Bollinger Bands

Bollinger Bands¶

The idea of bollinger bands is that we may see reversion to a rolling mean over time. Essentially, what one does is takes the rolling mean as well as two standard deviations above and below and you see at what times there is a breakout from these bands. Some people believe that if there is a breakout it will reverse, but others do not believe this is a viable trading strategy. This dataset is purposely created in a way for this to work, but in the real world you will have to determine for yourself what you think is correct or not.

In [10]:

#Let's create bollinger bands by adding two standard deviations above and below

#Create the lower and upper bands
upper_band = rolling_mean + std*2
lower_band = rolling_mean - std*2

#Plot the bands
df.plot(kind="line",color="k")
rolling_mean.plot(kind="line",color="b")
upper_band.plot(kind="line",color="r")
lower_band.plot(kind="line",color="r")
plt.show()

To identify what points are below or above the bollinger bands, we can use our boolean indexing. Let's create two indices, one for above the upper band and one for below the lower band.

In [11]:

#Index for above the upper bound
i1 = df > upper_band

#Index for above the lower bound
i2 = df < lower_band

print(i1[:5])
print()
print(i2[:5])

0    False
1    False
2    False
3    False
4    False
dtype: bool

0    False
1    False
2    False
3    False
4    False
dtype: bool

We may also want to combine the two to make an index that tells us whether the price is either above or below the bands. First, we can see one example of where the price is above the band below (we also see no points are below the bands):

In [12]:

print(i1[55:65])
print()
print(i2[55:65])

55    False
56    False
57    False
58    False
59     True
60    False
61    False
62    False
63    False
64    False
dtype: bool

55    False
56    False
57    False
58    False
59    False
60    False
61    False
62    False
63    False
64    False
dtype: bool

To combine them as an or statement, we can use the | (pipe) operator which will go index by index comparing if either is true.

In [13]:

#Create an index for either i1 or i2 being true
i3 = i1 | i2
print(i3[55:65])

55    False
56    False
57    False
58    False
59     True
60    False
61    False
62    False
63    False
64    False
dtype: bool

The reason you need to use the pipe operator is because pandas uses C libraries under the hood. The shorthand way to achieve this without setting variables would be the following (notice you need parantheses around the two conditions):

In [14]:

#Achieves the same goal:
print( (df > upper_band) | (df < lower_band) )

0      False
1      False
2      False
3      False
4      False
       ...
195    False
196    False
197    False
198    False
199    False
Length: 200, dtype: bool

We are able to use this to index into the actual data.

In [15]:

print(df[(df > upper_band) | (df < lower_band)])

33      95.581040
34      94.909794
36      93.779819
59     100.498860
68     101.962135
75      94.446699
76      93.640495
77      93.968470
90     102.108535
91     102.793692
92     102.918407
117    102.772938
147    109.376186
148    109.608680
149    110.291232
186    107.934320
187    108.675057
188    109.455722
189    109.882610
190    111.542948
191    112.412441
192    114.817100
193    116.428156
194    117.587206
dtype: float64

Data Science

Data Science

Bollinger Bands

Bollinger Bands¶

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