Riskfolio-Lib Tutorial:¶

import numpy as np
import pandas as pd
import yfinance as yf
import warnings

warnings.filterwarnings("ignore")
pd.options.display.float_format = '{:.4%}'.format

# 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()

# Downloading data
data = yf.download(assets, start = start, end = end)
data = data.loc[:,('Adj Close', slice(None))]
data.columns = assets

# Calculating returns

Y = data[assets].pct_change().dropna()

display(Y.head())

import riskfolio as rp

# Building the portfolio object
port = rp.Portfolio(returns=Y)

# Calculating optimal portfolio

# Select method and estimate input parameters:

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)

# Estimate optimal portfolio:

port.alpha = 0.05
model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)
rm = 'MV' # Risk measure used, this time will be variance
obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe
hist = True # Use historical scenarios for risk measures that depend on scenarios
rf = 0 # Risk free rate
l = 0 # Risk aversion factor, only useful when obj is 'Utility'

w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)

display(w.T)

# Plotting the composition of the portfolio

ax = rp.plot_pie(w=w, title='Sharpe Mean Variance', others=0.05, nrow=25, cmap = "tab20",
                 height=6, width=10, ax=None)

asset_classes = {'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'], 
                 'Industry': ['Consumer Discretionary','Consumer Discretionary',
                              'Consumer Discretionary', 'Consumer Staples',
                              'Consumer Staples','Energy','Financials',
                              'Financials','Financials','Financials',
                              'Health Care','Health Care','Industrials','Industrials',
                              'Industrials','Health care','Industrials',
                              'Information Technology','Information Technology',
                              'Materials','Telecommunications Services','Utilities',
                              'Utilities','Telecommunications Services','Financials']}

asset_classes = pd.DataFrame(asset_classes)
asset_classes = asset_classes.sort_values(by=['Assets'])

views = {'Disabled': [False, False, False],
         'Type': ['Classes', 'Classes', 'Classes'],
         'Set': ['Industry', 'Industry', 'Industry'],
         'Position': ['Energy', 'Consumer Staples', 'Materials'],
         'Sign': ['>=', '>=', '>='],
         'Weight': [0.08, 0.1, 0.09], # Annual terms 
         'Type Relative': ['Classes', 'Classes', 'Classes'],
         'Relative Set': ['Industry', 'Industry', 'Industry'],
         'Relative': ['Financials', 'Utilities', 'Industrials']}

views = pd.DataFrame(views)

display(views)

P, Q = rp.assets_views(views, asset_classes)

display(pd.DataFrame(P.T))
display(pd.DataFrame(Q))

# Estimate Black Litterman inputs:

port.blacklitterman_stats(P, Q/252, rf=rf, w=w, delta=None, eq=True)

# Estimate optimal portfolio:

model='BL'# Black Litterman
rm = 'MV' # Risk measure used, this time will be variance
obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe
hist = False # Use historical scenarios for risk measures that depend on scenarios

w_bl = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)

display(w_bl.T)

# Plotting the composition of the portfolio

ax = rp.plot_pie(w=w_bl, title='Sharpe Black Litterman', others=0.05, nrow=25,
                 cmap = "tab20", height=6, width=10, ax=None)

points = 50 # Number of points of the frontier

frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)

display(frontier.T.head())

# Plotting the efficient frontier

label = 'Max Risk Adjusted Return Portfolio' # Title of point
mu = port.mu_bl # Expected returns of Black Litterman model
cov = port.cov_bl # Covariance matrix of Black Litterman model
returns = port.returns # Returns of the assets

ax = rp.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,
                      rf=rf, alpha=0.05, cmap='viridis', w=w_bl, label=label,
                      marker='*', s=16, c='r', height=6, width=10, ax=None)

# Plotting efficient frontier composition

ax = rp.plot_frontier_area(w_frontier=frontier, cmap="tab20", height=6, width=10, ax=None)

w_s.style.format("{:.2%}").background_gradient(cmap='YlGn')

import matplotlib.pyplot as plt

# Plotting a comparison of assets weights for each portfolio

fig = plt.gcf()
fig.set_figwidth(14)
fig.set_figheight(6)
ax = fig.subplots(nrows=1, ncols=1)

w_s.plot.bar(ax=ax)