def outputSummaryStats(self, curve, plot):
returns = curve['return']
if hasattr(self.dataHandler, "benchmarkData"):
benchmarkReturns = self.dataHandler.benchmarkData['return']
benchmarkReturns.name = self.benchmark
else:
benchmarkReturns = None
perf_metric, perf_df, rollingRisk = createPerformanceTearSheet(
returns=returns, benchmarkReturns=benchmarkReturns, plot=plot)
if self.portfolioType == PortfolioType.FullNotional:
positons = curve.drop([
'cash', 'commission', 'total', 'return', 'margin',
'equity_curve', 'pnl'
],
axis=1)
else:
positons = curve.drop([
'commission', 'total', 'return', 'margin', 'equity_curve',
'pnl'
],
axis=1)
aggregated_positons = createPostionTearSheet(positons, plot=plot)
transactions = extractTransactionFromFilledBook(self.filledBook.view())
turnover_rate = createTranscationTearSheet(transactions,
positons,
plot=plot)
if plot:
plt.show()
return perf_metric, perf_df, rollingRisk, aggregated_positons, transactions, turnover_rate
def outputSummaryStats(self, curve, other_curves, plot):
returns = curve['return']
if hasattr(self.dataHandler, "benchmarkData"):
benchmarkReturns = self.dataHandler.benchmarkData['return']
benchmarkReturns.name = self.benchmark
else:
benchmarkReturns = None
perf_metric, perf_df, rollingRisk = createPerformanceTearSheet(returns=returns,
benchmarkReturns=benchmarkReturns,
other_curves=other_curves,
plot=plot)
if self.portfolioType == PortfolioType.FullNotional:
positons = curve.drop(['cash', 'commission', 'total', 'return', 'margin', 'equity_curve', 'pnl'], axis=1)
else:
positons = curve.drop(['commission', 'total', 'return', 'margin', 'equity_curve', 'pnl'], axis=1)
aggregated_positons = createPostionTearSheet(positons, plot=plot)
transactions = extractTransactionFromFilledBook(self.filledBook.view())
turnover_rate = createTranscationTearSheet(transactions, positons, plot=plot)
if plot:
plt.show()
return perf_metric, perf_df, rollingRisk, aggregated_positons, transactions, turnover_rate