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 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())
'secret': '',
'enableRateLimit': True
})
ohlcv = exchange.fetch_ohlcv(i, timeframe, limit=limit)
ohlcv = exchange.convert_ohlcv_to_trading_view(ohlcv)
df = pd.DataFrame(ohlcv)
df.t = df.t.apply(lambda x: datetime.datetime.fromtimestamp(x))
df = df.dropna()
df = df.set_index(df['t'])
df = df.drop(['t'], axis=1)
dataset = df
dataset = dataset.dropna()
data_[i] = dataset.c
data_.dropna(axis=1, inplace=True)
returns = risk_models.returns_from_prices(data_, log_returns=True)
S = CovarianceShrinkage(data_, frequency=180).ledoit_wolf()
hrp = HRPOpt(returns, cov_matrix=S)
# weights = hrp.optimize()
# hrp.portfolio_performance(verbose=True);
weights = {w: 1 / len(pair) for w in pair}
hrp.set_weights(weights)
w = hrp.portfolio_performance(verbose=True, frequency=180)
st.write("Expected annual return: {:.2f}%".format(w[0] * 100))
st.write("Annual volatility: {:.2f}%".format(w[1] * 100))
st.write("Sharpe Ratio: {:.2f}".format(w[2]))
returns = risk_models.returns_from_prices(data_, log_returns=True)
returns["sum"] = returns.sum(axis=1)