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()
def ta_mean_returns(df: Typing.PatchedPandas, period=20) -> _PANDAS:
return _wcs(f"mean_return_{period}",
df.pct_change().rolling(period).mean())
def ta_performance(df: Typing.PatchedPandas):
delta = df.pct_change() + 1
return delta.cumprod()
def ta_returns(df: Typing.PatchedPandas, period=1):
return _wcs("return", df.pct_change(periods=period))
def ta_returns(df: Typing.PatchedPandas):
return _wcs("return", df.pct_change())
