def test_continuous_count(self):
arr1 = nparr([False, True, True, False, True, False])
assert numpy.array_equal(
ArrayIndicator.consecutive_count_of_True(arr1), [0, 1, 2, 0, 1, 0])
arr1 = nparr([True, True, True, False, True, True])
assert numpy.array_equal(
ArrayIndicator.consecutive_count_of_True(arr1), [1, 2, 3, 0, 1, 2])
def plot_df_with_min_max(df):
fig, ax = plt.subplots()
val = StockAxisPlot((fig, ax), df)
is_min = ArrayIndicator.is_min_poses(df.low.values, 5)
is_max = ArrayIndicator.is_max_poses(df.high.values, 5)
pos_shift = (max(df.high) - min(df.low)) / 30
is_min_sr = df.low[is_min]
is_max_sr = df.high[is_max]
val.add_scatter_point('test', is_min_sr - pos_shift, color='b', marker='^')
val.add_scatter_point('test', is_max_sr + pos_shift, color='b', marker='v')
val.plot()
plt.show()
def test_min_poses(self):
arr = numpy.arange(4)
arr[0] = 10
arr = numpy.concatenate((arr, arr[::-1]))
max_arr = ArrayIndicator.is_max_poses(arr, 5)
assert numpy.all(
max_arr == [False, False, False, True, True, False, False, False])
def test_len_and_slope(self):
arr1 = nparr([1, 2, 1, 0, 1, 2, 3, 2, 1, 2, 3, 4], dtype=numpy.float64)
val2 = ArrayIndicator.trend_len_and_slope(arr1, window_len=5)
assert numpy.array_equal(val2[0], [0, 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3])
assert numpy.allclose(
val2[1],
[0., 1., 0., -0.33333333, 1., 1., 1., -1., -1., 1., 1., 1.])
def test_even_count(self):
val = ArrayIndicator.even_count(numpy.asarray([1, 1, 1, 0, 0]))
numpy.array_equal(val, [0, 1, 2, 0, 1])
def test_drop_count(self):
val = ArrayIndicator.rise_count(numpy.asarray([3, 2, 1, 2, 1]))
numpy.array_equal(val, [0, 1, 2, 0, 1])
def test_mdd_poses(self):
arr1 = nparr([1, 2, 1, 1.5, 0, 1, 2])
poses = ArrayIndicator.mdd_poses(arr1)
assert poses == (1, 4)
def even_count_of_previous_days(df):
arr = df.close.values
is_even = (arr == np_shift(arr, 1, 0))
count = ArrayIndicator.consecutive_count_of_True(is_even)
return np_shift(count, 1, 0)
def down_count_of_previous_days(df):
arr = df.close.values
is_up = arr - np_shift(arr, 1, 0) < 0
count = ArrayIndicator.consecutive_count_of_True(is_up)
return np_shift(count, 1, 0)
def _support_high_low_strength(df, window_len):
is_max = ArrayIndicator.is_max_poses(df.high, window_len)
is_min = ArrayIndicator.is_min_poses(df.low, window_len)
pass