def find_experiment_ids_by_assets(assets):
assets_str = pg.array2pgarray(assets)
query = '''
SELECT * FROM experiments
WHERE asset_columns = \'{0}\'
'''.format(assets_str)
return pd.read_sql(query, conn)
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
output['backtest_id'] = backtest_id
outputs.append(output)
output_table = pd.DataFrame(outputs)
output_table.to_csv('dcc_backtest_statistics.csv', index=False)