def test_cov_to_corr():
df = get_data()
rets = risk_models.returns_from_prices(df).dropna()
test_corr = risk_models.cov_to_corr(rets.cov())
pd.testing.assert_frame_equal(test_corr, rets.corr())
with pytest.warns(RuntimeWarning) as w:
test_corr_numpy = risk_models.cov_to_corr(rets.cov().values)
assert len(w) == 1
assert str(w[0].message) == "cov_matrix is not a dataframe"
np.testing.assert_array_almost_equal(test_corr_numpy, rets.corr().values)
def plot_covariance(cov_matrix,
plot_correlation=False,
show_tickers=True,
ax=None):
"""Generate a basic plot of the covariance (or correlation) matrix,
given a covariance matrix.
Args:
cov_matrix (pd.DataFrame, np.ndarray): covariance matrix
plot_correlation (bool): whether to plot the correlation matrix
instead, defaults to False. Optional.
show_tickers (bool): whether to use tickers as labels (not
recommended for large portfolios). Optional. Defaults to True.
ax (matplolib.axis, None): Axis to plot on. Optional. New axis will
be created if none is specified.
Returns:
matplotlib axis
"""
if plot_correlation:
matrix = risk_models.cov_to_corr(cov_matrix)
else:
matrix = cov_matrix
if ax is None:
_, ax = plt.subplots()
if show_tickers:
ax.set_xticks(np.arange(0, matrix.shape[0], 1))
ax.set_xticklabels(matrix.index)
ax.set_yticks(np.arange(0, matrix.shape[0], 1))
ax.set_yticklabels(matrix.index)
plt.xticks(rotation=90)
return ax
def test_corr_to_cov():
df = get_data()
rets = risk_models.returns_from_prices(df).dropna()
test_corr = risk_models.cov_to_corr(rets.cov())
new_cov = risk_models.corr_to_cov(test_corr, rets.std())
pd.testing.assert_frame_equal(new_cov, rets.cov())
with pytest.warns(RuntimeWarning) as w:
cov_numpy = risk_models.corr_to_cov(test_corr.to_numpy(), rets.std())
assert len(w) == 1
assert str(w[0].message) == "corr_matrix is not a dataframe"
assert isinstance(cov_numpy, pd.DataFrame)
np.testing.assert_equal(cov_numpy.to_numpy(), new_cov.to_numpy())
def test_corr_to_cov():
df = get_data()
rets = risk_models.returns_from_prices(df).dropna()
test_corr = risk_models.cov_to_corr(rets.cov())
new_cov = risk_models.corr_to_cov(test_corr, rets.std())
pd.testing.assert_frame_equal(new_cov, rets.cov())
def CorrMap(sht, nameplot, S, color):
fig = plt.figure(figsize=(8, 8))
corrmatrice = risk_models.cov_to_corr(S)
ax = sns.heatmap(corrmatrice, annot=True, cmap=color)
fig = ax.get_figure()
sht.pictures.add(fig, name=nameplot, update=True)
def Markmain():
#Set Sheets
sht = xw.Book.caller().sheets['Optim']
shtdata = xw.Book.caller().sheets['Data']
shtfodata = xw.Book.caller().sheets['FoDatas']
sht.range('F17').value = 'Optimizing...'
#Clear Values
sht.range('D23').expand().clear_contents()
shtdata.range('A1').expand().clear_contents()
shtdata.range('J1').expand().clear_contents()
#Datas
listticker = xw.Range('B3').expand().value
startdate = sht.range('F3').value
enddate = sht.range('F6').value
traintestdate = sht.range(
'F4'
).value #Dataset is divided in two sub: train (optimization) and test for backtest
#train, test = initialize(startdate, enddate, traintestdate, listticker)
train, test, tickers = loaddata(shtfodata, startdate, enddate,
traintestdate)
#Copy Data in Data Range
shtdata.range((1, 1)).value = train
shtdata.range((1, len(tickers) + 3)).value = test
#Set variables
ModelOptim = sht.range('F7').value
ReturnModel = sht.range('F8').value
RiskModel = sht.range('F9').value
RFrate = sht.range('F10').value
MinWeight = sht.range('F11').value
MaxWeight = sht.range('F12').value
InitialAmountInPortfolio = sht.range('F13').value
RefIndex = sht.range('F14').value
Gamma = sht.range('F15').value
EFBool = sht.range('F16').value
pltweights, rdf, cleanW, S, mu, perf = optimMarkowitz(
train, test, MinWeight, MaxWeight, ModelOptim, ReturnModel, RiskModel,
Gamma, RFrate)
numshares, left, listshares = getoptimprices(train, cleanW,
InitialAmountInPortfolio)
#Visualisation
corrmatrice = risk_models.cov_to_corr(S)
CorrMap(sht, 'CorrMatMark', corrmatrice, 'YlGnBu')
backtest_static(pltweights, rdf, ModelOptim, traintestdate, enddate,
RefIndex, sht)
if EFBool == "YES":
effrontier(mu, S, sht, 'EFMark')
#sht.range('F23').options(transpose=True).value = listshares
sht.range('D23').options(transpose=True).value = tickers
sht.range('E23').options(transpose=True).value = pltweights
sht.charts['MarkoWeights'].set_source_data(
sht.range((23, 4), (22 + len(tickers), 5)))
sht.range('D21').value = perf
sht.range('F17').value = 'Optimization Done'
