When I try to create a filter with Fine Selection I receive the error 'CoarseFundamental' object has no attribute 'OperationRatios'. I have tried changing the indicator to use ValuationRatios instead but the problem persists. This issue occurs in the filtered_fine indicator 'OperationRatios.ROIC.SixMonths (line 99)

import numpy as np import pandas as pd from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Data import * from datetime import timedelta from System.Collections.Generic import List from QuantConnect.Data.UniverseSelection import * class QualityMomentumModel(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 2, 10) # Set Start Date self.SetCash(1000) # Set Strategy Cash self.SPY = self.AddEquity("SPY", Resolution.Minute) #add SPY to use for trends #list of bond etfs for when markets down. self.AddEquity("TLT").Symbol self.AddEquity("IEF").Symbol self.BONDS = ['TLT', 'IEF'] # Add bonds # Set target number of securities to hold and top ROE qty to filter self.TARGET_SECURITIES = 5 self.TOP_ROE_QTY = 50 #First sort by ROE self.UniverseSettings.Resolution = Resolution.Minute #update the universe every minute #adding a universe of stocks self.AddUniverse(self.FineSelectionFunction) #determine how many symbols to select in the coarse filter self.num_coarse = 1000 self.num_fine = 50 #trend following filter self.TF_LOOKBACK = 200 self.TF_CURRENT_LOOKBACK = 20 #determining momentum self.MOMENTUM_LOOKBACK_DAYS = 126 #how many days to lookback self.MOMENTUM_SKIP_DAYS = 10 #how many days to skip overall_lookback = (self.MOMENTUM_LOOKBACK_DAYS + self.MOMENTUM_SKIP_DAYS) # Initialize any other variables before starting trading # setting the weights for each type of securitie self.stock_weights = pd.Series() self.bond_weights = pd.Series() #schedule function for selecting stocks and weights self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.select_stocks_set_weights) self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.BeforeMarketClose("SPY", 30), self.select_stocks_set_weights) #schedule function for making trades self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.trade) self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.BeforeMarketClose("SPY", 30), self.trade) #50 Day moving average of SPY self.spy_ma_fast = self.SMA("SPY", 50) #200 Day moving average of SPY self.spy_ma_slow = self.SMA("SPY", 200) self.trend_up = self.spy_ma_fast >= self.spy_ma_slow self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) 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: Slice object keyed by symbol containing the stock data ''' def CoarseSelectionFunction(self, coarse): '''Drop securities which have no fundamental data or have too low prices. Select those with highest by dollar volume''' selectedcoarse = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 10], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in selectedcoarse[:self.num_coarse]] coarselist = list[Symbol]() for x in selectedcoarse: list.Add(x.Symbol) return list def FineSelectionFunction(self, fine): filtered_fine = [x for x in fine if x.OperationRatios.ROIC.SixMonths and x.ValuationRatios.CashReturn and x.ValuationRatios.FCFYield and x.OperationRatios.LongTermDebtEquityRatio.NineMonths] value = [x for x in filtered_fine if x.ValuationRatios.CashReturn and x.ValuationRatios.FCFYield] quality = [x for x in value if x.OperationRatios.ROIC and x.OperationRatios.LongTermDebtEquityRatio] returns_overall = sorted(filtered_fine, key = lambda f: f.OperationRatios.RevenueGrowth.overall_lookback) returns_recent = sorted(filtered_fine, key = lambda f: f.OperationRatios.RevenueGrowth.MOMENTUM_SKIP_DAYS) momentum = sorted(filtered_fine, key = lambda f: f.returns_overall + f.returns_recent) top_quality = sorted(filtered_fine, key = lambda f: f.OperationRatios.ROE.OneMonth) return [x.Symbol for x in top_quality[:self.TOP_ROE_QTY]] #search for top 50 equities with the highest ROE return [x.Symbol for x in momentum[:self.TARGET_SECURITIES]] #search for specified number of securities with the highest momentum top_quality_momentum = list[Symbol]() for x in momentum, top_quality: list.Add(x.Symbol) return list

Any help is appreciated as this is my first attempt at creating an algorithm.