class Analysis():
def __init__(self, code):
self.code = code
self.data = pandas.read_csv('backend/posts.csv')
self.flairs = {}
self.r = Requests(code)
self.name = self.r.getName()
self.total_flairs = 0
def findFlairs(self):
for i in range(len(self.data)):
if (self.name or self.code) in (self.data.loc[i, 'Title'].upper()):
if self.data.loc[i, 'Flair'] in self.flairs:
self.flairs[self.data.loc[i, 'Flair']] += 1
self.total_flairs += 1
else:
self.flairs[self.data.loc[i, 'Flair']] = 1
self.total_flairs += 1
def return_prediction(self):
voilitity = (self.flairs['Discussion'] + self.flairs['DD'] +
self.flairs['YOLO'] + self.flairs['Options'] -
self.flairs['Fundamentals']) / self.total_flairs
try:
score = (self.flairs['Gain'] -
self.flairs['Loss']) * voilitity # 0 to 100
except:
score = 0
t = self.r.getAnalysis()
b = t['buy']
s = t['sell']
sb = t['strongBuy']
ss = t['strongSell']
finScore = (2 * sb + b - s - 2 * ss) / (b + s + sb + ss)
return (finScore + score) / 2
