[Series help] Avoid small transactions in the rebalancing of my portfolio

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posted Dec 14, 2018

Hi there,

I'm trying to avoid small transactions in the rebalancing of my portfolio.
The idea of the code is calculate the difference between the new optimal portfolio and my current portfolio positions and avoid the transaction if the difference is under the 5%.

    weights = opt.calculate_optimal_portfolio(objective=objective, constraints=constraints)  
    for s, v in weights.iteritems():  
        position = context.portfolio.positions[s].amount  
        current_alloc = position * data.current(s, 'price') / context.portfolio.portfolio_value  
        diff = (p - current_alloc)  
        weights[s] = v if abs(diff) > 0.05 else current_alloc  
    objective = opt.TargetWeights(weights)  
    order_optimal_portfolio(objective=objective, constraints=constraints)

But it doesn't work.

TypeError: TargetWeights() expected a value of type dict or
pandas.core.series.Series for argument 'weights', but got
pandas.indexes.base.Index instead.

What would be the best way to solve this problem?

Thanks

1 response

Cal Mitchell

Dec 20, 2018

Hi Martin,

If I was trying to limit small transactions, I would calculate the weight of each position in before_trading_start() by iterating over the portfolio object (similar to what you have already done), then convert the output of the dataframe column you are using as an alpha factor into weight space with code like this:

alpha_factor = context.pipeline_output['combined_score']  
asset_weights = alpha_factor/alpha_factor.abs().sum()

Then I would compare asset_weights (which now represent "ideal" portoflio weights) to the current position weights with DataFrame.diff(), and filter out anything that isn't more different than .05.

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