The ADX indicator is currently not 'built-in' as a factor. However, one can create a custom factor. Something like this
class ADX(CustomFactor):
"""
Average Directional Movement Index
Momentum indicator. Smoothed DX
**Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquitypricing.close
**Default Window Length:** 29
https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI
"""
inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]
window_length = 29
def compute(self, today, assets, out, high, low, close):
# positive directional index
plus_di = 100 * np.cumsum(plus_dm_helper(high, low) / trange_helper(high, low, close), axis=0)
# negative directional index
minus_di = 100 * np.cumsum(minus_dm_helper(high, low) / trange_helper(high, low, close), axis=0)
# full dx with 15 day burn-in period
dx_frame = (np.abs(plus_di - minus_di) / (plus_di + minus_di) * 100.)[14:]
# 14-day EMA
span = 14.
decay_rate = 2. / (span + 1.)
weights = weights_long = np.full(span, decay_rate, float) ** np.arange(span + 1, 1, -1)
# return EMA
out[:] = np.average(dx_frame, axis=0, weights=weights)
The code above relies on several helper functions.
def plus_dm_helper(high, low):
"""
Returns positive directional movement. Abstracted for use with more complex factors
https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI
Parameters
----------
high : np.array
matrix of high prices
low : np.array
matrix of low prices
Returns
-------
np.array : matrix of positive directional movement
"""
# get daily differences between high prices
high_diff = (high - np.roll(high, 1, axis=0))[1:]
# get daily differences between low prices
low_diff = (np.roll(low, 1, axis=0) - low)[1:]
# matrix of positive directional movement
return np.where(((high_diff > 0) | (low_diff > 0)) & (high_diff > low_diff), high_diff, 0.)
def minus_dm_helper(high, low):
"""
Returns negative directional movement. Abstracted for use with more complex factors
https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI
Parameters
----------
high : np.array
matrix of high prices
low : np.array
matrix of low prices
Returns
-------
np.array : matrix of negative directional movement
"""
# get daily differences between high prices
high_diff = (high - np.roll(high, 1, axis=0))[1:]
# get daily differences between low prices
low_diff = (np.roll(low, 1, axis=0) - low)[1:]
# matrix of megative directional movement
return np.where(((high_diff > 0) | (low_diff > 0)) & (high_diff < low_diff), low_diff, 0.)
def trange_helper(high, low, close):
"""
Returns true range
http://www.macroption.com/true-range/
Parameters
----------
high : np.array
matrix of high prices
low : np.array
matrix of low prices
close: np.array
matrix of close prices
Returns
-------
np.array : matrix of true range
"""
# define matrices to be compared
close = close[:-1]
high = high[1:]
low = low[1:]
# matrices for comparison
high_less_close = high - close
close_less_low = close - low
high_less_low = high - low
# return maximum value for each cel
return np.maximum(high_less_close, close_less_low, high_less_low)
There is a great post which provides custom factors for many technical signals. Check it out https://www.quantopian.com/posts/ta-lib-for-pipeline.
Good luck.
