本文整理匯總了Python中pandas.ols方法的典型用法代碼示例。如果您正苦於以下問題：Python pandas.ols方法的具體用法？Python pandas.ols怎麽用？Python pandas.ols使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas的用法示例。
在下文中一共展示了pandas.ols方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_window_ols_full
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def test_window_ols_full(ols_data):
y, x = ols_data['y'], ols_data['x']
res1 = _window_ols(y, x, window_type='full_sample')
res2 = _window_ols(y, x)
res3 = pd.ols(y=y, x=x, window_type='full_sample')
assert_ols_equal(res1, res2)
assert_ols_equal(res1, res3) 示例2: test_window_ols_rolling
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def test_window_ols_rolling(ols_data):
y, x = ols_data['y'], ols_data['x']
res1 = _window_ols(y, x, window_type='rolling', window=100)
res2 = pd.ols(y=y, x=x, window_type='rolling', window=100)
assert_ols_equal(res1, res2) 示例3: test_window_ols_expanding
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def test_window_ols_expanding(ols_data):
y, x = ols_data['y'], ols_data['x']
res1 = _window_ols(y, x, window_type='expanding')
res2 = pd.ols(y=y, x=x, window_type='expanding')
assert_ols_equal(res1, res2) 示例4: test_solve_rect
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def test_solve_rect(self):
if not _have_statsmodels:
raise nose.SkipTest("no statsmodels")
b = Series(np.random.randn(N), self.frame.index)
result = pmath.solve(self.frame, b)
expected = ols(y=b, x=self.frame, intercept=False).beta
self.assert_(np.allclose(result, expected)) 示例5: __init__
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def __init__(self, y_data, x_data, lookback):
TechnicalIndicator.__init__(self)
self.y_data = y_data
self.x_data = x_data
self.lookback = lookback
self.value = 'PAIR_%s_%s_%s' %(y_data, x_data, lookback)
self.ols = self.value
self.hedge_ratio = 'HEDGE_RATIO_%s_%s_%s' %(y_data, x_data, lookback)
self.spread = 'SPREAD_%s_%s_%s' %(y_data, x_data, lookback)
self.zscore = 'ZSCORE_%s_%s_%s' %(y_data, x_data, lookback)
self.logger.info('Initialized - %s' %self) 示例6: results
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def results(self, data_frame):
y_value = data_frame[self.y_data]
x_value = data_frame[self.x_data]
if self.lookback >= len(x_value):
return ([self.value, self.hedge_ratio, self.spread, self.zscore], \
[pd.Series(np.nan), pd.Series(np.nan), pd.Series(np.nan), pd.Series(np.nan)])
ols_result = pd.ols(y=y_value, x=x_value, window=self.lookback)
hedge_ratio = ols_result.beta['x']
spread = y_value - hedge_ratio * x_value
data_frame[self.value] = ols_result.resid
data_frame[self.hedge_ratio] = hedge_ratio
data_frame[self.spread] = spread
data_frame[self.zscore] = (spread - \
pd.rolling_mean(spread, self.lookback)) / \
pd.rolling_std(spread, self.lookback) 示例7: equations
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def equations(self):
eqs = {}
for col, ts in iteritems(self.y):
# TODO: Remove in favor of statsmodels implemetation
model = pd.ols(y=ts, x=self.x, window=self._window,
window_type=self._window_type,
min_periods=self._min_periods)
eqs[col] = model
return eqs 示例8: get_alpha_beta
# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import ols [as 別名]
def get_alpha_beta(self, bm_rets):
if isinstance(bm_rets, pd.Series):
bm = CumulativeRets(bm_rets)
elif isinstance(bm_rets, CumulativeRets):
bm = bm_rets
else:
raise ValueError('bm_rets must be series or CumulativeRetPerformace not %s' % (type(bm_rets)))
bm_freq = guess_freq(bm_rets)
if self.pds_per_year != bm.pds_per_year:
tgt = {'B': 'dly', 'W': 'weekly', 'M': 'monthly', 'Q': 'quarterly', 'A': 'annual'}.get(bm_freq, None)
if tgt is None:
raise ValueError('No mapping for handling benchmark with frequency: %s' % bm_freq)
tmp = getattr(self, tgt)
y = tmp.rets
y_ann = tmp.ltd_ann
else:
y = self.rets
y_ann = self.ltd_ann
x = bm.rets.truncate(y.index[0], y.index[-1])
x_ann = bm.ltd_ann
model = pd.ols(x=x, y=y)
beta = model.beta[0]
alpha = y_ann - beta * x_ann
return pd.Series({'alpha': alpha, 'beta': beta}, name=bm_freq)