# ### Sampling date
# %%
sampling_date = dt.datetime(year=2019, month=12, day=31)
# %%
sampling_date = dt.datetime(year=2021, month=12, day=31)
# %% [markdown]
# ### Data
# %%
option = "logvar_capm_resid" # "spy_capm_decomp"
# %%
data = DataMap("../data")
df_idio_var = data.load_historic(sampling_date=sampling_date,
column="var_idio")
df_logvar_resid = data.load_historic(sampling_date=sampling_date,
column="logvar_capm_resid")
df_var = data.load_historic(sampling_date=sampling_date, column="var")
df_spy_var = data.load_spy_data(series="var").loc[df_idio_var.index]
df_info = data.load_asset_estimates(
sampling_date=sampling_date,
columns=["ticker", "comnam", "last_size", "mean_size"])
# %% [markdown]
# ### Tickers
# %%
ticker_list = (data.load_historic(
# import pytest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from euraculus.data import DataMap
datamap = DataMap(datapath="/home/rubelrennfix/projects/euraculus/data")
class TestPrepareLogVariances:
"""This class serves to test various cases of preparing log variance data."""
def test_full_column(self):
df_var = pd.DataFrame(data=[[1], [1], [1]])
df_noisevar = pd.DataFrame(data=[[2], [2], [2]])
output = datamap.prepare_log_variances(df_var=df_var, df_noisevar=df_noisevar)
expected = np.log(pd.DataFrame(data=[[1], [1], [1]]))
assert_frame_equal(output, expected)
def test_empty_column(self):
_ = np.nan
df_var = pd.DataFrame(data=[[_], [_], [_]])
df_noisevar = pd.DataFrame(data=[[_], [_], [_]])
output = datamap.prepare_log_variances(df_var=df_var, df_noisevar=df_noisevar)
expected = np.log(pd.DataFrame(data=[[_], [_], [_]]))
assert_frame_equal(output, expected)
def test_zero_column(self):
df_var = pd.DataFrame(data=[[0], [0], [0]])