def vortex_index(high, low, close, window=20):
true_range = np.maximum.reduce(
[high[1:] - low[1:], high[1:] - close[:-1], low[1:] - close[:-1]])
true_range = offset(rolling_func(true_range, window, [np.sum])[0], 1)
uptrend = offset(np.abs(high[1:] - low[:-1]), 1)
downtrend = offset(np.abs(low[1:] - high[:-1]), 1)
vi_plus = divide(rolling_func(uptrend, window, [np.sum])[0], true_range)
vi_minus = divide(rolling_func(downtrend, window, [np.sum])[0], true_range)
vi_plus[:window] = np.nan
vi_minus[:window] = np.nan
return replace_nan(vi_plus), replace_nan(vi_minus)
def aroon(high, low, window=20):
aroon_up = rolling_func(high, window + 1, [np.argmax])[0] / window
aroon_down = rolling_func(low, window + 1, [np.argmin])[0] / window
return aroon_down * 100, aroon_up * 100
def b_bands(price, window=20, multiplier=2):
mean, std = rolling_func(price, window, [np.mean, np.std])
lower = mean - multiplier * std
upper = mean + multiplier * std
return upper, mean, lower
def rolling_min(series, window=20):
return rolling_func(series, window, [np.min])[0]
def rolling_max(series, window=20):
return rolling_func(series, window, [np.max])[0]
def sma(series, window=20):
return rolling_func(series, window, [np.mean])[0]
def cci(high, low, close, window=20):
typical_price = true_price(high, low, close)
ma_price, std = rolling_func(typical_price, window, [np.mean, np.std])
cci = divide(typical_price - ma_price, 0.015 * std)
return replace_nan(cci)
def vwma(price, volume, window=20):
volume_sum = rolling_func(volume, window, [np.sum])[0]
price_volume = rolling_func(price * volume, window, [np.sum])[0]
vwma = divide(price_volume, volume_sum)
return replace_nan(vwma)