def bollinger_bands(self, period=20, stds=2, verbose=False):
"""[The Bolinger Bands is essentially a confidence interval of
stds Deviations where the price should be based on the last
period periods of prices]
Keyword Arguments:
period {int} -- [The period over which to look over the
prices] (default: {20})
stds {int} -- [The number of standard deviations the bands
should take up] (default: {2})
Returns:
[float[], float[]] -- [Upper Bolinger Band, Lower Bolinger Band vectors respectively]
"""
tp = self.typical_prices()
ma = Indicators.sma(prices=tp, period=period)
std = Indicators.calc_std(prices=tp, period=period)
bolu = np.array([ma[i] + stds * std[i] for i in range(len(ma))])
bold = np.array([ma[i] + stds * std[i] for i in range(len(ma))])
return bolu, bold
def test_sma(self):
prices = np.arange(10)
expected = np.array([0, 0, 0, 0, 0, 2, 3, 4, 5, 6])
assert (Indicators.sma(period=5, prices=prices) == expected).all()
def test_sma(self, period):
prices = np.arange(10)
print(Indicators.sma(prices, period))
def sma_feature(eq, period):
sma_vec = np.array(Indicators.sma(eq.closes, period))
sma_vec = sma_vec.T
return [Indicator(sma_vec)]