Practical Portfolio Optimisation

# https://github.com/robertmartin8/PyPortfolioOpt
!pip install PyPortfolioOpt

import pypfopt
print(f'\n Library version: {pypfopt.__version__}')

#Output:  Library version: 1.4.2

from pypfopt import expected_returns
from pypfopt import EfficientFrontier

# json: for pretty print of a dictionary: https://stackoverflow.com/questions/44689546/how-to-print-out-a-dictionary-nicely-in-python/44689627
import json

# get all prices for one day in a row
df_pivot = stocks_prices.pivot('Date','Ticker','Close').reset_index()

mu = expected_returns.capm_return(df_pivot.set_index('Date'))
# Other options for the returns values: expected_returns.ema_historical_return(df_pivot.set_index('Date'))
# Other options for the returns values: expected_returns.mean_historical_return(df_pivot.set_index('Date'))
print(f'Expected returns from each stock: {mu} \n')

S = risk_models.CovarianceShrinkage(df_pivot.set_index('Date')).ledoit_wolf()

# Weights between 0 and 1 - we don't allow shorting
ef = EfficientFrontier(mu, S, weight_bounds=(0, 1))
ef.min_volatility()
weights_min_volatility = ef.clean_weights()

print(f'Portfolio weights for min volatility optimisation (lowest level of risk): {json.dumps(weights_min_volatility, indent=4, sort_keys=True)} \n')
print(f'Portfolio performance: {ef.portfolio_performance(verbose=True, risk_free_rate=0.0124)} \n')
# Risk-free rate : 10Y TBonds rate on 21-Jul-2021 https://www.cnbc.com/quotes/US10Y

pd.Series(weights_min_volatility).plot.barh(title = 'Optimal Portfolio Weights (min volatility) by PyPortfolioOpt');

ef.max_sharpe()
weights_max_sharpe = ef.clean_weights()


print(f'Portfolio weights for max Sharpe optimisation (highest level of risk): {json.dumps(weights_max_sharpe, indent=4, sort_keys=True)} \n')
print(f'Portfolio performance: {ef.portfolio_performance(verbose=True, risk_free_rate=0.0124)} \n')

#OUTPUT:
#Expected returns from each stock: Ticker
#BA      0.214378
#BUD     0.140177
#CHTR    0.128496
#NKE     0.198096
#NVDA    0.395522
#PTR     0.220099
#SHOP    0.292879
#XOM     0.129524
#Name: mkt, dtype: float64 

#Portfolio weights for min volatility optimisation (lowest level of risk): {
 #   "BA": 0.0,
 #  "BUD": 0.37214,
 # "CHTR": 0.35668,
 # "NKE": 0.03077,
 #  "NVDA": 0.0,
 #  "PTR": 0.04423,
 #  "SHOP": 0.14425,
 #  "XOM": 0.05194
#} 

# Expected annual return: 16.3%
# Annual volatility: 8.9%
# Sharpe Ratio: 1.69
# Portfolio performance: (0.16280187012757663, 0.08898359416885636, 1.6902202201697114) 

# Portfolio weights for max Sharpe optimisation (highest return-per-risk): {
#   "BA": 0.01318,
#   "BUD": 0.23948,
#   "CHTR": 0.15528,
#   "NKE": 0.0412,
#   "NVDA": 0.07844,
#   "PTR": 0.11107,
#    "SHOP": 0.36135,
#    "XOM": 0.0
#} 

# Expected annual return: 22.6%
# Annual volatility: 10.7%
# Sharpe Ratio: 1.92
# Portfolio performance: (0.2258118694269962, 0.10704509290593174, 1.9226651483021082) 

from pypfopt import DiscreteAllocation

latest_prices = df_pivot.set_index('Date').iloc[-1]  # prices as of the day you are allocating
da = DiscreteAllocation(weights_max_sharpe, latest_prices, total_portfolio_value = INVESTMENT, short_ratio=0.0)
alloc, leftover = da.lp_portfolio()
print(f"Discrete allocation performed with ${leftover:.2f} leftover")
alloc

# OUTPUT (5-Aug-2021)
# Discrete allocation performed with $115.07 leftover
# {'BUD': 8, 'CHTR': 1, 'NKE': 1, 'NVDA': 1, 'PTR': 6}

# 10% sampling - we invent not in all days, but only in 10% of days in the last 3.5 years
df_stats = df_portfolio_value_daily.sample(frac=0.1, random_state=42).describe()
df_stats

# Defined previosly: SELECTED_PORTFOLIO = {'BA': 2, 'BUD': 1, 'NKE': 1, 'PTR': 1, 'SHOP': 0, 'XOM': 1, 'NVDA':2, 'CHTR':1 }

# Get all major metrics for one portfolio:
print(f'Selected portfolio: {SELECTED_PORTFOLIO}')
print(f"Mean return in 3 days = {df_stats['return_3d']['mean']-1 :.2%}")
print(f"Volatility in 3 days = {df_stats['volatility_3d']['mean'] :.1%}")
print(f"Mean return_per_volatility in 3 days = {df_stats['return_per_volatility_3d']['mean'] :.3f}")
print(f"Median return_per_volality in 3 days = {df_stats['return_per_volatility_3d']['50%'] :.3f}")
print(f"25% drawdown in 3 days = {df_stats['return_3d']['25%']-1 :.1%}")
print(f"Max drawdown in 3 days = {df_stats['return_3d']['min']-1 :.1%}")