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Hey Zheng,

I'd recommend checking out the 200-50 EMA Momentum Universe Tutorial in Bootcamp.

That should get you started in the right direction 😊

Hey Financial Freedom with Zheng Tian,

you can also look at different algos provided by QC on github:

LIST

EmaCrossUniverseSelectionAlgorithm

EmaCrossUniverseSelectionFrameworkAlgorithm

MovingAverageCrossAlgorithm

SmaCrossUniverseSelectionAlgorithm

Hope it helps 😊

Good day

I was able to use the above algo to create this project. However, when I backtest the algo, it shows errors. May I know what are the issues with the codes? Please explain to me the reason in simple English as I am extremely new to this platform. thank you

Hey Financial Freedom with Zheng Tian,

The problem is that you might try to get information from a symbol that does not have data yet. For this you can add this line:

if not data.ContainsKey(symbol) or not data.Bars.ContainsKey(symbol):

     continue

#line 43

The other error is insufficient buying power. This has to do with the way you set up your strategy. Either lower the percentage in the holdings or change the number of symbols that you buy.

I attached your code with the added line and another version which takes the history request into the class and sorts/selects symbols from the values of the selection data. I also adjusted a few other things so it will give another result(only for testing purposes.

#region imports

from AlgorithmImports import *

#endregion

from Alphas.MacdAlphaModel import MacdAlphaModel



class WellDressedYellowGreenFish(QCAlgorithm):



    def Initialize(self):

        self.SetStartDate(2018, 1, 1)  # Set Start Date

        self.SetCash(5000)  # Set Strategy Cash

        #self.AddAlpha(MacdAlphaModel(12, 26, 9, MovingAverageType.Simple, Resolution.Daily))

        self.SetBenchmark("SPY")

        self.AddUniverse(self.CoarseSelectionFunction)

        self.UniverseSettings.Resolution = Resolution.Daily

        self.averages = { }

        self.SetWarmUp(400)

        self.n = 0

    def CoarseSelectionFunction(self, universe):  

        selected = []

        universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)  

        universe = [c for c in universe if c.Price > 100][:100]



        for coarse in universe:  

            symbol = coarse.Symbol

            

            if symbol not in self.averages:

                # 1. Call history to get an array of 200 days of history data

                #history = self.History(symbol, 200, Resolution.Daily)

                

                #2. Adjust SelectionData to pass in the history result

                self.averages[symbol] = SelectionData(self, symbol) 



            self.averages[symbol].update(coarse.EndTime, coarse.AdjustedPrice)

            

        symb = list(filter(lambda x: x.fast > x.mid and x.mid > x.slow, self.averages.values()))   

         

         #if self.averages[symbol].fast > self.averages[symbol].mid and self.averages[symbol].mid >  self.averages[symbol].slow:

                #selected.append(symbol)

        self.Debug([x.symbol.Value for x in symb[:2]])

        return [x.symbol for x in symb[:2]]



 

        

    def OnData(self, data):

        if self.IsWarmingUp:

            return

        for symbol, symbol_data in self.averages.items():

            if not data.ContainsKey(symbol) or not data.Bars.ContainsKey(symbol):

                continue

            tolerance = 0.0015

            holdings = self.Portfolio[symbol].Quantity if self.Portfolio.ContainsKey(symbol) else 0

            signalDeltaPercent = (symbol_data.macd.Current.Value - symbol_data.macd.Signal.Current.Value)/symbol_data.macd.Fast.Current.Value

            if holdings <= 0 and signalDeltaPercent > tolerance:  # 0.01%

                if self.n == 2: return

                # longterm says buy as well

                self.Debug(symbol.Value)

                self.SetHoldings(symbol, 0.4)

                self.n += 1

            elif holdings >= 0 and signalDeltaPercent < -tolerance:

                self.Liquidate(symbol)

                self.n -= 1

                



class SelectionData:

    #3. Update the constructor to accept a history array

    def __init__(self, algorithm, symbol):

       

        self.symbol = symbol

        self.slow = ExponentialMovingAverage(200)

        self.mid = ExponentialMovingAverage(13)

        self.fast = ExponentialMovingAverage(5)

        self.macd = MovingAverageConvergenceDivergence(50, 200, 9, MovingAverageType.Exponential)

        history = algorithm.History(symbol, 200, Resolution.Daily)

        #4. Loop over the history data and update the indicators

        for bar in history.itertuples():

            self.fast.Update(bar.Index[1], bar.close)

            self.slow.Update(bar.Index[1], bar.close)

            self.mid.Update(bar.Index[1], bar.close)

            self.macd.Update(bar.Index[1], bar.close)

            

    def is_ready(self):

        return self.slow.IsReady and self.fast.IsReady and self.mid.IsReady

        

    def update(self, time, price):

        self.fast.Update(time, price)

        self.mid.Update(time, price)

        self.slow.Update(time, price)

        self.macd.Update(time, price)

        fast = self.fast.Current.Value

        slow = self.slow.Current.Value

        mid = self.mid.Current.Value

Please accept this answer if you're satisfied with my answer, hope it helps 😊

Good day ever since the recent update, my bot suddenly does not work. May I know which code I need to change so that it would work again? thank you