['experiment_id', 'backtest_id', 'parameters_id'])
outputs = list()
for bt in scenarios:
print(bt[0])
experiment_id, backtest_id, parameters_id = bt[0]
results = bt[1]
experiment = db.load_experiment(experiment_id)
backtest_params = db.load_backtests([backtest_id])
returns = db.load_returns(experiment)
excess_returns = returns.values
train, test = backtest.split_returns(
excess_returns,
backtest_params['update_periods'],
backtest_params['trailing_periods'])
portfolios = pg.decimallistseries2nparray(results.portfolio_weights)
portfolio_returns = pg.decimallistseries2nparray(results.test_returns)
train_mean = ps.mean(portfolios[0,:], train[0])
train_variance = ps.variance(portfolios[0,:], train[0])
eta = results.eta.iloc[0]
backtest_statistics = ps.rkw_statistics(
portfolios, portfolio_returns, eta=eta)
test_statistics = backtest_statistics['statistics']
agg_returns = backtest_statistics['aggregate_returns']
output = test_statistics
output['aggregate_returns'] = pg.array2pgarray(agg_returns)
output['train_mean_return'] = train_mean
output['train_variance'] = train_variance
output['parameters_id'] = parameters_id
output['experiment_id'] = experiment_id
