def test_MOM():
values = np.array([90.0, 88.0, 89.0])
result = func.MOM(values, timeperiod=1)
assert_np_arrays_equal(result, [np.nan, -2, 1])
result = func.MOM(values, timeperiod=2)
assert_np_arrays_equal(result, [np.nan, np.nan, -1])
result = func.MOM(values, timeperiod=3)
assert_np_arrays_equal(result, [np.nan, np.nan, np.nan])
result = func.MOM(values, timeperiod=4)
assert_np_arrays_equal(result, [np.nan, np.nan, np.nan])
def get_technical_indicators(dataset):
# Create 7 and 21 days Moving Average
dataset['ma5'] = dataset['Close'].rolling(window=5).mean()
dataset['ma10'] = dataset['Close'].rolling(window=10).mean()
# Create MACD
dataset['26ema'] = func.EMA(dataset['Close'],26)
dataset['12ema'] = func.EMA(dataset['Close'],12)
dataset['MACD'] = (dataset['12ema']-dataset['26ema'])
# Create Bollinger Bands
dataset['10sd'] = dataset['Close'].rolling(10).std()
dataset['upper_band'] = dataset['ma5'] + (dataset['10sd']*2)
dataset['lower_band'] = dataset['ma10'] - (dataset['10sd']*2)
# Create Exponential moving average
dataset['ema'] = dataset['Close'].ewm(com=0.5).mean()
# Create Momentum
dataset['momentum'] = func.MOM(dataset['Close'],20)
# ROC
dataset['roc'] = func.ROC(dataset['Close'],10)
# Willam
dataset['wpr'] = func.WILLR(dataset['High'], dataset['Low'], dataset['Close'], timeperiod=14)
# ATR
dataset['atr'] = func.ATR(dataset['High'], dataset['Low'], dataset['Close'], timeperiod=14)
return dataset
bb_low = (ma5 - sd_10 * 2)['2007-02-08':]
print(sum(sum(np.isnan(bb_low.values))))
bb_low_open = (ma5_open - sd_10_open * 2)['2007-02-08':]
print(sum(sum(np.isnan(bb_low_open.values))))
ema = Close.ewm(com=0.5).mean()['2007-02-08':]
print(sum(sum(np.isnan(ema.values))))
ema_open = Open.ewm(com=0.5).mean()['2007-02-08':]
print(sum(sum(np.isnan(ema_open.values))))
roc = Close.apply(lambda x: func.ROC(x, 10), axis=0)['2007-02-08':]
print(sum(sum(np.isnan(roc.values))))
roc_open = Open.apply(lambda x: func.ROC(x, 10), axis=0)['2007-02-08':]
print(sum(sum(np.isnan(roc_open.values))))
mom = Close.apply(lambda x: func.MOM(x, 20), axis=0)['2007-02-08':]
print(sum(sum(np.isnan(mom.values))))
mom_open = Open.apply(lambda x: func.MOM(x, 20), axis=0)['2007-02-08':]
print(sum(sum(np.isnan(mom_open.values))))
Close = Close['2007-02-08':]
Open = Open['2007-02-08':]
High = High['2007-02-08':]
Low = Low['2007-02-08':]
#%%
def KalmanFilterAverage(x):
# Construct a Kalman filter
def test_input_wrong_type():
a1 = np.arange(10, dtype=int)
with assert_raises(Exception):
func.MOM(a1)
