god['DER'].append(float(tempDER[-1]))
god['SA'].append(float(tempSA[-1]))
god['TM'].append(float(tempTM[-1]))
god['IT'].append(float(tempIT[-1]))
god_time.append(msg["news"]["time"])
f = open('data.txt', 'w')
f.write(str(buzz))
f.write('\n')
f.write(str(buzz_time))
f.write('\n')
f.write(str(assoc))
f.write('\n')
f.write(str(assoc_time))
f.write('\n')
f.write(str(alpha))
f.write('\n')
f.write(str(alpha_time))
f.write('\n')
f.write(str(god))
f.write('\n')
f.write(str(god_time))
f.close()
#from assignQValue.py
estimate_distribution_mean()
#run tradersbot
t.onNews = news_Callback
t.run()
}
pending_orders.append(new_order)
except:
print('Unable to parse headline: Unknown error')
DEBUG = True
algo_bot = None
if len(sys.argv) >= 4:
algo_bot = TradersBot(host=sys.argv[1],
id=sys.argv[2],
password=sys.argv[3])
# DEBUG = False
CANCEL_TRADES = False
else:
algo_bot = TradersBot('127.0.0.1', 'trader0', 'trader0')
algo_bot.onAckRegister = onAckRegister
algo_bot.onMarketUpdate = onMarketUpdate
algo_bot.onTraderUpdate = onTraderUpdate
algo_bot.onTrade = onTrade
algo_bot.onAckModifyOrders = onAckModifyOrders
algo_bot.onNews = onNews
if not DEBUG:
def f(*args):
return None
print = f
algo_bot.run()
newsBody = msg["news"]["body"]
newsBody = str(newsBody).split(' ')
vals = [float(item.replace(';','')) for item in newsBody if ';' in item]
vals.append(float(newsBody[-1]))
news.append(vals)
print 'Data ', news
if len(news) >= 2:
x = []
d1 = np.array(news[-2])
d0 = np.delete(np.array(news[-1]),[2])
x = np.append(x, d0)
x = np.append(x,d1)
print 'Prediction: ', ridge.predict([x])[0]
t.onNews = updatePredict
def hi(msg,order):
print "hey"
t.onAckRegister = hi
# ######################################################
# # each time AAPL trade happens for $x, make bid
# # and ask at $x-0.02, $x+0.02, respectively
# def marketMake(msg, order):
# for trade in msg["trades"]:
# if trade["ticker"] == 'AAPL':
# px = trade["price"]
# order.addBuy('AAPL', 10, px - 0.02)
# order.addSell('AAPL', 10, px + 0.02)
news_items = news.split(';')
news_items = [item.split() for item in news_items]
names = [item[0] for item in news_items]
numbers = [[float(item[4])] for item in news_items]
reading = zip(names, numbers)
reading = pd.DataFrame.from_items(reading)
outcry_data = pd.concat([outcry_data, reading])
model = smf.ols(
formula='TAMIT ~ GDP + CPI + RS + HS + PPI + MTIS + U + MS + PI',
data=outcry_data)
res = model.fit()
tamit_pred = res.predict(reading)
logfile = open("outcry_log.txt", "a")
logfile.write("Prediction " + str(pred_counter) + ": " + str(tamit_pred) +
"\n")
logfile.close()
pred_counter += 1
t.onNews = get_data
## onMarketUpdate
## onTraderUpdate
## onTrade
### RUN BOT
t.run()
from tradersbot import TradersBot
'''t = TradersBot('mangocore.pw:10914', '*****@*****.**', 'never-open-students')'''
t = TradersBot('localhost', 'trader0', 'trader0')
def showNews(msg, order):
a = msg['news']['body'].split()
val = []
for word in a:
word = word.strip(';')
try:
num = float(word)
val.append(num)
except:
continue
print val
idx =
t.onNews = showNews
t.run()