# Calculating Bollinger Band Correctly

My bollinger band comes out like the below, which doesn't seem right. Any idea what is wrong with my code for calculating upper and lower bollinber bands?

I obtained my data from here

start, end = dt.datetime(1976, 1, 1), dt.datetime(2013, 12, 31)
here are my bollinger calculations


calculation for bollinger band

ave = pd.stats.moments.rolling_mean(self[name], window)
std = pd.stats.moments.rolling_std(self[name], window)
self['upper'] = ave + (2 * std)
self['lower'] = ave - (2 * std)


• I am not into python but looks like that your average (ave) time series does not look right in relation to "SP", at least ave does not converge with "SP". – Matt May 13 '14 at 5:53
• Perhaps try to plot ave to see where it is, and perhaps on a different window plot std to see its size. Providing a full code might be helpful as well. – Bach May 13 '14 at 6:41
• @Bach I second the recommendation on providing more code (or perhaps a simplified version that can reproduce the plot from scratch). – John May 14 '14 at 14:35
• Seems like the rolling Standard deviation for the $lower$ band is slightly lagging the $SP$ ... Make sure the rolling window is the same for both the upper & lower bands – Rime Dec 11 '14 at 6:45

In Pandas 0.19.2++:

def Bolinger_Bands(stock_price, window_size, num_of_std):

rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std  = stock_price.rolling(window=window_size).std()
upper_band = rolling_mean + (rolling_std*num_of_std)
lower_band = rolling_mean - (rolling_std*num_of_std)

return rolling_mean, upper_band, lower_band

def main():

price_series = get_data(ticker, dates) # it is a Pandas series...

rolling_avg_price, upper_band, lower_band = Bolinger_Bands(price_series, 20, 2)

do_other_processing(rolling_avg_price, upper_band, lower_band)
...

def bbands(price, length=30, numsd=2):
""" returns average, upper band, and lower band"""
ave = pd.stats.moments.rolling_mean(price,length)
sd = pd.stats.moments.rolling_std(price,length)
upband = ave + (sd*numsd)
dnband = ave - (sd*numsd)
return np.round(ave,3), np.round(upband,3), np.round(dnband,3)

sp['ave'], sp['upper'], sp['lower'] = bbands(sp.Close, length=30, numsd=1)
sp= sp[-200:]
sp.plot()


• Pandas stats deprecated. I used ave = price.rolling(window=20, center=False).mean() and sd = price.rolling(window=20,center=False).std() – Gabriel Dec 26 '16 at 19:19

I believe that the answers given here are incorrect as they return the sample standard deviation while the the population measure is the correct calculation for Bollinger Bands. The bands usign the sample calc will be too wide. Pandas does not appear to allow a choice between the sample and population calculations for either solution presented here.

sd = pd.stats.moments.rolling_std(price,length)
rolling_std  = stock_price.rolling(window=window_size).std()


Numpy does allow a choice, so it should be used until a proper pandas solution is presented.

a = np.array([1,2,3,4,5])
print np.std(a, ddof=1) # sample
print np.std(a, ddof=0) # population
>>>
1.58113883008
1.41421356237
>>>


https://docs.scipy.org/doc/numpy/reference/generated/numpy.std.html

John Bollinger, CFA, CMT

Well, it appears that pandas has caught up with me. This works correctly now.

b = pd.DataFrame([1,2,3,4,5])
print b.rolling(window=5).std() # sample
print b.rolling(window=5).std(ddof=0) # population


Try to plot the rolling mean against your quotes for SP and see if it makes sense. Although you line of code to compute the rolling mean is correct, there might be something wrong in the data that you pass as input.