Issues with MACD and MEA functions about trading-bot HOT 9 OPEN

`from botlog import BotLog
from botindicators import BotIndicators
from bottrade import BotTrade
from datetime import datetime

class BotStrategy(object):
    def __init__(self):
	self.output = BotLog()
	self.prices = []
	self.closes = [] # Needed for Momentum Indicator
	self.trades = []
	self.currentPrice = ""
	self.currentClose = ""
	self.numSimulTrades = 1
	self.indicators = BotIndicators()

def tick(self,candlestick):
	self.currentPrice = float(candlestick.priceAverage)
	self.prices.append(self.currentPrice)
	
	#self.currentClose = float(candlestick['close'])
	#self.closes.append(self.currentClose)
	
	self.output.log("DateTime: "+ str(datetime.utcfromtimestamp(candlestick.startTime).strftime("%Y-%b-%d %H:%M:%S"))+"\tPrice: "+str(candlestick.priceAverage)+"\tMoving Average: "+str(self.indicators.movingAverage(self.prices,15)))

	self.evaluatePositions()
	self.updateOpenTrades()
	self.showPositions()

def evaluatePositions(self):
	openTrades = []
	for trade in self.trades:
		if (trade.status == "OPEN"):
			openTrades.append(trade)

	if (len(openTrades) < self.numSimulTrades):
                    if (self.indicators.MACD(self.prices)[2] > 0):
		#if (self.currentPrice < self.indicators.movingAverage(self.prices,15)): # switch to MACD based strategy - moving average versus current price is a dumb strategy.
			self.trades.append(BotTrade(self.currentPrice,stopLoss=self.currentPrice *.001)) #should add a better calculation to create dynamic stop loss later

	for trade in openTrades:
                    if (self.indicators.MACD(self.prices)[2] < 0):
		#if (self.currentPrice > self.indicators.movingAverage(self.prices,15)):
			trade.close(self.currentPrice)

def updateOpenTrades(self):
	for trade in self.trades:
		if (trade.status == "OPEN"):
			trade.tick(self.currentPrice)

def showPositions(self):
	for trade in self.trades:
		trade.showTrade()`

def EMA(self, prices, period):
	if (len(prices) <= period):
		return 0

	x = numpy.asarray(prices)
	weights = None
	weights = numpy.exp(numpy.linspace(-1., 0., period))
	weights /= weights.sum()

	a = numpy.convolve(x, weights, mode='full')[:len(x)]
	a[:period] = a[period]
	return a

def movingAverage(self, prices, period):
	if (len(prices) > 1):
		return sum(prices[-period:]) / float(len(prices[-period:]))

def EMA(self, prices, period):

	if (len(prices) <= period):
		return

	x = numpy.asarray(prices)
	weights = None
	weights = numpy.exp(numpy.linspace(-1., 0., period))
	weights /= weights.sum()

	a = numpy.convolve(x, weights, mode='full')[:len(x)]
	a[:period] = a[period]
	return a

def MACD(self, prices, nslow=26, nfast=12):
	emaslow = self.EMA(prices, nslow)
	emafast = self.EMA(prices, nfast)

	if not (emafast is None or emaslow is None):
		return emaslow, emafast, emafast - emaslow