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Yep it's this

since data isn't defined anywhere (note that in OnData, LEAN automatically passes the latest stream of data as an argument). 

Bit of a hacky solution:

This schedules the function ToggleRebalance() to be called instead of rebalance() every month, then once it's set to True, we can call rebalance within OnData but pass in the data argument.

Cleaner solution would be to wrap the logic up into an AlphaModel or Indicator.

Thanks again Adam. I've got all that now but it doesn't like my data as an argument in self.rebalance(data). How do I need to change this statement for the toggle.rebalance flag?

"if data[self.spy.Symbol].Low>self.sma200.Current.Value:"

Can you post what you have so far?

In that answer's workaround, the scheduling should only trigger ToggleRebalance (remove the scheduled event call to rebalance if you haven't) and the rebalance function needs to be defined with an additional 'data' argument.

Sure, my data argument is this condition i've used on some other strategies that has worked:

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 *

class MonthlyROERebalanceAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2015,1,1)  #Set Start Date
        self.SetEndDate(2020,8,1)    #Set End Date
        self.SetCash(100000)            #Set Strategy Cash
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.agg = self.AddEquity("AGG", Resolution.Daily).Symbol
        #self.SetBenchmark("QQQ")
        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Daily
        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        # - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.numberOfSymbols = 250
        self.numberOfSymbolsFine = 20
        self._changes = None
        self.fineFilteredSymbols = []
        self.sma200 = self.SMA("SPY", 200)
        self.SetWarmUp(200)
        self.shouldRebalance = False
        
        # Create the monthly rebalance logic. Check SPY 30 minutes after market open to determine the first trading day of the month.
        self.Schedule.On(self.DateRules.MonthStart(self.spy),
        self.TimeRules.AfterMarketOpen(self.spy,30),
        self.self.ToggleRebalance)
    
    def ToggleRebalance(self):
        self.shouldRebalance = True
    
    def OnData(self, data):
        #make sure SPY and AGG has data to prevent missing data errors
        if data.ContainsKey("SPY") == False: return
        if data.ContainsKey("AGG") == False: return
        if not self.sma200.IsReady: return
        pass
    
        # Free cash buffer, increase in case buy orders are rejected due to insufficient funds
        self.Settings.FreePortfolioValuePercentage = 0.05

        # Coarse selection by sorting the symbols by daily dollar volume and define this list as "numbersOfSymbol"
    def CoarseSelectionFunction(self, coarse):
            # sort descending by daily dollar volume and only those that have fundamental data
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume and x.HasFundamentalData, reverse=True)
            # return the symbol objects of the top entries from our sorted collection
        return [ x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbols] ]
        
    # sort the data by Return on Equity and take the top ranked symbols as 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
            # sort descending by Return On Equity
        sortedByROE = sorted(fine, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
            # take the top entries from our sorted collection
        self.fineFilteredSymbols = [ x.Symbol for x in sortedByROE[:self.numberOfSymbolsFine] ]
        return self.fineFilteredSymbols
 
        if self.shouldRebalance:
            self.rebalance(data)
                data[self.spy.Symbol].Low>self.sma200.Current.Value
            self.shouldRebalance = False    # Turn this off until the next ToggleRebalance() call
            
    def rebalance(self):
        # if SPY is above 200 SMA then sell and AGG and go long stocks.
        if data[self.spy.Symbol].Low>self.sma200.Current.Value
            for symbol in self.fineFilteredSymbols:
                self.SetHoldings("AGG", 0)
                self.SetHoldings(symbol, 1 / self.numberOfSymbolsFine )
        # if SPY is below 200 SMA then go long AGG, no stocks.
        if data[self.spy.Symbol].Low<self.sma200.Current.Value:
            self.SetHoldings("AGG", 1)

    def OnSecuritiesChanged(self, changes):
    # Liquidate securities no longer in universe
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)

Made a couple of changes to your code and small design suggestions, marked with ' ### CHANGE '.

Explanations:

1. CHANGE (1): In general, check for boolean flags in Python with 'if' or 'if not'
2. CHANGE (2): 'pass' not needed; used as placeholder for empty functions
3. CHANGE (3): Put this in Initialize, no need to set this everytime data is streamed into OnData()
4. CHANGE (4): Seems to be in the wrong place. Moved to OnData()
5. CHANGE (5): Added the 'data' argument into function definition

Thanks Adam, that was a huge help.

Once I got all this together it didn't like my regime change condition "if data[self.spy.Symbol].Low>self.sma200.Current.Value:". I changed it to "if data[self.spy.].Low>self.sma200.Current.Value:" and its working.