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