def add_indicators(self):
self.candles = self.candles.drop(['Open_time', 'Close_time'], axis=1)
self.candles['EMA - 15'] = ema(self.candles['Close'].tolist(), 15)
self.candles['aaron down'] = aroon_down(self.candles['Close'].tolist(), 25)
self.candles['aaron up'] = aroon_up(self.candles['Close'].tolist(), 25)
self.candles['tenkansen'] = tenkansen(self.candles['Close'].tolist())
self.candles['kijunsen'] = kijunsen(self.candles['Close'].tolist())
self.candles['momentun'] = momentum(self.candles['Close'], 15)
return self.candles
def preProcessCalculation(df, limit):
df = df.reset_index()
adx = np.array(
average_directional_index(df['close'], df['high'], df['low'], limit))
mfi = np.array(
money_flow_index(df['close'], df['high'], df['low'], df['volume'],
limit - 1))
atr = np.array(average_true_range(df['close'], limit))
obv = np.array(on_balance_volume(df['close'], df['volume']))
rocr = np.array(rate_of_change(df['close'], limit))
mom = np.array(momentum(df['close'], limit))
return adx, mfi, atr, obv, rocr, mom
ans = simple_moving_average(read_float_with_comma(x), 10)
open = dataset_train.iloc[:, 1].values
high = dataset_train.iloc[:, 2].values
low = dataset_train.iloc[:, 3].values
close = read_float_with_comma(dataset_train.iloc[:, 4].values)
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)
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
def test_momentum_invalid_period(self):
period = 128
with self.assertRaises(Exception) as cm:
momentum.momentum(self.data, period)
expected = "Error: data_len < period"
self.assertEqual(str(cm.exception), expected)
def test_momentum_period_10(self):
period = 10
mom = momentum.momentum(self.data, period)
np.testing.assert_array_equal(mom, self.momentum_period_10_expected)