def __init__(self, alpha):
if isinstance(alpha, AlphaBase):
self.alpha = alpha.get_alphas()
else:
self.alpha = api.format(alpha)
self.alpha = self.alpha[np.isfinite(self.alpha)]
self.startdate = self.alpha.index[0]
def __init__(self, alpha):
if isinstance(alpha, AlphaBase):
self.alpha = alpha.get_alphas()
else:
self.alpha = api.format(alpha)
self.alpha = self.alpha[np.isfinite(self.alpha)]
self.startdate = self.alpha.index[0]
def test_format(self):
df1 = pd.DataFrame(np.random.randn(20, 40),
index=DATES[:20],
columns=SIDS[:40])
df2 = api.format(df1)
self.assertTrue((df2.index == pd.to_datetime(df1.index)).all() and \
(list(df2.columns) == SIDS))
def get_alphas(self):
"""Return the generated alphas in a DataFrame."""
if self._alphas is not None:
return self._alphas
df = format(pd.DataFrame(self.alphas).T, full_sids=False)
self._alphas = df
return df
def __init__(self, alpha):
if isinstance(alpha, AlphaBase):
self.alpha = alpha.get_alphas()
else:
self.alpha = api.format(alpha)
self.dates = np.unique(self.alpha.index.date)
self.startdate = self.dates[0]
self.freq = len(self.alpha) / len(self.dates)
self.freq = str(240 / self.freq) + 'min'
def __init__(self, alpha):
if isinstance(alpha, AlphaBase):
self.alpha = alpha.get_alphas()
else:
self.alpha = api.format(alpha)
self.dates = np.unique(self.alpha.index.date)
self.startdate = self.dates[0]
self.freq = len(self.alpha) / len(self.dates)
self.freq = str(240 / self.freq) + 'min'
def __init__(self, alpha, n, rank=None):
self.alpha = api.format(alpha)
self.rank_alpha = self.alpha.rank(axis=1, ascending=False)
self.rank_alpha = self.rank_alpha[self.rank_alpha <= n]
if rank is None:
self.alpha = (self.rank_alpha <= n).astype(float)
else:
if rank < 0:
self.alpha = self.alpha[self.rank_alpha <= n]
else:
self.alpha = rank - np.floor(self.rank_alpha / (n + 1) * rank)
self.alpha = api.scale(self.alpha)
self.dates = dateutil.to_datestr(self.alpha.index)
def set_index_components(cls, index, components):
"""Call this method to set index components data so that for future uses, there is no need to interact with MongoDB."""
with cls.mongo_lock:
cls.index_components[index] = api.format(components).fillna(False)
def set_returns(cls, returns):
"""Call this method to set returns so that for future uses, there is no need to interact with MongoDB."""
with cls.mongo_lock:
cls.returns = api.format(returns)
def set_index_components(cls, index, components):
"""Call this method to set index components data so that for future uses, there is no need to interact with MongoDB."""
with cls.mongo_lock:
cls.index_components[index] = api.format(components).fillna(False)
def set_returns(cls, returns):
"""Call this method to set returns so that for future uses, there is no need to interact with MongoDB."""
with cls.mongo_lock:
cls.returns = api.format(returns)