def findSimilar(currentPatternIndex):
#takes a pattern and checks past data,
#returns list of previous pattern start points in past data and 'pattern closeness score'
reqCloseScore = settings.reqCloseScore
methodNumber = settings.closeMethodNumber
predictionDistance = settings.futureDistance
historicData = settings.historicData
similarPatternsIndex = []
pastData = settings.historicData
patternLength = settings.patternLength
currentPattern = historicData[currentPatternIndex : currentPatternIndex + patternLength]
for startPoint in range( 0 , len(pastData) - predictionDistance - patternLength):
patternToCheck = pastData[startPoint : (startPoint + patternLength)]
#print(str(startPoint))
#print('toCheck : ' + str(len(patternToCheck)))
#print('current : ' + str(len(currentPattern)))
closeScore = comparePattern(currentPattern, patternToCheck, methodNumber)
if closeScore > reqCloseScore:
if closeScore != 100:
similarPatternsIndex.append( [startPoint , closeScore] )
return similarPatternsIndex
#yList = futureResults(similarPatterns)
yList = normalisedFuture
print(yList)
print(int(len(yList)))
print(xList)
print(int(len(xList)))
#pyplot.scatter(xList, yList)
pyplot.scatter(25,(historicData[startPosition + length + futureDistance])*ratio,color = 'blue')
pyplot.show()
pastPatterns = generatePastPatterns(len(currentPattern), historicData)
similarPatterns = []
from shapeComparison import comparePattern
for i in pastPatterns:
#print('Past pattern : '+str(i))
if comparePattern(currentPattern,i[0],1) > 35:
similarPatterns.append(i)
futurePrices = []
def futureResults(similarPatterns):
futureResults = []
fururePrices = []
for i in similarPatterns:
futurePrice = historicData[i[1] + len(currentPattern) + futureDistance]
futurePrices.append(futurePrice)
futurePercentage = percentageChange(historicData[i[1] + len(currentPattern)],futurePrice)
futureResults.append(futurePercentage)
return futureResults
print('Found ' +str(len(similarPatterns)) + ' similar patterns')
print('Done')
print(int(len(yList)))
print(xList)
print(int(len(xList)))
#pyplot.scatter(xList, yList)
pyplot.scatter(
25, (historicData[startPosition + length + futureDistance]) * ratio,
color='blue')
pyplot.show()
pastPatterns = generatePastPatterns(len(currentPattern), historicData)
similarPatterns = []
from shapeComparison import comparePattern
for i in pastPatterns:
#print('Past pattern : '+str(i))
if comparePattern(currentPattern, i[0], 1) > 35:
similarPatterns.append(i)
futurePrices = []
def futureResults(similarPatterns):
futureResults = []
fururePrices = []
for i in similarPatterns:
futurePrice = historicData[i[1] + len(currentPattern) + futureDistance]
futurePrices.append(futurePrice)
futurePercentage = percentageChange(
historicData[i[1] + len(currentPattern)], futurePrice)
futureResults.append(futurePercentage)
return futureResults
