I would guess that because of the other constraints it is impossible for the optimizer to keep the algorithm exactly dollar neutral

The optimize method is working, and does calculate weights, which meet the constraints -including the DollarNeutral constraint. However, the trades aren't being executed. Looking at the logs, there are a lot of unfilled orders at the end of each day. Many of the stocks being traded have low trading volume and the slippage model is limiting the shares which can be ordered.

The easy way to check this is to lower the initial portfolio balance from $10M to $100k and run a backtest. Check the logs and one won't see all the 'unfilled order' messages. The backtest also now meets the 'Net Dollar Exposure' criteria.

One technique I like in debugging is to use calculate_optimal_portfolio. This way one can record the output of of the optimizer. Something like this

    objective = opt.MaximizeAlpha(alphas)  
    constraints = [max_turnover,max_leverage,sector_style_risk,dollar_neutral]

    opt_weights = opt.calculate_optimal_portfolio(objective,constraints)

    # Record the output of the optimizer to see whats going on  
    gross_exposure = opt_weights.abs().sum()  
    net_exposure = opt_weights.sum()  
    record(gross=gross_exposure, net=net_exposure)

    # Order using the calculated opt_weights  
    algo.order_optimal_portfolio(opt.TargetWeights(opt_weights),[])

The attached algo has this change. In debugging, one can look at the gross exposure and the net exposure calculated by the optimizer. The values are 1 and 0 throughout the backtest. This is what's expected. Since the target weights should result in a 'Net Dollar Exposure' close to zero, there must be an issue with the execution.

Hope that helps. Good algo.

@Dan Whitnable Thank you so much!