def _fxc_login(self):
"""
Logs into ForexConnectClient and returns a session.
"""
while 1:
try:
fxc = fx.ForexConnectClient(s.FX_USER, s.FX_PASS, s.FX_ENVR,
s.URL)
if fxc.is_connected() == True:
break
except RuntimeError:
sleep(0.1)
return fxc
def _scouting(self):
"""
Continuiosly poles FXCM for avaiable offers to trade.
"""
url = "http://pricehistory.fxcorporate.com/pricearchive/catalog.xml"
while True:
try:
fxc = fx.ForexConnectClient(s.FX_USER, s.FX_PASS, s.FX_ENVR,
s.URL)
if fxc.is_connected() == True:
break
except RuntimeError:
pass
print("Scout started..")
offers = []
tracked = []
fxo = []
while True:
fxo = [x for x in fxc.get_offers() if x not in fxo]
if len(fxo) > len(offers):
offers = []
xml = urllib2.urlopen(url).read()
with open(self.XML_FILE, 'wb') as f:
f.write(xml)
with open(self.XML_FILE, 'r') as f:
soup = BeautifulSoup(f.read(), 'lxml-xml')
for symbol in soup.find_all('symbol'):
if symbol['price-stream'] == 'Default':
if symbol['name'] in fxo:
offers.append(symbol['name'])
tracked = [x for x in offers if x not in tracked]
self._reporting(tracked)
# TODO : Remove offer from Live tracking
sleep(0.1)
"""
An example getting user infomation.
"""
import sys
import forexconnect
import lib.login_manager as lm
username, password, connection = lm.get_login_params()
try:
client = forexconnect.ForexConnectClient(username, password, connection)
except:
lm.clear_cache()
sys.exit()
print "Balance:", client.get_balance()
print "UsedMargin:", client.get_used_margin()
trades = client.get_trades()
for t in trades:
print t
client.logout()
def historical_prices(hist_queue, live_queue, event):
"""
Contacts the database and retrives the latest date, then continues
with the historical data mining to the present date
"""
def collect_data(fxc, instrument, time_frame, dbdate):
"""
Gets the data
"""
time_delta = TimeDelta().get_delta(time_frame)
to_date = None
fm_date, to_date = DateRange().get_date_block(
time_delta, dbdate, to_date)
log(instrument).debug("[>>] Starting Block : %s %s %s %s" % \
(instrument, str(fm_date), str(to_date), time_frame))
breakout = 0
while True:
breakdate = datetime.datetime.now() # - datetime.timedelta(minutes = 5)
if to_date >= breakdate or fm_date >= breakdate:
breakout = 1
d = datetime.datetime.now()
to_date = d.replace(second=00, microsecond=00)
try:
data = fxc.get_historical_prices(
str(instrument), fm_date,
to_date, str(time_frame))
data = [d.__getstate__()[0] for d in data]
data = [x for x in data if dbdate not in x.values()]
except (KeyError, IndexError):
data = []
if data != []:
hist_queue.put(HistDataEvent(
data, instrument, time_frame))
log(instrument).debug("[:)] Data Collected : %s %s %s %s" % \
(instrument, str(fm_date), str(to_date), time_frame))
fm_date, to_date = DateRange().get_date_block(
time_delta, fm_date, to_date)
else:
log(instrument).debug("[??] Skipping Block : %s %s %s %s" % \
(instrument, str(fm_date), str(to_date), time_frame))
fm_date, to_date = DateRange().get_date_block(
time_delta, fm_date, to_date)
del data
if breakout == 1:
break
fxoffer = event.fxoffer
while True:
try:
fxc = fx.ForexConnectClient(s.FX_USER, s.FX_PASS,
s.FX_ENVR, s.URL
)
if fxc.is_connected() == True:
break
except RuntimeError:
pass
for offer, time_frames in fxoffer.iteritems():
for time_frame in time_frames:
dbdate = DatabaseManager().return_date(offer, time_frame)
collect_data(fxc, offer, time_frame, dbdate)
log(offer).debug("[^^] TFrame Complete : %s |%s|" % (offer, time_frame))
log(offer).debug("[<>] Offer Complete : %s |%s|" % (offer, time_frame))
print("[^^] Hist complete : %s" % offer)
live_queue.put(LiveReadyEvent(offer))
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import forexconnect as fc
import settings
# Clients
fxcm = fc.ForexConnectClient(
settings.FXCM_USERNAME,
settings.FXCM_PASSWORD,
settings.FXCM_ACCOUNT
)
# conn = psql.connect(database=pg_database, user=pg_username, password=pg_password, host=pg_host, port=pg_port)
def get_chart(currency_pair):
# Get the last days worth of tick data, fit to pandas dataframe, resample to size
now = datetime.now()
then = now - datetime.timedelta(days = 1)
data = client.get_historical_prices(currency_pair, then, now)
attributes = ['date', 'open', 'high', 'low', 'close']
dataframe = pd.DataFrame([[getattr(i, j) for j in attributes] for i in data], columns = attributes)
print(dataframe.to_string())
dataframe.resample('10Min').agg({'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last'})
# Resample the chart to timeframe, the idea is last time period should be at the beginning and newest at the end
# This way we can check MACD at beginning is different to end MACD
# Augment the chart by adding a column for MACD position, and other indicators I guess
# Figure out if the MACD at the beginning is different to the MACD at the end of the timetrame