import unittest2
from MultinomialNaiveBayes import MultinomialNaiveBayes as mnb
my_bayes = mnb("test.db")
#If no db, uncomment and run once, then recomment (so nums are not duplicated).
my_bayes.train("Abracadabra", [
["super", "awesome", "mega"],
["a", "a", "a", "a"],
["a", "b", "c"]])
my_bayes.train("Battery", [
["sad", "sad", "negative"],
["my", "apple", "is", "red"],
["my", "my", "my", "what", "teeth", "you", "awesome"]])
my_bayes.train("Jesus", [
["sad", "sad", "negative"],
["my", "apple", "is", "red"],
["my", "my", "my", "what", "teeth", "you", "awesome"]])
class test(unittest2.TestCase):
def testUniqueWords1(self):
cur = my_bayes.conn.cursor()
cur.execute("SELECT COUNT(*) FROM Battery")
self.failUnless(cur.fetchone()[0], 10)
def testUniqueWords2(self):
cur = my_bayes.conn.cursor()
import unittest2
from MultinomialNaiveBayes import MultinomialNaiveBayes as mnb
my_bayes = mnb("test.db")
#If no db, uncomment and run once, then recomment (so nums are not duplicated).
my_bayes.train(
"Abracadabra",
[["super", "awesome", "mega"], ["a", "a", "a", "a"], ["a", "b", "c"]])
my_bayes.train("Battery",
[["sad", "sad", "negative"], ["my", "apple", "is", "red"],
["my", "my", "my", "what", "teeth", "you", "awesome"]])
my_bayes.train("Jesus",
[["sad", "sad", "negative"], ["my", "apple", "is", "red"],
["my", "my", "my", "what", "teeth", "you", "awesome"]])
class test(unittest2.TestCase):
def testUniqueWords1(self):
cur = my_bayes.conn.cursor()
cur.execute("SELECT COUNT(*) FROM Battery")
self.failUnless(cur.fetchone()[0], 10)
def testUniqueWords2(self):
cur = my_bayes.conn.cursor()
cur.execute("SELECT COUNT(*) FROM Abracadabra")
self.failUnless(cur.fetchone()[0], 6)
currProfit += (+currPrice - nextPrice)
totProfit += currProfit
if predic["NEUT"] > predic["POS"] and predic["NEUT"] > predic[
"NEG"]:
print(" NOTHING")
pass
print("profit: " + str(currProfit))
print("total profit: " + str(totProfit))
#3. Update Bayesian classifier.
returns = math.log(nextPrice / currPrice)
if returns >= MIN_POS:
clas = "POS"
elif returns <= MAX_NEG:
clas = "NEG"
elif MAX_NEG < returns < MIN_POS:
clas = "NEUT"
bayes.train(clas, zip(*prevArts)[3])
except KeyError:
print(
"KeyError, probably because of beginning or ending of time range"
)
bayes = mnb("backtest.db", ["POS", "NEG", "NEUT"])
train(
tickerData[ticker], bayes, start,
datetime.datetime(2016, 01, 14, 9, 30,
0).replace(tzinfo=EST).astimezone(pytz.utc))
backtest(tickerData[ticker], bayes, start, end)
print(" BUY")
currProfit += (-currPrice + nextPrice)
totProfit += currProfit
elif predic["NEG"] > predic ["POS"] and predic["NEG"] > predic["NEUT"]:
print(" SELL")
currProfit += (+currPrice - nextPrice)
totProfit += currProfit
if predic["NEUT"] > predic ["POS"] and predic["NEUT"] > predic["NEG"]:
print(" NOTHING")
pass
print("profit: "+str(currProfit))
print("total profit: "+str(totProfit))
#3. Update Bayesian classifier.
returns = math.log(nextPrice / currPrice)
if returns >= MIN_POS:
clas = "POS"
elif returns <= MAX_NEG:
clas = "NEG"
elif MAX_NEG < returns < MIN_POS:
clas = "NEUT"
bayes.train(clas, zip(*prevArts)[3])
except KeyError:
print("KeyError, probably because of beginning or ending of time range")
bayes = mnb("backtest.db", ["POS", "NEG", "NEUT"])
train(tickerData[ticker], bayes, start, datetime.datetime(2016, 01, 14, 9, 30, 0).replace(tzinfo=EST).astimezone(pytz.utc))
backtest(tickerData[ticker], bayes, start, end)