class TestDataFrames(TestCase):
def setUp(self):
self.dates = pd.date_range(start='2015-05-13', periods=5)
self.column_names = ['a', 'b', 'c', 'd', 'e']
self.prices_values = [[1., 1., 1., 1, 1.], [2., 2., 2., 2., 2.],
[3., 3., 3., 3., 3.], [4., 4., 4., 4., 4.],
[5., 5., 5., 5., 5.]]
self.test_prices_df = PricesDataFrame(data=self.prices_values,
index=self.dates,
columns=self.column_names)
self.log_returns_values = [
[0.693147, 0.693147, 0.693147, 0.693147, 0.693147],
[0.405465, 0.405465, 0.405465, 0.405465, 0.405465],
[0.287682, 0.287682, 0.287682, 0.287682, 0.287682],
[0.223144, 0.223144, 0.223144, 0.223144, 0.223144]
]
self.test_log_returns_df = LogReturnsDataFrame(
data=self.log_returns_values,
index=self.dates[1:],
columns=self.column_names)
self.simple_returns_values = [
[1.000000, 1.000000, 1.000000, 1.000000, 1.000000],
[0.500000, 0.500000, 0.500000, 0.500000, 0.500000],
[0.333333, 0.333333, 0.333333, 0.333333, 0.333333],
[0.250000, 0.250000, 0.250000, 0.250000, 0.250000]
]
self.test_simple_returns_df = SimpleReturnsDataFrame(
data=self.simple_returns_values,
index=self.dates[1:],
columns=self.column_names)
def test_num_of_columns(self):
self.assertEqual(self.test_prices_df.num_of_columns, 5)
def test_prices_dataframe_dtypes(self):
dtypes = self.test_prices_df.dtypes
self.assertEqual({dtype("float64")}, set(dtypes))
def test_log_returns_dataframe_dtypes(self):
dtypes = self.test_log_returns_df.dtypes
self.assertEqual({dtype("float64")}, set(dtypes))
def test_simple_returns_dataframe_dtypes(self):
dtypes = self.test_simple_returns_df.dtypes
self.assertEqual({dtype("float64")}, set(dtypes))
def test_prices_to_log_returns(self):
expected_dataframe = self.test_log_returns_df
actual_dataframe = self.test_prices_df.to_log_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_simple_to_log_returns(self):
expected_dataframe = self.test_log_returns_df
actual_dataframe = self.test_simple_returns_df.to_log_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_log_to_log_returns(self):
expected_dataframe = self.test_log_returns_df
actual_dataframe = self.test_log_returns_df.to_log_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_log_to_simple_returns(self):
expected_dataframe = self.test_simple_returns_df
actual_dataframe = self.test_log_returns_df.to_simple_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_simple_to_simple_returns(self):
expected_dataframe = self.test_simple_returns_df
actual_dataframe = self.test_simple_returns_df.to_simple_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_prices_to_simple_returns(self):
expected_dataframe = self.test_simple_returns_df
actual_dataframe = self.test_prices_df.to_simple_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_log_returns_to_prices(self):
expected_dataframe = self.test_prices_df
actual_dataframe = self.test_log_returns_df.to_prices()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_simple_returns_to_prices(self):
expected_dataframe = self.test_prices_df
actual_dataframe = self.test_simple_returns_df.to_prices()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_prices_to_prices(self):
expected_dataframe = self.test_prices_df
actual_dataframe = self.test_prices_df.to_prices()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_min_max_normalized(self):
normalized_prices = [[0.00, 0.00, 0.00, 0.00, 0.00],
[0.25, 0.25, 0.25, 0.25, 0.25],
[0.50, 0.50, 0.50, 0.50, 0.50],
[0.75, 0.75, 0.75, 0.75, 0.75],
[1.00, 1.00, 1.00, 1.00, 1.00]]
expected_dataframe = PricesDataFrame(data=normalized_prices,
index=self.dates,
columns=self.column_names)
actual_dataframe = self.test_prices_df.min_max_normalized()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_exponential_average(self):
smoothed_values = [[1.000000, 1.000000, 1.000000, 1.000000, 1.000000],
[1.940000, 1.940000, 1.940000, 1.940000, 1.940000],
[2.936400, 2.936400, 2.936400, 2.936400, 2.936400],
[3.936184, 3.936184, 3.936184, 3.936184, 3.936184],
[4.936171, 4.936171, 4.936171, 4.936171, 4.936171]]
expected_dataframe = PricesDataFrame(data=smoothed_values,
index=self.dates,
columns=self.column_names)
actual_dataframe = self.test_prices_df.exponential_average()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_aggregate_by_year(self):
dates = pd.DatetimeIndex(
['2015-06-01', '2015-12-30', '2016-01-01', '2016-05-01'])
test_dataframe = SimpleReturnsDataFrame(
data=self.simple_returns_values, index=dates)
expected_aggregated_rets = [[
2.000000, 2.000000, 2.000000, 2.000000, 2.000000
], [0.666666, 0.666666, 0.666666, 0.666666, 0.666666]]
expected_dataframe = SimpleReturnsDataFrame(
data=expected_aggregated_rets,
index=pd.DatetimeIndex(['2015-12-31', '2016-12-31']))
actual_dataframe = test_dataframe.aggregate_by_year()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
self.assertEqual({dtype("float64")}, set(actual_dataframe.dtypes))
def test_rolling_time_window(self):
actual_result = self.test_prices_df.rolling_time_window(
window_length=2, step=1, func=lambda x: x.mean())
expected_values = [[1.5, 1.5, 1.5, 1.5,
1.5], [2.5, 2.5, 2.5, 2.5, 2.5],
[3.5, 3.5, 3.5, 3.5, 3.5],
[4.5, 4.5, 4.5, 4.5, 4.5]]
expected_index = self.test_prices_df.index[-4:].copy(deep=True)
expected_columns = ['a', 'b', 'c', 'd', 'e']
expected_result = QFDataFrame(expected_values, expected_index,
expected_columns)
assert_dataframes_equal(expected_result,
actual_result,
absolute_tolerance=1e-20)
actual_result = self.test_prices_df.rolling_time_window(
window_length=2, step=1, func=lambda x: x.mean().mean())
expected_values = [1.5, 2.5, 3.5, 4.5]
expected_index = self.test_prices_df.index[-4:].copy(deep=True)
expected_result = QFSeries(expected_values, expected_index)
assert_series_equal(expected_result,
actual_result,
absolute_tolerance=1e-20)
self.assertEqual(dtype("float64"), actual_result.dtypes)
def test_total_cumulative_return(self):
actual_result = self.test_prices_df.total_cumulative_return()
expected_result = PricesSeries(index=self.test_prices_df.columns,
data=[4.0, 4.0, 4.0, 4.0, 4.0])
assert_series_equal(expected_result, actual_result)
self.assertEqual(dtype("float64"), actual_result.dtypes)
def test_stats_functions(self):
qf_df = QFDataFrame(data=self.prices_values,
index=self.dates,
columns=self.column_names)
max_qf_df = qf_df.max()
expected_max = QFSeries([5, 5, 5, 5, 5],
index=['a', 'b', 'c', 'd', 'e'])
self.assertEqual(type(max_qf_df), QFSeries)
assert_series_equal(max_qf_df, expected_max)
self.assertEqual(dtype("float64"), max_qf_df.dtypes)
def test_squeeze(self):
qf_df = QFDataFrame(data=self.prices_values,
index=self.dates,
columns=self.column_names)
self.assertEqual(type(qf_df[['a']]), QFDataFrame)
self.assertEqual({dtype("float64")}, set(qf_df[['a']].dtypes))
self.assertEqual(type(qf_df[['a']].squeeze()), QFSeries)
self.assertEqual(dtype("float64"), qf_df[['a']].squeeze().dtypes)
self.assertEqual(type(qf_df[['a', 'b']].squeeze()), QFDataFrame)
self.assertEqual({dtype("float64")},
set(qf_df[['a', 'b']].squeeze().dtypes))
self.assertEqual(type(qf_df.iloc[[0]]), QFDataFrame)
self.assertEqual({dtype("float64")}, set(qf_df.iloc[[0]].dtypes))
self.assertEqual(type(qf_df.iloc[[0]].squeeze()), QFSeries)
self.assertEqual("float64", qf_df.iloc[[0]].squeeze().dtypes)
def test_concat(self):
full_df = QFDataFrame(data=self.prices_values,
index=self.dates,
columns=self.column_names)
# Concatenate along index (axis = 0)
number_of_rows = len(full_df)
half_df = full_df.iloc[:number_of_rows // 2]
second_half_df = full_df.iloc[number_of_rows // 2:]
concatenated_df = pd.concat([half_df, second_half_df])
self.assertEqual(type(concatenated_df), QFDataFrame)
self.assertEqual({dtype("float64")}, set(concatenated_df.dtypes))
assert_dataframes_equal(concatenated_df, full_df)
# Concatenate along columns (axis = 1)
number_of_columns = full_df.num_of_columns
half_df = full_df.loc[:, full_df.columns[:number_of_columns // 2]]
second_half_df = full_df.loc[:,
full_df.columns[number_of_columns // 2:]]
concatenated_df = pd.concat([half_df, second_half_df], axis=1)
self.assertEqual(type(concatenated_df), QFDataFrame)
self.assertEqual({dtype("float64")}, set(concatenated_df.dtypes))
assert_dataframes_equal(concatenated_df, full_df)
def test_concat_series(self):
index = [1, 2, 3]
series_1 = QFSeries(data=[17., 15., 16.], index=index)
series_2 = QFSeries(data=[18., 19., 20.], index=index)
df = pd.concat([series_1, series_2], axis=1)
self.assertEqual(type(df), QFDataFrame)
self.assertEqual({s.dtypes
for s in [series_1, series_2]}, set(df.dtypes))
series = pd.concat([series_1, series_2], axis=0)
self.assertEqual(type(series), QFSeries)
self.assertEqual("float64", series.dtypes)
self.assertEqual({s.dtypes
for s in [series_1, series_2]}, {series.dtypes})
class TestDataFrames(TestCase):
def setUp(self):
self.dates = pd.date_range(start='2015-05-13', periods=5)
self.column_names = ['a', 'b', 'c', 'd', 'e']
self.prices_values = [[1, 1, 1, 1, 1], [2, 2, 2, 2,
2], [3, 3, 3, 3, 3],
[4, 4, 4, 4, 4], [5, 5, 5, 5, 5]]
self.test_prices_df = PricesDataFrame(data=self.prices_values,
index=self.dates,
columns=self.column_names)
self.log_returns_values = [
[0.693147, 0.693147, 0.693147, 0.693147, 0.693147],
[0.405465, 0.405465, 0.405465, 0.405465, 0.405465],
[0.287682, 0.287682, 0.287682, 0.287682, 0.287682],
[0.223144, 0.223144, 0.223144, 0.223144, 0.223144]
]
self.test_log_returns_df = LogReturnsDataFrame(
data=self.log_returns_values,
index=self.dates[1:],
columns=self.column_names)
self.simple_returns_values = [
[1.000000, 1.000000, 1.000000, 1.000000, 1.000000],
[0.500000, 0.500000, 0.500000, 0.500000, 0.500000],
[0.333333, 0.333333, 0.333333, 0.333333, 0.333333],
[0.250000, 0.250000, 0.250000, 0.250000, 0.250000]
]
self.test_simple_returns_df = SimpleReturnsDataFrame(
data=self.simple_returns_values,
index=self.dates[1:],
columns=self.column_names)
def test_num_of_columns(self):
self.assertEqual(self.test_prices_df.num_of_columns, 5)
def test_prices_to_log_returns(self):
expected_dataframe = self.test_log_returns_df
actual_dataframe = self.test_prices_df.to_log_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_simple_to_log_returns(self):
expected_dataframe = self.test_log_returns_df
actual_dataframe = self.test_simple_returns_df.to_log_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_log_to_log_returns(self):
expected_dataframe = self.test_log_returns_df
actual_dataframe = self.test_log_returns_df.to_log_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_log_to_simple_returns(self):
expected_dataframe = self.test_simple_returns_df
actual_dataframe = self.test_log_returns_df.to_simple_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_simple_to_simple_returns(self):
expected_dataframe = self.test_simple_returns_df
actual_dataframe = self.test_simple_returns_df.to_simple_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_prices_to_simple_returns(self):
expected_dataframe = self.test_simple_returns_df
actual_dataframe = self.test_prices_df.to_simple_returns()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_log_returns_to_prices(self):
expected_dataframe = self.test_prices_df
actual_dataframe = self.test_log_returns_df.to_prices()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_simple_returns_to_prices(self):
expected_dataframe = self.test_prices_df
actual_dataframe = self.test_simple_returns_df.to_prices()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_prices_to_prices(self):
expected_dataframe = self.test_prices_df
actual_dataframe = self.test_prices_df.to_prices()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_min_max_normalized(self):
normalized_prices = [[0.00, 0.00, 0.00, 0.00, 0.00],
[0.25, 0.25, 0.25, 0.25, 0.25],
[0.50, 0.50, 0.50, 0.50, 0.50],
[0.75, 0.75, 0.75, 0.75, 0.75],
[1.00, 1.00, 1.00, 1.00, 1.00]]
expected_dataframe = PricesDataFrame(data=normalized_prices,
index=self.dates,
columns=self.column_names)
actual_dataframe = self.test_prices_df.min_max_normalized()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_exponential_average(self):
smoothed_values = [[1.000000, 1.000000, 1.000000, 1.000000, 1.000000],
[1.940000, 1.940000, 1.940000, 1.940000, 1.940000],
[2.936400, 2.936400, 2.936400, 2.936400, 2.936400],
[3.936184, 3.936184, 3.936184, 3.936184, 3.936184],
[4.936171, 4.936171, 4.936171, 4.936171, 4.936171]]
expected_dataframe = PricesDataFrame(data=smoothed_values,
index=self.dates,
columns=self.column_names)
actual_dataframe = self.test_prices_df.exponential_average()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_aggregate_by_year(self):
dates = pd.DatetimeIndex(
['2015-06-01', '2015-12-30', '2016-01-01', '2016-05-01'])
test_dataframe = SimpleReturnsDataFrame(
data=self.simple_returns_values, index=dates)
expected_aggregated_rets = [[
2.000000, 2.000000, 2.000000, 2.000000, 2.000000
], [0.666666, 0.666666, 0.666666, 0.666666, 0.666666]]
expected_dataframe = SimpleReturnsDataFrame(
data=expected_aggregated_rets,
index=pd.DatetimeIndex(['2015-12-31', '2016-12-31']))
actual_dataframe = test_dataframe.aggregate_by_year()
assert_dataframes_equal(expected_dataframe, actual_dataframe)
def test_rolling_time_window(self):
actual_result = self.test_prices_df.rolling_time_window(
window_length=2, step=1, func=lambda x: x.mean())
expected_values = [[1.5, 1.5, 1.5, 1.5,
1.5], [2.5, 2.5, 2.5, 2.5, 2.5],
[3.5, 3.5, 3.5, 3.5, 3.5],
[4.5, 4.5, 4.5, 4.5, 4.5]]
expected_index = self.test_prices_df.index[-4:].copy(deep=True)
expected_columns = ['a', 'b', 'c', 'd', 'e']
expected_result = QFDataFrame(expected_values, expected_index,
expected_columns)
assert_dataframes_equal(expected_result,
actual_result,
absolute_tolerance=1e-20)
actual_result = self.test_prices_df.rolling_time_window(
window_length=2, step=1, func=lambda x: x.mean().mean())
expected_values = [1.5, 2.5, 3.5, 4.5]
expected_index = self.test_prices_df.index[-4:].copy(deep=True)
expected_result = QFSeries(expected_values, expected_index)
assert_series_equal(expected_result,
actual_result,
absolute_tolerance=1e-20)
def test_total_cumulative_return(self):
actual_result = self.test_prices_df.total_cumulative_return()
expected_result = pd.Series(index=self.test_prices_df.columns,
data=[4.0, 4.0, 4.0, 4.0, 4.0])
assert_series_equal(expected_result, actual_result)