def runLambda(q: Q, f):
connections.create_connection()
s = Stock.search()
r = s.query(q)
# for stock in r.scan():
stock = Stock.get(id="FR0000076887")
f(stock)
stock.save()
def main():
stock = Stock.get(id="FR0000035370")
#ibg.reqMatchingSymbols(20, stock.symbol)
c = stock.contract()
#c.primaryExchange = "SBF"
result = ibg.getFundamentalData(stock.contract(), "ReportsFinStatements")
f = open("data/" + stock.symbol + ".xml", "w")
f.write(result)
f.close()
print(result)
def pollIB():
s = Stock.search()
s.query = Bool(must_not=[Q('exists', field='lastFinPoll'), Match(notValid=True)])
print(json.dumps(s.query.to_dict()))
#sit = iter(s.scan())
#for i in range(0,10):
for stock in s.scan():
#stock = next(sit)
print(stock.symbol, stock.isin)
result = getFundamentalData(stock)
if result == None:
print("Invalid")
stock.notValid = True
else:
stock.parseFinancials(result)
stock.lastFinPoll = datetime.datetime.now()
stock.save()
def getFundamentalData(stock: Stock):
fName = 'data/' + stock.isin + ".xml"
if (os.path.isfile(fName)):
with open(fName, "r") as f:
ret = f.read()
if ret == '':
return None
else:
return ret
time.sleep(10)
ret = ibg.getFundamentalData(stock.contract(), "ReportsFinStatements")
with open(fName, "w") as f:
if ret == None:
f.write('')
else:
f.write(ret)
return ret
def generateDF(self):
s = Stock.search()
r = s.query(Bool(must=[Q('exists', field='lastFinPoll')], must_not=[Match(notValid=True)]))
tuples = []
dates = self.generateDateDimension()
data = []
for stock in r:
print(stock.symbol)
for d in dates:
tuples.append((d, stock.symbol))
row = []
for f in self.fields:
row.append(stock.getMetric(d, f))
data.append(row)
mindex = pandas.MultiIndex.from_tuples(tuples)
df = pandas.DataFrame(data=data, index=mindex, columns=self.fields)
return df
def parseCsv(self, spamreader): it = iter(spamreader) header = next(it) docs = [] for row in it: dictRow = to_dict(header, row) if (dictRow != None): s = Stock() s.name = dictRow['Name'] s.isin = dictRow['ISIN'] s.meta.id = s.isin s.symbol = dictRow['Symbol'] s.market = dictRow['Market'] s.currency = dictRow['Trading Currency'] docs.append(s) bulk(connections.get_connection(), (d.to_dict(True) for d in docs))
def generateDF2(self):
s = Stock.search()
r = s.query(Bool(must=[Q('exists', field='lastFinPoll')],
must_not=[Match(notValid=True)]))
tuples = []
dates = self.generateDateDimension()
data = []
i = 0
for stock in r.scan():
i = i + 1
if i % 100 == 0:
print(i)
#print(stock.symbol)
sdf = stock.getMetrics(dates, self.fields)
data.append(sdf)
# mindex = pandas.MultiIndex.from_tuples(tuples)
# df = pandas.DataFrame(data=data, index=mindex, columns=self.fields)
df = pandas.concat(data)
return df
header = next(it) docs = [] for row in it: dictRow = to_dict(header, row) if (dictRow != None): s = Stock() s.name = dictRow['Name'] s.isin = dictRow['ISIN'] s.meta.id = s.isin s.symbol = dictRow['Symbol'] s.market = dictRow['Market'] s.currency = dictRow['Trading Currency'] docs.append(s) bulk(connections.get_connection(), (d.to_dict(True) for d in docs)) def parse(self): with open(self.fileName, newline='') as csvfile: spamreader = csv.reader(csvfile, delimiter=';', quotechar='"') self.parseCsv(spamreader) if __name__ == "__main__": enParser = EuroNextParser() connections.create_connection() Stock.init() enParser.downloadAndParse()
def computeIntermediatePeriods(stock: Stock):
print(stock.symbol)
fins = stock.fins
fins.sort(key=lambda fp: fp['@Type'] + ":" + fp['startDate'])
stock.fins = fins