import similarity
import display
import grouping
import tradingmeasure
import parameters as para
import util
import constants as const
#testOutputFilename = 'testresults.txt'
testOutputFilename = 'testresults_test.txt'
#testOutputFilename = 'results_confidenceSellOrKeep_fyh_nocond_byFirst.txt'
# test all algos
algosToTest = {
'acf': similarity.tsdist('acfDistance'),
'ar.lpc.ceps': similarity.tsdist('ar.lpc.cepsDistance'),
#'ar.mah': similarity.tsdist('ar.mahDistance'), #Need to retrieve p-value
'ar.pic': similarity.tsdist('ar.picDistance'),
'ccor': similarity.tsdist('ccorDistance'),
#'cdm': similarity.tsdist('cdmDistance'), # (USE) SLOW / INTERNAL ERROR 5 IN MEMCOMPRESS...?
'cid': similarity.tsdist('cidDistance'),
'cor': similarity.tsdist('corDistance'),
'cort': similarity.tsdist('cortDistance'),
'dissimapprox': similarity.tsdist('dissimapproxDistance'),
'dissim': similarity.tsdist('dissimDistance'),
'dtw': similarity.tsdist('dtwDistance'),
'edr_005': similarity.tsdist('edrDistance', 0.05),
'edr_01': similarity.tsdist('edrDistance', 0.1),
'edr_025': similarity.tsdist('edrDistance', 0.25),
'edr_05': similarity.tsdist('edrDistance', 0.5),
'erp_01': similarity.tsdist('erpDistance', 0.1),
import grouping
import tradingmeasure
import pyswarm
import dataselect
import util
import constants as const
# We try to find the optimal linear combination of these parameters.
# Edit this list to configure the parameters to use.
# Make sure that these parameters have been defined by parameters.readFile() first.
parametersToOptimise = ['Close', 'RatioClose', 'AvgClose', 'DiffCloseSign', 'RunningRatio']
# Test multiple algorithms
# The average score of the algorithms is used as the objective function.
algosToTest = {
'sts': similarity.tsdist('stsDistance'),
'inf.norm': similarity.tsdist('inf.normDistance'),
'cort': similarity.tsdist('cortDistance'),
'lcss_05': similarity.tsdist('lcssDistance', 0.05),
'minkowski_25': similarity.lpNorms(2.5), #otherwise known as lp-norms
'minkowski_30': similarity.lpNorms(3),
'lbKeogh_3': similarity.tsdist('lb.keoghDistance', 3),
'dtw': similarity.tsdist('dtwDistance'),
'euclidean': similarity.tsdist('euclideanDistance'),
'fourier': similarity.tsdist('fourierDistance'),
'dissim': similarity.tsdist('dissimDistance'),
}
# Test only sts (overwrites the previous algosToTest if not commented out)
algosToTest = {
'sts': similarity.tsdist('stsDistance'),
