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Morgan Liu INVESTOR

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I'm trying to make a simple equity screener that keeps stocks that cost < $10 from 5 days ago so I can eventually build more complicated screeners based on history data. When I run the code below over a 10 day backtest, it doesn't finish after 10 minutes, so I stopped it. Any recommendations on quicklly screening the full equity database?

from clr import AddReference

AddReference("System")

AddReference("QuantConnect.Algorithm")

AddReference("QuantConnect.Indicators")

AddReference("QuantConnect.Common")



from System import *

from QuantConnect import *

from QuantConnect.Data import *

from QuantConnect.Algorithm import *

from QuantConnect.Indicators import *

from System.Collections.Generic import List

import decimal as d



### <summary>

### In this algorithm we demonstrate how to perform some technical analysis as

### part of your coarse fundamental universe selection

### </summary>

### <meta name="tag" content="using data" />

### <meta name="tag" content="indicators" />

### <meta name="tag" content="universes" />

### <meta name="tag" content="coarse universes" />

class TestAlgorithm(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(2010,01,01)  #Set Start Date

        self.SetEndDate(2010,01,10)    #Set End Date

        self.SetCash(100000)           #Set Strategy Cash



        self.UniverseSettings.Resolution = Resolution.Daily

        self.AddUniverse(self.CoarseSelectionFunction)





    def CoarseSelectionFunction(self, coarse):

        '''select for all U.S. stocks where share price from 5 days ago was < $10'''

        matchingSymbols = []

        for cf in coarse:

            self.AddSecurity(SecurityType.Equity, cf.Symbol.Value)

            hist = self.History(cf.Symbol.Value,5,Resolution.Daily)

            if hist['close'][0] <10:

                matchingSymbols.append(cf.Symbol)

        

        return matchingSymbols

        

    # this event fires whenever we have changes to our universe

    def OnSecuritiesChanged(self, changes):

        # liquidate removed securities

        for security in changes.RemovedSecurities:

            if security.Invested:

                self.Liquidate(security.Symbol)



        for security in changes.AddedSecurities:

            self.SetHoldings(security.Symbol, 0.1)

Update Backtest

Morgan Liu

Accepted Answer

Update Backtest

Notebook

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Jared Broad

STAFF ,

You don't need to / can't add the securities like that in coarse selection (no idea what it'll do!). The security price is included in the coarse fundamental object: See Price.

https://www.quantconnect.com/docs#Universes-Coarse-Universe-Selection

If you want the price from 5 days ago you should cache the days in a rolling window; Alexandre Catarino should be able to help with that =)

Alexandre Catarino

100.5k ,

The EmaCrossUniverseSelectionAlgorithm is the best example for universe selection algorithms that rely on one or more indicators. The "trick" is in the definition of the class that will cache the information:

class SymbolData(object):

    def __init__(self, symbol):

        self.symbol = symbol

        self.history = RollingWindow[float](5)

        self.screen1 = False



    def update(self, value):

        self.history.Add(float(value))

        if self.history.IsReady:

            self.screen1 = self.history[4] < 10

Then you need to store the instances of SymbolData object in a dictionay and use it in the universe selector function:

# In Initialize

self.averages = { }



def CoarseSelectionFunction(self, coarse):

    '''select for all U.S. stocks where share price from 5 days ago was < $10'''

    matchingSymbols = []

    for cf in coarse:

        if cf.Symbol not in self.averages:

            self.averages[cf.Symbol] = SymbolData(cf.Symbol)



        # Updates the SymbolData object with current EOD price

        avg = self.averages[cf.Symbol]

        avg.update(cf.Price)

            

        if avg.screen1:

            matchingSymbols.append(cf.Symbol)

                

    return matchingSymbols

In your algorithm, you will need to define another metric to rank and select the securities, since a lot of them are trading below $10.

Morgan Liu

40 ,

Thanks, Jared and Alexandre.

Morgan Liu

40 ,

Is there a way to use open, high and low price from previous days? I see it used in some indicators, so I assume that I could learn C# and create a custom indicator if there's not a faster way.

Morgan Liu INVESTOR

Update Backtest

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