def test_risk_matrix_and_returns_data():
# Test the switcher method for simple calls
df = get_data()
for method in {
"sample_cov",
"semicovariance",
"exp_cov",
# FIXME: this fails "min_cov_determinant",
"ledoit_wolf",
"ledoit_wolf_constant_variance",
"ledoit_wolf_single_factor",
"ledoit_wolf_constant_correlation",
"oracle_approximating",
}:
S = risk_models.risk_matrix(df, method=method)
assert S.shape == (20, 20)
assert S.notnull().all().all()
assert risk_models._is_positive_semidefinite(S)
S2 = risk_models.risk_matrix(expected_returns.returns_from_prices(df),
returns_data=True,
method=method)
pd.testing.assert_frame_equal(S, S2)
def main():
prices, market_prices, mkt_caps, symbols, config = load_data()
#covar = risk_models.risk_matrix(prices, method='exp_cov', span=180)
#covar = risk_models.risk_matrix(prices, method='semicovariance')
#covar = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
covar = risk_models.risk_matrix(prices, method='oracle_approximating')
rets_bl, covar_bl = calc_black_litterman(market_prices, mkt_caps, covar,
config, symbols)
kelly_w = kelly_optimize(rets_bl, covar_bl, config)
max_quad_util_w, max_quad_util_ef = max_quad_utility_weights(
rets_bl, covar_bl, config)
min_vol_w, min_vol_ef = min_volatility_weights(rets_bl, covar_bl, config)
max_sharpe_w, max_sharpe_ef = max_sharpe_weights(rets_bl, covar_bl, config)
cla_max_sharpe_w, cla_max_sharpe_cla = cla_max_sharpe_weights(
rets_bl, covar_bl, config)
cla_min_vol_w, cla_min_vol_cla = cla_min_vol_weights(
rets_bl, covar_bl, config)
#ax = plotting.plot_efficient_frontier(cla_max_sharpe_cla, showfig=False)
#plt.title('Efficient Frontier via CLA Max Sharpe Optimization')
#plt.show()
#ax = plotting.plot_efficient_frontier(cla_min_vol_cla, showfig=False)
#plt.title('Efficient Frontier via CLA Min Volatility Optimization')
#plt.show()
weights_df = pd.merge(kelly_w,
max_quad_util_w,
left_index=True,
right_index=True)
weights_df = pd.merge(weights_df,
max_sharpe_w,
left_index=True,
right_index=True)
weights_df = pd.merge(weights_df,
cla_max_sharpe_w,
left_index=True,
right_index=True)
weights_df = pd.merge(weights_df,
min_vol_w,
left_index=True,
right_index=True)
weights_df = pd.merge(weights_df,
cla_min_vol_w,
left_index=True,
right_index=True)
weights_df.to_csv('portfolio_weight_results.csv')
plot_heatmap(weights_df, 'Portfolio Weighting (%)', 'Optimization Method',
'Security')
def test_risk_matrix_additional_kwargs():
df = get_data()
S = risk_models.sample_cov(df)
S2 = risk_models.risk_matrix(df, frequency=2)
pd.testing.assert_frame_equal(S / 126, S2)
S = risk_models.risk_matrix(
df, method="semicovariance", benchmark=0.0004, frequency=52
)
assert S.shape == (20, 20)
assert S.notnull().all().all()
assert risk_models._is_positive_semidefinite(S)
S = risk_models.risk_matrix(
expected_returns.returns_from_prices(df),
returns_data=True,
method="exp_cov",
span=60,
fix_method="diag",
)
assert S.shape == (20, 20)
assert S.notnull().all().all()
assert risk_models._is_positive_semidefinite(S)
def test_risk_matrix_not_implemented():
df = get_data()
with pytest.raises(NotImplementedError):
risk_models.risk_matrix(df, method="fancy_new!")
returns3=pd.to_numeric(returns3)
d4 = raw_return(prices=returns3, lag=True)
target=qs.stats.volatility(d4)
#for protfolio new
d1=pd.read_csv("prices.csv",parse_dates=True, index_col="תאריך")
stocks_t0=d1.dropna(thresh=len(d1) , axis=1)
m1 = expected_returns.mean_historical_return(stocks_t0)
m2=m1[m1 < m1.quantile(.95)]
stocks_t0=stocks_t0[m2.index]
stocks_t0=stocks_t0.sort_index()
stocks_t0=stocks_t0.loc["2015-05-27":"2016-05-26"]
stocks_t0=stocks_t0.dropna(thresh=len(stocks_t0) , axis=1)
stocks_t0=stocks_t0.sort_index(ascending=False)
S=risk_models.risk_matrix(stocks_t0,frequency=len(stocks_t0))
mu=expected_returns.mean_historical_return(stocks_t0,frequency=len(stocks_t0))
ef = EfficientFrontier(mu, S)
##target=target+0.02
weights = ef.efficient_risk(target_volatility=target)
ef.portfolio_performance(verbose=True)
wei=pd.Series(weights)
##d7 = raw_return(prices=stocks_t0, lag=True)
d7=stocks_t0.pct_change().dropna()
d7=d7[wei.index]
returns1=wei*d7
d8=returns1.sum(axis=1)
d8=d8.sort_index()
#year t1
def generate_outlook(t):
df = pd.read_csv("index-125.csv", index_col=None, header=0)## read index
d2=get_prices(df)# put index in correct way
###build dates
w="-05-27"
a=t+w
x=str(pd.to_numeric(a[:4])+1)+a[4:]
y=str(pd.to_numeric(a[:4])+2)+a[4:]
##find index returns
index_t0=d2.loc[a:x]
d3=index_t0.sort_index()
returns3 =d3.iloc[:,0]
returns3=pd.to_numeric(returns3)
index_t0 = raw_return(prices=returns3, lag=True)
target=qs.stats.volatility(index_t0)
#for protfolio for t0
d1=pd.read_csv("prices.csv",parse_dates=True, index_col="תאריך")##read data
stocks_t0=d1.dropna(thresh=len(d1) , axis=1)
m1 = expected_returns.mean_historical_return(stocks_t0)
m2=m1[m1 < m1.quantile(.95)]##drop top 5%
stocks_t0=stocks_t0[m2.index]
stocks_t0=stocks_t0.sort_index()
stocks_t0=stocks_t0.loc[a:x]
stocks_t0=stocks_t0.dropna(thresh=len(stocks_t0) , axis=1)
stocks_t0=stocks_t0.sort_index(ascending=False)
S=risk_models.risk_matrix(stocks_t0,frequency=len(stocks_t0))
mu=expected_returns.mean_historical_return(stocks_t0,frequency=len(stocks_t0))
ef = EfficientFrontier(mu, S)
##target=target+0.017##use this if target is not correct
weights = ef.efficient_risk(target_volatility=target)##build weights for EF
ef.portfolio_performance(verbose=True)
wei=pd.Series(weights)#change to pandas series
d7=stocks_t0.pct_change().dropna()
d7=d7[wei.index]
returns1=wei*d7
d8=returns1.sum(axis=1)
portfolio_t0=d8.sort_index()
name_index_t0="index_t0"+str(t)+".html"
name_portfolio_t0="portfolio_t0"+str(t)+".html"
qs.reports.html(index_t0,output=name_index_t0)
qs.reports.html(portfolio_t0,output=name_portfolio_t0)
#year t1
df3=d1.dropna(thresh=len(d1) , axis=1)
df3=df3.sort_index()
stocks_t0=df3.loc[x:y]
stocks_returns = raw_return(prices=stocks_t0,lag=True)
new_rets=stocks_returns[wei.index]
protfiolio=wei*new_rets
port_t1=protfiolio.sum(axis=1)
df = pd.read_csv("index-125.csv", index_col=None, header=0)
d2=get_prices(df)
index_t1=d2.loc[x:y]
index_t1=index_t1.sort_index()
returns3 =index_t1.iloc[:,0]
returns3=pd.to_numeric(returns3)
index_t1 = raw_return(prices=returns3, lag=True)
qs.reports.metrics(port_t1,mode='full')
name_index_t1="index_t1"+str(t)+".html"
name_portfolio_t1="portfolio_t1"+str(t)+".html"
name_compare_graph="compare_graph_t1"+str(t)+".html"
qs.reports.html(index_t1,output=name_index_t1)
qs.reports.html(port_t1,output=name_portfolio_t1)
qs.reports.html(port_t1,index_t1,output=name_compare_graph)
