This module has functions that allows us to create charts that helps us to analyze quickly the properties of our optimal portfolios.
The following example construct the portfolios and the efficient frontier that will be plot using the functions of this module.
import numpy as np
import pandas as pd
import yfinance as yf
import riskfolio as rp
# Date range
start = '2016-01-01'
end = '2019-12-30'
# Tickers of assets
assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',
'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',
'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']
assets.sort()
# Tickers of factors
factors = ['MTUM', 'QUAL', 'VLUE', 'SIZE', 'USMV']
factors.sort()
tickers = assets + factors
tickers.sort()
# Downloading the data
data = yf.download(tickers, start = start, end = end)
data = data.loc[:,('Adj Close', slice(None))]
data.columns = tickers
returns = data.pct_change().dropna()
Y = returns[assets]
X = returns[factors]
# Creating the Portfolio Object
port = rp.Portfolio(returns=Y)
# To display dataframes values in percentage format
pd.options.display.float_format = '{:.4%}'.format
# Choose the risk measure
rm = 'MSV' # Semi Standard Deviation
# Estimate inputs of the model (historical estimates)
method_mu='hist' # Method to estimate expected returns based on historical data.
method_cov='hist' # Method to estimate covariance matrix based on historical data.
port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)
mu = port.mu
cov = port.cov
# Estimate the portfolio that maximizes the risk adjusted return ratio
w1 = port.optimization(model='Classic', rm=rm, obj='Sharpe', rf=0.0, l=0, hist=True)
# Estimate points in the efficient frontier mean - semi standard deviation
ws = port.efficient_frontier(model='Classic', rm=rm, points=20, rf=0, hist=True)
# Estimate the risk parity portfolio for semi standard deviation
w2 = port.rp_optimization(model='Classic', rm=rm, rf=0, b=None, hist=True)
# Estimate the risk parity portfolio for semi standard deviation
w2 = port.rp_optimization(model='Classic', rm=rm, rf=0, b=None, hist=True)
# Estimate the risk parity portfolio for risk factors
port.factors = X
port.factors_stats(method_mu=method_mu,
method_cov=method_cov,
feature_selection='stepwise',
dict_risk=dict(stepwise='Forward'))
w3 = port.rp_optimization(model='FC', rm='MV', rf=0, b_f=None)
# Estimate the risk parity portfolio for principal components
port.factors = X
port.factors_stats(method_mu=method_mu,
method_cov=method_cov,
feature_selection='PCR',
dict_risk=dict(n_components=0.95))
w4 = port.rp_optimization(model='FC', rm='MV', rf=0, b_f=None)PlotFunctions