def __init__(self, feed_dict):
# initiate common vatiables
self.returns = feed_dict['input']
self.covar = self.returns.cov()
del feed_dict['input']
columns = []
for col in self.returns.columns:
columns.append(col.split('_')[0])
# save parameters in case replication is desired
self.Parameters = deepcopy(feed_dict)
objective, features, constraints, simulations, methods, tol, maxiter, disp = oc.unfeeder(
feed_dict)
# features
if 'benchmark' in features:
self.benchmark = features['benchmark']
else:
raise AttributeError('Alpha requires a benchmark.')
self.periodicity = _embed_periodicity(features['periodicity'])
if methods == 'arithmetic':
self.mean_returns = self.returns.mean() * self.periodicity
self.benchmark_mean = self.benchmark.mean() * self.periodicity
if methods == 'geometric':
self.mean_returns = _geomean(self.returns, self.periodicity)
self.benchmark_mean = _geomean(self.benchmark, self.periodicity)
if 'margin_int_rate' in features:
self.margin_rate = features['margin_int_rate']
else:
self.margin_rate = 0
# constraints
if 'min_weights' in constraints:
self.weight_bounds = tuple(
zip(constraints['min_weights'], constraints['max_weights']))
else:
self.weight_bounds = None
constraints = self.get_constraints(constraints)
# optimal portfolio
self.guess = np.random.normal(0, 0.5, len(self.mean_returns)) / 10
self.OptiParam = minimize(self.optimize_alpha,
self.guess,
method='SLSQP',
bounds=self.weight_bounds,
tol=tol,
constraints=constraints,
options={
'maxiter': maxiter,
'disp': disp
})
if not self.OptiParam['success']:
print('Warning!', self.OptiParam['message'])
tanw = self.OptiParam['x']
tanww = pd.DataFrame(tanw).T
tanww.columns = columns
tanr = self.weighted_annual_return(tanw)
margin = np.sum(np.abs(tanw)) - 1
tanra = tanr - self.margin_rate * margin
tans = self.weighted_annual_stddev(tanw)
self.Optimal = {
'Weights': tanww,
'Return': tanr,
'AdjReturn': tanra,
'StdDev': tans,
'Alpha': tanra - self.benchmark_mean,
'Series': self.weighted_return(tanw)
}
# Constituents
self.ConstituentAlpha = self.get_alphas(columns)
def __init__(self, feed_dict, full_analysis):
# initiate common vatiables
self.returns = feed_dict['input']
self.covar = self.returns.cov()
del feed_dict['input']
columns = []
self.inital_weights = []
for col in self.returns.columns:
name = col.split('_')[0]
self.inital_weights.append(get_quote_yahoo(name).marketCap)
columns.append(name)
self.inital_weights = np.array(self.inital_weights)
self.inital_weights = self.inital_weights / np.sum(self.inital_weights)
# save parameters in case replication is desired
self.Parameters = deepcopy(feed_dict)
objective, features, constraints, simulations, methods, tol, maxiter, disp = oc.unfeeder(
feed_dict)
try:
views = []
chg = []
for view in objective['target']:
chg.append(view[0] / 100)
views.append(view[1:])
views = np.array(views)
chg = np.array(chg)
except:
raise AttributeError(
'Black Litterman requires target views to be specified.')
# features
if 'benchmark' in features:
self.benchmark = features['benchmark']
else:
raise AttributeError('Black Litterman requires a benchmark.')
self.periodicity = _embed_periodicity(features['periodicity'])
if methods == 'arithmetic':
self.mean_returns = self.returns.mean() * self.periodicity
self.benchmark_mean = self.benchmark.mean() * self.periodicity
if methods == 'geometric':
self.mean_returns = _geomean(self.returns, self.periodicity)
self.benchmark_mean = _geomean(self.benchmark, self.periodicity)
if 'margin_int_rate' in features:
self.margin_rate = features['margin_int_rate']
else:
self.margin_rate = 0
if 'rf' in features:
self.rf = features['rf']
else:
self.rf = 0
# constraints
if 'min_weights' in constraints:
self.weight_bounds = tuple(
zip(constraints['min_weights'], constraints['max_weights']))
else:
self.weight_bounds = None
constraints = self.get_constraints(constraints)
# return computation
A = (self.benchmark_mean - self.rf) / (
(self.benchmark.std()**2) * self.periodicity)
pi = A * np.matmul(np.mat(self.covar * self.periodicity),
self.inital_weights)
omega = np.matmul(
np.matmul(views, np.mat(self.covar * self.periodicity)), views.T)
e1 = np.mat(
np.mat(self.covar * self.periodicity).I +
np.matmul(np.matmul(views.T,
np.mat(omega).I), views)).I
e2 = np.matmul(np.mat(self.covar * self.periodicity).I,
pi) + np.matmul(np.matmul(views.T,
np.mat(omega).I), chg).T
self.Expected_Returns = np.matmul(e1, e2)
# optimal portfolio
self.OptiParam = minimize(self.optimize_sharpe_ratio,
self.inital_weights,
method='SLSQP',
bounds=self.weight_bounds,
tol=tol,
constraints=constraints,
options={
'maxiter': maxiter,
'disp': disp
})
if not self.OptiParam['success']:
print('Warning!', self.OptiParam['message'])
tanw = self.OptiParam['x']
tanww = pd.DataFrame(tanw).T
tanww.columns = columns
tanr = self.weighted_annual_return(tanw)
margin = np.sum(np.abs(tanw)) - 1
tanra = tanr - self.rf - self.margin_rate * margin
tans = self.weighted_annual_stddev(tanw)
self.Optimal = {
'Weights': tanww,
'Return': tanr,
'AdjReturn': tanra,
'StdDev': tans,
'Sharpe': tanra / tans,
'Series': self.weighted_return(tanw)
}
# efficent frontier
if full_analysis == True:
analyze = np.linspace(max(self.Optimal['Return'] / 4, 0.0001),
min(self.Optimal['Return'] * 4, 0.5),
simulations)
efficent = []
eff = []
for ret in analyze:
e = self.optimize_efficent(ret, constraints, tol, maxiter,
disp)
we = e['x']
wew = pd.DataFrame(we).T
wew.columns = columns
margin = np.sum(np.abs(we)) - 1
re = self.weighted_annual_return(
we) - self.rf - self.margin_rate * margin
se = self.weighted_annual_stddev(we)
efficent.append({
'Weights': wew,
'Returns': re,
'StdDevs': se,
'Sharpe': re / se
})
eff.append(e)
self.EfficentParam = eff
self.EfficentFrontier = efficent
# close
self.ConstituentSharpe = self.get_sharpes(columns)
def __init__(self, feed_dict, full_analysis):
# initiate common vatiables
self.returns = feed_dict['input']
self.covar = self.returns.cov()
del feed_dict['input']
columns = []
for col in self.returns.columns:
columns.append(col.split('_')[0])
self.bins = oc.get_bins(self.returns)
# save parameters in case replication is desired
self.Parameters = deepcopy(feed_dict)
self.Parameters.update({'num_bins': deepcopy(self.bins)})
objective, features, constraints, simulations, methods, tol, maxiter, disp = oc.unfeeder(
feed_dict)
if objective['target']:
self.target = objective['target']
else:
self.target = 0.0
# features
if 'benchmark' in features:
self.benchmark = features['benchmark']
else:
raise AttributeError('Beta requires a benchmark.')
self.periodicity = _embed_periodicity(features['periodicity'])
if methods == 'arithmetic':
self.mean_returns = self.returns.mean() * self.periodicity
if methods == 'geometric':
self.mean_returns = _geomean(self.returns, self.periodicity)
if 'margin_int_rate' in features:
self.margin_rate = features['margin_int_rate']
else:
self.margin_rate = 0
# constraints
if 'min_weights' in constraints:
self.weight_bounds = tuple(
zip(constraints['min_weights'], constraints['max_weights']))
else:
self.weight_bounds = None
constraints = self.get_constraints(constraints)
# optimal portfolio
self.guess = np.random.normal(0, 0.5, len(self.mean_returns)) / 10
self.OptiParam = minimize(self.optimize_beta,
self.guess,
method='SLSQP',
bounds=self.weight_bounds,
tol=tol,
constraints=constraints,
options={
'maxiter': maxiter,
'disp': disp
})
if not self.OptiParam['success']:
print('Warning!', self.OptiParam['message'])
tanw = self.OptiParam['x']
tanww = pd.DataFrame(tanw).T
tanww.columns = columns
tanr = self.weighted_annual_return(tanw)
margin = np.sum(np.abs(tanw)) - 1
tanra = tanr - self.margin_rate * margin
tans = self.weighted_annual_stddev(tanw)
tanss = self.weighted_return(tanw)
beta = self.benchmark.cov(tanss) / self.benchmark.std()
self.Optimal = {
'Weights': tanww,
'Return': tanr,
'AdjReturn': tanra,
'StdDev': tans,
'Beta': beta,
'Series': tanss
}
# Beta frontier
if full_analysis == True:
analyze = np.linspace(max(self.Optimal['Return'] / 4, 0.0001),
min(self.Optimal['Return'] * 4, 0.5),
simulations)
efficent = []
eff = []
for ret in analyze:
self.ret = ret
e = self.optimize_betas(ret, constraints, tol, maxiter, disp)
we = e['x']
wew = pd.DataFrame(we).T
wew.columns = columns
margin = np.sum(np.abs(we)) - 1
re = self.weighted_annual_return(
we) - self.margin_rate * margin
se = self.weighted_annual_stddev(we)
be = self.benchmark.cov(
self.weighted_return(we)) / self.benchmark.std()
efficent.append({
'Weights': wew,
'Returns': re,
'StdDevs': se,
'Beta': be
})
eff.append(e)
self.EfficentParam = eff
self.EfficentFrontier = efficent
# Constituents
self.ConstituentBeta = self.get_betas(columns)
def __init__(self, feed_dict, full_analysis):
# initiate common vatiables
self.returns = feed_dict['input']
self.covar = self.returns.cov()
del feed_dict['input']
columns = []
for col in self.returns.columns:
columns.append(col.split('_')[0])
self.bins = oc.get_bins(self.returns)
# save parameters in case replication is desired
self.Parameters = deepcopy(feed_dict)
self.Parameters.update({'num_bins': deepcopy(self.bins)})
objective, features, constraints, simulations, methods, tol, maxiter, disp = oc.unfeeder(
feed_dict)
if objective['target']:
self.dist_type = objective['target']
else:
self.dist_type = False
# features
if 'benchmark' in features:
self.benchmark = features['benchmark']
else:
raise AttributeError('SharpeEdge requires a benchmark.')
self.periodicity = _embed_periodicity(features['periodicity'])
if methods == 'arithmetic':
self.mean_returns = self.returns.mean() * self.periodicity
self.benchmark_mean = self.benchmark.mean() * self.periodicity
if methods == 'geometric':
self.mean_returns = _geomean(self.returns, self.periodicity)
self.benchmark_mean = _geomean(self.benchmark, self.periodicity)
## Benchmark AE param
self.benchmark_stdev = self.benchmark.std() * np.sqrt(self.periodicity)
if self.dist_type == False:
if self.bins < 100:
dist_list = [st.triang, st.dgamma, st.levy_stable, st.gennorm]
else:
dist_list = [st.norm, st.johnsonsu, st.t]
dist = oc.get_distribution(self.benchmark, self.bins, dist_list)
loc, scale, args = oc.unpack_dist_params(dist[1])
self.benchmark_p_loss = dist[0].cdf(0, loc=loc, scale=scale, *args)
self.benchmark_p_profit = 1 - self.benchmark_p_loss
self.benchmark_sharpeedge = (
self.benchmark_mean * self.benchmark_p_profit) / (
self.benchmark_stdev * self.benchmark_p_loss)
self.benchmark_dist = (dist[0].name, dist[1], dist[2])
else:
self.benchmark_p_loss = st.percentileofscore(self.benchmark,
0) / 100
self.benchmark_p_profit = 1 - self.benchmark_p_loss
self.benchmark_sharpeedge = (
self.benchmark_mean * self.benchmark_p_profit) / (
self.benchmark_stdev * self.benchmark_p_loss)
if 'margin_int_rate' in features:
self.margin_rate = features['margin_int_rate']
else:
self.margin_rate = 0
if 'rf' in features:
self.rf = features['rf']
else:
self.rf = 0
# constraints
if 'min_weights' in constraints:
self.weight_bounds = tuple(
zip(constraints['min_weights'], constraints['max_weights']))
else:
self.weight_bounds = None
constraints = self.get_constraints(constraints)
# optimal portfolio
self.guess = np.random.normal(0, 0.5, len(self.mean_returns)) / 10
self.OptiParam = minimize(self.optimize_ae,
self.guess,
method='SLSQP',
bounds=self.weight_bounds,
tol=tol,
constraints=constraints,
options={
'maxiter': maxiter,
'disp': disp
})
if not self.OptiParam['success']:
print('Warning!', self.OptiParam['message'])
tanw = self.OptiParam['x']
tanww = pd.DataFrame(tanw).T
tanww.columns = columns
tanr = self.weighted_annual_return(tanw)
margin = np.sum(np.abs(tanw)) - 1
tanra = tanr - self.margin_rate * margin
tans = self.weighted_annual_stddev(tanw)
if self.dist_type == False:
ae, a, pp, pl, dist = self.optimize_ae(tanw, True)
self.Optimal = {
'Weights': tanww,
'Return': tanr,
'AdjReturn': tanra,
'StdDev': tans,
'SharpeEdge': ae,
'Sharpe': (tanra - self.rf) / tans,
'Alpha': a,
'Prob_Profit': pp,
'Prob_Loss': pl,
'Distribution': dist,
'Series': self.weighted_return(tanw)
}
else:
ae, a, pp, pl = self.optimize_var(tanw, True)
self.Optimal = {
'Weights': tanww,
'Return': tanr,
'AdjReturn': tanra,
'StdDev': tans,
'SharpeEdge': ae,
'Sharpe': (tanra - self.rf) / tans,
'Alpha': a,
'Prob_Profit': pp,
'Prob_Loss': pl,
'Series': self.weighted_return(tanw)
}
# AlphaEdge frontier
if full_analysis == True:
analyze = np.linspace(max(self.Optimal['Return'] / 4, 0.0001),
min(self.Optimal['Return'] * 4, 0.5),
simulations)
efficent = []
eff = []
for ret in analyze:
self.ret = ret
e = self.optimize_vars(ret, constraints, tol, maxiter, disp)
we = e['x']
wew = pd.DataFrame(we).T
wew.columns = columns
margin = np.sum(np.abs(we)) - 1
re = self.weighted_annual_return(
we) - self.margin_rate * margin
se = self.weighted_annual_stddev(we)
efficent.append({
'Weights': wew,
'Returns': re,
'StdDevs': se,
'SharpeEdge': -self.optimize_ae(we)
})
eff.append(e)
self.EfficentParam = eff
self.EfficentFrontier = efficent
# Constituents
self.ConstituentSharpeEdge = self.get_aes(columns)