## Storing Patterns

```'''
so we suspect that possibly

To compare patterns:
use a % change calculation to calculate similarity between each %change
movement in the pattern finder. From those numbers, subtract them from 100, to
get a "how similar" #. From this point, take all 10 of the how similars,
and average them. Whichever pattern is MOST similar, is the one we will assume
we have found.
'''

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import numpy as np
import time

delimiter=',',
converters={0:mdates.strpdate2num('%Y%m%d%H%M%S')})
####DEFINE######
#CHANGE#
patternAr = []
performanceAr = []

def percentChange(startPoint,currentPoint):

return ((float(currentPoint)-startPoint)/abs(startPoint))*100.00

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()

# required to do a pattern array, because the liklihood of an identical
# %change across millions of patterns is fairly likely and would
# cause problems. IF it was a problem of identical patterns,
# then it wouldnt matter, but the % change issue
# would cause a lot of harm. Cannot have a list as a dictionary Key.

#MOVE THE ARRAYS THEMSELVES#

x = len(avgLine)-30
y = 11
currentStance = 'none'

while y < x:
pattern = []
p1 = percentChange(avgLine[y-10], avgLine[y-9])
p2 = percentChange(avgLine[y-10], avgLine[y-8])
p3 = percentChange(avgLine[y-10], avgLine[y-7])
p4 = percentChange(avgLine[y-10], avgLine[y-6])
p5 = percentChange(avgLine[y-10], avgLine[y-5])
p6 = percentChange(avgLine[y-10], avgLine[y-4])
p7 = percentChange(avgLine[y-10], avgLine[y-3])
p8 = percentChange(avgLine[y-10], avgLine[y-2])
p9 = percentChange(avgLine[y-10], avgLine[y-1])
p10= percentChange(avgLine[y-10], avgLine[y])

outcomeRange = avgLine[y+20:y+30]
currentPoint = avgLine[y]
#Define##########################
#########change to try except for safety
try:
avgOutcome = reduce(lambda x, y: x + y, outcomeRange) / len(outcomeRange)
except Exception, e:
print str(e)
avgOutcome = 0
#Define
futureOutcome = percentChange(currentPoint, avgOutcome)

#print some logics
'''
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)

#can use .index to find the index value, then search for that value to get the matching information.
# so like, performanceAr.index(12341)
patternAr.append(pattern)
performanceAr.append(futureOutcome)

y+=1
#####
endTime = time.time()
print len(patternAr)
print len(performanceAr)
print 'Pattern storing took:', endTime-startTime
#####

def graphRawFX():

fig=plt.figure(figsize=(10,7))
ax1 = plt.subplot2grid((40,40), (0,0), rowspan=40, colspan=40)
ax1.plot(date,bid)

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()

plt.show()

```

The next tutorial: • Introduction

• Quick Look at our Data

• Basics

• Percent Change

• Finding Patterns

• Storing Patterns
• Current Pattern

• Predicting outcomes

• More predicting

• Increasing pattern complexity

• More on Patterns

• Displaying all patterns

• Variables in patterns

• Past outcomes as possible predictions

• Predicting from patterns

• Average outcomes as predictions