I used the example of Moving Average Cross in Python (by Quantconnect) https://www.quantconnect.com/forum/discussion/1013/moving-average-cross-in-python/p1

class MovingAverageCrossAlgorithm(QCAlgorithm):
'''In this example we look at the canonical 15/30 day moving average cross. This algorithm
will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
back below the 30.'''

def __init__(self):
self.symbol = "SPY"
self.previous = None
self.fast = None
self.slow = None
self.stopMarketTicket = None
self.quantity = -1

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2019, 1, 1) #Set Start Date
self.SetEndDate(2020, 1, 1) #Set End Date
self.SetCash(1000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.AddSecurity(SecurityType.Equity, self.symbol, Resolution.Hour)

# create a 15 day exponential moving average
self.fast = self.EMA(self.symbol, 15, Resolution.Hour);

# create a 30 day exponential moving average
self.slow = self.EMA(self.symbol, 30, Resolution.Hour);

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

data: TradeBars IDictionary object with your stock data
# a couple things to notice in this method:
# 1. We never need to 'update' our indicators with the data, the engine takes care of this for us
# 2. We can use indicators directly in math expressions
# 3. We can easily plot many indicators at the same time

# wait for our slow ema to fully initialize
if not self.slow.IsReady:

holdings = self.Portfolio[self.symbol].Quantity
self.Log("Holdings: {0}".format(self.Portfolio[self.symbol].Quantity))

# we only want to go long if we're currently short or flat
if holdings <= 0 and self.quantity==-1:
# if the fast is greater than the slow, we'll go long
if self.fast.Current.Value > self.slow.Current.Value:
self.Log("BUY >> {0}".format(self.Securities[self.symbol].Price))
self.stopMarketTicket = self.StopLimitOrder(self.symbol, self.CalculateOrderQuantity(self.symbol, 1.0), 0.95*self.slow.Current.Value, self.slow.Current.Value)
self.quantity = 0
# self.SetHoldings(self.symbol, 0.67)

# we only want to liquidate if we're currently long
# if the fast is less than the slow we'll liquidate our long
if holdings > 0 and self.fast.Current.Value < self.slow.Current.Value:
self.Log("SELL >> {0}".format(self.Securities[self.symbol].Price))
self.quantity = -1

def OnOrderEvent(self, orderEvent):

if orderEvent.Status != OrderStatus.Filled:

#2. Check if we hit our stop loss (Compare the orderEvent.Id with the stopMarketTicket.OrderId)
# It's important to first check if the ticket isn't null (i.e. making sure it has been submitted)
if self.stopMarketTicket is not None and self.stopMarketTicket.OrderId == orderEvent.OrderId:
self.Log("Asset Price >> {0}".format(self.Portfolio[self.symbol].Price))

however, I wanted to have a stop limit order and hence, I updated the code accordingly. Now since, we are putting the order, quantconnect will issue a ticket which needs to be matched in another method `Onorderevent`. I am using `self.quantity` but for some reason my order never gets filled and `holdings` are always `0.0`.