def test_max():
s1 = Stream.source([-1, 2, -3, 4, -5], dtype="float")
s2 = Stream.source([-1, 2, 3, 2, 1], dtype="float")
w = s1.max(s2).rename("w")
expected = [-1, 2, 3, 4, 1]
assert_op([w], expected)
def test_reduce_prod():
s1 = Stream.source([1, 2, 3, 4, 5, 6, 7], dtype="float").rename("s1")
s2 = Stream.source([3, 6, 4, 2, 0, 4, 10], dtype="float").rename("s2")
w = Stream.reduce([s1, s2]).prod().rename("w")
expected = [3, 12, 12, 8, 0, 24, 70]
assert_op([w], expected)
def test_reduce_sum():
s1 = Stream.source([1, 2, 3, 4, 5, 6, 7], dtype="float").rename("s1")
s2 = Stream.source([7, 6, 5, 4, 3, 2, 1], dtype="float").rename("s2")
w = Stream.reduce([s1, s2]).sum().rename("w")
expected = [8, 8, 8, 8, 8, 8, 8]
assert_op([w], expected)
def test_reduce_max():
s1 = Stream.source([1, 2, 3, 4, 5, 6, 7], dtype="float").rename("s1")
s2 = Stream.source([-3, 6, 4, 2, 0, 4, 10], dtype="float").rename("s2")
w = Stream.reduce([s1, s2]).max().rename("w")
expected = [1, 6, 4, 4, 5, 6, 10]
assert_op([w], expected)
def test_generic():
s1 = Stream.source(["hello", "my", "name", "is"], dtype="string")
s2 = Stream.source([1, 2, 3, 4, 5, 6])
g1 = s1.apply(lambda x: x[0]).rename("g1")
g2 = s2.lag().rename("g2")
feed = DataFeed([g1, g2])
feed.compile()
feed.next()
assert feed.next() == {"g1": "m", "g2": 1}
def test_cummax():
for array in arrays:
s = Stream.source(array, dtype="float")
w = s.cummax(skipna=True).rename("w")
expected = list(pd.Series(array).cummax(skipna=True))
assert_op([w], expected)
for array in arrays:
s = Stream.source(array, dtype="float")
w = s.cummin(skipna=False).rename("w")
expected = list(pd.Series(array).cummin(skipna=False))
assert_op([w], expected)
def test_diff():
for array in arrays:
s = Stream.source(array, dtype="float")
w = s.diff(periods=1).rename("w")
expected = list(pd.Series(array).diff(periods=1))
assert_op([w], expected)
for array in arrays:
s = Stream.source(array, dtype="float")
w = s.diff(periods=2).rename("w")
expected = list(pd.Series(array).diff(periods=2))
assert_op([w], expected)
def test_float_accessor():
s = Stream.source([1, 2, 3, 4, 5])
w = s.float.square().rename("w")
assert_op([w], [1, 4, 9, 16, 25])
def test_neg():
s = Stream.source([3, -4, 6, -7, 2, -6], dtype="float")
s1 = s.neg().rename("s1")
s2 = (-s).rename("s2")
assert_op([s1, s2], [-3, 4, -6, 7, -2, 6])
def test_abs():
s = Stream.source([3, -4, 6, -7, 2, -6], dtype="float")
s1 = s.abs().rename("s1")
s2 = abs(s).rename("s2")
assert_op([s1, s2], [3, 4, 6, 7, 2, 6])
def test_log():
for array in arrays:
s = Stream.source(array, dtype="float")
w = s.log().rename("w")
expected = list(pd.Series(array).apply(np.log))
assert_op([w], expected)
def test_cumprod():
for array in arrays:
s = Stream.source(array, dtype="float")
w = s.cumprod().rename("w")
expected = list(pd.Series(array).cumprod())
assert_op([w], expected)
def test_rolling_median():
for array, config in product(arrays, configurations):
s = Stream.source(array, dtype="float")
w = s.rolling(**config).median().rename("w")
expected = list(pd.Series(array).rolling(**config).median())
assert_op([w], expected)
def test_sub():
expected = [0, 1, 2, 3, 4, 5]
# (left, right) : (Stream, Stream)
s1 = Stream.source([1, 2, 3, 4, 5, 6], dtype="float")
s2 = Stream.source([1, 1, 1, 1, 1, 1], dtype="float")
w1 = s1.sub(s2).rename("w1")
w2 = (s1 - s2).rename("w2")
assert_op([w1, w2], expected)
# (left, right) : (Stream, float)
w1 = s1.sub(1).rename("w1")
w2 = (s1 - 1).rename("w2")
assert_op([w1, w2], expected)
def test_rsub():
# (left, right) : (float, Stream)
s1 = 6
s2 = Stream.source([1, 2, 3, 4, 5, 6], dtype="float")
w = (s1 - s2).rename("w")
assert_op([w], [5, 4, 3, 2, 1, 0])
def test_radd():
# (left, right) : (float, Stream)
s1 = 1
s2 = Stream.source([1, 2, 3, 4, 5, 6], dtype="float")
w = (s1 + s2).rename("w")
assert_op([w], [2, 3, 4, 5, 6, 7])
def test_div():
expected = [1, 2, 3, 4, 5, 6]
# (left, right) : (Stream, Stream)
s1 = Stream.source([2, 4, 6, 8, 10, 12], dtype="float")
s2 = Stream.source([2, 2, 2, 2, 2, 2], dtype="float")
w1 = s1.div(s2).rename("w1")
w2 = (s1 / s2).rename("w2")
assert_op([w1, w2], expected)
# (left, right) : (Stream, float)
w1 = s1.div(2).rename("w1")
w2 = (s1 / 2).rename("w2")
assert_op([w1, w2], expected)
def test_clamp_min():
array = [-1, 2, -3, 4, -5]
s = Stream.source(array, dtype="float")
w = s.clamp_min(0).rename("w")
expected = list(pd.Series(array).apply(lambda x: x if x > 0 else 0))
assert_op([w], expected)
def test_ffill():
array = [-1, np.nan, -3, 4, np.nan]
s = Stream.source(array, dtype="float")
w = s.ffill().rename("w")
expected = list(pd.Series(array).ffill())
assert_op([w], expected)
def test_rdiv():
expected = [6, 3, 2, 3 / 2, 6 / 5, 1]
# (left, right) : (Stream, Stream)
s = Stream.source([2, 4, 6, 8, 10, 12], dtype="float")
# (left, right) : (Stream, float)
w = (12 / s).rename("w")
assert_op([w], expected)
def test_rmul():
expected = [2, 4, 6, 8, 10, 12]
# (left, right) : (Stream, Stream)
s = Stream.source([1, 2, 3, 4, 5, 6], dtype="float")
# (left, right) : (Stream, float)
w = (2 * s).rename("w")
assert_op([w], expected)
def test_add():
# (left, right) : (Stream, Stream)
s1 = Stream.source([3, -4, 6, -7, 2, -6], dtype="float")
s2 = Stream.source([-3, 4, -6, 7, -2, 6], dtype="float")
w1 = s1.add(s2).rename("w1")
w2 = (s1 + s2).rename("w2")
assert_op([w1, w2], 6 * [0])
# (left, right) : (Stream, float)
s1 = Stream.source([1, 2, 3, 4, 5, 6], dtype="float")
s2 = 1
w1 = s1.add(s2).rename("w1")
w2 = (s1 + s2).rename("w2")
assert_op([w1, w2], [2, 3, 4, 5, 6, 7])
def test_bool_methods():
s = Stream.source(["hello", "my", "name", "i", "am", "the", "data", "feed"], dtype="string")
w1 = s.upper().rename("w1")
w2 = s.lower().rename("w2")
w3 = w1.endswith("E").invert().rename("w3")
feed = DataFeed([w1, w2, w3])
feed.compile()
assert feed.next() == {"w1": "HELLO", "w2": "hello", "w3": True}
def test_ewm_mean():
array = [1, np.nan, 3, 4, 5, 6, np.nan, 7]
s = Stream.source(array, dtype="float")
for config in configurations:
w = s.ewm(**config).mean().rename("w")
expected = list(pd.Series(array).ewm(**config).mean())
assert_op([w], expected)
def test_pow():
array = [1, -2, 3, -4, 5, -6]
s = Stream.source(array, dtype="float")
s1 = s.pow(3).rename("s1")
s2 = (s**3).rename("s2")
expected = list(pd.Series(array)**3)
assert_op([s1, s2], expected)
def test_str_accessor():
s = Stream.source(["hello", "my", "name", "i", "am", "the", "data", "feed"])
w1 = s.str.upper().rename("w1")
w2 = s.str.lower().rename("w2")
w3 = w1.str.endswith("E").rename("w3")
feed = DataFeed([w1, w2, w3])
feed.compile()
assert feed.next() == {"w1": "HELLO", "w2": "hello", "w3": False}
def test_pct_change():
configs = [
{
"periods": 1,
"fill_method": None
},
{
"periods": 1,
"fill_method": "pad"
},
{
"periods": 1,
"fill_method": "ffill"
},
{
"periods": 2,
"fill_method": None
},
{
"periods": 2,
"fill_method": "pad"
},
{
"periods": 2,
"fill_method": "ffill"
},
]
for array, config in product(arrays, configs):
s = Stream.source(array, dtype="float")
w = s.pct_change(**config).rename("w")
expected = list(pd.Series(array).pct_change(**config))
print(config)
assert_op([w], expected)