def hrp(my_portfolio, perf=True) -> list:
# changed to take in desired timeline, the problem is that it would use all historical data
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
prices = prices.filter(my_portfolio.portfolio)
# sometimes we will pick a date range where company isn't public we can't set price to 0 so it has to go to 1
prices = prices.fillna(1)
rets = expected_returns.returns_from_prices(prices)
hrp = HRPOpt(rets)
hrp.optimize()
weights = hrp.clean_weights()
wts = weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
hrp.portfolio_performance(verbose=True)
return flatten(result)
def test_portfolio_performance():
df = get_data()
returns = df.pct_change().dropna(how="all")
hrp = HRPOpt(returns)
with pytest.raises(ValueError):
hrp.portfolio_performance()
hrp.optimize()
assert hrp.portfolio_performance()
def test_portfolio_performance():
df = get_data()
returns = df.pct_change().dropna(how="all")
hrp = HRPOpt(returns)
with pytest.raises(ValueError):
hrp.portfolio_performance()
hrp.optimize()
np.testing.assert_allclose(
hrp.portfolio_performance(),
(0.21353402380950973, 0.17844159743748936, 1.084579081272277),
)
def HRP():
sht = xw.Book.caller().sheets['Optim']
sht.range('N17').value = 'Optimizing...'
listticker = sht.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 = train.pct_change().dropna(), test.pct_change().dropna()
hrp = HRPOpt(train)
weights = hrp.optimize()
perf = hrp.portfolio_performance(verbose=False)
fig = plt.figure(figsize=(8, 8))
ax = hrp.plot_dendrogram(showfig=False) # to plot dendrogram
fig = ax.get_figure()
sht.range('P23').value = weights
sht.range('P21').value = perf
#Visualisation
sht.pictures.add(fig, name="HRPCluster", update=True)
sht.charts['HRPweights'].set_source_data(
sht.range((23, 16), (22 + len(listticker), 17)))
#Done
sht.range('N17').value = 'Optimization Done'
def test_pass_cov_matrix():
df = get_data()
S = CovarianceShrinkage(df).ledoit_wolf()
hrp = HRPOpt(cov_matrix=S)
hrp.optimize()
perf = hrp.portfolio_performance()
assert perf[0] is None and perf[2] is None
np.testing.assert_almost_equal(perf[1], 0.10002783894982334)
'RRC': 0.0,
'BBY': 0.0,
'MA': 0.0,
'PFE': 0.0,
'JPM': 0.14379,
'SBUX': 0.0}
Expected annual return: 15.3%
Annual volatility: 28.7%
Sharpe Ratio: 0.46
"""
# Hierarchical risk parity
hrp = HRPOpt(returns)
weights = hrp.optimize()
hrp.portfolio_performance(verbose=True)
print(weights)
plotting.plot_dendrogram(hrp) # to plot dendrogram
"""
Expected annual return: 10.8%
Annual volatility: 13.2%
Sharpe Ratio: 0.66
{'AAPL': 0.022258941278778397,
'AMD': 0.02229402179669211,
'AMZN': 0.016086842079875,
'BABA': 0.07963382071794091,
'BAC': 0.014409222455552262,
'BBY': 0.0340641943824504,
'FB': 0.06272994714663534,
'GE': 0.05519063444162849,
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)
returns["cum"] = returns['sum'].cumsum(axis=0)
returns = returns.reset_index()
plt.figure(figsize=(12, 6))
plt.plot(returns.cum)
st.pyplot()
shift_d = shift_d
Prop = returns
st.write(f"## Moroccan Assets Allocation using Efficient Frontier")
st.pyplot()
################################## Herarchical Risk Parity ###############################################################
if model_name == 'HRP':
hrp_ETF = HRPOpt(returns)
hrp_Mor = HRPOpt(moroccan_stocks_returns)
weights = hrp_ETF.optimize(linkage_method='single')
wei = hrp_Mor.optimize(linkage_method='single')
hrp_ETF.portfolio_performance(verbose=True)
hrp_Mor.portfolio_performance(verbose=True)
dfhrpETF = pd.DataFrame([weights])
dfhrpMor = pd.DataFrame([wei])
if len(ETF_name) == 0 & len(moroccan_stocks) == 0:
st.write("Please select stocks!!!")
if len(ETF_name) != 0:
result_pct = dfhrpETF.div(dfhrpETF.sum(1), axis=0)
ax = result_pct.plot(kind='bar', figsize=(20, 6), width=0.8, color=colors_list, edgecolor=None)
plt.legend(labels=dfhrpETF.columns, fontsize=20)
plt.xticks(fontsize=20)
for spine in plt.gca().spines.values():
spine.set_visible(False)
plt.yticks([])