Created
October 31, 2020 09:00
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def exponentialMovingAverage(period, data): | |
#--- Exponential Moving Average | |
# data: array, time series data e.g. daily close prices | |
# period: integer, number of periods form time series array to include in calculation | |
#--- import libraries | |
import numpy as np | |
#--- get first non nan index | |
for i in range(len(data)): | |
if np.isnan(data[i]) == False: | |
firstNonNan = i | |
break | |
#--- get last non nan index | |
for i in reversed(range(len(data))): | |
if np.isnan(data[i]) == False: | |
lastNonNan = i | |
break | |
#--- define variables | |
sma = data[firstNonNan:period+firstNonNan].sum()/period # the first simple moving average | |
m = 2/(period+1) # weighting factor | |
#--- define output array | |
out = np.zeros(len(data)) | |
#--- calculate EMA | |
for i in range(len(data)): | |
#--- where data item is the p'th item, use the SMA | |
if i < firstNonNan: | |
out[i] = np.nan | |
elif i > lastNonNan: | |
out[i] = np.nan | |
elif i == period-1+firstNonNan: | |
out[i] = sma | |
elif i > period-1+firstNonNan: | |
#--- the EMA calculation | |
out[i] = ((data[i] - out[i-1]) * m) + out[i-1] | |
#--- mathematically equivalent | |
# out[i] = m * data[i] + (1-m) * out[i-1] | |
elif i < period-1+firstNonNan: | |
out[i] = np.nan | |
return out |
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