def initializeWorkers(self):
'''
initializes workers from scratch
'''
# creates 7 dimensional array
for ma in self.mas:
for md in self.mds:
for smooth in self.smooths:
for percent in self.percents:
for riseTol in self.riseTols:
for lossTol in self.lossTols:
curKey = (ma, md, smooth, percent, riseTol,
lossTol)
print curKey
# initialize
self.workers[curKey] = observer(
smooth, md, ma, percent, lossTol, riseTol)
# take the first 100 points in data file
self.workers[curKey].loadData(
self.price_data[0, 0:100].tolist(),
self.price_data[1, 0:100].tolist())
# cycle over the rest of the historical data
for i in range(100,
len(self.price_data[0, :])):
self.workers[curKey].step(
self.price_data[0, i],
self.price_data[1, i])
def initializeWorkers(self):
'''
initializes workers from scratch
'''
# how many initial data points to load so that moving windows are defined.
initialLoadN = 2*max(max(self.mas),max(self.mds),max(self.smooths))
# time this process
timer= time.time()
# creates 7 dimensional array
for ma in self.mas:
for md in self.mds:
for smooth in self.smooths:
for percent in self.percents:
for riseTol in self.riseTols:
for lossTol in self.lossTols:
curKey = (ma,md,smooth,percent,riseTol,lossTol)
# visual to see that things are running.
print curKey
# initialize
self.workers[curKey] = observer(smooth,md,ma,percent,lossTol,riseTol)
# take the first 100 points in data file
self.workers[curKey].loadData(self.price_data[0,0:initialLoadN ].tolist(),self.price_data[1,0:initialLoadN ].tolist())
# cycle over the rest of the historical data
for i in xrange(initialLoadN,len(self.price_data[0,:])):
self.workers[curKey].step(self.price_data[0,i],self.price_data[1,i])
# Display how long the initialization took.
duration = round((time.time() - timer)/60,1)
print 'It took %s minutes to intiialize %s observer. %s minutes per observer.' % (duration,self.numWorkers,round(duration/self.numWorkers,2))
def initializeWorkers(self):
'''
initializes workers from scratch
'''
# how many initial data points to load so that moving windows are defined.
initialLoadN = 2*max(max(self.mas),max(self.mds),max(self.smooths))
# time this process
timer= time.time()
# creates 7 dimensional array
for ma in self.mas:
for md in self.mds:
for smooth in self.smooths:
for percent in self.percents:
for riseTol in self.riseTols:
for lossTol in self.lossTols:
curKey = (ma,md,smooth,percent,riseTol,lossTol)
# visual to see that things are running.
print curKey
# initialize
self.workers[curKey] = observer(smooth,md,ma,percent,lossTol,riseTol)
# take the first 100 points in data file
self.workers[curKey].loadData(self.price_data[0,0:initialLoadN ].tolist(),self.price_data[1,0:initialLoadN ].tolist())
# cycle over the rest of the historical data
for i in range(initialLoadN,len(self.price_data[0,:])):
self.workers[curKey].step(self.price_data[0,i],self.price_data[1,i])
# Display how long the initialization took.
duration = round((time.time() - timer)/60,1)
print 'It took %s minutes to intiialize %s observer. %s minutes per observer.' % (duration,self.numWorkers,round(duration/self.numWorkers,2))
def initializeWorkers(self):
'''
initializes workers from scratch
'''
# creates 7 dimensional array
for ma in self.mas:
for md in self.mds:
for smooth in self.smooths:
for percent in self.percents:
for riseTol in self.riseTols:
for lossTol in self.lossTols:
curKey = (ma,md,smooth,percent,riseTol,lossTol)
print curKey
# initialize
self.workers[curKey ] = observer(smooth,md,ma,percent,lossTol,riseTol)
# take the first 100 points in data file
self.workers[curKey].loadData(self.price_data[0,0:100].tolist(),self.price_data[1,0:100].tolist())
# cycle over the rest of the historical data
for i in range(100,len(self.price_data[0,:])):
self.workers[curKey].step(self.price_data[0,i],self.price_data[1,i])
#!/usr/bin/python
'''
This is just a functional test of observer class
'''
# These are the modules created for this project
from observer import *
from common import *
# Load data
datas= loadData(data='data/test_data.txt')
#datas= loadData(data='data/btc_usd_btce.txt')
# Not a best performing parameter set -- highlights potential problems
# Might show that something is not working as intended .
# ma md smooth percent riseTol lossTol
# current best: 100 40 10 0.1 0.7 0.1
x = observer(smooth=10,md=40,ma=100,percent=0.1,riseTolerance=0.7,lossTolerance=0.1)
# Start with the first 100 points in the set
x.loadData(datas[0,0:100].tolist(),datas[1,0:100].tolist())
# "step" through the rest.
for i in range(100,len(datas[0,:])):
x.step(datas[0,i],datas[1,i])
# Make a plot
#x.plot_trades()as
'''
# These are the modules created for this project
from observer import *
from common import *
# Load data
datas = loadData(data='data/test_data.txt')
#datas= loadData(data='data/btc_usd_btce.txt')
# Not a best performing parameter set -- highlights potential problems
# Might show that something is not working as intended .
# ma md smooth percent riseTol lossTol
# current best: 100 40 10 0.1 0.7 0.1
x = observer(smooth=10,
md=40,
ma=100,
percent=0.1,
riseTolerance=0.7,
lossTolerance=0.1)
# Start with the first 100 points in the set
x.loadData(datas[0, 0:100].tolist(), datas[1, 0:100].tolist())
# "step" through the rest.
for i in range(100, len(datas[0, :])):
x.step(datas[0, i], datas[1, i])
# Make a plot
#x.plot_trades()as
