def portfolio():
df1 = pd.read_csv("tests/data/input/bitfinex_(BTC,ETH)USD_d.csv").tail(100)
df1 = df1.rename({"Unnamed: 0": "date"}, axis=1)
df1 = df1.set_index("date")
df2 = pd.read_csv("tests/data/input/bitstamp_(BTC,ETH,LTC)USD_d.csv").tail(
100)
df2 = df2.rename({"Unnamed: 0": "date"}, axis=1)
df2 = df2.set_index("date")
ex1 = Exchange("bitfinex", service=execute_order)(
Stream.source(list(df1['BTC:close']), dtype="float").rename("USD-BTC"),
Stream.source(list(df1['ETH:close']), dtype="float").rename("USD-ETH"))
ex2 = Exchange("binance", service=execute_order)(
Stream.source(list(df2['BTC:close']), dtype="float").rename("USD-BTC"),
Stream.source(list(df2['ETH:close']), dtype="float").rename("USD-ETH"),
Stream.source(list(df2['LTC:close']), dtype="float").rename("USD-LTC"))
p = Portfolio(USD, [
Wallet(ex1, 10000 * USD),
Wallet(ex1, 10 * BTC),
Wallet(ex1, 5 * ETH),
Wallet(ex2, 1000 * USD),
Wallet(ex2, 5 * BTC),
Wallet(ex2, 20 * ETH),
Wallet(ex2, 3 * LTC),
])
return p
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_runs_with_external_feed_only(portfolio):
df = pd.read_csv("tests/data/input/bitfinex_(BTC,ETH)USD_d.csv").tail(100)
df = df.rename({"Unnamed: 0": "date"}, axis=1)
df = df.set_index("date")
bitfinex_btc = df.loc[:, [name.startswith("BTC") for name in df.columns]]
bitfinex_eth = df.loc[:, [name.startswith("ETH") for name in df.columns]]
ta.add_all_ta_features(
bitfinex_btc,
colprefix="BTC:",
**{k: "BTC:" + k
for k in ['open', 'high', 'low', 'close', 'volume']})
ta.add_all_ta_features(
bitfinex_eth,
colprefix="ETH:",
**{k: "ETH:" + k
for k in ['open', 'high', 'low', 'close', 'volume']})
streams = []
with NameSpace("bitfinex"):
for name in bitfinex_btc.columns:
streams += [
Stream.source(list(bitfinex_btc[name]),
dtype="float").rename(name)
]
for name in bitfinex_eth.columns:
streams += [
Stream.source(list(bitfinex_eth[name]),
dtype="float").rename(name)
]
feed = DataFeed(streams)
action_scheme = ManagedRiskOrders()
reward_scheme = SimpleProfit()
env = default.create(portfolio=portfolio,
action_scheme=action_scheme,
reward_scheme=reward_scheme,
feed=feed,
window_size=50,
enable_logger=False)
done = False
obs = env.reset()
while not done:
action = env.action_space.sample()
obs, reward, done, info = env.step(action)
assert obs.shape[0] == 50
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_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_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_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_expanding_max():
for array, config in product(arrays, configurations):
s = Stream.source(array, dtype="float")
w = s.expanding(**config).max().rename("w")
expected = list(pd.Series(array).expanding(**config).max())
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_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_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_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_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_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_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_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_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_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)
def test_boolean():
s = Stream.source(["hello", "my", "name", "is", "slim", "shady"],
dtype="string")
s.startswith("m").invert()
