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'
'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)
hrp.plot_dendrogram() # 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,
'GM': 0.05557666024185722,