def test_corr_sanity(self):
# GH 3155
df = DataFrame(
np.array(
[
[0.87024726, 0.18505595],
[0.64355431, 0.3091617],
[0.92372966, 0.50552513],
[0.00203756, 0.04520709],
[0.84780328, 0.33394331],
[0.78369152, 0.63919667],
]
)
)
res = mom.rolling_corr(df[0], df[1], 5, center=True)
self.assertTrue(all([np.abs(np.nan_to_num(x)) <= 1 for x in res]))
# and some fuzzing
for i in range(10):
df = DataFrame(np.random.rand(30, 2))
res = mom.rolling_corr(df[0], df[1], 5, center=True)
try:
self.assertTrue(all([np.abs(np.nan_to_num(x)) <= 1 for x in res]))
except:
print(res)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index,
major_axis=df.columns,
minor_axis=df.columns)
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(
x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(
x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5
),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def test_rolling_corr_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = mom.rolling_corr(s1, s2, window=3, min_periods=2)
expected = Series([None, None, 1.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = mom.rolling_corr(s1, s2a, window=3, min_periods=2)
assert_series_equal(result, expected)
def test_rolling_corr_diff_length(self):
# GH 7512
s1 = Series([1, 2, 3], index=[0, 1, 2])
s2 = Series([1, 3], index=[0, 2])
result = mom.rolling_corr(s1, s2, window=3, min_periods=2)
expected = Series([None, None, 1.0])
assert_series_equal(result, expected)
s2a = Series([1, None, 3], index=[0, 1, 2])
result = mom.rolling_corr(s1, s2a, window=3, min_periods=2)
assert_series_equal(result, expected)
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = moments.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
a = tm.makeTimeSeries()
b = tm.makeTimeSeries()
a[:5] = np.nan
b[:10] = np.nan
result = moments.rolling_corr(a, b, len(a), min_periods=1)
assert_almost_equal(result[-1], a.corr(b))
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = mom.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
# test for correct bias correction
a = tm.makeTimeSeries()
b = tm.makeTimeSeries()
a[:5] = np.nan
b[:10] = np.nan
result = mom.rolling_corr(a, b, len(a), min_periods=1)
assert_almost_equal(result[-1], a.corr(b))
def test_rolling_corr_pairwise(self):
panel = mom.rolling_corr_pairwise(self.frame, 10, min_periods=5)
correl = panel.ix[:, 1, 5]
exp = mom.rolling_corr(self.frame[1], self.frame[5],
10, min_periods=5)
tm.assert_series_equal(correl, exp)
def test_rolling_corr_pairwise(self):
panel = mom.rolling_corr_pairwise(self.frame, 10, min_periods=5)
correl = panel.ix[:, 1, 5]
exp = mom.rolling_corr(self.frame[1], self.frame[5],
10, min_periods=5)
tm.assert_series_equal(correl, exp)
def test_corr_sanity(self):
#GH 3155
df = DataFrame(
np.array([[0.87024726, 0.18505595], [0.64355431, 0.3091617],
[0.92372966, 0.50552513], [0.00203756, 0.04520709],
[0.84780328, 0.33394331], [0.78369152, 0.63919667]]))
res = mom.rolling_corr(df[0], df[1], 5, center=True)
self.assertTrue(all([np.abs(np.nan_to_num(x)) <= 1 for x in res]))
# and some fuzzing
for i in range(10):
df = DataFrame(np.random.rand(30, 2))
res = mom.rolling_corr(df[0], df[1], 5, center=True)
print(res)
self.assertTrue(all([np.abs(np.nan_to_num(x)) <= 1 for x in res]))
def test_expanding_corr_pairwise(self):
result = mom.expanding_corr(self.frame)
rolling_result = mom.rolling_corr(self.frame, len(self.frame), min_periods=1)
for i in result.items:
assert_almost_equal(result[i], rolling_result[i])
def test_expanding_corr_pairwise(self):
result = mom.expanding_corr(self.frame)
rolling_result = mom.rolling_corr(self.frame,
len(self.frame),
min_periods=1)
for i in result.items:
assert_almost_equal(result[i], rolling_result[i])
def test_expanding_corr(self):
A = self.series.dropna()
B = (A + randn(len(A)))[:-5]
result = mom.expanding_corr(A, B)
rolling_result = mom.rolling_corr(A, B, len(A), min_periods=1)
assert_almost_equal(rolling_result, result)
def test_expanding_corr(self):
A = self.series.dropna()
B = (A + randn(len(A)))[:-5]
result = mom.expanding_corr(A, B)
rolling_result = mom.rolling_corr(A, B, len(A), min_periods=1)
assert_almost_equal(rolling_result, result)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)
functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
from pandas.core.common import isnull
from pandas.core.config import option_context
db = InfluxDB(db_name='Econ')
series_1 = db.interpret('cpi_us_owners_equiv_yoy')
series_2 = db.interpret('avg(zillow_median_sale_price_yoy,M)')
series_1.rename(columns={'value':'value_1'}, inplace=True)
series_2.rename(columns={'value':'value_2'}, inplace=True)
periods = 60
analysis_type = 'snapshot'
if(analysis_type=='snapshot'):
for i in range(-36, 37): # go back i periods
shifted_1 = series_1.shift(i)
combined = concat([shifted_1['value_1'],series_2['value_2']],axis=1,join='outer')
combined = combined.loc[isnull(combined['value_1']) != True]
combined = combined.loc[isnull(combined['value_2']) != True]
results = rolling_corr(combined['value_1'],combined['value_2'],window=periods)
print '%d,%.4f' % (i, results[-1:].values)
elif(analysis_type=='history'):
shifted_1 = series_1.shift(0)
combined = concat([shifted_1['value_1'],series_2['value_2']],axis=1,join='outer')
combined = combined.loc[isnull(combined['value_1']) != True]
combined = combined.loc[isnull(combined['value_2']) != True]
results = rolling_corr(combined['value_1'],combined['value_2'],window=periods)
with option_context('display.max_rows', 9999, 'display.max_columns', 3):
print results
else:
print 'type %s not recognized' % analysis_type
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = moments.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
def decide_by_history(self, x, last_b=None):
self.record_history(x)
window = self.window
port = pd.DataFrame(self.history)
n, m = port.shape
weights = 1. / m * np.ones(port.shape)
CORR, EX = rolling_corr(port, port.shift(window))
if self.c_version:
try:
from scipy import weave
except ImportError:
warnings.warn(
'scipy.weave is not available in python3, falling back to python version'
)
self.c_version = False
if self.c_version is False:
for t in range(n - 1):
M = CORR[t, :, :]
mu = EX[t, :]
# claim[i,j] is claim from stock i to j
claim = np.zeros((m, m))
for i in range(m):
for j in range(m):
if i == j: continue
if mu[i] > mu[j] and M[i, j] > 0:
claim[i, j] += M[i, j]
# autocorrelation
if M[i, i] < 0:
claim[i, j] += abs(M[i, i])
if M[j, j] < 0:
claim[i, j] += abs(M[j, j])
# calculate transfer
transfer = claim * 0.
for i in range(m):
total_claim = sum(claim[i, :])
if total_claim != 0:
transfer[i, :] = weights[t,
i] * claim[i, :] / total_claim
# update weights
weights[t + 1, :] = weights[t, :] + np.sum(
transfer, axis=0) - np.sum(transfer, axis=1)
else:
def get_weights_c(c, mu, w):
code = """
int t,i,j;
float claim [Nc[1]] [Nc[1]];
float transfer [Nc[1]] [Nc[1]];
for (t=0; t<Nc[0]-1; t++) {
for (i=0; i<Nc[1]; i++) {
for (j=0; j<Nc[1]; j++) {
claim[i][j] = 0.;
transfer[i][j] = 0.;
}
}
for (i=0; i<Nc[1]; i++) {
for (j=0; j<Nc[1]; j++) {
if(i != j){
if(MU2(t,i) > MU2(t,j) && C3(t,i,j) > 0){
claim[i][j] += C3(t,i,j);
if(C3(t,i,i) < 0)
claim[i][j] -= C3(t,i,i);
if(C3(t,j,j) < 0)
claim[i][j] -= C3(t,j,j);
}
}
}
}
for (i=0; i<Nc[1]; i++) {
float total_claim=0.;
for (j=0; j<Nc[1]; j++) {
total_claim += claim[i][j];
}
if(total_claim != 0){
for (j=0; j<Nc[1]; j++) {
transfer[i][j] = W2(t,i) * claim[i][j] / total_claim;
}
}
}
for (i=0; i<Nc[1]; i++) {
W2(t+1,i) = W2(t,i);
for (j=0; j<Nc[1]; j++) {
W2(t+1,i) += transfer[j][i] - transfer[i][j];
}
}
}
"""
return weave.inline(code, ['c', 'mu', 'w'])
get_weights_c(CORR, EX, weights)
return weights[-1, :]
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
result = moments.rolling_corr(A, B, 50, min_periods=25)
assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])
