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):
sol = mvportfolio.portfolio(
er_train,
**portfolio_params)
portfolio = sol
returns = er_test.dot(portfolio)
result = {'portfolio': array2str(portfolio)}
result['test_returns'] = 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}
