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Islander INVESTOR

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Hi,

consider the following script, it simply buy Futures at 10UTC and sell at 17UTC:

import numpy as np

from System import *

from NodaTime import DateTimeZone



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







class ScheduledEventsAlgorithm(QCAlgorithm):

    '''Basic template algorithm simply initializes the date range and cash'''



    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(2019, 6, 1)   #Set Start Date

        self.SetEndDate(2020, 6, 1)     #Set End Date

        self.SetCash(100000)            #Set Strategy Cash

        #Timezone Setting

        self.SetTimeZone(DateTimeZone.Utc)

        #

        



        # Setup Interactive Broker

        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)

        

        #Adding Instruments 

        self.futureES = self.AddSecurity(SecurityType.Future, Futures.Indices.SP500EMini, Resolution.Hour);

        self.futureES.SetFilter(timedelta(0), timedelta(182))

        

        #Logging / Debugs

        self.Logging_On = True

        self.Debug_On = False

        

        # Set Schedule to buy and sell

        self.Schedule.On(self.DateRules.EveryDay(self.futureES.Symbol), self.TimeRules.At(10, 0), self.ESBuy)

        self.Schedule.On(self.DateRules.EveryDay(self.futureES.Symbol), self.TimeRules.At(13, 0), self.ESSell)





    def OnData(self, data):

        # If we haven't already stored the front contract, we need to find it

        pass

        

    def ESBuy(self):

        if self.Debug_On:

            self.Debug("ESBuy: Fired at : {0}".format(self.Time))

        self.MarketOrder(self.futureES.Symbol, 1)



    

    def ESSell(self):

        if self.Debug_On:

            self.Debug("ESSell: Fired at : {0}".format(self.Time))

        self.Liquidate

It fails with the following error:

The security with symbol '/ES' is marked as non-tradable.

what is the best way to implement such a simple strategy with this asset?

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Islander

Accepted Answer

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Adam May

21 ,

The problem from the error message isn't the schedule you're trying to implement, but the contract you're trying to trade.

'/ES' is the abstract e-Mini Future, not a specific tradeable instrument. It's used for determining the market hours, tick sizes, etc that are all the same for each of the expiries related to this contract.

To add a specific expiry for the e-Mini, you need to do the following:

var symbol = QuantConnect.Symbol.CreateFuture(Futures.Indices.SP500EMini, Market.CME, expiryDate);

var future = AddFutureContract(symbol);

Noting the expiry date at the end of the first line.

Alternatively, take a look at the documentation around Universe Selection. This will help with automagically making sure you're using the right contract, but is a higher barrier to get to

Derek Melchin

STAFF ,

Hi Islander,

As Adam pointed out above, the symbol the algorithm is trying to trade is the canonical symbol. See the attached backtest logs for reference.

To trade futures, we need to select an individual contract to trade. I recommend reviewing our Bootcamp lesson on futures for more information and examples.

Going forward, please attach a backtest of an algorithm instead of embedding the code in the message body.

Best,
Derek Melchin

Islander

1.6k ,

Thanks Adam May and Derek Melchin .

Consider the attached backtest, I would like to adjust this algo in the way it trades every day and it opens a position at 16:00UTC and it closes at 17:00UTC. Could you please indicate me what parts I need to change?

Derek Melchin

STAFF ,

Hi Islander,

To have the algorithm open positions at 16:00UTC and close them at 17:00UTC, we need to adjust our alpha model initializer to be

self.SetAlpha(ConstantFutureContractAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(minutes=59)))

and add the following conditional to the ShouldEmitInsights method

if not (utcTime.hour == 16 and utcTime.minute == 0):

    return False

To ensure the positions are closed when insights have expired, we need to adjust the definition of the CreateTargets method inside the SingleSharePortfolioConstructionModel. See the attached backtest for reference.

Best,
Derek Melchin

Islander

1.6k ,

Ok Thanks Derek Melchin

I assume that timedelta(minutes=59)) gives how long it should keep the position open. Please clarify. Thanks.

But suppose now that when I generate insights I would like to evaluate time as in your example but also some indicators in another symbol.

Something like the following:

        fastPeriod = 20

        slowPeriod = 60

        self._tolerance = 1 + 0.001

        self.IsUpTrend = False

        self.IsDownTrend = False

        self.SetWarmUp(max(fastPeriod, slowPeriod))



        # Adds SPY to be used in our EMA indicators

        equity = self.AddEquity("SPY", Resolution.Daily)

        self._fast = self.EMA(equity.Symbol, fastPeriod, Resolution.Daily)

        self._slow = self.EMA(equity.Symbol, slowPeriod, Resolution.Daily)

Where exactly should I put this code and how can I pass to ShouldEmitInsight ?

Derek Melchin

STAFF ,

Hi Islander,

Yes, the timedelta states how long to hold the position for. Review our documentation on insights for more information.

To take the EMA factors into account when generating the insights, I recommend replacing the current alpha model with a custom one. We place the code in the constructor. Note that we warm up the EMAs before using them too.

class MyAlphaModel(AlphaModel):

    def __init__(self, algorithm):

        fastPeriod = 20

        slowPeriod = 60

        self._tolerance = 1 + 0.001

        self.IsUpTrend = False

        self.IsDownTrend = False

        

        symbol = algorithm.AddEquity("SPY", Resolution.Daily).Symbol

        self._fast = algorithm.EMA(symbol, fastPeriod, Resolution.Daily)

        self._slow = algorithm.EMA(symbol, slowPeriod, Resolution.Daily)

        

        # Warm up history

        history = algorithm.History(symbol, slowPeriod, Resolution.Daily).loc[symbol]

        for idx, row in history.iterrows():

            self._fast.Update(idx, row.close)

            self._slow.Update(idx, row.close)

    

    def Update(self, algorithm, slice):

        if not (algorithm.UtcTime.hour == 16 and algorithm.UtcTime.minute == 0 and algorithm.UtcTime.second == 0):

            return []

            

        insights = []

        for symbol in slice.Keys:

            if symbol.SecurityType != SecurityType.Future:

                continue

            insights.append(Insight.Price(symbol, timedelta(minutes=59), InsightDirection.Up))

        

        return insights

See the attached backtest for reference.

Best,
Derek Melchin

Islander INVESTOR

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