The error you are getting is a bit misleading. The TargetWeights objective expects a series with the index being the security objects and the values being the associated weights. You only supplied the weights. By default pandas will assign integers as the index values. The error happens when the objective tries to get the 'end_date' attribute from the integer index (remember it is expecting a security) and it fails.

So, simply supply the securities one wishes to trade as the index. Something like this:

    # Specify a series with the weights we want  
    # Need to specify BOTH the securities (as the index) and the weight  
    my_weights = pd.Series(index=context.my_stocks, data=context.target_weight)  

See the attached algo. Good luck!

Thank you so much Dan.

I have seen the approach you mentioned but my problem is, I do not know how to provide the list of stocks I will be trading. Say my algorithm picks stocks based on sentiment similar to the tutorial, then I am not sure how to create a list of the stocks selected. Is there a simpler way of just automatically equally weighting each stock, rather than having to assign them in a such a list.

You need to be able to tell the TargetWeights method two things: which stocks to trade, and, what the weight of each stock should be. (Otherwise how would it know which stocks to choose. You need to tell it).

Which tutorial are you referring to? Is it Lesson 3 'Pipeline API' (https://www.quantopian.com/tutorials/getting-started#lesson3) ?

When using a pipeline to fetch data, the data is always returned in a dataframe. The dataframe columns are whatever factors, filters, or classifiers you define. The rows are always the security objects (ie the stocks). To get a list of stocks one simply needs to turn the index into a list. Something like this:

my_list_of_stocks = pipeline.output.index.tolist()

Python and Pandas is pretty forgiving. One often doesn't need to supply a strict python list. Often times the index is fine. Just 'list like' is often good enough.

The other thing to remember is that most of the time one wouldn't want to trade ALL the stocks returned in pipeline.output. So, selecting a subset of the dataframe is quite typical. Using the 'Lesson 3' pipeline, we could take the 50 stocks with the highest 'sentiment_score' something like this:

my_best_stock_list = pipeline.output.nlargest(50, 'sentiment_score').index.tolist()

The above could also be done with filters as part of the pipeline definition. Personal preference which way one goes.

Hope that helps?

This helps a lot, thanks so much Dan.

I was indeed referring to lesson 3 from the first tutorial, but I misunderstood where the stock selection process took place.

Your explanation makes perfect sense. In the tutorial they retrieve the sentiment score from context.pipeline_data.sentiment_score and pass that to the optimizer, and let the optimizer do the selection process.

If I create equal weights instead, I have not yet clarified which stocks to select, as you said, I don't want to trade all the stocks.

Thanks so much for the quick help!!!

Glad to help.

The tutorial (jumping ahead to Lesson 7) uses the objective MaximizeAlpha. Unlike the TargetWeights objective it can assign a zero weight to a security if others contribute more to the alpha. In this way it can be thought of as part of the selection process. Perhaps a better way to think of the stocks provided in the index is these are the 'universe' of potential securities to trade. One will always want to prune the list to a meaningful subset of the total securities. The contest requires at least paring down to the QTradableStocksUS.

One challenge you may have going forward is to select stocks you want to long and stocks you want to short. These can become two different 'universes' and you may want to treat them differently. Search the forum posts on ways to do that if you find yourself headed in that direction.

Again, good luck!

I see, that makes sense.

Yeah, I will check those out, thanks for all the help!!!