I am recieving the follwing error
Runtime Error: In Scheduled Event 'SPY: MonthStart: SPY: 0 min after MarketOpen', AttributeError : 'EquityHolding' object has no attribute 'historicalPERatio' AttributeError : 'EquityHolding' object has no attribute 'historicalPERatio' (Open Stacktrace)I cannnot seem to figure out why I am getting the error. The following is some of my code
#All appropriate variables are imported before this line
# Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
class FrameworkAlgorithm(QCAlgorithm):
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2014,1,1) #Set Start Date
self.SetEndDate(2015,1,1) #Set End Date
self.SetCash(50000) #Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.AddEquity("SPY", Resolution.Daily)
self.SetBenchmark("SPY")
self.__numberOfSymbols = 100
self.__numberOfSymbolsFine = 30
self._changes = None
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))
self.historicalPERatio = {}
self.Y = {}
self.rollingWindowSize = 20;
self.averages = { };
def CoarseSelectionFunction(self, coarse):
x = list(coarse)
CoarseWithFundamental = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
#in between above and Return statement is the rest of the function
return top
def FineSelectionFunction(self, fine):
self.r = [x for x in fine if x.ValuationRatios.PERatio]
for x in self.r:
if x.Symbol not in self.historicalPERatio.keys():
# Set up rolling window for new ticker
self.historicalPERatio[x.Symbol] = RollingWindow[Decimal](self.rollingWindowSize)
self.historicalPERatio[x.Symbol].Add(x.ValuationRatios.PERatio) #your job is to find the decimal current version for all the following variables
#self.prices_y[x.Symbol] = list(history.loc[x.Value]['close'])[1:]
self.r = [x for x in fine if PredictionEngine(x.historicalPERatio, x.Y ).predictionScore() > float(x.Price) or PredictionEngine(x.historicalPERatio , x.Y ).predictionScore() < float(x.Price)]
r = self.r
topFine = sorted(self.r, key=lambda x: x.ValuationRatios.PERatio, reverse = False)
self.topFine = [x.Symbol for x in topFine]
self.symbols = [i.Symbol for i in topFine]
self.y = self.History(self.symbols, self.rollingWindowSize, Resolution.Daily)
for i in self.symbols:
self.Debug(i.Symbol)
return self.symbols
def OnData(self, data):
symbols = self.symbols
for x in symbols:
if PredictionEngine(x.historicalPERatio,x.y).predictionScore() > float(x.Price):
self.SetHoldings(security, 0.1)
print("Security")
self.Debug(security)
self._changes = None
# if we have no changes, do nothing
def Rebalancing(self):
print("b")
for security in self.Portfolio.Values:
if PredictionEngine(security.historicalPERatio,security.y).predictionScore() < float(security.Price)*1.08 and security.Invested:
self.Liquidate(security.Symbol)
class PredictionEngine(object):
def __init__(self,historicalPERatio):
self.historicalPERatio = historicalPERatio
self.y = Y
def predictionScore(self):
scalar = StandardScaler()
pe = pd.DataFrame(self.historicalPERatio)
#after this line is where I do all the magical things needed to predict prices
return predictions
Share New Research 
Douglas Stridsberg
Line 65. A symbol does not have a "historicalPERatio" property. Looks like you meant to use self.historicalPERatio[x].
Line 76. A security holding object also doesn't have a "historicalPERatio" property. Again, it looks like you meant to use self.historicalPERatio[security.Symbol].
Subarno Sen
Even after the fix I am still recieving the following error
Runtime Error: In Scheduled Event 'SPY: MonthStart: SPY: 0 min after MarketOpen', Trying to retrieve an element from a collection using a key that does not exist in that collection throws a KeyError exception. To prevent the exception, ensure that the key exist in the collection and/or that collection is not empty. KeyError : (<QuantConnect.Symbol object at 0x7f0ed33c6860>,) (Open Stacktrace)Daniel Chen
Hi Subarno,
Thank you for posting your code. I think the CoarseSelectionFunction() part is not complete in your algorithm: the top variable is not defined. Here I give you a clear and nice version to do Coarse Selection:
def CoarseSelectionFunction(self, coarse): selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in selected[:self.num_coarse]]Besides, these algorithm examples using Coarse and Fine selection might be helpful to you. It would be easier to build new algorithms based on existing examples provided by QC. I would also recommend you work on Boot Camps to get familiar with the API in the fastest way. Thank you!
Subarno Sen
I don't believe that is waht is causing the error because even after I implmented the recommended fix I still continue to recieve the following error which is basically identical to the one I got earlier
BacktestingRealTimeHandler.Run(): There was an error in a scheduled event SPY: MonthStart: SPY: 0 min after MarketOpen. The error was KeyError : (<QuantConnect.Symbol object at 0x7f123c874f28>,)11 | 03:00:39:Algorithm (3fed5b18f528e93379b9ac8fc25a8625) Completed.12 | 03:00:40:Runtime Error: In Scheduled Event 'SPY: MonthStart: SPY: 0 min after MarketOpen', Trying to retrieve an element from a collection using a key that does not exist in that collection throws a KeyError exception. To prevent the exception, ensure that the key exist in the collection and/or that collection is not empty. KeyError : (<QuantConnect.Symbol object at 0x7f123c874f28>,) (Open Stacktrace)Yiyun Wu
Hi Subarno,
Would you mind attaching the most up-to-date version code so we can take a look and see why there is an error?
Thanks!
Subarno Sen
import pandas as pd #import datetime import requests import sys from time import time, sleep import time as systime from statistics import mean, median import numpy as np import math import collections, itertools from sklearn.ensemble import RandomForestRegressor import tensorflow as tf #import tensorflow_probability as tfp from sklearn.preprocessing import Imputer, StandardScaler, Normalizer, MinMaxScaler from sklearn.decomposition import KernelPCA, PCA from sklearn.preprocessing import Imputer, StandardScaler #sorted(sortedByfactor1, key=lambda x: x.ValuationRatios.NormalizedPERatio, reverse = False) # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from clr import AddReference AddReference("System.Core") AddReference("System.Collections") AddReference("QuantConnect.Common") AddReference("QuantConnect.Algorithm") from System import * from System.Collections.Generic import List from QuantConnect import * from QuantConnect.Algorithm import QCAlgorithm from QuantConnect.Data.UniverseSelection import * # Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks. class FrameworkAlgorithm(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2014,1,1) #Set Start Date self.SetEndDate(2015,1,1) #Set End Date self.SetCash(50000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.AddEquity("SPY", Resolution.Daily).Symbol self.SetBenchmark("SPY") self.__numberOfSymbols = 400 self.__numberOfSymbolsFine = 200 self._changes = None self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing)) self.splotName = 'Strategy Info' sPlot = Chart(self.splotName) sPlot.AddSeries(Series('Leverage', SeriesType.Line, 0)) self.AddChart(sPlot) self.historicalPERatio = {} self.Y = {} self.rollingWindowSize = 90; self.averages = { }; # Your New Python File def CoarseSelectionFunction(self, coarse): selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in selected[:self.__numberOfSymbols]] #return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols]] #this is the code to get fundamentals data def FineSelectionFunction(self, fine): self.r = [x for x in fine if x.ValuationRatios.PERatio ] for x in self.r: if x.Symbol not in self.historicalPERatio.keys(): # Set up rolling window for new ticker self.historicalPERatio[x.Symbol] = RollingWindow[Decimal](self.rollingWindowSize) #self.historicalCFOPerShare[x.Symbol] = RollingWindow[Decimal](self.rollingWindowSize) self.historicalPERatio[x.Symbol].Add(x.ValuationRatios.PERatio) #your job is to find the decimal current version for all the following variables #self.prices_y[x.Symbol] = list(history.loc[x.Value]['close'])[1:] #self.r = [x for x in self.r if FScore(x.historicalPERatio, x.Y ).predictionScore() > float(x.Price) or FScore(x.historicalPERatio, x.Y ).predictionScore() < float(x.Price)] #self.a = (x for x in self.r r = self.r #self.a = sorted(r, key=lambda x: r, reverse = False) #sorted(filtered_fine, key=lambda x: filtered_fine.predictionScore(), reverse = False) #factor = [x for x in filtered_fine if FScore(x. #a = self.a topFine = sorted(self.r, key=lambda x: x.ValuationRatios.PERatio, reverse = False) #self.dollarVolumeBySymbol[x.Symbol] #self.topFine = [x.Symbol for x in topFine] #self.Debug(topFine) #topFine = [x for x in topFine if FScore(x.historicalPERatio, x.y).predictionScore()] """ for x in topFine: FinalFactor.append(FScore(x.historicalPricetoCashRatio,x.TotalDebtEquityRatioGrowth,x.CFOPerShare,x.FCFRatio,x.currentPrice).predictionScore) self.Debug(FinalFactor) finalFactor = sorted(FinalFactor, key=lambda x: x.PredictionScore, reverse = False) #SortedFinalFactor = sorted(FinalFactor, key=lambda x: x.ValuationRatios.NormalizedPERatio, reverse = False) """ #dict = {} #for i in self.Securities: #price = self.Securities[i].Price #dict[i] = price #self.price.append(dict,ignore_index=True) #self.Debug(self.price) #df_price = pd.DataFrame(self.price, columns=self.price.keys()) #rets = np.log(df_price).dropna()#.diff().dropna() """ df_price = pd.DataFrame(self.price, columns=self.price.keys()) rets = np.log(df_price).diff().dropna() lock_value = df_price.iloc[-1] """ #symbols = self.symbols self.symbols = [x.Symbol for x in topFine[:self.__numberOfSymbolsFine]] self.y = self.History(self.symbols, self.rollingWindowSize, Resolution.Daily) for i in self.symbols: self.Debug(i.Symbol) #self.Debug(self.symbols) self._changes = None return [x.Symbol for x in topFine[:self.__numberOfSymbolsFine] ] #BluePrints for completing this: """ 2. put the data into dataframes and split it into training and test 3. plug it into the prediction function, standardize the prediction and assign the alpha value to each security. THen do a print statement of the dataframe with the alpha values associated with the securities 4. conduct optimization using cvxpy? """ def OnSecuritiesChanged(self, changes): self._changes = changes def OnData(self, data): print("asdf") symbols = self.symbols for x in symbols: if FScore(historicalPERatio[x],y[x]).predictionScore() > float(x.Price): #USE THE SAME FORMAT AS FACTOR 1 in THE CLONE EXCEPT MAKE IT SO THAT IT IS BASED ON PRICE self.SetHoldings(x.Symbo, 0.1) print("Security") self.Debug(x.Symbol) #self.historicalPERatio[x.Symbol] self._changes = None #self.Debug("asdf") # if we have no changes, do nothing #if self.changes == None: return # liquidate removed securities #for security in self._changes.RemovedSecurities: #if security.Invested: #for security in self.symbols: #self.SetHoldings(security, 0.1*finalFactor) def Rebalancing(self): print("b") for security in self.Portfolio.Values: if FScore(self.historicalPERatio[security.Symbol],self.y[security.Symbol]).predictionScore() < float(security.Price)*1.08 and security.Invested: self.Liquidate(security.Symbol) #self.SetHoldings(security,0) #elif finalFactor > price*1.02 && factor or holding <=0: #self.SetHoldings(security,0) class FScore(object): def __init__(self,historicalPERatio): self.historicalPERatio = historicalPERatio self.y = Y def predictionScore(self): vector_factor_direction= 0 scalar = StandardScaler() p = 1 pe = pd.DataFrame(self.historicalPERatio) X = pe X = scalar.fit_transform(X) X = pca.fit_transform(X) Y = self.Y Y = scalar.fit_transform(Y) #This is the place where I generate my predictions return predictionsSubarno Sen
This is my full code
Alexandre Catarino
Hi Subarno Sen ,
I cannot reproduce the issue with this code. There are other bugs...
For example:
Line 144: It is a list of Symbol, so items don't have Symbol attribute.
Line 171: self in historicalPERatio is missing, we get a missing attribute exception
Could you please try to fix the other bugs until we can reproduce the one you reported.
I would suggest using the Debugging Feature.
Subarno Sen
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