def data_to_indicators(data, o, h, l, c, v, window) -> pd.DataFrame:
"""
Given a DataFrame of stock prices, return the data along with the corresponding indicators
:param data: Data in C-OHLV format
:param window: default value of window for indicators (only for indicators that support this)
:param o: name of column containing 'OPEN' values
:param h: name of column containing 'HIGH' values
:param l: name of column containing 'LOW' values
:param c: name of column containing 'CLOSE' values
:param v: name of column containing 'VOLUME' values
:return: DataFrame with all indicators
"""
df = data.reset_index(drop=True)
# Momentum
# TODO PMO
df['macd_' + str(window)] = macd(df[c], n_fast=int(window / 2), n_slow=window)
df['rsi_' + str(window)] = rsi(df[c], n=window)
df['wr_' + str(window)] = wr(df[h], df[l], df[c], lbp=window)
df['mfi_' + str(window)] = money_flow_index(df[h], df[l], df[c], df[v], n=window)
df['stochk_' + str(window)] = stoch(df[h], df[l], df[c], n=window)
df['stochd_' + str(window)] = stoch_signal(df[h], df[l], df[c], n=window, d_n=3)
# ROC
df['roc_' + str(window)] = [(df[c][i] - df[c][i - window]) / df[c][i - window] if i >= window else np.nan
for i in range(0, df[c].size)]
# Smoothing
df['sma_' + str(window)] = simple_moving_average.simple_moving_average(df[c], period=window)
df['wma_' + str(window)] = weighted_moving_average.weighted_moving_average(df[c], period=window)
df['ema_' + str(window)] = exponential_moving_average.exponential_moving_average(np.array(df[c]), period=window)
df['hma_' + str(window)] = hull_moving_average.hull_moving_average(np.array(df[c]), period=window)
# df['cma_' + str(window)] = [df[c][i - int(window / 2): i + int(window / 2)].mean()
# if (i >= int(window / 2) and (i + int(window / 2)) < df[c].size) else np.nan
# for i in range(0, df[c].size)]
# Overbought/Oversold Signals
df['cci_' + str(window)] = cci(df[h], df[l], df[c], n=window, c=0.015)
# Volume
df['adl'] = acc_dist_index(df[h], df[l], df[c], df[v])
df['cmf_' + str(window)] = chaikin_money_flow(df[h], df[l], df[c], df[v], n=window)
df['obv'] = on_balance_volume(df[c], df[v])
df['emv_' + str(window)] = ease_of_movement(df[h], df[l], df[c], df[v], n=window)
# Volatility
df['atr_' + str(window)] = average_true_range(df[h], df[l], df[c], n=window)
df['mass_ind_' + str(window)] = mass_index(df[h], df[l], n=window / 2, n2=window)
# Trends
# How will model know b/w Ichmoku A and B?
df['ichimoku_a'] = ichimoku_a(df[h], df[l], n1=9, n2=26)
df['ichimoku_b'] = ichimoku_b(df[h], df[l], n2=26, n3=52)
series_aroon_up = aroon_up(df[c], n=window)
series_aroon_down = aroon_down(df[c], n=window)
df['aroon_ind_' + str(window)] = series_aroon_up - series_aroon_down
df['adx_' + str(window)] = adx(df[h], df[l], df[c], n=window)
return df
def test_wma_invalid_period(self):
period = 128
with self.assertRaises(Exception) as cm:
weighted_moving_average.weighted_moving_average(self.data, period)
expected = "Error: data_len < period"
self.assertEqual(str(cm.exception), expected)
#dataset_train = dataset_test
from pyti.simple_moving_average import simple_moving_average
x = dataset_train.iloc[:, 4].values
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,
def test_wma_period_10(self):
period = 10
wma = weighted_moving_average.weighted_moving_average(
self.data, period)
np.testing.assert_array_equal(wma, self.wma_period_10_expected)
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