def eval_scenario(returns_params, backtest_params, portfolio_params):
excess_returns = data.load_excess_returns(**returns_params)
# Setup backtest return splits.
training_returns, testing_returns = (
backtest.split_returns(excess_returns, **backtest_params))
results = list()
period = 0
for er_train, er_test in zip(training_returns, testing_returns):
try:
sol = dccportfolio.portfolio_sphere(
er_train,
extended_return=True, verbose=False,
**portfolio_params)
except LinAlgError:
logging.error('Linear algebra error encountered.')
sol = ('', '', '', '', 'linear_algebra_error')
portfolio = sol[0]
status = sol[4]
if status == 'solution_found':
returns = er_test.dot(portfolio)
else:
returns = ''
sol_keys = ['portfolio', 'objective', 'sdp_solution', 'constraints', 'status']
result = {
k: pg.array2str(s)
for k, s in zip(sol_keys, sol)
}
result['test_returns'] = pg.array2str(returns)
result['period'] = period
period += 1
results.append(result)
logging.debug('period = {0}'.format(period))
return {'kwargs': {'returns': returns_params,
'backtest': backtest_params,
'portfolio': portfolio_params},
'results': results}
def eval_scenario(returns_params, portfolio_params):
excess_returns = data.load_excess_returns(**returns_params)
try:
sol = dccportfolio.portfolio_sphere(
excess_returns,
extended_return=True, verbose=False,
**portfolio_params)
except LinAlgError:
logging.error('Linear algebra error encountered.')
sol = ('', '', '', '', 'linear_algebra_error')
sol_keys = ['portfolio', 'obj', 'sdp_solution', 'g', 'status']
result = {
k: pg.array2str(s)
for k, s in zip(sol_keys, sol)
}
return {
'kwargs': {
'returns': returns_params,
'portfolio': portfolio_params},
'result': result}
