def ta_volume_as_time(df: _t.PatchedPandas, volume="Volume"):
if df.ndim > 1:
res = df.copy()
res.index = df[volume].cumsum()
return res
else:
return _pd.Series(df.index, index=df.cumsum())
def ta_week(po: _t.PatchedPandas):
if not isinstance(po.index, _pd.DatetimeIndex):
df = po.copy()
df.index = _pd.to_datetime(df.index)
else:
df = po
return (df.index.week / 52.0).to_series(index=po.index, name="week")
def ta_sinusoidial_week_day(po: _t.PatchedPandas):
if not isinstance(po.index, _pd.DatetimeIndex):
df = po.copy()
df.index = _pd.to_datetime(df.index)
else:
df = po
return _np.sin(2 * _np.pi * (df.index.dayofweek / 6.0)).to_series(
index=po.index, name="dow")
def ta_sinusoidal_week(po: _t.PatchedPandas):
if not isinstance(po.index, _pd.DatetimeIndex):
df = po.copy()
df.index = _pd.to_datetime(df.index)
else:
df = po
return _np.sin(2 * _np.pi *
(df.index.isocalendar().week / 52.0)).rename("week")
def ta_ewma_covariance(df: Typing.PatchedPandas,
convert_to='returns',
alpha=0.97):
data = df.copy()
if convert_to == 'returns':
data = df.pct_change()
if convert_to == 'log-returns':
data = _np.log(df) - _np.log(df.shift(1))
data.columns = data.columns.to_list()
return data.ewm(com=alpha).cov()
