def test_SMA():
expected = func.SMA(ford_2012['close'], 10)
assert_np_arrays_equal(expected,
abstract.Function('sma', ford_2012, 10).outputs)
assert_np_arrays_equal(
expected,
abstract.Function('sma')(ford_2012, 10, price='close'))
assert_np_arrays_equal(expected,
abstract.Function('sma')(ford_2012, timeperiod=10))
expected = func.SMA(ford_2012['open'], 10)
assert_np_arrays_equal(
expected,
abstract.Function('sma', ford_2012, 10, price='open').outputs)
assert_np_arrays_equal(
expected,
abstract.Function('sma', price='low')(ford_2012, 10, price='open'))
assert_np_arrays_not_equal(
expected,
abstract.Function('sma', ford_2012, 10, price='open')(timeperiod=20))
assert_np_arrays_not_equal(
expected,
abstract.Function('sma', ford_2012)(10, price='close'))
assert_np_arrays_not_equal(
expected,
abstract.Function('sma', 10)(ford_2012, price='high'))
assert_np_arrays_not_equal(
expected,
abstract.Function('sma', price='low')(ford_2012, 10))
input_arrays = {'foobarbaz': ford_2012['open']}
assert_np_arrays_equal(expected,
abstract.SMA(input_arrays, 10, price='foobarbaz'))
def test_call_doesnt_cache_parameters():
sma = abstract.Function('SMA', timeperiod=10)
expected = func.SMA(ford_2012['close'], 20)
output = sma(ford_2012, timeperiod=20)
assert_np_arrays_equal(expected, output)
expected = func.SMA(ford_2012['close'], 10)
output = sma(ford_2012)
assert_np_arrays_equal(expected, output)
def test_MAVP():
a = np.array([1, 5, 3, 4, 7, 3, 8, 1, 4, 6], dtype=float)
b = np.array([2, 4, 2, 4, 2, 4, 2, 4, 2, 4], dtype=float)
result = func.MAVP(a, b, minperiod=2, maxperiod=4)
assert_np_arrays_equal(
result,
[np.nan, np.nan, np.nan, 3.25, 5.5, 4.25, 5.5, 4.75, 2.5, 4.75])
sma2 = func.SMA(a, 2)
assert_np_arrays_equal(result[4::2], sma2[4::2])
sma4 = func.SMA(a, 4)
assert_np_arrays_equal(result[3::2], sma4[3::2])
result = func.MAVP(a, b, minperiod=2, maxperiod=3)
assert_np_arrays_equal(result, [
np.nan, np.nan, 4, 4, 5.5, 4.666666666666667, 5.5, 4, 2.5,
3.6666666666666665
])
sma3 = func.SMA(a, 3)
assert_np_arrays_equal(result[2::2], sma2[2::2])
assert_np_arrays_equal(result[3::2], sma3[3::2])
def test_pandas_series():
import pandas
input_df = pandas.DataFrame(ford_2012)
output = talib.SMA(input_df['close'], 10)
expected = pandas.Series(func.SMA(ford_2012['close'], 10),
index=input_df.index)
pandas.util.testing.assert_series_equal(output, expected)
# Test kwargs
output = talib.SMA(real=input_df['close'], timeperiod=10)
pandas.util.testing.assert_series_equal(output, expected)
# Test talib.func API
output = func.SMA(input_df['close'], timeperiod=10)
pandas.util.testing.assert_series_equal(output, expected)
# Test multiple outputs such as from BBANDS
_, output, _ = talib.BBANDS(input_df['close'], 10)
expected = pandas.Series(func.BBANDS(ford_2012['close'], 10)[1],
index=input_df.index)
pandas.util.testing.assert_series_equal(output, expected)
def test_pandas():
import pandas
input_df = pandas.DataFrame(ford_2012)
input_dict = dict((k, pandas.Series(v)) for k, v in ford_2012.items())
expected_k, expected_d = func.STOCH(ford_2012['high'], ford_2012['low'], ford_2012['close']) # 5, 3, 0, 3, 0
output = abstract.Function('stoch', input_df).outputs
assert_true(isinstance(output, pandas.DataFrame))
assert_np_arrays_equal(expected_k, output['slowk'])
assert_np_arrays_equal(expected_d, output['slowd'])
output = abstract.Function('stoch', input_dict).outputs
assert_true(isinstance(output, list))
assert_np_arrays_equal(expected_k, output[0])
assert_np_arrays_equal(expected_d, output[1])
expected = func.SMA(ford_2012['close'], 10)
output = abstract.Function('sma', input_df, 10).outputs
assert_true(isinstance(output, pandas.Series))
assert_np_arrays_equal(expected, output)
output = abstract.Function('sma', input_dict, 10).outputs
assert_true(isinstance(output, np.ndarray))
assert_np_arrays_equal(expected, output)
