def test_dendrogram_plot(self):
"""
Test if dendrogram plot object is correctly rendered.
"""
herc = HierarchicalEqualRiskContribution()
herc.allocate(asset_prices=self.data, optimal_num_clusters=5)
dendrogram = herc.plot_clusters(assets=self.data.columns)
assert dendrogram.get('icoord')
assert dendrogram.get('dcoord')
assert dendrogram.get('ivl')
assert dendrogram.get('leaves')
assert dendrogram.get('color_list')
class HierarchicalEqualRiskContributionStrategy(object):
def __init__(self):
# Get the data:
self.loadDirectory = os.path.expandvars(
'${HOME}/Desktop/quant-research-env/DARWINStrategyContentSeries/Data/ClosePricePortfolio.csv'
)
self.saveDirectory = os.path.expandvars(
'${HOME}/Desktop/quant-research-env/DARWINStrategyContentSeries/OnlinePortfolioStrategies/_OPTIMIZATIONS/HERC/'
)
self.DF_CLOSE = self._loadTechnicalDataset()
def _loadTechnicalDataset(self):
# Load it:
ASSET_UNIVERSE = pd.read_csv(self.loadDirectory,
index_col=0,
parse_dates=True,
infer_datetime_format=True)
print('¡Asset Universe file LOADED!')
return ASSET_UNIVERSE
def _generateAllocations(self):
# Create object:
self.STRATEGY = HierarchicalEqualRiskContribution()
# Allocate:
self.STRATEGY.allocate(
asset_names=self.DF_CLOSE.columns,
asset_prices=self.DF_CLOSE,
#risk_measure='expected_shortfall',
risk_measure='conditional_drawdown_risk',
linkage='ward')
# Plot portfolio metrics:
self._plotOptimalPortfolio()
self._plotClusters()
def _plotOptimalPortfolio(self):
print(
f'Optimal number of clusters: {self.STRATEGY.optimal_num_clusters}'
)
# Get weights:
weights = self.STRATEGY.weights
y_pos = np.arange(len(weights.columns))
# Create the figure:
f1, ax = plt.subplots(figsize=(10, 5))
# Create the plots:
ax.bar(list(weights.columns), weights.values[0], label='Assets')
plt.xticks(y_pos, rotation=45, size=10)
plt.xticks(y_pos, rotation=45, size=10)
ax.grid(True)
plt.grid(linestyle='dotted')
plt.xlabel('Assets',
horizontalalignment='center',
verticalalignment='center',
fontsize=14,
labelpad=20)
plt.ylabel('Asset Weights',
horizontalalignment='center',
verticalalignment='center',
fontsize=14,
labelpad=20)
ax.legend(loc='best')
plt.title(
f'Optimal portfolio for {self.STRATEGY.__class__.__name__} optimization'
)
plt.subplots_adjust(left=0.09,
bottom=0.20,
right=0.94,
top=0.90,
wspace=0.2,
hspace=0)
f1.canvas.set_window_title('OPTIMIZATION METHODS')
# In PNG:
plt.savefig(self.saveDirectory + 'OptimalPortfolio.png')
# Show:
plt.show()
def _plotClusters(self):
# Create the figure:
f1, ax = plt.subplots(figsize=(10, 5))
# Create the plots:
self.STRATEGY.plot_clusters(self.DF_CLOSE.columns)
ax.grid(True)
plt.grid(linestyle='dotted')
plt.title(
f'Dendrogram for {self.STRATEGY.__class__.__name__} optimization')
plt.xticks(rotation=45)
plt.subplots_adjust(left=0.09,
bottom=0.20,
right=0.94,
top=0.90,
wspace=0.2,
hspace=0)
f1.canvas.set_window_title('OPTIMIZATION METHODS')
# In PNG:
plt.savefig(self.saveDirectory + 'Dendogram.png')
# Show:
plt.show()
def _predictOutcome(self):
pass