def ta_z_norm(df: Typing.PatchedPandas,
period=200,
ddof=1,
demean=True,
lag=0):
# (value - mean) / std
s = df.rolling(period).std(ddof=ddof)
a = (df - df.rolling(period).mean().shift(lag)) if demean else df
return (a / s / 4).rename(df.name)
def ta_z_norm(df: Typing.PatchedPandas, period=200, ddof=1, demean=True, lag=0):
if has_indexed_columns(df):
return pd.DataFrame(
{col: ta_z_norm(df[col], period, ddof, demean) for col in df.columns},
index=df.index
)
# (value - mean) / std
s = df.rolling(period).std(ddof=ddof)
a = (df - df.rolling(period).mean().shift(lag)) if demean else df
return (a / s / 4).rename(df.name)
def ta_ncdf_compress(df: Typing.PatchedPandas, period=200, lower_percentile=25, upper_percentile=75) -> Typing.PatchedPandas:
if has_indexed_columns(df):
return pd.DataFrame(
{col: ta_ncdf_compress(df[col], period, lower_percentile, upper_percentile) for col in df.columns},
index=df.index
)
f50 = df.rolling(period).mean().rename("f50")
fup = df.rolling(period).apply(lambda x: np.percentile(x, upper_percentile)).rename("fup")
flo = df.rolling(period).apply(lambda x: np.percentile(x, lower_percentile)).rename("flo")
return pd.Series(norm.cdf(0.5 * (df - f50) / (fup - flo)) - 0.5, index=df.index, name=df.name)
def ta_ncdf_compress(df: Typing.PatchedPandas,
period=200,
lower_percentile=25,
upper_percentile=75) -> Typing.PatchedPandas:
f50 = df.rolling(period).mean().rename("f50")
fup = df.rolling(period).apply(
lambda x: np.percentile(x, upper_percentile)).rename("fup")
flo = df.rolling(period).apply(
lambda x: np.percentile(x, lower_percentile)).rename("flo")
return pd.Series(norm.cdf(0.5 * (df - f50) / (fup - flo)) - 0.5,
index=df.index,
name=df.name)
