def test_relative_candles(self):
df = DF_TEST[-2:].copy()
relative = ta_realative_candles(df)
np.testing.assert_array_almost_equal(
np.array([0.002469, 0.002021, 0.002533, -0.002829]),
relative.values[-1])
def test_multi_channel(self):
df = DF_TEST.copy()
club = ta_candles_as_culb(df)
self.assertGreaterEqual(club["upper"].values.max(), 0)
self.assertGreaterEqual(club["lower"].values.max(), 0)
np.testing.assert_array_almost_equal(
np.array([3.120900e+02, 6.402011e-05, 4.861506e-03, 9.995516e-01]),
club.iloc[-1].values, 5)
def test_multi_channel(self):
df = DF_TEST.copy()
ohlc_values = df[["Open", "High", "Low", "Close"]].iloc[-1].values
culb = ta_candles_as_culb(df, volume=None)
culb_values = culb.iloc[-1].values
self.assertGreaterEqual(culb["upper"].values.max(), 0)
self.assertGreaterEqual(culb["lower"].values.max(), 0)
self.assertEqual(ohlc_values[1] > ohlc_values[-1], culb_values[-1] < 0)
np.testing.assert_array_almost_equal(np.array([3.120900e+02, 6.402011e-05, 4.861506e-03, -4.48435e-04]),
culb_values, 5)
def test_one_hot_encoder_vec(self):
df = pd.DataFrame({"a": [1, 2, 3, 4, 4, 5]})
encoded = ta_one_hot_encode_discrete(df["a"])
print(repr(encoded))
self.assertListEqual(df.index.tolist(), encoded.index.tolist())
np.testing.assert_array_equal(
np.array([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0],
[0, 0, 0, 1, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1]]),
encoded._.values)