def min_var(my_portfolio, perf=True) -> list:
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
prices = prices.filter(my_portfolio.portfolio)
mu = expected_returns.capm_return(prices)
S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
ef = EfficientFrontier(mu, S)
ef.add_objective(objective_functions.L2_reg,
gamma=my_portfolio.diversification)
if my_portfolio.min_weights is not None:
ef.add_constraint(lambda x: x >= my_portfolio.min_weights)
if my_portfolio.max_weights is not None:
ef.add_constraint(lambda x: x <= my_portfolio.max_weights)
ef.min_volatility()
weights = ef.clean_weights()
wts = weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
ef.portfolio_performance(verbose=True)
return flatten(result)
def test_custom_convex_abs_exposure():
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(None, None))
ef.add_constraint(lambda x: cp.norm(x, 1) <= 2)
ef.convex_objective(
objective_functions.portfolio_variance,
cov_matrix=ef.cov_matrix,
weights_sum_to_one=False,
)
def test_custom_convex_objective_market_neutral_efficient_risk():
target_risk = 0.19
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(-1, 1))
ef.efficient_risk(target_risk, market_neutral=True)
built_in = ef.weights
# Recreate the market-neutral efficient_risk optimiser using this API
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(-1, 1))
ef.add_constraint(lambda x: cp.sum(x) == 0)
ef.add_constraint(
lambda x: cp.quad_form(x, ef.cov_matrix) <= target_risk**2)
ef.convex_objective(lambda x: -x @ ef.expected_returns,
weights_sum_to_one=False)
custom = ef.weights
np.testing.assert_allclose(built_in, custom, atol=1e-7)
def mean_var(my_portfolio, vol_max=0.15, perf=True) -> list:
# changed to take in desired timeline, the problem is that it would use all historical data
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
prices = prices.filter(my_portfolio.portfolio)
# sometimes we will pick a date range where company isn't public we can't set price to 0 so it has to go to 1
prices = prices.fillna(1)
mu = expected_returns.capm_return(prices)
S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
ef = EfficientFrontier(mu, S)
ef.add_objective(objective_functions.L2_reg,
gamma=my_portfolio.diversification)
if my_portfolio.min_weights is not None:
ef.add_constraint(lambda x: x >= my_portfolio.min_weights)
if my_portfolio.max_weights is not None:
ef.add_constraint(lambda x: x <= my_portfolio.max_weights)
ef.efficient_risk(vol_max)
weights = ef.clean_weights()
wts = weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
ef.portfolio_performance(verbose=True)
return flatten(result)
def efficient_frontier(my_portfolio, perf=True) -> list:
# changed to take in desired timeline, the problem is that it would use all historical data
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
df = prices.filter(my_portfolio.portfolio)
# sometimes we will pick a date range where company isn't public we can't set price to 0 so it has to go to 1
df = df.fillna(1)
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# optimize for max sharpe ratio
ef = EfficientFrontier(mu, S)
if my_portfolio.min_weights is not None:
ef.add_constraint(lambda x: x >= my_portfolio.min_weights)
if my_portfolio.max_weights is not None:
ef.add_constraint(lambda x: x <= my_portfolio.max_weights)
weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
wts = cleaned_weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
pred = ef.portfolio_performance(verbose=True)
return flatten(result)
def test_constrained_ef_plot_risk():
ef = EfficientFrontier(*setup_efficient_frontier(data_only=True),
weight_bounds=(None, None))
ef.add_constraint(lambda w: w[0] >= 0.2)
ef.add_constraint(lambda w: w[2] == 0.15)
ef.add_constraint(lambda w: w[3] + w[4] <= 0.10)
# 100 portfolios with risks between 0.10 and 0.30
risk_range = np.linspace(0.157, 0.40, 100)
ax = plotting.plot_efficient_frontier(ef,
ef_param="risk",
ef_param_range=risk_range,
show_assets=True,
showfig=False)
assert len(ax.findobj()) == 137
