def test_quasi_diag():
prices = get_data()
# compute returns
returns = prices.pct_change().dropna(axis=0, how="all").fillna(0.0)
np.testing.assert_allclose(returns.cov().values,
np.genfromtxt(resource("covariance2.csv")))
np.testing.assert_allclose(returns.corr().values,
np.genfromtxt(resource("correlation2.csv")))
cor = returns.corr().values
links = linkage(dist(cor), method="single")
# uncomment this line if you want to generate a new test resource
# np.savetxt(resource("links.csv"), links, delimiter=",")
np.testing.assert_array_almost_equal(
links, np.loadtxt(resource("links.csv"), delimiter=','))
node = tree(links)
ids = node.pre_order()
assert ids == [
11, 7, 19, 6, 14, 5, 10, 13, 3, 1, 4, 16, 0, 2, 17, 9, 8, 18, 12, 15
]
ordered_tickers = prices.keys()[ids].to_list()
print(ordered_tickers)
assert ordered_tickers == [
'UAA', 'WMT', 'SBUX', 'AMD', 'RRC', 'GE', 'T', 'XOM', 'BABA', 'AAPL',
'AMZN', 'MA', 'GOOG', 'FB', 'PFE', 'GM', 'BAC', 'JPM', 'SHLD', 'BBY'
]
def test_hrp():
prices = get_data()
root = hrp(prices=prices, method="ward")
x = pd.read_csv(resource("weights_hrp.csv"),
squeeze=True,
index_col=0,
header=0)
x.index.name = None
pd.testing.assert_series_equal(x, root.weights, check_exact=False)
def test_hrp():
prices = get_data()
variance, weights = hrp(prices=prices)
w = pd.Series(index=prices.keys(), data=weights,
name="Weights").sort_index()
x = pd.read_csv(resource("weights_hrp.csv"),
squeeze=True,
index_col=0,
header=None)
x.name = "Weights"
x.index.name = None
pd.testing.assert_series_equal(x, w, check_exact=False)
def test_hrp():
prices = get_data()
root = hrp(prices=prices)
# uncomment this line if you want generating a new file
# root.weights_series(index=list(prices.keys())).to_csv(resource("weights_hrp.csv"), header=False)
x = pd.read_csv(resource("weights_hrp.csv"),
squeeze=True,
index_col=0,
header=None)
x.name = "Weights"
x.index.name = None
pd.testing.assert_series_equal(x, root.weights, check_exact=False)