beta == 0.0
def test_low_volatility_instrument(mock_data_fetcher):
"""Test that instruments with very low correlation to market have near-zero beta."""
# Create price data with very small, uncorrelated movements
tiny_movements = [0.0001 if i % 2 == 0 else -0.0001 for i in range(100)]
market_movements = [0.01 if i % 3 == 0 else -0.01 for i in range(100)]
mock_data_fetcher.fetch_data.return_value = create_price_data(tiny_movements)
mock_data_fetcher.fetch_market_data.return_value = create_price_data(
market_movements
)
beta = get_beta("TLT")
assert abs(beta) < 0.1 # Low volatility instruments should have beta near 0
def test_market_correlated_etf(mock_data_fetcher):
"""Test that market-correlated ETFs have significant positive beta."""
# Create correlated but dampened market movements
market_moves = [0.01, -0.01, 0.02, -0.015, 0.025] * 20
etf_moves = [0.007, -0.007, 0.014, -0.011, 0.018] * 20 # ~0.7x market moves
mock_data_fetcher.fetch_data.return_value = create_price_data(etf_moves)
mock_data_fetcher.fetch_market_data.return_value = create_price_data(market_moves)
beta = get_beta("MCHI")
assert 0.5 < beta < 1.0 # Should have significant but less than market beta
def test_high_beta_stock(mock_data_fetcher):
"""Test that volatile stocks can have beta > 1."""
# Create amplified market movements
market_moves = [0.01, -0.01, 0.02, -0.015, 0.025] * 20
stock_moves = [0.015, -0.015, 0.03, -0.022, 0.037] * 20 # ~1.5x market moves
mock_data_fetcher.fetch_data.return_value = create_price_data(stock_moves)
mock_data_fetcher.fetch_market_data.return_value = create_price_data(market_moves)
beta = get_beta("AAPL")
assert beta > 1.0 # Should have higher than market beta
def test_market_benchmark(mock_data_fetcher):
"""Test that perfectly correlated instrument has beta ≈ 1."""
# Use exact same movements for both
moves = [0.01, -0.01, 0.02, -0.015, 0.025] * 20
mock_data_fetcher.fetch_data.return_value = create_price_data(moves)
mock_data_fetcher.fetch_market_data.return_value = create_price_data(moves)
beta = get_beta("SPY")
assert abs(beta - 1.0) < 0.01 # Should be very close to 1.0
def test_insufficient_data(mock_data_fetcher):
"""Test that instruments with insufficient data points return beta of 0."""
mock_data_fetcher.fetch_data.return_value = create_price_data(
[0.01]
) # Only 2 prices
mock_data_fetcher.fetch_market_data.return_value = create_price_data([0.01])
beta = get_beta("NEWSTOCK")
assert beta == 0.0
def test_data_fetch_failure(mock_data_fetcher):
"""Test that data fetching failures raise appropriate errors."""
mock_data_fetcher.fetch_data.return_value = None
with pytest.raises(RuntimeError, match="Failed to fetch data"):
get_beta("INVALID")
def test_all_null_data(mock_data_fetcher):
"""Test that all-null data returns beta of 0.0."""
invalid_data = pd.DataFrame({"Close": [None, None, None]})
mock_data_fetcher.fetch_data.return_value = invalid_data
mock_data_fetcher.fetch_market_data.return_value = invalid_data
beta = get_beta("BADDATA")
assert beta == 0.0 # Should return 0.0 as we can't calculate meaningful beta
def test_invalid_data_format(mock_data_fetcher):
"""Test that data with invalid format raises appropriate errors."""
# Data without required 'Close' column
invalid_data = pd.DataFrame({"Wrong_Column": [1, 2, 3]})
mock_data_fetcher.fetch_data.return_value = invalid_data
mock_data_fetcher.fetch_market_data.return_value = create_price_data([0.01] * 10)
with pytest.raises(KeyError): # Should raise KeyError when trying to access 'Close'
get_beta("BADFORMAT")
|