def overall_metrics(self, timestamp='one_month', metrics=None, save=False, db_id=None):
'''
Use zipline results to compute some performance indicators and store it in database
'''
perfs = dict()
# If no rolling perfs provided, computes it
if metrics is None:
metrics = self.rolling_performances(timestamp=timestamp, save=False, db_id=db_id)
riskfree = np.mean(metrics['Treasury.Returns'])
#NOTE Script args define default database table name (test), make it consistent
if db_id is None:
db_id = self.configuration['algorithm'] + pd.datetime.strftime(pd.datetime.now(), format='%Y%m%d')
perfs['Name'] = db_id
perfs['Sharpe.Ratio'] = qstk_get_sharpe_ratio(metrics['Returns'].values, risk_free=riskfree)
perfs['Returns'] = (((metrics['Returns'] + 1).cumprod()) - 1)[-1]
perfs['Max.Drawdown'] = max(metrics['Max.Drawdown'])
perfs['Volatility'] = np.mean(metrics['Volatility'])
perfs['Beta'] = np.mean(metrics['Beta'])
perfs['Alpha'] = np.mean(metrics['Alpha'])
perfs['Benchmark.Returns'] = (((metrics['Benchmark.Returns'] + 1).cumprod()) - 1)[-1]
if save:
self.datafeed.stock_db.save_performances(perfs)
return perfs
def overall_metrics(self, timestamp='one_month', metrics=None, save=False, db_id=None):
'''
Use zipline results to compute some performance indicators and store it in database
'''
perfs = dict()
# If no rolling perfs provided, computes it
if metrics is None:
metrics = self.rolling_performances(timestamp=timestamp, save=False, db_id=db_id)
riskfree = np.mean(metrics['Treasury.Returns'])
#NOTE Script args define default database table name (test), make it consistent
if db_id is None:
db_id = self.configuration['algorithm'] + pd.datetime.strftime(pd.datetime.now(), format='%Y%m%d')
perfs['Name'] = db_id
perfs['Sharpe.Ratio'] = qstk_get_sharpe_ratio(metrics['Returns'].values, risk_free=riskfree)
perfs['Returns'] = (((metrics['Returns'] + 1).cumprod()) - 1)[-1]
perfs['Max.Drawdown'] = max(metrics['Max.Drawdown'])
perfs['Volatility'] = np.mean(metrics['Volatility'])
perfs['Beta'] = np.mean(metrics['Beta'])
perfs['Alpha'] = np.mean(metrics['Alpha'])
perfs['Benchmark.Returns'] = (((metrics['Benchmark.Returns'] + 1).cumprod()) - 1)[-1]
if save:
self.datafeed.stock_db.save_performances(perfs)
return perfs
def overall_metrics(self, timestamp='one_month', metrics=None):
'''
Use zipline results to compute some performance indicators
'''
perfs = dict()
# If no rolling perfs provided, computes it
if metrics is None:
metrics = self.rolling_performances(timestamp=timestamp)
riskfree = np.mean(metrics['treasury_period_return'])
perfs['sharpe'] = qstk_get_sharpe_ratio(
metrics['algorithm_period_return'].values, risk_free=riskfree)
perfs['algorithm_period_return'] = (
((metrics['algorithm_period_return'] + 1).cumprod()) - 1)[-1]
perfs['max_drawdown'] = max(metrics['max_drawdown'])
perfs['algo_volatility'] = np.mean(metrics['algo_volatility'])
perfs['beta'] = np.mean(metrics['beta'])
perfs['alpha'] = np.mean(metrics['alpha'])
perfs['benchmark_period_return'] = (
((metrics['benchmark_period_return'] + 1).cumprod()) - 1)[-1]
return perfs
