volumep = data_original.ix[:, 'Volume'].tolist()
ma30 = pd.DataFrame(closep).rolling(14).mean().values.tolist()
ma30 = [v[0] for v in ma30]
ma60 = pd.DataFrame(closep).rolling(30).mean().values.tolist()
ma60 = [v[0] for v in ma60]
nine_period_high = pd.rolling_max(pd.DataFrame(highp), window=ROLLING / 2)
nine_period_low = pd.rolling_min(pd.DataFrame(lowp), window=ROLLING / 2)
ichimoku = (nine_period_high + nine_period_low) / 2
ichimoku = ichimoku.replace([np.inf, -np.inf], np.nan)
ichimoku = ichimoku.fillna(0.).values.tolist()
macd_indie = moving_average_convergence(pd.DataFrame(closep))
wpr = williams_percent_r(closep)
rsi = relative_strength_index(closep, ROLLING / 2)
volatility1 = pd.DataFrame(closep).rolling(ROLLING).std().values #.tolist()
volatility2 = pd.DataFrame(closep).rolling(ROLLING).var().values #.tolist()
volatility = volatility1 / volatility2
volatility = [v[0] for v in volatility]
rolling_skewness = pd.DataFrame(closep).rolling(ROLLING).skew().values
rolling_kurtosis = pd.DataFrame(closep).rolling(ROLLING).kurt().values
X, Y = [], []
for i in range(WINDOW, len(data_original) - 1, STEP):
try:
o = openp[i:i + WINDOW]
def test_wpr(self):
wpr = williams_percent_r.williams_percent_r(self.data)
np.testing.assert_array_equal(wpr, self.wpr_expected)
def williams_percent(dataframe):
from pyti.williams_percent_r import williams_percent_r
return williams_percent_r(dataframe['close'])
ma30 = pd.DataFrame(closep).rolling(14).mean().values.tolist()
ma30 = [v[0] for v in ma30]
ma60 = pd.DataFrame(closep).rolling(30).mean().values.tolist()
ma60 = [v[0] for v in ma60]
nine_period_high = pd.rolling_max(pd.DataFrame(highp), window= ROLLING / 2)
nine_period_low = pd.rolling_min(pd.DataFrame(lowp), window= ROLLING / 2)
ichimoku = (nine_period_high + nine_period_low) /2
ichimoku = ichimoku.replace([np.inf, -np.inf], np.nan)
ichimoku = ichimoku.fillna(0.).values.tolist()
macd_indie = moving_average_convergence(pd.DataFrame(closep))
wpr = williams_percent_r(closep)
rsi = relative_strength_index(closep, ROLLING / 2)
volatility1 = pd.DataFrame(closep).rolling(ROLLING).std().values#.tolist()
volatility2 = pd.DataFrame(closep).rolling(ROLLING).var().values#.tolist()
volatility = volatility1 / volatility2
volatility = [v[0] for v in volatility]
rolling_skewness = pd.DataFrame(closep).rolling(ROLLING).skew().values
rolling_kurtosis = pd.DataFrame(closep).rolling(ROLLING).kurt().values
X, Y = [], []
for i in range(WINDOW, len(data_original)-1, STEP):
try:
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 = []
tmp.append((int)(close[_] > sma[_]))
tmp.append(wma[_])
tmp.append(mome[_])
tmp.append(rsi[_])
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_wpr(self):
period = 14
wpr = williams_percent_r.williams_percent_r(self.high_data, self.low_data, self.close_data, period)
print (wpr.tolist())
np.testing.assert_array_equal(wpr, self.wpr_expected)
