Account leverage is defined as the sum of the absolute value of long and short positions divided by the portfolio value. For a leverage of one, the sum of the absolute values of position weights must equal 1.

So, if one has a series with all the securities and weights one wishes to hold, then divide the series by the sum of the absolute value of the weights to ensure the gross leverage is 1 something like this

    weights = weights / weights.abs().sum  

The general ordering logic would look like this.

    # Make two series of the longs and shorts and associated weights  
    # Ensure that all the short weights are negative (this is what tells order to short them)  
    # This would come from the algo's selection and weighting process  
    # Make sure to include any current securities if those are to be held

    # Combine the two series  
    weights = pd.concat([longs, shorts])

    # Divide by the sum of the abs value of the weights to ensure the gross leverage is 1  
    weights = weights / weights.abs().sum  
    # Order the stocks  
    for stock, weight in weights.iteritems():  
        if data.can_trade(stock):  
            order_target_percent(stock, weight)  
    # Close any positions not in our long or short list  
    for stock, position in context.portfolio.positions.iteritems():  
        if data.can_trade(stock) and stock not in weights.index:  
            order_target_percent(stock, 0)

Using the order_optimal_portfolio method is almost exactly the same but takes a few less lines of code and can be easily enhanced to incorporate other constraints. The attached algo shows a simple algo which incorporates both order approaches.

Thank You! This was just I needed.