def test_logcalc_return_type(self):
"""check if the value returned by the calc_return
function is of type numpy.float64 when the return type
is log.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if different return type encountered.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
out_return = functions.calc_return(data_input, start, end, return_type='log')
out_type = str(type(out_return))
if out_type == "<class 'numpy.float64'>" or out_type == "<type 'numpy.float64'>":
out_bool = 1
else:
out_bool = 1
self.assertEqual(out_bool, 1)
def test_num_rows_portfolio(self):
"""check if track_portfolio returns a number of records
equal to the number of unique date_time indices.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected num_rows encountered.
Raises:
Raises AssertionError Values not equal
"""
stock = functions.invest_dataframe(FILE_NAME)
bond = functions.invest_dataframe(BOND_FILE_NAME)
stockshare = TEST_STOCKSHARE
alloc = [(stock, stockshare), (bond, 1 - stockshare)]
start = pd.Timestamp(str(BOND_START_YEAR) + '-01-02 00:00:00', tz=None)
end = pd.Timestamp(str(BOND_END_YEAR) + '-01-03 00:00:00', tz=None)
x_portfolio = functions.track_portfolio(INITIAL_INV, alloc, QUARTER, start, end)
rows_to_have = x_portfolio.index.nunique()
self.assertEqual(len(x_portfolio), rows_to_have)
def test_date_time_indices(self):
"""check if index is a DateTime index
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected columns encountered.
Raises:
Raises AssertionError: Type not equal
"""
df_to_test = functions.invest_dataframe(FILE_NAME)
self.assertEqual(type(df_to_test.index), pd.core.indexes.datetimes.DatetimeIndex)
def test_calc_risk_return_val(self):
"""verify that the output of the calc_risk function is
greater than or equal to zero.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected value encountered.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
out_return = functions.calc_risk(data_input, start, end)
self.assertGreaterEqual(out_return, 0)
def test_exccalc_risk_return_type(self):
"""check if calc_risk function raises an exception
when risk type is neither proba or stddev.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected risk_type encountered.
Raises:
Raises AssertionError if Exception not raised
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
with self.assertRaises(Exception):
functions.calc_risk(data_input, start, end, risk_type='null')
def test_return_list_type(self):
"""check if the output of the invest_dataframe function
is a numpy array (numpy.ndarray).
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected return type encountered.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
out_return = functions.return_list(data_input, start, end)
self.assertEqual(np.ndarray, type(out_return))
def test_calc_return_type(self):
"""check if the value returned by the calc_return
function is of type float.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if different return type encountered.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
out_return = functions.calc_return(data_input, start, end, return_type='percent')
self.assertEqual(float, type(out_return))
def test_num_rows(self):
"""check if number of records is as expected.
Since that each date occurs only once, number
of records should be the number of unique date_time
indices.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error.
Raises:
Raises AssertionError Values not equal
"""
df_to_test = functions.invest_dataframe(FILE_NAME)
rows_to_have = df_to_test.index.nunique()
self.assertEqual(len(df_to_test), rows_to_have)
def test_return_rate(self):
"""check that calc_return gives a valid return rate
between 0 and 100, when the return_type is percent.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else raises
an error if unexpected return value encountered.
Raises:
Raises AssertionError Values not equal
"""
df_t = functions.invest_dataframe(FILE_NAME)
start = pd.Timestamp(str(BOND_START_YEAR) + '-01-02 00:00:00', tz=None)
end = pd.Timestamp(str(BOND_END_YEAR) + '-01-03 00:00:00', tz=None)
ror_percent = functions.calc_return(df_t, start, end, return_type='percent', annualize=True)
self.assertGreaterEqual(ror_percent, 0)
self.assertLessEqual(ror_percent, 100)
def test_exccalc_return_type(self):
"""check if exception raised when return type is neither
log or percent.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if no exception raised with an unexpected
return type.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
with self.assertRaises(Exception):
functions.calc_return(data_input, start, end, return_type='null')
def test_file_name(self):
"""check if the index of output dataframe has the
same name as the input file.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected column name encountered.
Raises:
Raises AssertionError Values not equal
"""
df_to_test = functions.invest_dataframe(FILE_NAME)
out_file_name = list(df_to_test)[0]
char_one = "/"
char_two = "."
break1 = [pos for pos, char in enumerate(FILE_NAME) if char == char_one]
break2 = [pos for pos, char in enumerate(FILE_NAME) if char == char_two]
in_file_name = FILE_NAME[break1[-1] + 1:break2[-1]]
self.assertEqual(in_file_name, out_file_name)
def test_return_list_num_rows(self):
"""check if number of rows returned by the return_list
function is less than or equal to the number of days
being considered in the calculation.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected num_rows encountered.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
out_return = functions.return_list(data_input, start, end)
num_days_str = str(end - start)
num_days = int(num_days_str[:num_days_str.find(" ")])
self.assertLessEqual(out_return.shape[0], num_days)
def test_num_rows_with_data(self):
"""Check that the number of rows in the dataframe
returned is greater than the number of rows in the
input data file.
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay
else raises an error if unexpected number of
rows encountered.
Raises:
Raises AssertionError Values not equal
"""
len_file = 0
with open(FILE_NAME) as file_open:
for _ in enumerate(file_open):
len_file += 1
df_to_test = functions.invest_dataframe(FILE_NAME)
rows_output = df_to_test.index.shape[0]
self.assertGreater(rows_output, len_file)
def test_probcalc_risk_return_type(self):
"""check if calc_risk function returns an output
of type numpy.float64 when risk type = proba
Args:
No special arguments as it is a unittest.
Returns:
No return values. Passes the test if all okay else
raises an error if unexpected return type encountered.
Raises:
Raises AssertionError Values not equal
"""
data_input = functions.invest_dataframe(FILE_NAME)
start = TEST_START
end = TEST_END
out_return = functions.calc_risk(data_input, start, end, risk_type='proba')
out_type = str(type(out_return))
if out_type == "<class 'numpy.float64'>" or out_type == "<type 'numpy.float64'>":
out_bool = 1
else:
out_bool = 1
self.assertEqual(out_bool, 1)
"""
This file contains the functions which take the user inputs
from the frontend, send them to the backend to interact with
the data, then returns the information to the frontend for graphing.
"""
from backend.functions import invest_dataframe, track_portfolio_cache, label_risk_return
# Dictionary translating descriptions of investment classes to data sets
# Expand as necessary in the future.
INVESTMENT_CLASS_DICT = {
'U.S. large-cap stocks (S&P 500 index)':
invest_dataframe('./Data/SP500.csv'),
'U.S. large-cap stocks (Wilshire index)':
invest_dataframe('./Data/WILLLRGCAP.csv'),
'U.S. mid-cap stocks (Wilshire index)':
invest_dataframe('./Data/WILLMIDCAP.csv'),
'U.S. small-cap stocks (Wilshire index)':
invest_dataframe('./Data/WILLSMLCAP.csv'),
'U.S. corporate bonds (investment-grade, AAA rated)':
invest_dataframe('./Data/BAMLCC0A1AAATRIV.csv'),
'U.S. corporate bonds (investment-grade, BBB rated)':
invest_dataframe('./Data/BAMLCC0A4BBBTRIV.csv'),
'U.S. Treasury bonds, total market (S&P index)':
invest_dataframe('./Data/SPUSBOND.csv'),
'U.S. Treasury bonds, 0-1 year (S&P index)':
invest_dataframe('./Data/SP01BOND.csv'),
'U.S. Treasury bonds, 1-3 year (S&P index)':
invest_dataframe('./Data/SP13BOND.csv'),
'U.S. Treasury bonds, 3-5 year (S&P index)':
invest_dataframe('./Data/SP35BOND.csv'),
'U.S. Treasury bonds, 5-7 year (S&P index)':
