def execute(self, dateValuesDict):
''' processing input'''
super(AvgDivProcessor, self).execute(dateValuesDict)
for dateValues in dateValuesDict.values():
stockMeasurement = StockMeasurement(dateValues)
data = {
'days': len(dateValues),
'avg': stockMeasurement.mean(),
'standard deviation': stockMeasurement.std(),
'alpha': stockMeasurement.alpha(),
'beta': stockMeasurement.beta()
}
return data
def analyze(self):
''' analyze '''
print 'Start analyzing'
buildBenchmarkValues()
returnRates = [[], [], [], [], [], [], []]
alphas = [[], [], [], [], [], [], []]
relativeReturnRates = [[], [], [], [], [], [], []]
for fileName in filter(lambda f: f.endswith('.xls'),
os.listdir(self.__workingDir)):
excelFile = os.path.join(self.__workingDir, fileName)
sheetNames = ExcelLib.getSheetNames(excelFile)
for sheetName in sheetNames:
with ExcelLib(excelFile) as excel:
excel.openSheet(sheetName=sheetName)
contry = sheetName.split('.')[-1] if len(
sheetName.split('.')) != 1 else 'DEFAULT'
benchmark = benchmarks[contry]
print 'Processing %s with benchmark %s' % (sheetName,
benchmark)
for index, duration in enumerate(self.dayIntervals):
data = []
broke = False
for i in range(1, duration + 1):
#print "row %d, duration %d" % (i, duration)
if broke:
break
try:
values = excel.readRow(i)
for j in range(len(values)):
values[j] = float(
values[j]) if j != 0 else values[j]
data.append(StockDailyType(*values))
except Exception:
print 'Analyzing %s break at %d' % (sheetName,
i)
broke = True
break
if data:
dateValues = sorted(data, key=itemgetter(0))
#print benchmarkValues[benchmark]
stockMeasurement = StockMeasurement(
dateValues, benchmark,
benchmarkValues[benchmark])
stockMeasurement.linearRegression()
returnRates[index].append(
stockMeasurement.returnRate())
alphas[index].append(stockMeasurement.alpha())
relativeReturnRates[index].append(
stockMeasurement.relativeReturnRate())
with open(os.path.join(self.__workingDir, 'output.txt'),
'w') as outputFile:
outputReturnRates = map(lambda x: sum(x) / len(x), returnRates)
outputAlphas = map(lambda x: sum(x) / len(x), alphas)
outputRelativeReturnRates = map(lambda x: sum(x) / len(x),
relativeReturnRates)
print "Days since going public %s" % self.dayIntervals
print "returnRates: %s" % outputReturnRates
print "alphas: %s" % outputAlphas
print "relativeReturnRates: %s" % outputRelativeReturnRates
outputFile.write("outputReturnRates %s\n" % outputReturnRates)
outputFile.write("outputAlphas %s\n" % outputAlphas)
outputFile.write("outputRelativeReturnRates %s\n" %
outputRelativeReturnRates)
outputFile.write("returnRates %s\n" % returnRates)
outputFile.write("alphas %s\n" % alphas)
outputFile.write("relativeReturnRates %s\n" % relativeReturnRates)
