from financialdata import StockData

from financialmodel import FinancialNetwork

from evolinotrainer import EvolinoTrainer, EvolinoEvaluation

from pylab import plot, show, ion, cla, subplot, title, figlegend, draw

from pybrain.tools.customxml.networkwriter import NetworkWriter

import numpy as np

import matplotlib.pyplot as plt

import pylab

name='acs'

data=StockData()

data.readData('stockprices/'+name+'.txt')

data.normalizeData()

data.delayInputs()

data.createSequentialDataSets()

model = FinancialNetwork(data.trainData.indim,data.trainData.outdim,hiddim=10)

trainer=EvolinoTrainer(model,data.trainData,

verbosity = 2)

trainInput = data.trainData.getField('input')

testInput = data.testData.getField('input')

trainTarget = data.trainData.getField('target')

testTarget = data.testData.getField('target')

wtRatio=1./3.

training_washout_separator=int(wtRatio*len(trainInput))

test_washout_separator=int(wtRatio*len(testInput))

washout_trainInput=trainInput[:training_washout_separator]

washout_testInput=testInput[:test_washout_separator]

forecast_trainInput=trainInput[training_washout_separator:]

forecast_testInput=testInput[test_washout_separator:]

target_trainInput=trainTarget[training_washout_separator:]

target_testInput=testTarget[test_washout_separator:]

ion() # switch matplotlib to interactive mode

for i in range(100):

print("======================")

print("====== NEXT RUN ======")

print("======================")

print("=== TRAINING")

# train the network for 1 epoch

trainer.trainEpochs( 1 )

print("=== PLOTTING\n")

# calculate the nets output for train and the test data

model.activate(washout_trainInput)

trnSequenceOutput = model.activate(forecast_trainInput)

model.activate(washout_testInput)

tstSequenceOutput = model.activate(forecast_testInput)

NetworkWriter.writeToFile(model.network,'trainednetworks/'+name+'.xml')

with open ('trainednetworks/trainresults/%s_train_results.txt'% (name),'w') as f:

for i in range(len(trnSequenceOutput)):

f.write('%.7f\t%.7f\n' % (target_trainInput[i],trnSequenceOutput[i]))

f.close()

with open ('trainednetworks/trainresults/%s_test_results.txt'% (name),'w') as f:

for i in range(len(tstSequenceOutput)):

f.write('%.7f\t%.7f\n' % (target_testInput[i],tstSequenceOutput[i]))

f.close()

# plot training data

sp = subplot(211) # switch to the first subplot

cla() # clear the subplot

targetline = plot(target_trainInput,"r-") # plot the targets

outputline = plot(trnSequenceOutput,"b-") # plot the actual output

# plot test data

sp = subplot(212)

cla()

plot(target_testInput,"r-")

plot(tstSequenceOutput,"b-")

# draw everything

draw()

show()