Source Code for Module QSTK.qstkutil.bollinger

 1  # python imports 
 2  import cPickle 
 3  from pylab import * 
 4  from pandas import * 
 5  import datetime as dt 
 6   
 7  #qstk imports 
 8  import qstkutil.DataAccess as da 
 9  import qstkutil.dateutil as du 
10   
11 -def calcavg(period): 
12          sum = zeros(len(period.columns)) 
13          count=0 
14          for day in period.index: 
15                  sum+=period.xs(day) 
16                  count+=1 
17          return(sum/count) 
18   
19 -def calcdev(period): 
20          avg=calcavg(period) 
21          devs=zeros(len(period.columns)) 
22          count=0 
23          for day in period.index: 
24                  devs+=(period.xs(day)-avg*ones(len(period.columns)))*(period.xs(day)-avg*ones(len(period.columns))) 
25                  count+=1 
26          return(sqrt(devs/count)) 
27   
28 -def calcbvals(adjclose, timestamps, stocks, lookback): 
29          for i in adjclose.values: 
30                  if i == 'NaN': 
31                          adjclose.values[adjclose.values.index(i)]=1; 
32          bvals=DataMatrix(index=[timestamps[0]],columns=stocks,data=[zeros(len(stocks))])  
33          for i in range(1,len(timestamps)): 
34                  s=i-lookback 
35                  if s<0: 
36                          s=0 
37                  lookbackperiod=adjclose[s:i] 
38                  avg = calcavg(lookbackperiod) 
39                  stddevs = calcdev(lookbackperiod) 
40                  if not(0 in stddevs): 
41                          b=(adjclose[i:i+1]-avg*ones(len(lookbackperiod.columns)))/stddevs 
42                          bvals=bvals.append(DataMatrix(index=[timestamps[i]],columns=stocks,data=b)) 
43          return(bvals) 
44