def test_fso_expected_with_talib(self):
"""
Test the output that is returned from the fast stochastic oscillator
is the same as that from the ta-lib STOCHF function.
"""
window_length = 14
nassets = 6
closes = np.random.random_integers(1, 6, size=(50, nassets)) * 1.0
highs = np.random.random_integers(4, 6, size=(50, nassets)) * 1.0
lows = np.random.random_integers(1, 3, size=(50, nassets)) * 1.0
expected_out_k = []
for i in range(nassets):
e = talib.STOCHF(
high=highs[:, i],
low=lows[:, i],
close=closes[:, i],
fastk_period=window_length,
)
expected_out_k.append(e[0][-1])
expected_out_k = np.array(expected_out_k)
today = pd.Timestamp('2015')
out = np.empty(shape=(nassets, ), dtype=np.float)
assets = np.arange(nassets, dtype=np.float)
fso = FastStochasticOscillator()
fso.compute(today, assets, out, closes, lows, highs)
assert_equal(out, expected_out_k)
def test_fso_expected_with_talib(self):
"""
Test the output that is returned from the fast stochastic oscillator
is the same as that from the ta-lib STOCHF function.
"""
window_length = 14
nassets = 6
closes = np.random.random_integers(1, 6, size=(50, nassets))*1.0
highs = np.random.random_integers(4, 6, size=(50, nassets))*1.0
lows = np.random.random_integers(1, 3, size=(50, nassets))*1.0
expected_out_k = []
for i in range(nassets):
e = talib.STOCHF(
high=highs[:, i],
low=lows[:, i],
close=closes[:, i],
fastk_period=window_length,
)
expected_out_k.append(e[0][-1])
expected_out_k = np.array(expected_out_k)
today = pd.Timestamp('2015')
out = np.empty(shape=(nassets,), dtype=np.float)
assets = np.arange(nassets, dtype=np.float)
fso = FastStochasticOscillator()
fso.compute(
today, assets, out, closes, lows, highs
)
assert_equal(out, expected_out_k)
def test_fso_expected_basic(self):
"""
Simple test of expected output from fast stochastic oscillator
"""
fso = FastStochasticOscillator()
today = pd.Timestamp('2015')
assets = np.arange(3, dtype=np.float)
out = np.empty(shape=(3,), dtype=np.float)
highs = np.full((50, 3), 3)
lows = np.full((50, 3), 2)
closes = np.full((50, 3), 4)
fso.compute(today, assets, out, closes, lows, highs)
# Expected %K
assert_equal(out, np.full((3,), 200))
def test_fso_expected_with_talib(self, seed):
"""
Test the output that is returned from the fast stochastic oscillator
is the same as that from the ta-lib STOCHF function.
"""
import talib
window_length = 14
nassets = 6
rng = np.random.RandomState(seed=seed)
input_size = (window_length, nassets)
# values from 9 to 12
closes = 9.0 + (rng.random_sample(input_size) * 3.0)
# Values from 13 to 15
highs = 13.0 + (rng.random_sample(input_size) * 2.0)
# Values from 6 to 8.
lows = 6.0 + (rng.random_sample(input_size) * 2.0)
expected_out_k = []
for i in range(nassets):
fastk, fastd = talib.STOCHF(
high=highs[:, i],
low=lows[:, i],
close=closes[:, i],
fastk_period=window_length,
fastd_period=1,
)
expected_out_k.append(fastk[-1])
expected_out_k = np.array(expected_out_k)
today = pd.Timestamp('2015')
out = np.empty(shape=(nassets,), dtype=np.float)
assets = np.arange(nassets, dtype=np.float)
fso = FastStochasticOscillator()
fso.compute(
today, assets, out, closes, lows, highs
)
assert_equal(out, expected_out_k, array_decimal=6)
def test_fso_expected_with_talib(self, seed):
"""
Test the output that is returned from the fast stochastic oscillator
is the same as that from the ta-lib STOCHF function.
"""
window_length = 14
nassets = 6
rng = np.random.RandomState(seed=seed)
input_size = (window_length, nassets)
# values from 9 to 12
closes = 9.0 + (rng.random_sample(input_size) * 3.0)
# Values from 13 to 15
highs = 13.0 + (rng.random_sample(input_size) * 2.0)
# Values from 6 to 8.
lows = 6.0 + (rng.random_sample(input_size) * 2.0)
expected_out_k = []
for i in range(nassets):
fastk, fastd = talib.STOCHF(
high=highs[:, i],
low=lows[:, i],
close=closes[:, i],
fastk_period=window_length,
fastd_period=1,
)
expected_out_k.append(fastk[-1])
expected_out_k = np.array(expected_out_k)
today = pd.Timestamp('2015')
out = np.empty(shape=(nassets,), dtype=np.float)
assets = np.arange(nassets, dtype=np.float)
fso = FastStochasticOscillator()
fso.compute(
today, assets, out, closes, lows, highs
)
assert_equal(out, expected_out_k, array_decimal=6)
