def run(seq):
with open('log.dat', 'rb') as infile:
ledger = pickle.load(infile)
kmeans = genExtractor(seq, ledger)
functionLog = []
print("Thread " + str(seq) + " partito")
for i in range(1, POINTS_PER_SEQ):
short = randint(1, AVG_LEN)
# forcing long != short
long = short
while (long == short):
long = randint(1, AVG_LEN)
value = simulate(seq, short, long, kmeans, ledger, 0)
print("Num: " + str(i) + "\t" + "Seq: " + str(seq) + "\t" + "Short: " + str(short) + "\t" + "Long: " + str(long) + "\t" + "Value: " + str(float("{0:.2f}".format(value))))
# just keep configurations with value above X
if (value > 100):
functionLog.append(Function(seq, short, long, value))
with open('function' + str(seq) + '.dat', 'wb') as outfile:
pickle.dump(functionLog, outfile)
def run(seq):
with open('log.dat', 'rb') as infile:
ledger = pickle.load(infile)
kmeans = genExtractor(seq, ledger)
functionLog = []
print("Partita l'analisi per la sequenza", seq)
for i in range(1, POINTS_PER_SEQ):
try:
short = randint(1, AVG_LEN)
#forcing long > short
long = randint(short + 1, AVG_LEN + 1)
value = simulate(seq, short, long, kmeans, ledger, 0)
print("Num: " + str(i) + "\t" + "Seq: " + str(seq) + "\t" +
"Short: " + str(short) + "\t" + "Long: " + str(long) + "\t" +
"Value: " + str(float("{0:.2f}".format(value))))
# just keep configurations with value above X
if (value > 100):
functionLog.append(Function(seq, short, long, value))
with open('function' + str(seq) + '.dat', 'wb') as outfile:
pickle.dump(functionLog, outfile)
except:
print("Errore")
def run(seq, id):
with open('log.dat', 'rb') as infile:
ledger = pickle.load(infile)
kmeans = genExtractor(seq, ledger)
functionLog = []
print("Thread " + str(seq) + " partito")
for i in range(1, POINTS_PER_SEQ):
# modify values. INSERT HERE !!!!!!
short = randint(675, 1400)
long = short
while (long == short):
# modify values. INSERT HERE !!!!!!
long = randint(600, 800)
value = simulate(seq, short, long, kmeans, ledger, 0)
print("Num: " + str(i) + "\t" + "Seq: " + str(seq) + "\t" + "Short: " +
str(short) + "\t" + "Long: " + str(long) + "\t" + "Value: " +
str(float("{0:.2f}".format(value))))
functionLog.append(Function(seq, short, long, value))
with open('function' + str(id) + '.dat', 'wb') as outfile:
pickle.dump(functionLog, outfile)
import pickle
from recordclass import recordclass
import sys
import numpy
from functions import genExtractor, getAvg, getFloatingAvg, SuperQueue, simulate
Log = recordclass('Log', ['coin', 'value_ask', 'value_bid'])
SEQ_SIZE = int(sys.argv[1])
SHORT = int(sys.argv[2])
LONG = int(sys.argv[3])
with open('log.dat', 'rb') as infile:
ledger = pickle.load(infile)
kmeans = genExtractor(SEQ_SIZE, ledger)
value, history, coins = simulate(SEQ_SIZE, SHORT, LONG, kmeans, ledger, 1)
# printing statistics. 100 is the initial budget used for the simulation
print("\n\nFinal budget: " + str(float("{0:.4f}".format((value)))) + " with " +
str(coins) +
" coins. The final budget accounts for those coins as sold.")
print("Profit: " + str(float("{0:.4f}".format((value - 100)))))
with open('history', 'wb') as outfile:
pickle.dump(history, outfile)