def test_return_model_additional_kwargs():
df = get_data()
mkt_prices = get_benchmark_data()
mu1 = expected_returns.return_model(df,
method="capm_return",
market_prices=mkt_prices,
risk_free_rate=0.03)
mu2 = expected_returns.capm_return(df,
market_prices=mkt_prices,
risk_free_rate=0.03)
pd.testing.assert_series_equal(mu1, mu2)
def test_capm_with_benchmark():
df = get_data()
mkt_df = get_benchmark_data()
mu = expected_returns.capm_return(df,
market_prices=mkt_df,
compounding=True)
assert isinstance(mu, pd.Series)
assert list(mu.index) == list(df.columns)
assert mu.notnull().all()
assert mu.dtype == "float64"
correct_mu = np.array([
0.09115799375654746,
0.09905386632033128,
0.05676282405265752,
0.06291827346436336,
0.13147799781014877,
0.10239088012000815,
0.1311567086884512,
0.07339649698626659,
0.1301248935078549,
0.07620949056643983,
0.07629095442513395,
0.12163575425541985,
0.10400070536161658,
0.0781736030988492,
0.09185177050469516,
0.10245700691271296,
0.11268307946677197,
0.07870087187919145,
0.1275598841214107,
0.09536788741392595,
])
np.testing.assert_array_almost_equal(mu.values, correct_mu)
mu2 = expected_returns.capm_return(df,
market_prices=mkt_df,
compounding=False)
assert (mu2 >= mu).all()
def test_capm_no_benchmark():
df = get_data()
mu = expected_returns.capm_return(df)
assert isinstance(mu, pd.Series)
assert list(mu.index) == list(df.columns)
assert mu.notnull().all()
assert mu.dtype == "float64"
correct_mu = np.array(
[
0.22148462799238577,
0.2835429647498704,
0.14693081977908462,
0.1488989354304723,
0.4162399750335195,
0.22716772604184535,
0.3970337136813829,
0.16733214988182069,
0.31791477659742146,
0.17279931642386534,
0.15271750464365566,
0.351778014382922,
0.32859883451716376,
0.1501938182844417,
0.268295486802897,
0.31632339201710874,
0.27753479916328516,
0.16959588523287855,
0.3089119447773357,
0.2558719211959501,
]
)
np.testing.assert_array_almost_equal(mu.values, correct_mu)
# Test the (warning triggering) case that input is not a dataFrame
with pytest.warns(RuntimeWarning):
mu_np = expected_returns.capm_return(df.to_numpy())
mu_np.name = mu.name # These will differ.
mu_np.index = mu.index # Index labels would be tickers.
pd.testing.assert_series_equal(mu_np, mu)
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)
import matplotlib.pyplot as plt
# small portfolio to test
tickers = ['BLK', "BAC", "AAPL", "TM", "WMT",
"JD"] # "INTU", "MA", "UL", "CVS",
# "DIS", "AMD", "NVDA", "PBI", "TGT"]
ohlc = yf.download(tickers, period="max")
prices = ohlc["Adj Close"]
prices.tail()
#various types of expected returns; these will be used for expected future returns
mu = expected_returns.james_stein_shrinkage(prices)
mum = expected_returns.mean_historical_return(prices)
mummy = expected_returns.capm_return(prices)
#different risk models; guessing that you can also use the pandas.cov function
S = risk_models.semicovariance(prices)
T = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
plotting.plot_covariance(S)
plotting.plot_covariance(T)
#equal weights
initial_weights = np.array([1 / len(tickers)] * len(tickers))
print(initial_weights)
#transaction cost objective
ef = EfficientFrontier(mum, T)
# 1% broker commission
cov_matrix #voltilidade do portfólio port_volatility = np.sqrt(np.dot(pesos.T, np.dot(cov_matrix, pesos))) vol_ano = port_volatility * np.sqrt(252) print(vol_ano) #Otimização Max-Sharpe from pypfopt.expected_returns import capm_return #Estimadores de retorno #CAPM mu = capm_return(carteira_cripto, risk_free_rate=0.00157) from pypfopt import risk_models #matriz covariância from pypfopt.risk_models import CovarianceShrinkage #Modelos de Risco #estimativa de matriz de covariância S = CovarianceShrinkage(carteira_cripto).ledoit_wolf() #Técnica de Otimização - Fronteira Eficiente from pypfopt.efficient_frontier import EfficientFrontier from pypfopt import objective_functions ef = EfficientFrontier(mu, S) ef.add_objective(objective_functions.L2_reg, gamma=0.1)