def advanceOptiuon(self):
#every period calculates the appropriate statistic and prints
#to the console and dataset
data1 = self.sheetname.col_values(2)
data1[:] = [item for item in data1 if item != '']
dataframe1 = pd.DataFrame(data1)
#print(dataframe1)
dataframe2 = pd.DataFrame(data1[::99])
#ensures objects flow in and out of R and Python without catastrophic failures
pandas2ri.activate()
#the final R code used, prior code can't be loaded with this functionality
string = """statcalc <- function(x, size = 100){
x <- na.omit(x)
library(TTR)
library(stats)
xa <- ts(x)
arima <- arima(xa, c(0,0,0))
rsi <- RSI(xa, size - 1)[size]
list <- c(arima$coef, rsi)
return(as.matrix(list))
}
"""
statcalc = SignatureTranslatedAnonymousPackage(string, "statcalc")
newlist = statcalc.statcalc(dataframe1, len(dataframe1))
otherlist = statcalc.statcalc(dataframe2, len(dataframe2))
winsandlosses = float(data1[99]) - float(data1[0])
#the basic version of the algorithm, subject to change at a later date
rsi = float(otherlist[1])
if rsi > 80:
rsi = 0
if rsi < 35:
rsi = 1
gains = float(data1[99])
arima = otherlist[0]
arimaOverall = newlist[0]
if gains - float(arima) > 0:
topthird = 0
else:
topthird = 1
if gains - float(arimaOverall) > 0:
bottomthird = 0
else:
bottomthird = 1
midthird = (newlist[1]) / 100
arimaIndex = (float(2 / 3 * bottomthird) + float(1 / 3 * midthird) +
float(2 / 3 * topthird)) * 3 / 5
if (rsi / 2 + arimaIndex / 2) > .5:
rowtoinsert = [
'', '', 'period gain: ',
str(winsandlosses), 'it is recommended to: buy', 1,
'rsi index is: ' + str(rsi),
'arima index is: ' + str(arimaIndex)
]
else:
rowtoinsert = [
'', '', 'period gain: ',
str(winsandlosses), 'it is recommended to: sell', 0,
'rsi index is: ' + str(rsi),
'arima index is: ' + str(arimaIndex)
]
self.sheetname.insert_row(rowtoinsert)
