def getPrice(connection, f, tmp_f, pair="btc_usd"):
#get ticker
ticker = btceapi.getTicker(pair, connection)
#print ticker.high
#get asks/bids
asks, bids = btceapi.getDepth(pair)
ask_prices, ask_volumes = zip(*asks)
bid_prices, bid_volumes = zip(*bids)
#start list with all of the ticker info
curTrades = trades(coin='ltc',
updated=ticker.updated,
server_time=ticker.server_time,
buy=ticker.buy,
sell=ticker.sell)
#print out_list
#now we have a huge list with all the info, write to a single line in the csv file
line = ','.join(
(pair, str(time.mktime(time.localtime())), str(curTrades.buy))) + '\n'
#print line
f.write(line)
tmp_f.write(line)
def getPrice(connection,f,pair="btc_usd"):
#get ticker
ticker = btceapi.getTicker(pair, connection)
#print ticker.high
#get asks/bids
asks, bids = btceapi.getDepth(pair)
ask_prices, ask_volumes = zip(*asks)
bid_prices, bid_volumes = zip(*bids)
#start list with all of the ticker info
curTrades = trades(coin='ltc',updated=ticker.updated,server_time=ticker.server_time,buy=ticker.buy,sell=ticker.sell)
#print out_list
#now we have a huge list with all the info, write to a single line in the csv file
line = ','.join((pair,str(time.mktime(time.localtime())),str(curTrades.buy)))+'\n'
#print line
f.write(line)
def getPrice(connection,f,pair="btc_usd"):
#get ticker
ticker = btceapi.getTicker(pair, connection)
#print ticker.high
#get asks/bids
asks, bids = btceapi.getDepth(pair)
ask_prices, ask_volumes = zip(*asks)
bid_prices, bid_volumes = zip(*bids)
#start list with all of the ticker info
curTrades = trades(coin='ltc',updated=ticker.updated,server_time=ticker.server_time,buy=ticker.buy,sell=ticker.sell)
#print out_list
#now we have a huge list with all the info, write to a single line in the csv file
print pair
print curTrades.buy
# Pickle class using protocol 0.
pickle.dump(curTrades,f)
parse_dates=True,
date_parser=functools.partial(datetime.datetime.strptime, format = "%m/%d/%Y"))
## data[sheet_name].columns = ['DateTime', 'Open', 'High', 'Low', 'Close', 'Volume']
data[sheet_name].columns = ['DateTime', 'Close']
outrights = xls.sheet_names[0:12]
flies = xls.sheet_names[12:]
ButterflyData = data['BL-'+ButterflyName.upper()+' Comdty']
OutrightData = data['L '+OutrightName.upper()+' Comdty']
rolling_window = 20
trade_recorder = {}
m = pd.merge(OutrightData, ButterflyData, on = "DateTime", suffixes = ('_'+OutrightName, '_'+ButterflyName))
for i in range(HedgeRatioFrom, HedgeRatioTo,1):
index = 'hr_'+i.__str__()
trade_recorder[index] = trades()
m[index] = m['Close_'+OutrightName] + i*m['Close_'+ButterflyName]
m[index+'_mean'] = pd.rolling_mean(m[index], rolling_window)
m[index+'_std'] = pd.rolling_var (m[index], rolling_window)
for rn in range(1,len(m)):
if (m.iloc[rn-1][index] > m.iloc[rn-1][index+'_mean'] + EntryStd*m.iloc[rn-1][index+'_std']) and\
(m.iloc[rn][index] < m.iloc[rn-1][index+'_mean'] + EntryStd*m.iloc[rn-1][index+'_std']) and\
(trade_recorder[index].position == 0):
trade_recorder[index].add(m.iloc[rn]['DateTime'],
m.iloc[rn-1][index+'_mean'] + EntryStd*m.iloc[rn-1][index+'_std'],
-1,
"EnterShort")
if m.iloc[rn-1][index] < m.iloc[rn-1][index+'_mean'] - EntryStd*m.iloc[rn-1][index+'_std'] and\
m.iloc[rn][index] > m.iloc[rn-1][index+'_mean'] - EntryStd*m.iloc[rn-1][index+'_std'] and\
trade_recorder[index].position == 0:
trade_recorder[index].add(m.iloc[rn]['DateTime'],
