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financialdata.py
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financialdata.py
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__author__ = 'Andres'
import Quandl
import numpy as np
from pybrain.datasets.sequential import SequentialDataSet
from financialmodel import FinancialTrainedNetwork
import matplotlib.pyplot as plt
from pybrain.tools.customxml.networkreader import NetworkReader
from numpy import zeros, array, append
import pylab
ibexStocks={'abertis':'YAHOO/MC_ABE',
'acciona':'YAHOO/MC_ANA',
'acerinox':'YAHOO/MC_ACX',
'acs':'YAHOO/MC_ACS',
'bankinter':'YAHOO/MC_BKT',
'bbva':'YAHOO/MC_BBVA',
'caixabank':'YAHOO/MC_CABK',
'dia':'YAHOO/MC_DIA',
'enagas':'YAHOO/MC_ENG',
'endesa':'YAHOO/MC_ELE',
'ferrovial':'YAHOO/MC_FER',
'grifolsa':'YAHOO/MC_GRF',
'inditex':'YAHOO/MC_ITX',
'jazztel':'YAHOO/MC_JAZ',
'mapfre':'YAHOO/MC_MAP',
'ohl':'YAHOO/MC_OHL',
'repsol':'YAHOO/MC_REP',
'santander':'YAHOO/MC_SAN',
'telefonica':'YAHOO/MC_TEF',
'viscofan':'YAHOO/MC_VIS'}
class StockData:
def __init__(self):
self.data=[]
self.trainData=[]
self.testData=[]
def downloadData(self,stock,collapse,start="2012-12-01",end="2013-01-01"):
self.stock=stock
self.start=start
self.end=end
self.data = Quandl.get(ibexStocks[self.stock], authtoken="4bosWLqsiGqMtuuuYAcq", collapse=collapse, trim_start=self.start, trim_end=self.end, returns='numpy')
def saveData(self,name):
with open (name,'w') as f:
for i in range(len(self.data)):
if self.data[i][5]:
f.write("%.3f\t%.3f\t%.3f\t%.3f\t%d\t%.3f\n" % (self.data[i][1],self.data[i][2],self.data[i][3],self.data[i][4],self.data[i][5], self.data[i][4]))
else:
pass
def readData(self,name,delimiter='\t'):
with open(name) as f:
for line in f:
self.data.append(line.strip().split(delimiter))
for item in self.data:
for i in range(len(item)):
item[i]=float(item[i])
self.data=np.array(self.data)
def normalizeData(self):
def normalize(vector):
maximo=max(vector)
for i in range(len(vector)):
vector[i]=vector[i]/maximo
return vector
for i in range(self.data.shape[1]):
self.data[:,i]=normalize(self.data[:,i])
def delayInputs(self):
m=len(self.data)
for i in range(1,m):
self.data[i-1,-1]=self.data[i,-1]
self.data=np.delete(self.data,m-1,axis=0)
def createSequentialDataSets(self,testRatio=0.7):
ixSeparator=int(self.data.shape[0]*0.7)
trainData=self.data[0:ixSeparator]
testData=self.data[ixSeparator:]
self.trainData = SequentialDataSet(5,1)
self.testData= SequentialDataSet(5,1)
for i in range(len(trainData)):
self.trainData.addSample(trainData[i,0:5],trainData[i,5])
for i in range(len(testData)):
self.testData.addSample(testData[i,0:5],testData[i,5])
def plotData(self):
plt.plot(self.data[:,5],'b')
pylab.show()
class StockSample:
def __init__(self,stock,collapse,start,end):
self.stock=stock
data = Quandl.get(ibexStocks[stock], authtoken="4bosWLqsiGqMtuuuYAcq", collapse=collapse, trim_start=start, trim_end=end, returns='numpy')
self.data=[]
for i in range(len(data)):
row=[]
for j in range(1,6):
row.append(data[i][j])
self.data.append(row)
self.data=np.asarray(self.data)
def normalizeData(self):
def normalize(vector):
maximo=max(vector)
for i in range(len(vector)):
vector[i]=vector[i]/maximo
return vector
for i in range(self.data.shape[1]):
self.data[:,i]=normalize(self.data[:,i])
def calculateReturns(self):
self.returns=[]
for i in range(1,len(self.data)):
self.returns.append((self.data[i,3]-self.data[i-1,3])/self.data[i-1,3])
def forecastReturn(self):
self.expectedPrices=[]
model=FinancialTrainedNetwork(self.stock)
wtratio=1./3.
wtseparator=int(wtratio*self.data.shape[0])
washout_inputs=self.data[:wtseparator]
self.activation_inputs=self.data[wtseparator:]
model.activate(washout_inputs)
self.expectedPrices=model.activate(self.activation_inputs)
self.expectedReturn=(self.expectedPrices[-1]-self.activation_inputs[-1,3])/self.activation_inputs[-1,3]
def plotPredictions(self):
data=self.expectedPrices
data.insert(1,0)
plt.plot(data[1:],'b',self.activation_inputs[1:,3],'r')
plt.show()
def _getLastOutput(self):
if self.model.offset == 0:
return zeros(self.model.outdim)
else:
return self.model._out_layer.outputbuffer[self.network.offset - 1]