def testGetOvernightTrades(self):
'''
The only way to automate this is to just figure all the same data and compare it.
'''
indir = "data/"
for infile in self.infiles:
inpathfile = os.path.join(indir, infile)
os.path.exists(inpathfile)
dframe = pd.read_csv(inpathfile)
sl = list()
# Total up the shares found for each unique ticker/account to create our own
# list of overnight trades (unbalanced trades are overnight) Then verify its
# the same list as that from getOvernightTrades
for symbol in dframe['Symb'].unique():
# print(symbol)
df = dframe[dframe['Symb'] == symbol]
for account in df['Account'].unique():
df1 = df[df['Account'] == account]
buy = 0
for dummy, row in df1.iterrows():
# print(row['Side'], row['Qty'])
if row['Side'].startswith('S'):
buy = buy - row['Qty']
elif row['Side'].startswith('B'):
buy = buy + row['Qty']
else:
print('error', 'Side is ', row['Side'])
if buy != 0:
# Create the same data format used in getOvernightTrades
sl.append({
'ticker': symbol,
'shares': buy,
'before': 0,
'after': 0,
'acct': account
})
idf = InputDataFrame()
dframe = idf.mkShortsNegative(dframe)
st = idf.getOvernightTrades(dframe)
self.assertEqual(len(sl), len(st))
for trade in sl:
self.assertTrue(trade in st)
def test_MkShortNegative(self):
'''
Test the method Statement_DAS.mkShortsNegative
'''
rc = ReqCol()
for dummy in range(random.randint(2, 10)):
side, mult, shares = getTestSet(random.randint(4, 20))
testSet = list(zip(side, shares))
apd = pd.DataFrame(testSet, columns=[rc.side, rc.shares])
for i in range(len(side)):
# self.assertEqual(apd[rc.shares][i], shares[i])
self.assertEqual(apd[rc.shares][i], shares[i])
idf = InputDataFrame()
apd = idf.mkShortsNegative(apd)
for i in range(len(side)):
self.assertEqual(apd[rc.shares][i], shares[i] * mult[i])