def __init__(self, subparsers):
data_fetch_cli_parser = subparsers.add_parser('data_fetch', help='Commands for fetching bluetooth logs')
data_fetch_cli_parser.add_argument('-s', '--start', action='store_true',
help="Start fetching data")
data_fetch_cli_parser.add_argument('-t', '--timeout',
help="Set timeout for fetching logs")
data_fetch_cli_parser.add_argument('-dst', '--destination',
help="Set destination for storing logs")
data_fetch_cli_parser.set_defaults(func=self.__parse)
self.__data_fetch = DataFetch()
def __init__(self):
self.df = DataFetch()
self.ua = self.df.load_data(USER_AUTHOR)
self.ub = self.df.load_data(USER_BLOG)
self.ut = self.df.load_data(USER_TAGS)
#self.ul = self.df.load_data(USER_LANGUAGE)
self.posts = pickle.load(open("posts_dict_all.p", "rb"))
self.test_sample = pickle.load(open(TEST_SAMPLE_FILE,"rb"))
self.a_coef = 0.33
self.b_coef = 0.33
self.t_coef = 0.33
class DataFetchCli:
def __init__(self, subparsers):
data_fetch_cli_parser = subparsers.add_parser('data_fetch', help='Commands for fetching bluetooth logs')
data_fetch_cli_parser.add_argument('-s', '--start', action='store_true',
help="Start fetching data")
data_fetch_cli_parser.add_argument('-t', '--timeout',
help="Set timeout for fetching logs")
data_fetch_cli_parser.add_argument('-dst', '--destination',
help="Set destination for storing logs")
data_fetch_cli_parser.set_defaults(func=self.__parse)
self.__data_fetch = DataFetch()
def __parse(self, args):
if args.timeout is not None:
self.__data_fetch.set_timeout(int(args.timeout))
if args.destination is not None:
self.__data_fetch.set_destination(args.destination)
if args.start:
self.__data_fetch.start()
from db_connection import DBConnection from data_fetch import DataFetch from technical_indicators import TechnicalIndicators from trading_signals import TradingSignals from trade_sim import TradeSimulation from datetime import datetime start = datetime.now() mysql_conn = DBConnection().mysql_engine() data_fetch = DataFetch(mysql_conn) data_fetch.get_equities() data_fetch.get_dividends() tech_ind = TechnicalIndicators(mysql_conn) tech_ind.calc_tech_indicators() trade_sig = TradingSignals(mysql_conn) trade_sig.calc_signals() simulation = TradeSimulation(mysql_conn) simulation.run_simulation() print(datetime.now() - start)
class Predictions:
def __init__(self):
self.df = DataFetch()
self.ua = self.df.load_data(USER_AUTHOR)
self.ub = self.df.load_data(USER_BLOG)
self.ut = self.df.load_data(USER_TAGS)
#self.ul = self.df.load_data(USER_LANGUAGE)
self.posts = pickle.load(open("posts_dict_all.p", "rb"))
self.test_sample = pickle.load(open(TEST_SAMPLE_FILE,"rb"))
self.a_coef = 0.33
self.b_coef = 0.33
self.t_coef = 0.33
def logistic(self):
dimx = len(self.ua)
dimy = 3
ymat = np.zeros((dimx, dimy))
ctr = 0
for k,v in self.ua.iteritems():
user = k
a_val = v
try:
b_val = self.ub[user]
except:
b_val = 0.0
try:
t_val = self.ut[user]
except:
t_val = 0.0
ymat[ctr][0] = a_val
ymat[ctr][1] = b_val
ymat[ctr][2] = t_val
ctr+=1
print ymat
def no_logic(self, test_sample):
# Operate on values without any modification of weights.
threshold = 0.00072
predicted_list = []
for (i,j,b,a,t,l) in test_sample:
U = str(i)
post = str(j)
try:
P = self.posts[post]
A = self.posts[post]["author"] if not None else 0
B = self.posts[post]["blog"] if not None else 0
T = self.posts[post]["tags"] if not None else 0
a_val = 0.0 + self.ua[U][A]
b_val = 0.0 + self.ub[U][B]
t_val = 0.0
for tag in T:
try:
t_val+= 0.0 + self.ut[U][tag]
except:
t_val+= 0.0
except Exception, e:
print "In except"
print e
print len(self.posts)
A = a
B = b
T = t
try:
a_val = 0.0 + self.ua[U][A]
except:
a_val = 0.0
try:
b_val = 0.0 + self.ub[U][B]
except:
b_val = 0.0
t_val = 0.0
try:
for tag in T:
try:
t_val+= 0.0 + self.ut[U][tag]
except:
t_val+= 0.0
except:
t_val+=0.0
#print "Values for a_val {}, b_val {} and t_val {}".format(a_val,b_val,t_val)
val = 0.0 + (self.a_coef * a_val) + (self.b_coef * b_val) + (self.t_coef * t_val)
if val>threshold:
label = 1
else:
label = 0
predicted_list.append((i,j,l,label))
return predicted_list
