class TestPreprocess(TestCase):
def test_retrieve_fundamental_ratios(self):
self.preprocess = Preprocess()
try:
df = self.preprocess.retrieve_fundamental_ratios()
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_ticks(self):
self.preprocess = Preprocess(lag=7)
try:
df = self.preprocess.retrieve_ticks(lag=True)
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_bars(self):
self.preprocess = Preprocess(lag=7)
try:
df = self.preprocess.retrieve_bars()
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_open_close(self):
self.preprocess = Preprocess(lag=7)
try:
df = self.preprocess.retrieve_open_close()
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_mkt_caps(self):
self.preprocess = Preprocess(lag=7)
try:
df = self.preprocess.retrieve_mkt_caps(["GE", "MMM", "APPL"])
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_dividends(self):
self.preprocess = Preprocess(lag=7)
try:
df = self.preprocess.retrieve_dividends()
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_benchmark(self):
self.preprocess = Preprocess(lag=30)
try:
df = self.preprocess.retrieve_benchmark("snp500")
if not isinstance(df, pd.DataFrame) and not df.empty:
raise Exception
except:
self.fail()
def test_retrieve_return(self):
self.preprocess = Preprocess(lag=7)
try:
df1 = self.preprocess.retrieve_return(
) # non-split is a super set of split returns
if not isinstance(df1, pd.DataFrame) and not df1.empty:
raise Exception
except:
self.fail()
def test_retrieve_benchmark_change(self):
self.preprocess = Preprocess(lag=7)
try:
change = self.preprocess.retrieve_benchmark_change("snp500")
if not isinstance(change, float):
raise Exception
except:
self.fail()
def test_filter_column(self):
self.preprocess = Preprocess(density=0.5, lag=7)
data = [('symbol', ['A', 'B', 'C', 'D']), ('index', [150, 200, 50,
10]),
('date', [200, 210, 90, 20]), ('currency', [140, 215, 95, 30]),
('latestadate', [140, 215, 95, 40]),
('dense', [140, 215, np.NaN, 50]),
('sparse', [np.NaN, np.NaN, np.NaN, 60])]
df = pd.DataFrame.from_items(data)
filtered = self.preprocess.filter_column(df)
self.assertEqual(len(filtered.columns),
2) # only symbol and dense will survive
def test_fill_missing_value(self):
self.preprocess = Preprocess(lag=7)
data = [('x', [40, 30, np.NaN, np.NaN]), ('y', [np.NaN, 60, 50, 60])]
df = pd.DataFrame.from_items(data)
filled = self.preprocess.fill_missing_value(df)
self.assertFalse(filled.isnull().values.any())
def test_cap_outlier(self):
self.preprocess = Preprocess(lag=7, limit=3, outlier=4)
data = [('x', [
1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4,
4, 4, 8
]),
('y', [
1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3,
3, 4, 4, 4, 50
]),
('z', [
1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3,
3, 4, 4, 4, 10000
])]
df = pd.DataFrame.from_items(data)
dfc = df.copy(
) # method will operate on the same memory location without making copy
capped = self.preprocess.cap_outlier(df)
self.assertEqual(capped['x'].max(), dfc['x'].max())
self.assertTrue(capped['z'].max() < dfc['z'].max())
def test_scale_data(self):
self.preprocess = Preprocess(lag=7)
data = [('x', [1, 2, 3, 4]), ('y', [51, -6, 43, -8])]
df = pd.DataFrame.from_items(data)
scaled = self.preprocess.scale_data(df)
self.assertTrue(scaled['x'].max() <= 1)
self.assertTrue(scaled['y'].max() <= 1)
self.assertTrue(scaled['x'].min() >= 0)
self.assertTrue(scaled['y'].min() >= 0)
def test_get_data(self):
pass # this method is composed of other tested methods
