Algorithmic Trading Video Series #8 | Dyanimc Universes

Hi Louis Thanks for the video! I have one question: when we execute self.SetHoldings(self.portfolioTargets), what happens to the securities which are already in portfolio and in portfolioTargets too? I suppose if the ratio is same then nothing will happen, but if it's changed then it will be bought/sold according to the ratio specified? Am I interpreting it right? Thanks.

Hi Log Up

There would be no effect on other existing positions. The SetHoldings method is actually placing MarketOrder in quantity representing a proportion of the portfolio value for an asset. 

If you want to rebalance the portfolio, you could consider 

1. Liquidate all positions and use SetHoldings with a list of PortfolioTarget

self.SetHoldings([PortfolioTarget("SPY", 0.5), PortfolioTarget("AAPL", 0.5)], 
                 liquidateExistingHoldings=True)

2. using the Framework and InsightWeightingPortfolioConstructionModel. Active insight weights sum up larger than 1 will rebalance when new insight arrives. This could avoid excessive orders when singular order is desired instead of a whole portfolio change.

Best
Louis

HI Louis Szeto , thanks for the reply. Let's say I have SetHoldings with TSLA as 0.1(10%) previously, and in next iteration of OnData I set it to 0.2(20%) of my portfolio, then what will happen in this case?

Hi Log Up

For the same asset, it would hold the new set ratio (i.e. 20%). The other assets would not change their existing positions although (they will not automatically rebalance/adjust).

Best
Louis

Hi Louis, 

thank you for the videos. 

I am just a beginner and have watched your videos. 

Please I have some questions regarding  AddUniverse with CoarseSelectionFunction and FineSelectionFunction

In your videos, you have selected  

Coarse: Securities with FundamentalData that have DollarVolume > a certain amount. 

But I also want the Filter to include: Industry, segment, etc. 

Please how can I add those to my CoarseSelectionFunction? 

I am inspired by video #8 and the More Advanced Fundamental Investing bot video in your education series. 

Thank you. 

Samuel 

Hi Louis, 

Thank you for the very helpful videos, indeed this has really allowed me to gain knowledge I previously found to be very difficult, despite trying other tutorials too! Your approach to teaching is outstanding.

I have a question regarding customdata universe, and the use on onSecuritiesChanged function. I am getting this error while backtesting and I am not entirely sure why this is happening:

 Error Message

Runtime Error: 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.
  at on_securities_changed
    self.activeSecurities.remove(x.Symbol)
 in main.py: line 27

And this is my line of code:

 from AlgorithmImports import *
class CustomDataEMFAlgorithm(QCAlgorithm):

    def initialize(self):

        self.set_start_date(2008, 1, 8)
        self.set_end_date(2014, 7, 25)
        self.set_cash(100000)
        self.activeSecurities = set()

        universe = self.add_universe(MyCustomUniverseDataClass, "myCustomUniverse", self.selector_function)
        self.universe_settings.resolution = Resolution.DAILY

        self.portfolioTargets = []

     # Define the selector function
    def selector_function(self, data):
        sorted_data = sorted([ x for x in data if x["ARO_5YR"] < 33 ],
                            key=lambda x: x.end_time) #,
                            # reverse=True)
        self.Log('Stocks with discount percentile < 33: ' + str(len(sorted_data)))
        return [x.symbol for x in sorted_data]    

    def on_securities_changed(self, changes):
        # close positions in removed securities
        for x in changes.RemovedSecurities:
            self.Liquidate(x.Symbol)
            self.activeSecurities.remove(x.Symbol)      

        # can't open positions here since data might not be added correctly yet
        for x in changes.AddedSecurities:
            self.activeSecurities.add(x.Symbol)

        # adjust targets if universe has changed
        self.portfolioTargets = [PortfolioTarget(symbol, 1/len(self.activeSecurities)) 
                            for symbol in self.activeSecurities]
        
    def OnData(self, data):

        if self.portfolioTargets == []:
            return
        
        # for symbol in self.activeSecurities:
        #     if symbol not in data:
        #         return
        
        self.SetHoldings(self.portfolioTargets)
        
        self.portfolioTargets = []
       
# Example custom universe data; it is virtually identical to other custom data types.
class MyCustomUniverseDataClass(PythonData):		
    def get_source(self, config, date, is_live_mode):
        source = "https://raw.githubusercontent.com/XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.csv"
        return SubscriptionDataSource(source, SubscriptionTransportMedium.RemoteFile);
		
    def Reader(self, config, line, date, isLive):
        if not (line.strip() and line[0].isalnum()):
            return None
			
        items = line.split(",")
    
        # Generate required data, then return an instance of your class.
        data = MyCustomUniverseDataClass()
		
        try:
            data.end_time = datetime.strptime(items[1], '%Y-%m-%d')+timedelta(hours=20)

            # define Time as exactly 1 day earlier Time
            data.time = data.end_time - timedelta(1)

            data.symbol = Symbol.create(items[0], SecurityType.EQUITY, Market.USA)
            data.value = float(items[5])
            data["Open"] = float(items[2])
            data["High"] = float(items[3])
            data["Low"] = float(items[4])
            data["Close"] = float(items[5])
            data["Volume"] = int(items[6])
            data["ARO_5YR"] = float(items[7])
        except ValueError:
            return None
        
        return data

My csv data looks like this, with hundreds of tickers…:

Any help is highly appreciated!

Best