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'CoarseFundamental' object has no attribute

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.

Update Backtest







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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Hi Jimothy,

This is because on line 22, AddUniverse is called with a single function, and AddUniverse with a single function argument treats the passed function as a coarse filter function. I see a second AddUniverse on line 58, so I assume this is the desired universe, so I’ve commented out line 22, which solved the issue. Please see the attached backtest for reference (note: I added SetEndDate to speed up the backtest).

Best,
Shile Wen

0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Update Backtest





0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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