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Algorithim not taking orders

This bot looks to combine the G Score algorithim found in Quantconnect documentation, with some technical analysis written into it, collecting symbol data such as sma, and other stuff.

I suspect the list of stocks from the Fine filter isnt populating the self.tickers variable, and thus no orders are ever taken. Any ideas how I'd further troubleshoot this?

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Axist

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Update Backtest

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Vladimir

94.7k Pro ,

Axist

Try this slightly modified version of the algorithm you mentioned.

Vladimir

94.7k Pro ,

Axist

Did you have a chance to try what I recommended?

Looking forward to your response.

Axist

4.1k Pro ,

Yeah, so its the fundamental component that I am building the TA on top of. So I took what you had and attempted swapping it out for the universes in the first algo. I'm still work in progress, but you can kind of see what I am going about below. I deleted chunks to shorten it…

Essentially I am using a universe selector to populate stocks into different sectors. Then a KEI algo to pick the sectors to be trading in, then looking for top 3 stocks in that sector via assigning that sector's Fine results to a variable. Of those 3 stocks in the sector, check TA (SMA crossover or price above sma, etc) to provide a buy signal for that stock. In my first algorithim, I know I had the Coarse and Fine populating stocks, but it didnt seem to be getting added to the variable so that the rest of the algo could proceed to function.

class DynamicCalibratedGearbox(QCAlgorithm):

    def Initialize(self):        
        self.tech_ROA_key = 'TECH_ROA'
        self.SetStartDate(2019, 1, 1)  
        self.SetEndDate(2022, 6, 24)        
        self.SetCash(100000)        
        #self.SetBrokerageModel(AlphaStreamsBrokerageModel())
        #self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days=31)))
        #self.SetExecution(ImmediateExecutionModel())
        #self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(lambda time:None))        
        #self.AddUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseFilter, self.FineFilter))
        self.UniverseSettings.Resolution = Resolution.Daily        
        self.curr_month = -1
        self.tech_ROA = {}        
        self.symbols = None           
        self.quarters = 0    
        self.SetWarmUp(756, Resolution.Daily)
        
        #KEI
        quandl_symbol = "OECD/KEI_LOLITOAA_OECDE_ST_M"
        self.kei = self.AddData(NasdaqDataLink, quandl_symbol, Resolution.Daily).Symbol
        self.init = True
        self.sma = self.SMA(self.kei, 1)
        self.mom = self.MOMP(self.kei, 2)

        self.XLFsector_symbolDataBySymbol = {}
        self.XLEsector_symbolDataBySymbol = {}
        self.XLBsector_symbolDataBySymbol = {}
        self.XLIsector_symbolDataBySymbol = {}



        self.XLEstocks = []
        self.XLFstocks = []
        self.XLBstocks = []


        self.trade = True
        self.stateData = { }

        self.SPY = self.AddEquity('SPY', Resolution.Daily).Symbol
        self.stock = self.AddEquity('QQQ', Resolution.Hour).Symbol
        self.bond = self.AddEquity('TMF', Resolution.Hour).Symbol
        self.vix = self.AddEquity('VIX', Resolution.Minute).Symbol

        self.XLF = self.AddEquity('XLF', Resolution.Hour).Symbol
        self.XLE = self.AddEquity('XLE', Resolution.Hour).Symbol
        self.XLB = self.AddEquity('XLB', Resolution.Hour).Symbol
        self.XLI = self.AddEquity('XLI', Resolution.Hour).Symbol
        self.XLY = self.AddEquity('XLY', Resolution.Hour).Symbol
        self.XLP = self.AddEquity('XLP', Resolution.Hour).Symbol
        self.XLU = self.AddEquity('XLU', Resolution.Hour).Symbol
        self.XLK = self.AddEquity('XLK', Resolution.Hour).Symbol
        self.XLV = self.AddEquity('XLV', Resolution.Hour).Symbol
        self.XLC = self.AddEquity('XLC', Resolution.Hour).Symbol
       
       #Shorts
        self.SQQQ = self.AddEquity('SQQQ', Resolution.Daily).Symbol

        self.manually_selected = []
       
   # Stock Selector
        #self.UniverseSettings.Resolution = Resolution.Minute
        self.universeXLF = self.AddUniverse(self.coarse, self.XLFfine)
        self.universeXLE = self.AddUniverse(self.coarse, self.XLEfine)
       def Collectsymboldata(self):
       
        for symbol in self.XLFstocks:
           self.AddEquity(symbol, Resolution.Hour)
           
           ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
           sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
           sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close) 
           sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
           sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
           ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
           ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
           rsi = self.RSI(symbol, 14, Resolution.Daily)
           wilr = self.WILR(symbol, 14, Resolution.Daily)
           wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
           high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
           midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
           low = self.MIN(symbol, int(self.GetParameter("lowHist")), Resolution.Daily, Field.Low)
           stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
           
           symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
           self.XLFsector_symbolDataBySymbol[symbol] = symbolData
       
        for symbol in self.XLEstocks:
           self.AddEquity(symbol, Resolution.Hour)
           ema10 = self.EMA(symbol, 10, Resolution.Hour, Field.Close)
           sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
           sma7 = self.SMA(symbol, 7, Resolution.Hour, Field.Close)
           sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
           sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
           ema20 = self.EMA(symbol, 20, Resolution.Hour, Field.Close)
           ema50 = self.EMA(symbol, 50, Resolution.Hour, Field.Close)
           rsi = self.RSI(symbol, 14, Resolution.Daily)
           wilr = self.WILR(symbol, 14, Resolution.Daily)
           wilr_fast = self.WILR(symbol, 10, Resolution.Daily)
           high = self.MAX(symbol, int(self.GetParameter("highHist")), Resolution.Daily, Field.High)
           midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
           low = self.MIN(symbol, int(self.GetParameter("lowHist")), Resolution.Daily, Field.Low)
           stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
           
           symbolData = SymbolData(symbol, sma7, ema10, sma20, sma200, sma50, ema20, rsi, wilr, wilr_fast, high, midhigh, low, stoplow)
           self.XLEsector_symbolDataBySymbol[symbol] = symbolData
          
           
             
           ....
   
   
    def coarse(self, coarse):      


        if self.curr_month == self.Time.month:
            return Universe.Unchanged
       
        self.curr_month = self.Time.month
        if self.Time.month % 3 != 1:
            return Universe.Unchanged        
        self.quarters += 1
       
        return [c.Symbol for c in coarse if c.HasFundamentalData]



   



    def XLFfine(self, fine):
       
        tech_securities = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices and
                                                f.OperationRatios.ROA.ThreeMonths]
       
        for security in tech_securities:
            symbol = security.Symbol
            if symbol not in self.tech_ROA:
                self.tech_ROA[symbol] = deque(maxlen=12)
            self.tech_ROA[symbol].append(security.OperationRatios.ROA.ThreeMonths)


        if self.Time.month != 4 or (self.quarters < 12 ):
            return Universe.Unchanged


        tech_securities = [x for x in tech_securities if x.OperationRatios.ROA.OneYear and
                                                        x.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths and
                                                        x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths and
                                                        x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths and
                                                        x.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths and
                                                        x.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths and
                                                        x.MarketCap]


        tech_VARROA = {symbol:stat.variance(ROA) for symbol, ROA in self.tech_ROA.items() if len(ROA) == ROA.maxlen}
       
        if len(tech_VARROA) < 2:
            return Universe.Unchanged
       
        tech_VARROA_median = stat.median(tech_VARROA.values())
        tech_ROA1Y = {x.Symbol:x.OperationRatios.ROA.OneYear for x in tech_securities}
        tech_ROA1Y_median = stat.median(tech_ROA1Y.values())
        tech_CFROA = {x.Symbol: (
            x.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths
            / x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths
            ) for x in tech_securities}
        tech_CFROA_median = stat.median(tech_CFROA.values())


        tech_RD2MktCap = {x.Symbol: (
            x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths / x.MarketCap
            ) for x in tech_securities}
        tech_RD2MktCap_median = stat.median(tech_RD2MktCap.values())


        tech_CaPex2MktCap = {x.Symbol: (
            x.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths / x.MarketCap
            ) for x in tech_securities}
        tech_CaPex2MktCap_median = stat.median(tech_CaPex2MktCap.values())  


        tech_Ad2MktCap = {x.Symbol: (
            x.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths / x.MarketCap
            ) for x in tech_securities}
        tech_Ad2MktCap_median = stat.median(tech_Ad2MktCap.values())


        has_book = [f for f in fine if f.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths and f.MarketCap]
        sorted_by_BM = sorted(has_book, key=lambda x: x.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths / x.MarketCap)[:len(has_book)//4]
        tech_symbols = [f.Symbol for f in sorted_by_BM if f in tech_securities]
       
        ratioDicts_medians = [(tech_ROA1Y, tech_ROA1Y_median),
                                (tech_CFROA, tech_CFROA_median), (tech_RD2MktCap, tech_RD2MktCap_median),
                                (tech_CaPex2MktCap, tech_CaPex2MktCap_median), (tech_Ad2MktCap, tech_Ad2MktCap_median)]


    def XLEfine(self, fine):
       
        energy_securities = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy and
                                                f.OperationRatios.ROA.ThreeMonths]
       
        for security in energy_securities:
            symbol = security.Symbol
            if symbol not in self.energy_ROA:
                self.energy_ROA[symbol] = deque(maxlen=12)
            self.energy_ROA[symbol].append(security.OperationRatios.ROA.ThreeMonths)


        if self.Time.month != 4 or (self.quarters < 12 ):
            return Universe.Unchanged


        energy_securities = [x for x in energy_securities if x.OperationRatios.ROA.OneYear and
                                                        x.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths and
                                                        x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths and
                                                        x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths and
                                                        x.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths and
                                                        x.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths and
                                                        x.MarketCap]


        energy_VARROA = {symbol:stat.variance(ROA) for symbol, ROA in self.energy_ROA.items() if len(ROA) == ROA.maxlen}
       
        if len(energy_VARROA) < 2:
            return Universe.Unchanged
       
        energy_VARROA_median = stat.median(energy_VARROA.values())
        energy_ROA1Y = {x.Symbol:x.OperationRatios.ROA.OneYear for x in energy_securities}
        energy_ROA1Y_median = stat.median(energy_ROA1Y.values())
        energy_CFROA = {x.Symbol: (
            x.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths
            / x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths
            ) for x in energy_securities}
        energy_CFROA_median = stat.median(energy_CFROA.values())


        energy_RD2MktCap = {x.Symbol: (
            x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths / x.MarketCap
            ) for x in energy_securities}
        energy_RD2MktCap_median = stat.median(energy_RD2MktCap.values())


        energy_CaPex2MktCap = {x.Symbol: (
            x.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths / x.MarketCap
            ) for x in energy_securities}
        energy_CaPex2MktCap_median = stat.median(energy_CaPex2MktCap.values())  


        energy_Ad2MktCap = {x.Symbol: (
            x.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths / x.MarketCap
            ) for x in energy_securities}
        energy_Ad2MktCap_median = stat.median(energy_Ad2MktCap.values())


        has_book = [f for f in fine if f.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths and f.MarketCap]
        sorted_by_BM = sorted(has_book, key=lambda x: x.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths / x.MarketCap)[:len(has_book)//4]
        energy_symbols = [f.Symbol for f in sorted_by_BM if f in energy_securities]
       
        ratioDicts_medians = [(energy_ROA1Y, energy_ROA1Y_median),  
                                (energy_CFROA, energy_CFROA_median), (energy_RD2MktCap, energy_RD2MktCap_median),
                                (energy_CaPex2MktCap, energy_CaPex2MktCap_median), (energy_Ad2MktCap, energy_Ad2MktCap_median)]
       
    def compute_g_score_tech(symbol):
        g_score = 0
        if tech_CFROA[symbol] > tech_ROA1Y[symbol]:
                g_score += 1
        if symbol in tech_VARROA and tech_VARROA[symbol] < tech_VARROA_median:
                g_score += 1
        for ratio_dict, median in ratioDicts_medians:
            if symbol in ratio_dict and ratio_dict[symbol] > median:
                g_score += 1
        return g_score
 
        g_scores = {symbol:compute_g_score(symbol) for symbol in tech_symbols}
       
        return [symbol for symbol, g_score in g_scores.items() if g_score >= 5]


    def compute_g_score_energy(symbol):
        g_score = 0
        if energy_CFROA[symbol] > energy_ROA1Y[symbol]:
                g_score += 1
        if symbol in energy_VARROA and energy_VARROA[symbol] < energy_VARROA_median:
                g_score += 1
        for ratio_dict, median in ratioDicts_medians:
            if symbol in ratio_dict and ratio_dict[symbol] > median:
                g_score += 1
        return g_score
 
        g_scores = {symbol:compute_g_score(symbol) for symbol in energy_symbols}
       
        return [symbol for symbol, g_score in g_scores.items() if g_score >= 5]


   



    def Rebalance(self):
       if not self.mom.IsReady or not self.sma.IsReady: return
       initial_asset = self.stock if self.mom.Current.Value > 0 else self.bond
       
       if self.init:
           self.SetHoldings(initial_asset, .01)
           self.init = False
           
       keihist = self.History([self.kei], (int(self.GetParameter("keihist"))))
       #keihist = keihist['Value'].unstack(level=0).dropna()
       keihistlowt = np.nanpercentile(keihist, 15)
       keihistmidt = np.nanpercentile(keihist, 50)
       keihisthight = np.nanpercentile(keihist, 90)
       kei = self.sma.Current.Value
       keimom = self.mom.Current.Value
           
       if (keimom < 0 and kei < keihistmidt and  kei > keihistlowt) and not (self.Securities[self.bond].Invested)  :
           # DECLINE
           self.Liquidate()
           self.SetHoldings(self.XLP, .01)
           
           for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
               if not self.Portfolio[symbol].Invested : #and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
                   self.SetHoldings(symbol, .1, False, "Buy Signal")
                   
          #Stop Loss
#            for symbol, symbolData in self.XLPsector_symbolDataBySymbol.items():
#                if not (self.Securities[symbol].Close < symbolData.low.Current.Value) and (self.Securities[self.XLB].Invested):
#                    self.Liquidate()
#                    self.SetHoldings(self.XLP, .01)
           
           self.SetHoldings(self.bond, .4)
           self.Debug("STAPLES {0} >> {1}".format(self.XLP, self.Time))
       
       elif (keimom > 0 and kei < keihistlowt) and not (self.Securities[self.XLB].Invested)  :
           # RECOVERY
           self.Liquidate()
           self.SetHoldings(self.XLB, .01)
           
           for symbol, symbolData in self.XLEsector_symbolDataBySymbol.items():
               if not self.Portfolio[symbol].Invested : #and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
                   self.SetHoldings(symbol, .1, False, "Buy Signal")
           
           #XLB
           for symbol, symbolData in self.XLBsector_symbolDataBySymbol.items():
               if not self.Portfolio[symbol].Invested : #and (self.Securities[symbol].Close > symbolData.sma50.Current.Value):
                   self.SetHoldings(symbol, .1, False, "Buy Signal")

       
       elif (keimom < 0 and kei < 100 and not self.Securities[self.bond].Invested):
           self.Liquidate()
           self.SetHoldings(self.bond, .5)
       
       self.Plot("LeadInd", "SMA(LeadInd)", self.sma.Current.Value)
       self.Plot("LeadInd", "THRESHOLD", 100)
       self.Plot("MOMP", "MOMP(LeadInd)", self.mom.Current.Value)
       self.Plot("MOMP", "THRESHOLD", 0)

Axist

4.1k Pro ,

Provided a bit more of a cleaner backtest with the above logic. Some errors I am working through, and being able to convert the output from

self.energy_ROA = {} to self.XLEstocks = []

for example.

Vladimir

94.7k Pro ,

Axist

You have defined the topic of this thread as "Algorithm not taking orders".

You have defined the algorithm as "The G Score Algorithm found in the Quantconnect documentation".

Did you have a chance to try what I recommended on the algorithm problem?

Are you satisfied with my answer to your question?

I will be happy to answer questions about the KEI strategy in another thread.

Axist INVESTOR

Update Backtest

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