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