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'],