# Read local (.cfg files and command line args) or remote (ZMQ Messages) backtest, algorithm and manager configuration
args = engine.configure()
# See neuronquant/calculus/engine.py or zipline for details on results dataframe
results = engine.run_backtest()
'''--------------------------------------------------------------------------------------------- Results ----'''
log.info('Portfolio returns: {}'.format(results.portfolio_value[-1]))
if args['live'] or results.portfolio_value[-1] == 100000:
# Currently tests don't last more than 20min, analysis is not relevant, neither backtest without orders
sys.exit(0)
#TODO Implement in datafeed a generic save method (which could call the correct database save method)
#NOTE Could do a generic save client method (retrieve the correct model, with correct fields)
perf_series = engine.rolling_performances(timestamp='one_month', save=False, db_id='test')
#TODO save returns not ready yet, don't try to save
#TODO more benchmarks choice (zipline modification)
returns_df = engine.get_returns(benchmark='SP500', save=False)
risk_metrics = engine.overall_metrics(save=True, db_id='test')
log.info('\n\nReturns: {}% / {}%\nVolatility:\t{}\nSharpe:\t\t{}\nMax drawdown:\t{}\n\n'.format(
risk_metrics['Returns'] * 100.0,
risk_metrics['Benchmark.Returns'] * 100.0,
risk_metrics['Volatility'],
risk_metrics['Sharpe.Ratio'],
risk_metrics['Max.Drawdown']))
# If we work in local, draw a quick summary plot
if not args['remote']:
data = returns_df.drop(['Returns', 'Benchmark.Returns'], axis=1)
# Read local (.cfg files and command line args) or remote (ZMQ Messages) backtest, algorithm and manager configuration
args = engine.configure()
# See neuronquant/calculus/engine.py or zipline for details on results dataframe
results = engine.run_backtest()
'''--------------------------------------------------------------------------------------------- Results ----'''
log.info('Portfolio returns: {}'.format(results.portfolio_value[-1]))
if args['live'] or results.portfolio_value[-1] == 100000:
# Currently tests don't last more than 20min, analysis is not relevant, neither backtest without orders
sys.exit(0)
#TODO Implement in datafeed a generic save method (which could call the correct database save method)
#NOTE Could do a generic save client method (retrieve the correct model, with correct fields)
perf_series = engine.rolling_performances(timestamp='one_month', save=True, db_id=args['database'])
#TODO save returns not ready yet, don't try to save
#TODO more benchmarks choice (zipline modification)
returns_df = engine.get_returns(benchmark='^GSPC', save=False)
risk_metrics = engine.overall_metrics(save=True, db_id=args['database'])
#FIXME irrelevant results if no transactions were made
log.info('\n\nReturns: {}% / {}%\nVolatility:\t{}\nSharpe:\t\t{}\nMax drawdown:\t{}\n\n'.format(
risk_metrics['Returns'] * 100.0,
risk_metrics['Benchmark.Returns'] * 100.0,
risk_metrics['Volatility'],
risk_metrics['Sharpe.Ratio'],
risk_metrics['Max.Drawdown']))
# If we work in local, draw a quick summary plot
if not args['remote']:
