def portfolioAnalysis(posDF,
startDate,
endDate,
notional=10000000.,
benchmark='000300.zicn',
isweight=False):
secIDs = posDF['instrumentID']
data = get_equity_eod(instruments=secIDs,
start_date=startDate,
end_date=endDate)
close_data = data['closePrice']
close_data = close_data.fillna(method='pad')
close_data.fillna(value=0., inplace=True)
columns = close_data.columns
for instrument in columns:
if isweight and notional:
invest_value = posDF[posDF.instrumentID == instrument]['position'].iloc[0] * notional
volume = int(invest_value / close_data[instrument].values[0])
else:
volume = posDF[posDF.instrumentID == instrument]['position'].iloc[0]
close_data[instrument] *= volume
prices = close_data.sum(axis=1)
perf_metric, perf_df, rollingRisk = createPerformanceTearSheet(prices=prices, benchmark=benchmark)
return perf_metric, perf_df, rollingRisk
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
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 portfolioAnalysis(posDF,
startDate,
endDate,
notional=10000000.,
benchmark='000300.zicn',
isweight=False):
secIDs = posDF['instrumentID']
data = get_equity_eod(instruments=secIDs,
start_date=startDate,
end_date=endDate)
close_data = data['closePrice']
close_data = close_data.fillna(method='pad')
close_data.fillna(value=0., inplace=True)
columns = close_data.columns
for instrument in columns:
if isweight and notional:
invest_value = posDF[posDF.instrumentID ==
instrument]['position'].iloc[0] * notional
volume = int(invest_value / close_data[instrument].values[0])
else:
volume = posDF[posDF.instrumentID ==
instrument]['position'].iloc[0]
close_data[instrument] *= volume
prices = close_data.sum(axis=1)
perf_metric, perf_df, rollingRisk = createPerformanceTearSheet(
prices=prices, benchmark=benchmark)
return perf_metric, perf_df, rollingRisk
