According to Wikipedia, EWMA works well with stationary data, but it does not work as expected in the presence of trends, or seasonality. In those cases you should use a second or third order EWMA method, respectively. I decided to look at the pandas ewma function to see how it handled trends, and this is what I came up with:
import pandas, numpy as np
ewma = pandas.stats.moments.ewma
# make a hat function, and add noise
x = np.linspace(0,1,100)
x = np.hstack((x,x[::-1]))
x += np.random.normal( loc=0, scale=0.1, size=200 )
plot( x, alpha=0.4, label="Raw" )
# take EWMA in both directions with a smaller span term
fwd = ewma( x, span=15 ) # take EWMA in fwd direction
bwd = ewma( x[::-1], span=15 ) # take EWMA in bwd direction
c = np.vstack(( fwd, bwd[::-1] )) # lump fwd and bwd together
c = np.mean( c, axis=0 ) # average
# regular EWMA, with bias against trend
plot( ewma( x, span=20 ), 'b', label="EWMA, span=20" )
# "corrected" (?) EWMA
plot( c, 'r', label="Reversed-Recombined" )
legend(loc=8)
savefig( 'ewma_correction.png', fmt="png", dpi=100 )
As you can see, the EWMA bucks the trend uphill and downhill. We can correct for this (without having to implement a second-order scheme ourselves) by taking the EWMA in both directions and then averaging. I hope your data was stationary!