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examples.py
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examples.py
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import pandas as pd
import numpy as np
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt.value_at_risk import CVAROpt
from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
from pypfopt.hierarchical_risk_parity import HRPOpt
from pypfopt.cla import CLA
from pypfopt import black_litterman
from pypfopt.black_litterman import BlackLittermanModel
# Reading in the data; preparing expected returns and a risk model
df = pd.read_csv("tests/stock_prices.csv", parse_dates=True, index_col="date")
returns = df.pct_change().dropna()
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Long-only Maximum Sharpe portfolio, with discretised weights
ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe()
ef.portfolio_performance(verbose=True)
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(weights, latest_prices)
allocation, leftover = da.lp_portfolio()
print("Discrete allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))
"""
Expected annual return: 33.0%
Annual volatility: 21.7%
Sharpe Ratio: 1.43
11 out of 20 tickers were removed
Discrete allocation: {'GOOG': 0, 'AAPL': 5, 'FB': 11, 'BABA': 5, 'AMZN': 1,
'BBY': 7, 'MA': 14, 'PFE': 50, 'SBUX': 5}
Funds remaining: $8.42
"""
# Long-only minimum volatility portfolio, with a weight cap and regularisation
# e.g if we want at least 15/20 tickers to have non-neglible weights, and no
# asset should have a weight greater than 10%
ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.10), gamma=1)
weights = ef.min_volatility()
print(weights)
ef.portfolio_performance(verbose=True)
"""
{
"GOOG": 0.07350956640872872,
"AAPL": 0.030014017863649482,
"FB": 0.1,
"BABA": 0.1,
"AMZN": 0.020555866446753328,
"GE": 0.04052056082259943,
"AMD": 0.00812443078787937,
"WMT": 0.06506870608367901,
"BAC": 0.008164561664321555,
"GM": 0.1,
"T": 0.06581732376444831,
"UAA": 0.04764331094366604,
"SHLD": 0.04233556511047908,
"XOM": 0.06445358180591973,
"RRC": 0.0313848213281047,
"BBY": 0.02218378020003044,
"MA": 0.068553464907087,
"PFE": 0.059025401478094965,
"JPM": 0.015529411963789761,
"SBUX": 0.03711562842076907,
}
Expected annual return: 22.7%
Annual volatility: 12.7%
Sharpe Ratio: 1.63
"""
# A long/short portfolio maximising return for a target volatility of 10%,
# with a shrunk covariance matrix risk model
shrink = risk_models.CovarianceShrinkage(df)
S = shrink.ledoit_wolf()
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
weights = ef.efficient_risk(target_risk=0.10)
ef.portfolio_performance(verbose=True)
"""
Expected annual return: 29.8%
Annual volatility: 10.0%
Sharpe Ratio: 2.77
"""
# A market-neutral Markowitz portfolio finding the minimum volatility
# for a target return of 20%
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
weights = ef.efficient_return(target_return=0.20, market_neutral=True)
ef.portfolio_performance(verbose=True)
"""
Expected annual return: 20.0%
Annual volatility: 16.5%
Sharpe Ratio: 1.09
"""
# Custom objective
def utility_obj(weights, mu, cov_matrix, k=1):
return -weights.dot(mu) + k * np.dot(weights.T, np.dot(cov_matrix, weights))
ef = EfficientFrontier(mu, S)
ef.custom_objective(utility_obj, ef.expected_returns, ef.cov_matrix, 1)
ef.portfolio_performance(verbose=True)
"""
Expected annual return: 40.1%
Annual volatility: 29.2%
Sharpe Ratio: 1.30
"""
ef.custom_objective(utility_obj, ef.expected_returns, ef.cov_matrix, 2)
ef.portfolio_performance(verbose=True)
"""
Expected annual return: 36.6%
Annual volatility: 24.7%
Sharpe Ratio: 1.39
"""
# Black-Litterman
spy_prices = pd.read_csv(
"tests/spy_prices.csv", parse_dates=True, index_col=0, squeeze=True
)
delta = black_litterman.market_implied_risk_aversion(spy_prices)
mcaps = {
"GOOG": 927e9,
"AAPL": 1.19e12,
"FB": 574e9,
"BABA": 533e9,
"AMZN": 867e9,
"GE": 96e9,
"AMD": 43e9,
"WMT": 339e9,
"BAC": 301e9,
"GM": 51e9,
"T": 61e9,
"UAA": 78e9,
"SHLD": 0,
"XOM": 295e9,
"RRC": 1e9,
"BBY": 22e9,
"MA": 288e9,
"PFE": 212e9,
"JPM": 422e9,
"SBUX": 102e9,
}
prior = black_litterman.market_implied_prior_returns(mcaps, delta, S)
# 1. SBUX will drop by 20%
# 2. GOOG outperforms FB by 10%
# 3. BAC and JPM will outperform T and GE by 15%
views = np.array([-0.20, 0.10, 0.15]).reshape(-1, 1)
picking = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, -0.5, 0, 0, 0.5, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0.5, 0],
]
)
bl = BlackLittermanModel(S, Q=views, P=picking, pi=prior, tau=0.01)
rets = bl.bl_returns()
ef = EfficientFrontier(rets, S)
ef.max_sharpe()
print(ef.clean_weights())
ef.portfolio_performance(verbose=True)
"""
{'GOOG': 0.2015,
'AAPL': 0.2368,
'FB': 0.0,
'BABA': 0.06098,
'AMZN': 0.17148,
'GE': 0.0,
'AMD': 0.0,
'WMT': 0.0,
'BAC': 0.18545,
'GM': 0.0,
'T': 0.0,
'UAA': 0.0,
'SHLD': 0.0,
'XOM': 0.0,
'RRC': 0.0,
'BBY': 0.0,
'MA': 0.0,
'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.hrp_portfolio()
print(weights)
"""
{'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,
'GOOG': 0.049560084289929286,
'JPM': 0.017675709092515708,
'MA': 0.03812737349732021,
'PFE': 0.07786528342813454,
'RRC': 0.03161528695094597,
'SBUX': 0.039844436656239136,
'SHLD': 0.027113184241298865,
'T': 0.11138956508836476,
'UAA': 0.02711590957075009,
'WMT': 0.10569551148587905,
'XOM': 0.11175337115721229}
"""
# Crticial Line Algorithm
cla = CLA(mu, S)
print(cla.max_sharpe())
cla.portfolio_performance(verbose=True)
"""
{'GOOG': 0.020889868669945022,
'AAPL': 0.08867994115132602,
'FB': 0.19417572932251745,
'BABA': 0.10492386821217001,
'AMZN': 0.0644908140418782,
'GE': 0.0,
'AMD': 0.0,
'WMT': 0.0034898157701416382,
'BAC': 0.0,
'GM': 0.0,
'T': 2.4138966206946562e-19,
'UAA': 0.0,
'SHLD': 0.0,
'XOM': 0.0005100736411646903,
'RRC': 0.0,
'BBY': 0.05967818998203106,
'MA': 0.23089949598834422,
'PFE': 0.19125123325029705,
'JPM': 0.0,
'SBUX': 0.041010969970184656}
Expected annual return: 32.5%
Annual volatility: 21.3%
Sharpe Ratio: 1.43
"""
# CVaR optimisation - very buggy
vr = CVAROpt(returns)
vr.min_cvar()
print(vr.clean_weights())
"""
{'GOOG': 0.10886,
'AAPL': 0.0,
'FB': 0.02598,
'BABA': 0.57691,
'AMZN': 0.0,
'GE': 0.01049,
'AMD': 0.0138,
'WMT': 0.01581,
'BAC': 0.01049,
'GM': 0.03463,
'T': 0.01049,
'UAA': 0.07782,
'SHLD': 0.04184,
'XOM': 0.00931,
'RRC': 0.0,
'BBY': 0.01748,
'MA': 0.03782,
'PFE': 0.0,
'JPM': 0.0,
'SBUX': 0.00828}
"""