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. Arguments: 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: return 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.Liquidate(self.symbol) self.quantity = -1 def OnOrderEvent(self, orderEvent): if orderEvent.Status != OrderStatus.Filled: return #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`.