What's wrong with my universe selection?

Hi Mostafa,

1- You wrote SetStartData twice. So the second call overwrites the first.

2- Your coarse filter should always return 10 securities. Note you wrote < 500 instead of < 100 (as you wrote in the post).

3- In OnSecuritiesChanged, you log added securities, those are new securities, which were not in the universe before. That's why you do not always see 10 securities as some of them are already in there.

4- It's already ok.

Cheers,

Fred

Thanks Fred. I fixed the End date mistake, and used the (ActiveSecurities) instead of (AddedSecurities), and now I have also updated the code to include both Coarse and Fine Filters.

Now I have the following questions:

1. How to make a brand new universe selection every day for day trading? should I remove old securities one by one? how to do this the easiest way?
2. How to print the stock symbol only without the text following the symbol?
3. I saw different posts about challenges with scanning the pre-market gappers using the universe selection and that feature not available on QuantConnect as universe selection is happening only at fixed time every day 00.00. Is there any work-around for that? Can't we do this with custom code to save close price for all stocks at the end of every day and do the comparison 10-15 min before market open the following day? Is there any code samples for that available?
4. I'm not able to find the exact name of Fundamental data property for each of the following (if they are available in the data library from Morningstart:

• Market Cap
• Shares Float
• Short Float
• Target Price / Average Analysts Valuation of the stock

Thanks,

Mostafa

Hi Mostafa,

I am going to answer the rest when I get more time but for now the easy ones;

These are the CoarseFundamentals not listed in the morningstar library

class CoarseFundamental:
    # Gets the market for this symbol (string) 
    self.Market;
    # Gets the day's dollar volume for this symbol (decimal) 
    self.DollarVolume;
    # Gets the day's total volume (int) 
    self.Volume;
    # Returns whether the symbol has fundamental data for the given date (bool) 
    self.HasFundamentalData;
    # Gets the price factor for the given date (decimal) 
    self.PriceFactor;
    # Gets the split factor for the given date (decimal) 
    self.SplitFactor;
    # Gets the combined factor used to create adjusted prices from raw prices 
    # (decimal) 
    self.PriceScaleFactor;
    # Gets the split and dividend adjusted price (decimal) 
    self.AdjustedPrice;
    # The end time of this data. (DateTime) 
    self.EndTime;
    # Market Data Type of this data - does it come in individual price packets or 
    # is it grouped into OHLC. (MarketDataType) 
    self.DataType;
    # True if this is a fill forward piece of data (bool) 
    self.IsFillForward;
    # Current time marker of this data packet. (DateTime) 
    self.Time;
    # Symbol representation for underlying Security (Symbol) 
    self.Symbol;
    # Value representation of this data packet. All data requires a 
    # representative value for this moment in time. For streams of data this is 
    # the price now, for OHLC packets this is the closing price. (decimal) 
    self.Value;
    # As this is a backtesting platform we'll provide an alias of value as price. 
    # (decimal) 
    self.Price;

    # Return the URL string source of the file. This will be converted to a 
    # stream 
    self.GetSource(config, date, isLiveMode):
        return SubscriptionDataSource
    # Reader converts each line of the data source into BaseData objects. Each 
    # data type creates its own factory method, and returns a new instance of the 
    # object each time it is called. 
    self.Reader(config, line, date, isLiveMode):
        return BaseData
    # Return a new instance clone of this object, used in fill forward 
    self.Clone():
        return BaseData
    # Reader converts each line of the data source into BaseData objects. Each 
    # data type creates its own factory method, and returns a new instance of the 
    # object each time it is called. The returned object is assumed to be time 
    # stamped in the config.ExchangeTimeZone. 
    self.Reader(config, stream, date, isLiveMode):
        return BaseData
    # Indicates if there is support for mapping 
    self.RequiresMapping():
        return bool
    # Indicates that the data set is expected to be sparse 
    self.IsSparseData():
        return bool
    # Gets the default resolution for this data and security type 
    self.DefaultResolution():
        return Resolution
    # Gets the supported resolution for this data and security type 
    self.SupportedResolutions():
        return List<Resolution>
    # Specifies the data time zone for this data type. This is useful for custom 
    # data types 
    self.DataTimeZone():
        return DateTimeZone
    # Updates this base data with a new trade 
    self.UpdateTrade(lastTrade, tradeSize):
        pass
    # Updates this base data with new quote information 
    self.UpdateQuote(bidPrice, bidSize, askPrice, askSize):
        pass
    # Updates this base data with the new quote bid information 
    self.UpdateBid(bidPrice, bidSize):
        pass
    # Updates this base data with the new quote ask information 
    self.UpdateAsk(askPrice, askSize):
        pass
    # Updates this base data with a new trade 
    self.Update(lastTrade, bidPrice, askPrice, volume, bidSize, askSize):
        pass
    # Return a new instance clone of this object, used in fill forward 
    self.Clone(fillForward):
        return BaseData
    # Formats a string with the symbol and value. 
    self.ToString():
        return string

These are the FineFundamentals not listed in the morningstar library;

class FineFundamental:
    # The end time of this data. (DateTime) 
    self.EndTime;
    # Price * Total SharesOutstanding. The most current market cap for example, 
    # would be the most recent closing price x the most recent reported shares 
    # outstanding. For ADR share classes, market cap is price * (ordinary shares 
    # outstanding / adr ratio). (int) 
    self.MarketCap;
    # The instance of the CompanyReference class (CompanyReference) 
    self.CompanyReference;
    # The instance of the SecurityReference class (SecurityReference) 
    self.SecurityReference;
    # The instance of the FinancialStatements class (FinancialStatements) 
    self.FinancialStatements;
    # The instance of the EarningReports class (EarningReports) 
    self.EarningReports;
    # The instance of the OperationRatios class (OperationRatios) 
    self.OperationRatios;
    # The instance of the EarningRatios class (EarningRatios) 
    self.EarningRatios;
    # The instance of the ValuationRatios class (ValuationRatios) 
    self.ValuationRatios;
    # The instance of the CompanyProfile class (CompanyProfile) 
    self.CompanyProfile;
    # The instance of the AssetClassification class (AssetClassification) 
    self.AssetClassification;
    # Market Data Type of this data - does it come in individual price packets or 
    # is it grouped into OHLC. (MarketDataType) 
    self.DataType;
    # True if this is a fill forward piece of data (bool) 
    self.IsFillForward;
    # Current time marker of this data packet. (DateTime) 
    self.Time;
    # Symbol representation for underlying Security (Symbol) 
    self.Symbol;
    # Value representation of this data packet. All data requires a 
    # representative value for this moment in time. For streams of data this is 
    # the price now, for OHLC packets this is the closing price. (decimal) 
    self.Value;
    # As this is a backtesting platform we'll provide an alias of value as price. 
    # (decimal) 
    self.Price;

    # Return the URL string source of the file. This will be converted to a 
    # stream 
    self.GetSource(config, date, isLiveMode):
        return SubscriptionDataSource
    # Reader converts each line of the data source into BaseData objects. Each 
    # data type creates its own factory method, and returns a new instance of the 
    # object each time it is called. The returned object is assumed to be time 
    # stamped in the config.ExchangeTimeZone. 
    self.Reader(config, line, date, isLiveMode):
        return BaseData
    # Applies updated values from  to this instance 
    self.UpdateValues(update):
        pass
    # Reader converts each line of the data source into BaseData objects. Each 
    # data type creates its own factory method, and returns a new instance of the 
    # object each time it is called. The returned object is assumed to be time 
    # stamped in the config.ExchangeTimeZone. 
    self.Reader(config, stream, date, isLiveMode):
        return BaseData
    # Indicates if there is support for mapping 
    self.RequiresMapping():
        return bool
    # Indicates that the data set is expected to be sparse 
    self.IsSparseData():
        return bool
    # Gets the default resolution for this data and security type 
    self.DefaultResolution():
        return Resolution
    # Gets the supported resolution for this data and security type 
    self.SupportedResolutions():
        return List<Resolution>
    # Specifies the data time zone for this data type. This is useful for custom 
    # data types 
    self.DataTimeZone():
        return DateTimeZone
    # Updates this base data with a new trade 
    self.UpdateTrade(lastTrade, tradeSize):
        pass
    # Updates this base data with new quote information 
    self.UpdateQuote(bidPrice, bidSize, askPrice, askSize):
        pass
    # Updates this base data with the new quote bid information 
    self.UpdateBid(bidPrice, bidSize):
        pass
    # Updates this base data with the new quote ask information 
    self.UpdateAsk(askPrice, askSize):
        pass
    # Updates this base data with a new trade 
    self.Update(lastTrade, bidPrice, askPrice, volume, bidSize, askSize):
        pass
    # Return a new instance clone of this object, used in fill forward 
    self.Clone(fillForward):
        return BaseData
    # Return a new instance clone of this object, used in fill forward 
    self.Clone():
        return BaseData
    # Formats a string with the symbol and value. 
    self.ToString():
        return string

Here you can see how you can schedule your universe selection at your desired time interval;

https://www.quantconnect.com/docs/algorithm-framework/universe-selection#:~:text=%23%20Selection%20will%20run,function%20in%20algorithm.

Shares Float isn't available in QC but you can follow this posts explanation on how to calculate them yourselves or use shares outstanding instead;

https://www.quantconnect.com/forum/discussion/3936/looking-for-floating-stock-shares-number-on-equities/p1

You can check this posts on how to get the short float;

https://www.quantconnect.com/forum/discussion/10513/short-interest-data/p1

Rest when I have more time, thanks!