Wrong values for SharesOutstanding

Hi Stanislav,

It seems like you're trying to get the historical data for the number of shares outstanding for a given stock. In your code, you're using the History method with Fundamental type and the Equity Symbol. This is the correct approach as per the QuantConnect documentation.

However, it's important to note that if there is no data in the period you request, the history result is empty. This could potentially explain why you're seeing the same value for all items in the history.

Here's an example of how you can get historical fundamental data:

ibm = Symbol.Create("IBM", SecurityType.Equity, Market.USA)

# DataFrame objects
df_history = qb.History(Fundamental, ibm, timedelta(30))

# Fundamental objects
fundamental_history= self.History[Fundamental](ibm, timedelta(30))

# Fundamentals objects for all US Equities (including delisted companies)
fundamentals_history = self.History[Fundamentals](timedelta(30))

If you're still seeing the same value for all items in the history, it might be worth running a backtest on QuantConnect and comparing the result of self.History with the same data from another source. This can help you understand where the differences are coming from.

If you continue to observe significant discrepancies, I would recommend posting a detailed question on the QuantConnect forum with specific examples. The community there may be able to provide further insights.

Nope. AI doesn't help here. In my example I requested data for 10 years, not 30 days. And it reproduces for IBM and df_history = qb.History(Fundamental, ibm, timedelta(365)). 

I apologize for the confusion earlier. You're correct in your approach to request historical fundamental data for a period of 10 years. The code you've shared seems to be correct as per the QuantConnect documentation.

However, it's important to note that the data you receive depends on the availability of the data for the requested period. If there is no data available for the period you request, the history result will be empty.

If you're consistently seeing the same value for SharesOutstanding for all items in the history, it could be due to the data source or it could be a potential issue with the data provider.

As per the QuantConnect forum discussion, to access fundamental data in QuantConnect, you need to use the FineFundamental objects in your fine universe selection function or the Fundamentals property of the Equity objects in your algorithm.

Here's an example of how to use the FineFundamental object to select symbols based on their P/E ratio:

def SelectFine(self, fine: List[FineFundamental]) -> List[Symbol]:
    sorted_by_pe_ratio = sorted(fine, key=lambda f: f.ValuationRatios.PERatio, reverse=True)
    return [ f.Symbol for f in sorted_by_pe_ratio[:5] ]

To access the fundamental data outside of your fine fundamental selection function, you can use the Fundamentals property of the Equity objects in your algorithm:

fundamentals = self.Securities[symbol].Fundamentals

If you continue to observe discrepancies, I would recommend reaching out to the QuantConnect team or the community on the QuantConnect forum for further assistance.

It seems to be an issue only in research notebooks. 

In backtests it works fine:

# region imports
from AlgorithmImports import *
# endregion

class SquareFluorescentYellowFalcon(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2022, 6, 24)
        self.SetCash(100000)
        self.symbol = Symbol.Create("AAPL", SecurityType.Equity, Market.USA)
        self.sharesOutstanding = []
        self.dates = []
        self.AddUniverse(self.FundamentalFilterFunction)

    def OnData(self, data: Slice):
        self.dates.append(self.Time)
        fundamental = self.Fundamentals(self.symbol)
        outstanding = fundamental.CompanyProfile.SharesOutstanding
        self.sharesOutstanding.append(outstanding)

        if not self.Portfolio.Invested:
            self.SetHoldings(self.symbol, 1)

    def FundamentalFilterFunction(self, fundamental: List[Fundamental]):
        return [self.symbol]

    def OnEndOfAlgorithm(self) -> None:
        self.Debug("Algorithm done")
        #self.Debug(",".join([str(item) for item in self.sharesOutstanding[:100]]))

        for i, x in enumerate(self.sharesOutstanding):
            if i == 0 or x != self.sharesOutstanding[i - 1]:
                self.Debug(f"{i}: date: {self.dates[i]}, shares outstanding: {x}")
        self.Debug(f"completed. i: {i}")

logs:

520
|
5:35:54
:
Algorithm done
521
|
5:35:54
:
0: date: 2022-06-24 09:31:00, shares outstanding: 16185181000
522
|
5:35:54
:
780: date: 2022-06-28 09:31:00, shares outstanding: 16095378000
523
|
5:35:54
:
5850: date: 2022-07-18 09:31:00, shares outstanding: 16070752000
524
|
5:35:54
:
25351: date: 2022-09-27 09:31:00, shares outstanding: 15943425000
525
|
5:35:54
:
30811: date: 2022-10-17 09:31:00, shares outstanding: 15908118000
526
|
5:35:54
:
51693: date: 2023-01-04 09:31:00, shares outstanding: 15842407000
527
|
5:35:54
:
53253: date: 2023-01-10 09:31:00, shares outstanding: 15836213000
528
|
5:35:54
:
56373: date: 2023-01-23 09:31:00, shares outstanding: 15821946000
529
|
5:35:54
:
75874: date: 2023-04-04 09:31:00, shares outstanding: 15723406000
530
|
5:35:54
:
80944: date: 2023-04-24 09:31:00, shares outstanding: 15728702000
531
|
5:35:54
:
99875: date: 2023-07-05 09:31:00, shares outstanding: 15647868000
532
|
5:35:54
:
104945: date: 2023-07-24 09:31:00, shares outstanding: 15634232000
533
|
5:35:54
:
completed. i: 122885

it looks correct. it doesn't match exactly the data on tradingview though.

Hi Stanislav,

It's great to hear that you're able to retrieve the correct SharesOutstanding data in backtests. The discrepancy you're observing between the data in research notebooks and backtests could be due to the different ways data is handled in these two environments.

As for the difference between the data on QuantConnect and TradingView, it's important to note that different platforms may use different data sources, which can lead to slight discrepancies in the data. As mentioned in the QuantConnect documentation, QuantConnect uses institutional quality data from the Consolidated Tape Association (CTA) and Unlisted Trading Privileges (UTP) tick feeds, which cover the entire US tick feed. On the other hand, many charting websites use the Better Alternative Trading System (BATS), which only covers about 6-7% of the total market volume.

If you continue to observe significant discrepancies, I would recommend reaching out to the QuantConnect team or the community on the QuantConnect forum for further assistance.

I updated the code to be the minimum to reproduce the issue.

And TBH I also just wanted to try the project sharing feature because it's amazing.

Hi Stanislav,

Your code seems to be correct in terms of syntax and logic. However, the issue you're facing might be due to the way you're trying to access the SharesOutstanding data.

In your OnData method, you're trying to access the SharesOutstanding data using the Fundamentals property of the QCAlgorithm class. However, this property is updated only once per day at midnight and it might not contain the most recent data.

Here's an example of how you can access the SharesOutstanding data in the OnData method:

def OnData(self, data: Slice):
    if data.ContainsKey(self.symbol):
        self.outstanding = self.Securities[self.symbol].Fundamentals.CompanyProfile.SharesOutstanding

In this example, we're checking if the Slice object contains data for the symbol we're interested in. If it does, we're accessing the SharesOutstanding data using the Fundamentals property of the Security object.

As for the research notebook, you're using the History method with Fundamental type and the Equity Symbol. This is the correct approach as per the QuantConnect documentation. However, if there is no data in the period you request, the history result is empty. This could potentially explain why you're seeing the same value for all items in the history.

If you continue to observe discrepancies, I would recommend reaching out to the QuantConnect team or the community on the QuantConnect forum for further assistance.