def test_commodity_channel_index_invalid_period(self):
period = 128
with self.assertRaises(Exception) as cm:
commodity_channel_index.commodity_channel_index(
self.close_data, self.high_data, self.low_data, period)
expected = "Error: data_len < period"
self.assertEqual(str(cm.exception), expected)
def test_commodity_channel_index_invalid_data(self):
period = 6
self.close_data.append(1)
with self.assertRaises(Exception) as cm:
commodity_channel_index.commodity_channel_index(
self.close_data, self.high_data, self.low_data, period)
expected = (
"Error: mismatched data lengths, check to ensure that all input data is the same length and valid"
)
self.assertEqual(str(cm.exception), expected)
def cci(dataframe, period) -> ndarray:
from pyti.commodity_channel_index import commodity_channel_index
return commodity_channel_index(dataframe['close'], dataframe['high'],
dataframe['low'], period)
def cci(df, period):
return commodity_channel_index(df['close'].tolist(), df['high'].tolist(),
df['low'].tolist(), period)
def test_cci_period_10(self):
period = 10
cci = commodity_channel_index.commodity_channel_index(
self.close_data, self.high_data, self.low_data, period)
np.testing.assert_array_equal(cci, self.cci_period_10_expected)
wma = weighted_moving_average(close, period)
from pyti.momentum import momentum
mome = momentum(close, period)
from pyti.relative_strength_index import relative_strength_index
rsi = relative_strength_index(close, period)
from pyti.moving_average_convergence_divergence import moving_average_convergence_divergence
macd = moving_average_convergence_divergence(close,
short_period=1,
long_period=10)
from pyti.commodity_channel_index import commodity_channel_index
cci = commodity_channel_index(close,
high_data=high,
low_data=low,
period=period)
from pyti.williams_percent_r import williams_percent_r
willr = williams_percent_r(close)
from pyti.accumulation_distribution import accumulation_distribution
acd = accumulation_distribution(close_data=close,
low_data=low,
high_data=high,
volume=volume)
X = []
Y = []
for _ in range(10, len(open) - 1):
tmp = []
def convert(str):
period = 10
dataset_train = pd.read_csv(str)
x = dataset_train.iloc[:, 4].values
try:
open = dataset_train.iloc[:, 1].values
except Exception:
open = read_float_with_comma(dataset_train.iloc[:, 1].values)
try:
high = dataset_train.iloc[:, 2].values
except Exception:
high = read_float_with_comma(dataset_train.iloc[:, 2].values)
try:
low = dataset_train.iloc[:, 3].values
except Exception:
low = read_float_with_comma(dataset_train.iloc[:, 3].values)
try:
close = dataset_train.iloc[:, 4].values
except Exception:
close = read_float_with_comma(dataset_train.iloc[:, 4].values)
try:
volume = dataset_train.iloc[:, 5].values
except Exception:
volume = read_float_with_comma(dataset_train.iloc[:, 5].values)
from pyti.simple_moving_average import simple_moving_average
sma = simple_moving_average(close, period)
from pyti.weighted_moving_average import weighted_moving_average
wma = weighted_moving_average(close, period)
from pyti.momentum import momentum
mome = momentum(close, period)
from pyti.relative_strength_index import relative_strength_index
rsi = relative_strength_index(close, period)
from pyti.moving_average_convergence_divergence import moving_average_convergence_divergence
macd = moving_average_convergence_divergence(close,
short_period=1,
long_period=10)
from pyti.commodity_channel_index import commodity_channel_index
cci = commodity_channel_index(close,
high_data=high,
low_data=low,
period=period)
from pyti.williams_percent_r import williams_percent_r
willr = williams_percent_r(close)
from pyti.accumulation_distribution import accumulation_distribution
acd = accumulation_distribution(close_data=close,
low_data=low,
high_data=high,
volume=volume)
X = []
Y = []
for _ in range(10, len(open)):
tmp = []
tmp.append(sma[_])
tmp.append(wma[_])
tmp.append(mome[_])
tmp.append(rsi[_])
tmp.append(macd[_])
tmp.append(cci[_])
tmp.append(willr[_])
X.append(tmp)
Y.append([close[_]])
X, Y = np.array(X), np.array(Y)
return X, Y
