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Error Re-Adding Securities

I ran the original code but recieved this error: Runtime Error: QuantConnect.Scheduling.ScheduledEventException: Python.Runtime.PythonException: Exception : The security 'JNPR' 'Equity' has already been added. \

Ill attach the full error below.

from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
# import statsmodels.api as sm

class FundamentalFactorAlgorithm(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2004, 01, 01) #Set Start Date
self.SetEndDate(2018, 01, 01) #Set Start Date
self.SetCash(50000) #Set Strategy Cash


self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
self.holding_months = 1
self.num_screener = 100
self.num_stocks = 10
self.formation_days = 200
self.lowmom = False
self.month_count = self.holding_months
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), Action(self.monthly_rebalance))
self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(10, 0), Action(self.rebalance))
# rebalance the universe selection once a month
self.rebalence_flag = 0
# make sure to run the universe selection at the start of the algorithm even it's not the manth start
self.first_month_trade_flag = 1
self.trade_flag = 0
self.symbols = None

def CoarseSelectionFunction(self, coarse):
if self.rebalence_flag or self.first_month_trade_flag:
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
# rank the stocks by dollar volume
filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)

return [ x.Symbol for x in filtered[:200]]
else:
return self.symbols


def FineSelectionFunction(self, fine):
if self.rebalence_flag or self.first_month_trade_flag:
hist = self.History([i.Symbol for i in fine], 1, Resolution.Daily)
try:
filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
and float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][0] > 2e9]
except:
filtered_fine = [x for x in fine if (x.ValuationRatios.EVToEBITDA > 0)
and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)]

top = sorted(filtered_fine, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener]
self.symbols = [x.Symbol for x in top]

self.rebalence_flag = 0
self.first_month_trade_flag = 0
self.trade_flag = 1
return self.symbols
else:
return self.symbols

def OnData(self, data):
pass

def monthly_rebalance(self):
self.rebalence_flag = 1

def rebalance(self):
spy_hist = self.History([self.spy], 120, Resolution.Daily).loc[str(self.spy)]['close']
if self.Securities[self.spy].Price < spy_hist.mean():
for symbol in self.Portfolio.Keys:
if symbol.Value != "TLT":
self.Liquidate()
self.AddEquity("TLT")
self.SetHoldings("TLT", 1)
return

if self.symbols is None: return
chosen_df = self.calc_return(self.symbols)
chosen_df = chosen_df.iloc[:self.num_stocks]

self.existing_pos = 0
add_symbols = []
for symbol in self.Portfolio.Keys:
if symbol.Value == 'SPY': continue
if (symbol.Value not in chosen_df.index):
self.SetHoldings(symbol, 0)
elif (symbol.Value in chosen_df.index):
self.existing_pos += 1

weight = 0.99/len(chosen_df)
for symbol in chosen_df.index:
self.AddEquity(symbol)
self.SetHoldings(symbol, weight)

def calc_return(self, stocks):
hist = self.History(stocks, self.formation_days, Resolution.Daily)
current = self.History(stocks, 1, Resolution.Minute)

self.price = {}
ret = {}

for symbol in stocks:
if str(symbol) in hist.index.levels[0] and str(symbol) in current.index.levels[0]:
self.price[symbol.Value] = list(hist.loc[str(symbol)]['close'])
self.price[symbol.Value].append(current.loc[str(symbol)]['close'][0])

for symbol in self.price.keys():
ret[symbol] = (self.price[symbol][-1] - self.price[symbol][0]) / self.price[symbol][0]
df_ret = pd.DataFrame.from_dict(ret, orient='index')
df_ret.columns = ['return']
sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom)

return sort_return

Error code: 

Runtime Error: QuantConnect.Scheduling.ScheduledEventException: Python.Runtime.PythonException: Exception : The security 'JNPR' 'Equity' has already been added. at QuantConnect.Algorithm.QCAlgorithm.AddSecurity[T] (QuantConnect.SecurityType securityType, System.String ticker, QuantConnect.Resolution resolution, System.String market, System.Boolean fillDataForward, System.Decimal leverage, System.Boolean extendedMarketHours) [0x00083] in <a8b13edcb24c4fa4acb30df03c15ca94>:0 at QuantConnect.Algorithm.QCAlgorithm.AddEquity (System.String ticker, QuantConnect.Resolution resolution, System.String market, System.Boolean fillDataForward, System.Decimal leverage, System.Boolean extendedMarketHours) [0x00001] in <a8b13edcb24c4fa4acb30df03c15ca94>:0 at (wrapper managed-to-native) System.Reflection.MonoMethod.InternalInvoke(System.Reflection.MonoMethod,object,object[],System.Exception&) at System.Reflection.MonoMethod.Invoke (System.Object obj, System.Reflection.BindingFlags invokeAttr, System.Reflection.Binder binder, System.Object[] parameters, System.Globalization.CultureInfo culture) [0x00032] in <2e7c1c96edae44d496118948ca617c11>:0 at Python.Runtime.Dispatcher.Dispatch (System.Collections.ArrayList args) [0x00018] in <3e87c364169b46d2b45f0865ae967c08>:0 at __System_ActionDispatcher.Invoke () [0x00006] in <57842ce96c5c4ff8916c34f6c06bab21>:0 at QuantConnect.Scheduling.ScheduleManager+<>c__DisplayClass16_0.<On>b__0 (System.String name, System.DateTime time) [0x00000] in <32eb38a3b7cb4298b4d49e1623171594>:0 at QuantConnect.Scheduling.ScheduledEvent.OnEventFired (System.DateTime triggerTime) [0x00036] in <32eb38a3b7cb4298b4d49e1623171594>:0 at QuantConnect.Scheduling.ScheduledEvent.OnEventFired (System.DateTime triggerTime) [0x00086] in <32eb38a3b7cb4298b4d49e1623171594>:0 at QuantConnect.Scheduling.ScheduledEvent.Scan (System.DateTime utcTime) [0x0010f] in <32eb38a3b7cb4298b4d49e1623171594>:0 at QuantConnect.Lean.Engine.RealTime.BacktestingRealTimeHandler.SetTime (System.DateTime time) [0x00019] in <0c007253613f4c96834d5d4cb3c3b185>:0 at QuantConnect.Lean.Engine.AlgorithmManager.Run(QuantConnect.Packets.AlgorithmNodePacket job, QuantConnect.Interfaces.IAlgorithm algorithm, QuantConnect.Lean.Engine.DataFeeds.IDataFeed feed, QuantConnect.Lean.Engine.TransactionHandlers.ITransactionHandler transactions, QuantConnect.Lean.Engine.Results.IResultHandler results, QuantConnect.Lean.Engine.RealTime.IRealTimeHandler realtime, QuantConnect.Lean.Engine.Server.ILeanManager leanManager, QuantConnect.Lean.Engine.Alpha.IAlphaHandler alphas, System.Threading.CancellationToken token) [0x007d0] in <0c007253613f4c96834d5d4cb3c3b185>:0 at QuantConnect.Lean.Engine.Engine+<>c__DisplayClass8_1.<Run>b__3 () [0x000ac] in <0c007253613f4c96834d5d4cb3c3b185>:0

Update Backtest








Hi Phil. You add the same securities over and over again in this code

for symbol in chosen_df.index:
self.AddEquity(symbol)
self.SetHoldings(symbol, weight)

You should check if the security exists already:

for symbol in chosen_df.index:
if not self.Securities.ContainsKey(symbol):
self.AddEquity(symbol)
self.SetHoldings(symbol, weight)
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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