Source Code for Module QSTK.Bin.converter

 1  #
 
 2  # fundsToPNG.py
 
 3  #
 
 4  # Short script which produces a graph of funds 
 
 5  # over time from a pickle file.
 
 6  #
 
 7  # Drew Bratcher
 
 8  #
 
 9  
 
10  from pylab import * 
11  from qstkutil import DataAccess as da 
12  from qstkutil import tsutil as tsu 
13  from quicksim import quickSim 
14  from copy import deepcopy 
15  import math 
16  from pandas import * 
17  import matplotlib.pyplot as plt 
18  import cPickle 
19  
 
20 -def fundsToPNG(funds,output_file): 
21          plt.clf() 
22          if(type(funds)==type(list())): 
23                  for i in range(0,len(funds)): 
24                          plt.plot(funds[i].index,funds[i].values) 
25          else: 
26                  plt.plot(funds.index,funds.values) 
27          plt.ylabel('Fund Value') 
28          plt.xlabel('Date') 
29          plt.draw() 
30          savefig(output_file, format='png') 
31  
 
32 -def fundsAnalysisToPNG(funds,output_file): 
33          plt.clf() 
34          if(type(funds)!=type(list())): 
35                  print 'fundsmatrix only contains one timeseries, not able to analyze.' 
36          #convert to daily returns
 
37          count=list() 
38          dates=list() 
39          sum=list() 
40          for i in range(0,len(funds)): 
41                  ret=tsu.daily(funds[i].values) 
42                  for j in range(0, len(ret)): 
43                          if (funds[i].index[j] in dates): 
44                                  sum[dates.index(funds[i].index[j])]+=ret[j] 
45                                  count[dates.index(funds[i].index[j])]+=1 
46                          else: 
47                                  dates.append(funds[i].index[j])  
48                                  count.append(1) 
49                                  sum.append(ret[j]) 
50          #compute average
 
51          tot_ret=deepcopy(sum) 
52          for i in range(0,len(sum)): 
53                  tot_ret[i]=sum[i]/count[i] 
54          
 
55          #compute std
 
56          std=zeros(len(sum)) 
57          for i in range(0,len(funds)): 
58                  temp=tsu.daily(funds[i].values) 
59                  for j in range(0,len(temp)): 
60                          std[dates.index(funds[i].index[j])]=0 
61                          std[dates.index(funds[i].index[j])]+=math.pow(temp[j]-tot_ret[dates.index(funds[i].index[j])],2) 
62          
 
63          for i in range(1, len(std)): 
64  #               std[i]=math.sqrt(std[i]/count[i])+std[i-1]
 
65                  std[i]=math.sqrt(std[i]/count[i]) 
66          
 
67          #compute total returns
 
68          lower=deepcopy(tot_ret) 
69          upper=deepcopy(tot_ret) 
70          tot_ret[0]=funds[0].values[0] 
71          lower[0]=funds[0].values[0] 
72          upper[0]=lower[0] 
73  #       for i in range(1,len(tot_ret)):
 
74  #               tot_ret[i]=tot_ret[i-1]+(tot_ret[i])*tot_ret[i-1]
 
75  #               lower[i]=tot_ret[i-1]-(std[i])*tot_ret[i-1]
 
76  #               upper[i]=tot_ret[i-1]+(std[i])*tot_ret[i-1]
 
77          for i in range(1,len(tot_ret)): 
78                  lower[i]=(tot_ret[i]-std[i]+1)*lower[i-1] 
79                  upper[i]=(tot_ret[i]+std[i]+1)*upper[i-1] 
80                  tot_ret[i]=(tot_ret[i]+1)*tot_ret[i-1] 
81                  
 
82          
 
83          plt.clf() 
84          plt.plot(dates,tot_ret) 
85          plt.plot(dates,lower) 
86          plt.plot(dates,upper) 
87          plt.legend(('Tot_Ret','Lower','Upper'),loc='upper left') 
88          plt.ylabel('Fund Total Return') 
89          plt.ylim(ymin=0,ymax=2*tot_ret[0]) 
90          plt.draw() 
91          savefig(output_file, format='png') 
92