Past outcomes as possible predictions
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import numpy as np
from numpy import loadtxt
import time
from matplotlib import style
style.use("ggplot")
totalStart = time.time()
date,bid,ask = np.loadtxt('GBPUSD1d.txt', unpack=True,
delimiter=',',
converters={0:mdates.strpdate2num('%Y%m%d%H%M%S')})
def percentChange(startPoint,currentPoint):
try:
x = ((float(currentPoint)-startPoint)/abs(startPoint))*100.00
if x == 0.0:
return 0.000000001
else:
return x
except:
return 0.0001
def patternStorage():
'''
The goal of patternFinder is to begin collection of %change patterns
in the tick data. From there, we also collect the short-term outcome
of this pattern. Later on, the length of the pattern, how far out we
look to compare to, and the length of the compared range be changed,
and even THAT can be machine learned to find the best of all 3 by
comparing success rates.'''
startTime = time.time()
x = len(avgLine)-30
y = 31
currentStance = 'none'
while y < x:
pattern = []
p1 = percentChange(avgLine[y-30], avgLine[y-29])
p2 = percentChange(avgLine[y-30], avgLine[y-28])
p3 = percentChange(avgLine[y-30], avgLine[y-27])
p4 = percentChange(avgLine[y-30], avgLine[y-26])
p5 = percentChange(avgLine[y-30], avgLine[y-25])
p6 = percentChange(avgLine[y-30], avgLine[y-24])
p7 = percentChange(avgLine[y-30], avgLine[y-23])
p8 = percentChange(avgLine[y-30], avgLine[y-22])
p9 = percentChange(avgLine[y-30], avgLine[y-21])
p10= percentChange(avgLine[y-30], avgLine[y-20])
p11 = percentChange(avgLine[y-30], avgLine[y-19])
p12 = percentChange(avgLine[y-30], avgLine[y-18])
p13 = percentChange(avgLine[y-30], avgLine[y-17])
p14 = percentChange(avgLine[y-30], avgLine[y-16])
p15 = percentChange(avgLine[y-30], avgLine[y-15])
p16 = percentChange(avgLine[y-30], avgLine[y-14])
p17 = percentChange(avgLine[y-30], avgLine[y-13])
p18 = percentChange(avgLine[y-30], avgLine[y-12])
p19 = percentChange(avgLine[y-30], avgLine[y-11])
p20= percentChange(avgLine[y-30], avgLine[y-10])
p21 = percentChange(avgLine[y-30], avgLine[y-9])
p22 = percentChange(avgLine[y-30], avgLine[y-8])
p23 = percentChange(avgLine[y-30], avgLine[y-7])
p24 = percentChange(avgLine[y-30], avgLine[y-6])
p25 = percentChange(avgLine[y-30], avgLine[y-5])
p26 = percentChange(avgLine[y-30], avgLine[y-4])
p27 = percentChange(avgLine[y-30], avgLine[y-3])
p28 = percentChange(avgLine[y-30], avgLine[y-2])
p29 = percentChange(avgLine[y-30], avgLine[y-1])
p30= percentChange(avgLine[y-30], avgLine[y])
outcomeRange = avgLine[y+20:y+30]
currentPoint = avgLine[y]
try:
avgOutcome = reduce(lambda x, y: x + y, outcomeRange) / len(outcomeRange)
except Exception as e:
print(str(e))
avgOutcome = 0
futureOutcome = percentChange(currentPoint, avgOutcome)
'''
print 'where we are historically:',currentPoint
print 'soft outcome of the horizon:',avgOutcome
print 'This pattern brings a future change of:',futureOutcome
print '_______'
print p1, p2, p3, p4, p5, p6, p7, p8, p9, p10
'''
pattern.append(p1)
pattern.append(p2)
pattern.append(p3)
pattern.append(p4)
pattern.append(p5)
pattern.append(p6)
pattern.append(p7)
pattern.append(p8)
pattern.append(p9)
pattern.append(p10)
pattern.append(p11)
pattern.append(p12)
pattern.append(p13)
pattern.append(p14)
pattern.append(p15)
pattern.append(p16)
pattern.append(p17)
pattern.append(p18)
pattern.append(p19)
pattern.append(p20)
pattern.append(p21)
pattern.append(p22)
pattern.append(p23)
pattern.append(p24)
pattern.append(p25)
pattern.append(p26)
pattern.append(p27)
pattern.append(p28)
pattern.append(p29)
pattern.append(p30)
patternAr.append(pattern)
performanceAr.append(futureOutcome)
y+=1
endTime = time.time()
print(len(patternAr))
print(len(performanceAr))
print('Pattern storing took:', endTime-startTime)
def currentPattern():
mostRecentPoint = avgLine[-1]
cp1 = percentChange(avgLine[-31],avgLine[-30])
cp2 = percentChange(avgLine[-31],avgLine[-29])
cp3 = percentChange(avgLine[-31],avgLine[-28])
cp4 = percentChange(avgLine[-31],avgLine[-27])
cp5 = percentChange(avgLine[-31],avgLine[-26])
cp6 = percentChange(avgLine[-31],avgLine[-25])
cp7 = percentChange(avgLine[-31],avgLine[-24])
cp8 = percentChange(avgLine[-31],avgLine[-23])
cp9 = percentChange(avgLine[-31],avgLine[-22])
cp10= percentChange(avgLine[-31],avgLine[-21])
cp11 = percentChange(avgLine[-31],avgLine[-20])
cp12 = percentChange(avgLine[-31],avgLine[-19])
cp13 = percentChange(avgLine[-31],avgLine[-18])
cp14 = percentChange(avgLine[-31],avgLine[-17])
cp15 = percentChange(avgLine[-31],avgLine[-16])
cp16 = percentChange(avgLine[-31],avgLine[-15])
cp17 = percentChange(avgLine[-31],avgLine[-14])
cp18 = percentChange(avgLine[-31],avgLine[-13])
cp19 = percentChange(avgLine[-31],avgLine[-12])
cp20= percentChange(avgLine[-31],avgLine[-11])
cp21 = percentChange(avgLine[-31],avgLine[-10])
cp22 = percentChange(avgLine[-31],avgLine[-9])
cp23 = percentChange(avgLine[-31],avgLine[-8])
cp24 = percentChange(avgLine[-31],avgLine[-7])
cp25 = percentChange(avgLine[-31],avgLine[-6])
cp26 = percentChange(avgLine[-31],avgLine[-5])
cp27 = percentChange(avgLine[-31],avgLine[-4])
cp28 = percentChange(avgLine[-31],avgLine[-3])
cp29 = percentChange(avgLine[-31],avgLine[-2])
cp30= percentChange(avgLine[-31],avgLine[-1])
patForRec.append(cp1)
patForRec.append(cp2)
patForRec.append(cp3)
patForRec.append(cp4)
patForRec.append(cp5)
patForRec.append(cp6)
patForRec.append(cp7)
patForRec.append(cp8)
patForRec.append(cp9)
patForRec.append(cp10)
patForRec.append(cp11)
patForRec.append(cp12)
patForRec.append(cp13)
patForRec.append(cp14)
patForRec.append(cp15)
patForRec.append(cp16)
patForRec.append(cp17)
patForRec.append(cp18)
patForRec.append(cp19)
patForRec.append(cp20)
patForRec.append(cp21)
patForRec.append(cp22)
patForRec.append(cp23)
patForRec.append(cp24)
patForRec.append(cp25)
patForRec.append(cp26)
patForRec.append(cp27)
patForRec.append(cp28)
patForRec.append(cp29)
patForRec.append(cp30)
def graphRawFX():
fig=plt.figure(figsize=(10,7))
ax1 = plt.subplot2grid((40,40), (0,0), rowspan=40, colspan=40)
ax1.plot(date,bid)
ax1.plot(date,ask)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
plt.grid(True)
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
plt.gca().get_yaxis().get_major_formatter().set_useOffset(False)
ax1_2 = ax1.twinx()
ax1_2.fill_between(date, 0, (ask-bid), facecolor='g',alpha=.3)
plt.subplots_adjust(bottom=.23)
plt.show()
def patternRecognition():
plotPatAr = []
patFound = 0
for eachPattern in patternAr:
sim1 = 100.00 - abs(percentChange(eachPattern[0], patForRec[0]))
sim2 = 100.00 - abs(percentChange(eachPattern[1], patForRec[1]))
sim3 = 100.00 - abs(percentChange(eachPattern[2], patForRec[2]))
sim4 = 100.00 - abs(percentChange(eachPattern[3], patForRec[3]))
sim5 = 100.00 - abs(percentChange(eachPattern[4], patForRec[4]))
sim6 = 100.00 - abs(percentChange(eachPattern[5], patForRec[5]))
sim7 = 100.00 - abs(percentChange(eachPattern[6], patForRec[6]))
sim8 = 100.00 - abs(percentChange(eachPattern[7], patForRec[7]))
sim9 = 100.00 - abs(percentChange(eachPattern[8], patForRec[8]))
sim10 = 100.00 - abs(percentChange(eachPattern[9], patForRec[9]))
sim11 = 100.00 - abs(percentChange(eachPattern[10], patForRec[10]))
sim12 = 100.00 - abs(percentChange(eachPattern[11], patForRec[11]))
sim13 = 100.00 - abs(percentChange(eachPattern[12], patForRec[12]))
sim14 = 100.00 - abs(percentChange(eachPattern[13], patForRec[13]))
sim15 = 100.00 - abs(percentChange(eachPattern[14], patForRec[14]))
sim16 = 100.00 - abs(percentChange(eachPattern[15], patForRec[15]))
sim17 = 100.00 - abs(percentChange(eachPattern[16], patForRec[16]))
sim18 = 100.00 - abs(percentChange(eachPattern[17], patForRec[17]))
sim19 = 100.00 - abs(percentChange(eachPattern[18], patForRec[18]))
sim20 = 100.00 - abs(percentChange(eachPattern[19], patForRec[19]))
sim21 = 100.00 - abs(percentChange(eachPattern[20], patForRec[20]))
sim22 = 100.00 - abs(percentChange(eachPattern[21], patForRec[21]))
sim23 = 100.00 - abs(percentChange(eachPattern[22], patForRec[22]))
sim24 = 100.00 - abs(percentChange(eachPattern[23], patForRec[23]))
sim25 = 100.00 - abs(percentChange(eachPattern[24], patForRec[24]))
sim26 = 100.00 - abs(percentChange(eachPattern[25], patForRec[25]))
sim27 = 100.00 - abs(percentChange(eachPattern[26], patForRec[26]))
sim28 = 100.00 - abs(percentChange(eachPattern[27], patForRec[27]))
sim29 = 100.00 - abs(percentChange(eachPattern[28], patForRec[28]))
sim30 = 100.00 - abs(percentChange(eachPattern[29], patForRec[29]))
howSim = (sim1+sim2+sim3+sim4+sim5+sim6+sim7+sim8+sim9+sim10
+sim11+sim12+sim13+sim14+sim15+sim16+sim17+sim18+sim19+sim20
+sim21+sim22+sim23+sim24+sim25+sim26+sim27+sim28+sim29+sim30)/30.00
if howSim > 75:
patdex = patternAr.index(eachPattern)
patFound = 1
xp = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
#############
plotPatAr.append(eachPattern)
if patFound == 1:
fig = plt.figure(figsize=(10,6))
for eachPatt in plotPatAr:
futurePoints = patternAr.index(eachPatt)
if performanceAr[futurePoints] > patForRec[9]:
pcolor = '#24bc00'
else:
pcolor = '#d40000'
plt.plot(xp, eachPatt)
####################
plt.scatter(35, performanceAr[futurePoints],c=pcolor,alpha=.4)
plt.plot(xp, patForRec, '#54fff7', linewidth = 3)
plt.grid(True)
plt.title('Pattern Recognition')
plt.show()
dataLength = int(bid.shape[0])
print('data length is', dataLength)
toWhat = 37000
while toWhat < dataLength:
avgLine = ((bid+ask)/2)
avgLine = avgLine[:toWhat]
patternAr = []
performanceAr = []
patForRec = []
patternStorage()
currentPattern()
patternRecognition()
totalEnd = time.time()-totalStart
print('Entire processing took:',totalEnd,'seconds')
toWhat += 1
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